diff --git a/.gitignore b/.gitignore
index 3ddb942c4..b33099105 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,8 +10,11 @@ build/
 venv/
 .venv/
 .idea/
-results/
 logs/
+vectordb_bench/results/cloudleaderboard/
+
+# Worktrees
+.worktrees/
 
 # AI rules
 CLAUDE.md
diff --git a/Makefile b/Makefile
index e227e3b92..ef8207c55 100644
--- a/Makefile
+++ b/Makefile
@@ -1,8 +1,10 @@
-lint:
-	PYTHONPATH=`pwd` python3 -m black vectordb_bench --check
-	PYTHONPATH=`pwd` python3 -m ruff check vectordb_bench
-
 unittest:
 	PYTHONPATH=`pwd` python3 -m pytest tests/test_dataset.py::TestDataSet::test_download_small -svv
 
-.PHONY: lint unittest
+format:
+	PYTHONPATH=`pwd` python3 -m black vectordb_bench
+	PYTHONPATH=`pwd` python3 -m ruff check vectordb_bench --fix
+
+lint:
+	PYTHONPATH=`pwd` python3 -m black vectordb_bench --check
+	PYTHONPATH=`pwd` python3 -m ruff check vectordb_bench
diff --git a/README.md b/README.md
index ef498c263..85d34d76d 100644
--- a/README.md
+++ b/README.md
@@ -16,6 +16,11 @@ Prepare to delve into the world of VDBBench, and let it guide you in uncovering
 VDBBench is sponsored by Zilliz，the leading opensource vectorDB company behind Milvus. Choose smarter with VDBBench - start your free test on [zilliz cloud](https://zilliz.com/) today!
 
 **Leaderboard:** https://zilliz.com/benchmark
+
+## 🎈 Announcement 🎈
+
+**June 2026 update:** Full Text Search has landed in VectorDBBench. We now benchmark BM25-style retrieval across supported backends, starting with MS MARCO and HotpotQA datasets, payload profiles, recall, QPS, and load metrics ready to compare. See the [VectorDBBench Full Text Search Release Note](docs/release/2026-06-full-text-search.md) for the full rollout details and caveats.
+
 ## Quick Start
 ### Prerequirement
 ``` shell
@@ -27,11 +32,6 @@ python >= 3.11
 pip install vectordb-bench
 ```
 
-**Install all database clients**
-
-``` shell
-pip install 'vectordb-bench[all]'
-```
 **Install the specific database client**
 
 ```shell
@@ -42,12 +42,11 @@ All the database client supported
 | Optional database client | install command                             |
 |--------------------------|---------------------------------------------|
 | pymilvus, zilliz_cloud (*default*)     | `pip install vectordb-bench`                |
-| all (*clients requirements might be conflict with each other*) | `pip install vectordb-bench[all]`           |
 | qdrant                   | `pip install vectordb-bench[qdrant]`        |
 | pinecone                 | `pip install vectordb-bench[pinecone]`      |
 | weaviate                 | `pip install vectordb-bench[weaviate]`      |
 | elastic, aliyun_elasticsearch| `pip install vectordb-bench[elastic]`       |
-| pgvector, pgvectorscale, pgdiskann, alloydb | `pip install vectordb-bench[pgvector]`      |
+| pgvector, pgvectorscale, pgdiskann, alloydb, vectorchord | `pip install vectordb-bench[pgvector]`      |
 | pgvecto.rs               | `pip install vectordb-bench[pgvecto_rs]`    |
 | redis                    | `pip install vectordb-bench[redis]`         |
 | memorydb                 | `pip install vectordb-bench[memorydb]`      |
@@ -61,11 +60,14 @@ All the database client supported
 | oceanbase                | `pip install vectordb-bench[oceanbase]`     |
 | hologres                 | `pip install vectordb-bench[hologres]`      |
 | tencent_es               | `pip install vectordb-bench[tencent_es]`    |
-| alisql                   | `pip install 'vectordb-bench[alisql]'`      |
+| alisql                   | `pip install vectordb-bench[alisql]`      |
+| polardb                  | `pip install vectordb-bench[polardb]`       |
 | doris                    | `pip install vectordb-bench[doris]`         |
 | zvec                     | `pip install vectordb-bench[zvec]`          |
 | endee                    | `pip install vectordb-bench[endee]`         |
 | lindorm                  | `pip install vectordb-bench[lindorm]`       |
+| volc_mysql               | `pip install vectordb-bench[volc_mysql]`    |
+| adbpg                    | `pip install vectordb-bench[adbpg]`         |
 
 ### Run
 
@@ -91,11 +93,21 @@ Options:
 Commands:
   pgvectorhnsw
   pgvectorivfflat
+  vectorchordrq
+  volcmysqlhnsw
   test
   weaviate
 ```
 To list the options for each command, execute `vectordbbench [command] --help`
 
+Use `--note` or `--note-file` to preserve deployment, resource, client, network, and constraint context in each result JSON under `task_config.db_config.note`. The options are mutually exclusive. Prefer `--note-file` for structured or multiline context, and never include credentials, tokens, or sensitive connection details.
+
+```shell
+vectordbbench zillizautoindex \
+  --note-file ./run-context.json \
+  <other options>
+```
+
 ```text
 $ vectordbbench pgvectorhnsw --help
 Usage: vectordbbench pgvectorhnsw [OPTIONS]
@@ -114,6 +126,9 @@ Options:
                                   Case type
   --db-label TEXT                 Db label, default: date in ISO format
                                   [default: 2024-05-20T20:26:31.113290]
+  --note TEXT                     Run context stored with each result
+                                  [default: ""]
+  --note-file FILE                Read run context from a UTF-8 text file
   --dry-run                       Print just the configuration and exit
                                   without running the tasks
   --k INTEGER                     K value for number of nearest neighbors to
@@ -184,6 +199,34 @@ Options:
   --help                          Show this message and exit.
 ```
 
+### Run VectorChord (vchordrq) from command line
+
+VectorChord is a PostgreSQL extension for scalable vector similarity search using IVF + RaBitQ indexing.
+It is fully compatible with pgvector data types and provides faster queries and index builds.
+
+```shell
+vectordbbench vectorchordrq \
+  --user-name postgres --password '<password>' \
+  --host localhost --port 5432 --db-name vectordb \
+  --case-type Performance1536D50K \
+  --lists 1000 --probes 10 --epsilon 1.9 \
+  --spherical-centroids --build-threads 8 \
+  --max-parallel-workers 15
+```
+
+Key VectorChord-specific options:
+| Option | Description |
+|--------|-------------|
+| `--lists` | Number of IVF lists for vchordrq index |
+| `--probes` | Number of probes during search (default: 10) |
+| `--epsilon` | Reranking precision factor, 0.0-4.0 (default: 1.9) |
+| `--residual-quantization` | Enable residual quantization |
+| `--spherical-centroids` | L2-normalize centroids (recommended for cosine/IP) |
+| `--build-threads` | Number of threads for index building (1-255) |
+| `--degree-of-parallelism` | Degree of parallelism for index build (1-256) |
+| `--max-parallel-workers` | Sets max_parallel_workers & max_parallel_maintenance_workers |
+| `--max-scan-tuples` | Max tuples to scan before stopping (-1 for unlimited) |
+
 ### Run awsopensearch from command line
 
 ```shell
@@ -225,12 +268,49 @@ Options:
 
   --ondisk                        Ondisk mode with binary quantization(32x compression)
   --oversample-factor             Controls the degree of oversampling applied to minority classes in imbalanced datasets to improve model performance by balancing class distributions.(default 1.0)
-  
 
   # Quantization Type
   --quantization-type TEXT        which type of quantization to use valid values [fp32, fp16, bq]
   --help                          Show this message and exit.
   ```
+
+### Run awsopensearch serverless from command line
+
+OpenSearch Serverless (AOSS) is a serverless deployment option for Amazon OpenSearch Service. VDBBench supports AOSS with the `--serverless` flag, which uses AWS SigV4 authentication and automatically skips unsupported operations (cluster settings, force merge, manual refresh, warmup API).
+
+**Prerequisites:**
+- AWS credentials configured (via `~/.aws/credentials`, environment variables, or IAM role)
+- Serverless dependencies installed (included in the `opensearch` extra): `pip install 'vectordb-bench[opensearch]'`. This installs `opensearch-py`, `boto3`, and `requests-aws4auth`.
+- IAM identity policy allowing `aoss:APIAccessAll` on the collection
+- AOSS Data Access Policy granting index/collection permissions to the IAM principal
+
+**Example: Run performance test on OpenSearch Serverless**
+
+```shell
+NUM_PER_BATCH=100 vectordbbench awsopensearch --db-label aoss \
+  --serverless --aws-region us-east-1 \
+  --host <collection-id>.aoss.us-east-1.on.aws --port 443 \
+  --case-type Performance768D1M \
+  --m 16 --ef-construction 200 --ef-search 40 \
+  --number-of-shards 8 --number-of-replicas 0 \
+  --engine faiss --metric-type cosine \
+  --num-concurrency 80,100,120
+```
+
+OpenSearch Serverless-specific options:
+
+| Option | Description |
+|--------|-------------|
+| `--serverless` | Enable OpenSearch Serverless mode (uses AWS SigV4 auth) |
+| `--aws-region` | AWS region for the AOSS collection (default: `us-east-1`) |
+| `NUM_PER_BATCH` | Number of vectors per Serverless bulk request (default: `100`) |
+
+> **Notes:**
+> - `--user` and `--password` are not needed for Serverless mode
+> - `--engine` is accepted but ignored internally (AOSS manages the engine)
+> - `--force-merge-enabled`, `--refresh-interval`, `--flush-threshold-size`, and `--cb-threshold` are ignored for Serverless
+> - Keep `NUM_PER_BATCH` small enough for the Serverless bulk API request limits
+
 ### Run Elastic Cloud from command line
 
 Elastic Cloud supports multiple index types: HNSW, HNSW_INT8, HNSW_INT4, and HNSW_BBQ.
@@ -294,13 +374,13 @@ Options:
   # Connection
   --cloud-id TEXT                 Elastic Cloud ID  [required]
   --password TEXT                 Elastic Cloud password  [required]
-  
+
   # HNSW Index Parameters
   --m INTEGER                     HNSW M parameter  [default: 16]
   --ef-construction INTEGER       HNSW efConstruction parameter  [default: 100]
   --num-candidates INTEGER        Number of candidates for search  [default: 100]
   --element-type [float|byte]     Element type for vectors (float: 4 bytes, byte: 1 byte)  [default: float]
-  
+
   # Index Configuration
   --number-of-shards INTEGER      Number of shards  [default: 1]
   --number-of-replicas INTEGER    Number of replicas  [default: 0]
@@ -311,7 +391,7 @@ Options:
   --use-routing BOOLEAN           Whether to use routing  [default: False]
   --use-rescore BOOLEAN           Whether to use rescore  [default: False]
   --oversample-ratio FLOAT        Oversample ratio for rescore  [default: 2.0]
-  
+
   # Common Options
   --case-type [CapacityDim128|CapacityDim960|Performance768D100M|...]
                                   Case type
@@ -401,7 +481,8 @@ Execute tests for the index types: HGraph.
 NUM_PER_BATCH=10000 vectordbbench hologreshgraph --host Hologres_Endpoint --port 80 \
 --user ACCESS_ID --password ACCESS_KEY --database DATABASE_NAME \
 --m 64 --ef-construction 400 --case-type Performance768D10M \
---index-type HGraph --ef-search 400 --k 10 --num-concurrency 1,60,70,75,80,90,95,100,110,120
+--index-type HGraph --ef-search 400 --k 10 --num-concurrency 1,60,70,75,80,90,95,100,105,110,115,120,125,130 \
+--serial-cooldown 3
 ```
 
 To list the options for Hologres, execute `vectordbbench hologreshgraph --help`, The following are some Hologres-specific command-line options.
@@ -491,7 +572,7 @@ Lindorm supports index types: hnsw, ivfpq, or ivfbq.
 ```shell
 vectordbbench lindormhnsw --case-type Performance768D10M --index-name <index_name> --k 10 \
 --host <lindorm_host> --port <lindorm_port> --user <username> --password <password> --m 32 \
---ef-construction 400 --ef-search 150
+--ef-construction 400 --ef-search 150 --reorder-factor 2
 ```
 
 **Example: Run ivfpq index test**
@@ -525,8 +606,127 @@ To list the options for Lindorm, execute `vectordbbench lindormhnsw --help`, The
   --m INTEGER                     hnsw m  [required]
   --ef-construction INTEGER       hnsw ef-construction  [required]
   --ef-search INTEGER             hnsw ef-search  [required]
+  --reorder-factor INTEGER        reorder factor
+```
+
+### Run ADBPG (Aliyun AnalyticDB for PostgreSQL) from command line
+
+ADBPG Nova uses the fastann/Nova vector index engine with `USING ann` syntax.
+
+**Example: Run novamr index benchmark (BioASQ 1M, 1024-dim)**
+
+```shell
+vectordbbench adbpgnova --case-type Performance1024D1M --k 10 \
+--host <adbpg_host> --port 5432 --db-name postgres \
+--user-name <username> --password <password> \
+--algorithm novamr --hnsw-m 48 --ef-construction 600 \
+--ef-search 130 --max-scan-points 5000 --quantize-rescore-amp 2.0
 ```
 
+**Example: Run from config file**
+
+```shell
+vectordbbench adbpgnova --config-file adbpg_bioasq1m_novamr.yml
+```
+
+To list the options for ADBPG, execute `vectordbbench adbpgnova --help`. The following are some ADBPG-specific command-line options.
+
+```text
+  --user-name TEXT                Db username  [required]
+  --password TEXT                 Postgres database password  [$POSTGRES_PASSWORD]
+  --host TEXT                     Db host  [required]
+  --port INTEGER                  Postgres database port  [default: 5432]
+  --db-name TEXT                  Db name  [required]
+  --algorithm TEXT                algorithm  [default: novamr]
+  --hnsw-m INTEGER                hnsw_m  [default: 16]
+  --ef-construction INTEGER       ef_construction  [default: 200]
+  --ef-search INTEGER             ef_search  [default: 100]
+  --max-scan-points INTEGER       max scan points  [default: 2000]
+  --quantize-rescore-amp FLOAT    fastann.quantize_rescore_amp  [default: 1.0]
+  --nova-adaptive-gamma FLOAT     fastann.nova_adaptive_gamma  [default: 0.0]
+  --auto-reduction/--no-auto-reduction  Index WITH auto_reduction=on  [default: False]
+```
+
+### Run PolarDB from command line
+
+PolarDB supports index types: faiss_hnsw_flat, faiss_hnsw_pq, and faiss_hnsw_sq.
+
+**Example: Run faiss_hnsw_flat benchmark**
+
+```shell
+vectordbbench polardbhnswflat \
+  --case-type Performance768D1M \
+  --username <db_user> \
+  --password '<db_password>' \
+  --host <db_host> \
+  --port 3306 \
+  --m 16 \
+  --ef-construction 256 \
+  --ef-search 256 \
+  --insert-workers 64 \
+  --num-concurrency '10,20,40,60,80' \
+  --concurrency-duration 60 \
+  --task-label <task_label> \
+  --db-label <db_label> \
+  --skip-search-serial \
+  --post-load-index
+```
+
+To list the options for PolarDB, execute `vectordbbench polardbhnswflat --help`. The following are some PolarDB-specific command-line options.
+
+```text
+  --username TEXT                  Username  [required]
+  --password TEXT                  Password
+  --host TEXT                      Db host  [default: 127.0.0.1]
+  --port INTEGER                   Db Port  [default: 3306]
+  --database TEXT                  Database name  [default: vectordbbench]
+  --m INTEGER                      M parameter (max_degree) in HNSW
+  --ef-construction INTEGER        ef_construction parameter in HNSW
+  --ef-search INTEGER              polar_vector_index_hnsw_ef_search session variable
+  --insert-workers INTEGER         Number of concurrent threads for data insertion
+  --post-load-index / --inline-index
+                                   Create index after load or inline at table creation
+```
+
+### Run VolcMySQL from command line
+
+VolcMySQL is a MySQL-compatible service with a native `VECTOR` type and an HNSW vector index (created via `SECONDARY_ENGINE_ATTRIBUTE`). Optional quantization is configurable through `--quant-algorithm` (`NONE`, `SQ`, `PQ`) and `--quant-type` (`16_bit`, `8_bit`, `4_bit`, `binary`).
+
+```shell
+vectordbbench volcmysqlhnsw \
+  --case-type Performance1536D50K \
+  --username <db_user> \
+  --password '<db_password>' \
+  --host <db_host> \
+  --port 3306 \
+  --m 16 \
+  --ef-construction 128 \
+  --ef-search 100 \
+  --quant-algorithm SQ \
+  --quant-type 16_bit \
+  --num-concurrency '10,20,40,60,80' \
+  --concurrency-duration 30 \
+  --task-label <task_label> \
+  --db-label <db_label>
+```
+
+To list the options for VolcMySQL, execute `vectordbbench volcmysqlhnsw --help`. The following are some VolcMySQL-specific command-line options.
+
+```text
+  --username TEXT                  Username  [required]
+  --password TEXT                  Password  [required]
+  --host TEXT                      Db host  [default: 127.0.0.1]
+  --port INTEGER                   DB Port  [default: 3306]
+  --m INTEGER                      M parameter in HNSW vector indexing
+  --ef-search INTEGER              Session variable loose_hnsw_ef_search
+  --ef-construction INTEGER        HNSW ef_construction
+  --quant-algorithm [NONE|SQ|PQ]   Quantization algorithm
+  --quant-type [16_bit|8_bit|4_bit|binary]
+                                   Quantization type
+```
+
+> Note: vectors are loaded and queried over the raw-binary `VECTOR` path by default; the client auto-probes server support and falls back to `to_vector()` text when unavailable. Set `VDB_BINARY_VEC=0` to force the text path or `1` to force binary.
+
 #### Using a configuration file.
 
 The vectordbbench command can optionally read some or all the options from a yaml formatted configuration file.
@@ -644,6 +844,17 @@ vectordbbench batchcli --batch-config-file <your-yaml-configuration-file>
 ### Introduction
 To facilitate the presentation of test results and provide a comprehensive performance analysis report, we offer a [leaderboard page](https://zilliz.com/benchmark). It allows us to choose from QPS, QP$, and latency metrics, and provides a comprehensive assessment of a system's performance based on the test results of various cases and a set of scoring mechanisms (to be introduced later). On this leaderboard, we can select the systems and models to be compared, and filter out cases we do not want to consider. Comprehensive scores are always ranked from best to worst, and the specific test results of each query will be presented in the list below.
 
+### Cloud Leaderboard
+
+VectorDBBench now includes Cloud Leaderboard cases for production-oriented cloud vector database evaluation. These cases complement the original raw-performance leaderboard by measuring behaviors that matter for managed services:
+
+- `CloudInsertCase`: insert throughput plus searchable and indexed readiness delays.
+- `CloudPayloadSearchCase`: search performance when responses return IDs only, scalar metadata, or vectors.
+- `CloudMultiTenantSearchCase`: tenant-routed search for SaaS-shaped workloads.
+- `CloudColdLatencyCase`: cold and warm serial latency for first-query and cache-sensitive serving paths.
+
+The May 2026 release note explains why the Cloud Leaderboard was added, what changed, which systems were tested this round, and how to run each new case: [docs/release/2026-05-cloud-leaderboard.md](docs/release/2026-05-cloud-leaderboard.md).
+
 ### Scoring Rules
 
 1. For each case, select a base value and score each system based on relative values.
@@ -721,9 +932,9 @@ Now we can only run one task at the same time.
 ### Code Structure
 ![image](https://github.com/zilliztech/VectorDBBench/assets/105927039/8c06512e-5419-4381-b084-9c93aed59639)
 ### Client
-Our client module is designed with flexibility and extensibility in mind, aiming to integrate APIs from different systems seamlessly. As of now, it supports Milvus, Zilliz Cloud, Elastic Search, Pinecone, Qdrant Cloud, Weaviate Cloud, PgVector, Redis, Chroma, CockroachDB, etc. Stay tuned for more options, as we are consistently working on extending our reach to other systems.
+Our client module is designed with flexibility and extensibility in mind, aiming to integrate APIs from different systems seamlessly. As of now, it supports Milvus, Zilliz Cloud, Elastic Search, Pinecone, Qdrant Cloud, Weaviate Cloud, PgVector, VectorChord, Redis, Chroma, CockroachDB, VolcMySQL, etc. Stay tuned for more options, as we are consistently working on extending our reach to other systems.
 ### Benchmark Cases
-We've developed lots of comprehensive benchmark cases to test vector databases' various capabilities, each designed to give you a different piece of the puzzle. These cases are categorized into four main types:
+We've developed lots of comprehensive benchmark cases to test vector databases' various capabilities, each designed to give you a different piece of the puzzle. These cases are categorized into several main types:
 #### Capacity Case
 - **Large Dim:** Tests the database's loading capacity by inserting large-dimension vectors (GIST 100K vectors, 960 dimensions) until fully loaded. The final number of inserted vectors is reported.
 - **Small Dim:** Similar to the Large Dim case but uses small-dimension vectors (SIFT 500K vectors, 128 dimensions).
@@ -735,6 +946,11 @@ We've developed lots of comprehensive benchmark cases to test vector databases'
 #### Filtering Search Performance Case
 - **Int-Filter Cases:** Evaluates search performance with int-based filter expression (e.g.  "id >= 2,000").
 - **Label-Filter Cases:** Evaluates search performance with label-based filter expressions (e.g., "color == 'red'"). The test includes randomly generated labels to simulate real-world filtering scenarios.
+#### Full Text Search Performance Case
+- **FullTextSearchPerformance:** Measures BM25-style text retrieval over raw text documents. The case inserts documents, runs the backend optimization or index-readiness step, then measures recall, latency, and QPS for text queries.
+- **Datasets:** The initial FTS benchmark uses MS MARCO and HotpotQA in small, medium, and large corpus sizes.
+- **Ground truth:** Recall, MRR, and NDCG are computed against positive semantic relevance labels from `ir_datasets`.
+- **Payload profiles:** FTS supports IDs-only responses and text payload responses so users can compare pure retrieval throughput against response-size overhead.
 #### Streaming Cases
 - **Insertion-Under-Load Case:** Evaluates search performance while maintaining a constant insertion workload. VDBBench applies a steady stream of insert requests at a fixed rate to simulate real-world scenarios where search operations must perform reliably under continuous data ingestion.
 
diff --git a/docs/release/2026-05-cloud-leaderboard.md b/docs/release/2026-05-cloud-leaderboard.md
new file mode 100644
index 000000000..c9dc76a7b
--- /dev/null
+++ b/docs/release/2026-05-cloud-leaderboard.md
@@ -0,0 +1,170 @@
+# VectorDBBench Cloud Leaderboard Release Note
+
+May 2026
+
+The VectorDBBench Cloud Leaderboard moves beyond a single raw-throughput ranking. It evaluates managed vector databases around the behaviors production teams have to plan for: ingest readiness, payload-aware search, tenant-shaped workloads, cold latency, and cost at practical QPS targets.
+
+## Why we need a new leaderboard now
+
+The vector database market has moved past the "highest QPS wins" phase. Production teams choosing a managed vector database also care about budget, data freshness, tail latency, recall, metadata payloads, tenant isolation, and operational predictability.
+
+The existing VectorDBBench leaderboard remains useful for comparing baseline search performance across systems. But cloud buyers ask a wider set of questions:
+
+- When does newly inserted data become searchable?
+- When is it fully indexed?
+- What happens when search returns metadata or vectors instead of only IDs?
+- What happens when traffic is split across many tenants?
+- What does each reachable QPS tier cost?
+
+The Cloud Leaderboard is designed around those questions. It keeps performance visible, but puts it next to the readiness, payload, tenant, cold-start, and cost signals that determine what a customer can safely deploy.
+
+## What the Cloud Leaderboard changes
+
+The Cloud Leaderboard is a production cloud decision layer, not a replacement for the original raw-performance board. The main change is that benchmark cases now model cloud operating concerns directly instead of treating all products as simple warm search engines.
+
+The new cases add:
+
+- Insert readiness measurement, including client insert completion, searchable delay, and indexed delay.
+- Explicit response payload profiles: IDs only, scalar label metadata, or vector values.
+- Cloud cold-latency measurement for the first search path after idle or cache-cold conditions.
+- Multi-tenant search, where data is split into deterministic tenant labels or namespaces and queries are routed by tenant.
+- Cost-oriented interpretation, so raw QPS can be read together with monthly cost and readiness constraints.
+
+This matters because a top-line QPS table can hide important tradeoffs. A system can look strong on peak throughput while losing ground on recall, p99 latency, cold-start behavior, payload cost, or sustained cost at the same target QPS.
+
+## Who we tested this round
+
+This round focuses on three popular cloud vector databases:
+
+- Zilliz Cloud, including tiered and fixed-capacity configurations.
+- turbopuffer, including normal, pinned, and backpressure-related configurations where applicable.
+- Pinecone serverless.
+
+The tested matrix is intentionally cloud-oriented. It compares managed products and managed-service modes rather than only local or self-hosted engine behavior.
+
+## The new tests we added
+
+Version 2 adds four cloud-oriented cases in VectorDBBench. Each case is designed to expose a production behavior that a plain QPS benchmark can miss.
+
+### CloudInsertCase
+
+**Purpose.** CloudInsertCase measures write readiness, not just client-side insert speed. This is important for backfills, migrations, daily refreshes, and release workflows where a team needs to know when newly written vectors can safely take traffic.
+
+**How it works.** The case loads the dataset with `ConcurrentInsertRunner`, records insert completion time and rows per second, then polls the database until inserted data is fully searchable and fully indexed. The resulting metric separates:
+
+- `insert_completion_seconds`
+- `insert_rows_per_second`
+- `searchable_after_insert_seconds`
+- `indexed_after_searchable_seconds`
+
+Example: run LAION 100M insert readiness on Zilliz Cloud with a 10k batch size.
+
+```bash
+vectordbbench zillizautoindex \
+  --case-type CloudInsertCase \
+  --uri "$ZILLIZ_URI" \
+  --token "$ZILLIZ_TOKEN" \
+  --collection-name cloud_insert_laion100m_bs10k \
+  --cloud-insert-batch-size 10000 \
+  --load-concurrency 16 \
+  --skip-search-serial \
+  --skip-search-concurrent \
+  --task-label cloud-insert-zilliz-12cu
+```
+
+### CloudPayloadSearchCase
+
+**Purpose.** CloudPayloadSearchCase measures search when the response body resembles production traffic. Many applications return more than vector IDs: they return scalar metadata, labels, or the vector values themselves. That response payload can change throughput, latency, and even product ranking.
+
+**How it works.** The case extends the normal performance case with an explicit `payload_profile`. Supported profiles are:
+
+- `ids_only`
+- `scalar_label`
+- `vector`
+
+The case can also run unfiltered search, integer-filter search through `--cloud-filter-rate`, or scalar-label filter search through `--cloud-label-percentage`. It records QPS, latency, recall where applicable, and estimated response payload bytes per query.
+
+When `payload_profile` is `scalar_label`, VectorDBBench materializes scalar label data even for unfiltered runs. This keeps the loaded schema aligned with the requested response payload instead of only loading labels for scalar-label filter runs.
+
+Example: run vector-payload search on Pinecone with a highly selective integer filter.
+
+```bash
+vectordbbench pinecone \
+  --case-type CloudPayloadSearchCase \
+  --api-key "$PINECONE_API_KEY" \
+  --index-name "$PINECONE_INDEX" \
+  --payload-profile vector \
+  --cloud-filter-rate 0.001 \
+  --k 100 \
+  --num-concurrency 60,80 \
+  --concurrency-duration 30 \
+  --task-label cloud-payload-pinecone-vector-int-filter-0-1p
+```
+
+### CloudMultiTenantSearchCase
+
+**Purpose.** CloudMultiTenantSearchCase models SaaS-shaped traffic. Instead of treating the dataset as one flat global collection, it splits records across many tenants and routes each query to a tenant. This highlights products whose namespace, partition-key, or tenant-filter paths behave differently from single-tenant search.
+
+**How it works.** The case defaults to the Cohere 10M dataset and assigns each row to a deterministic tenant by `row_id % tenant_count`. During search, queries are routed to the corresponding tenant label or namespace. The case supports the same payload profiles and optional filter modes as payload search.
+
+Tenant routing labels and scalar payload labels are separate concepts. A multi-tenant run can route by tenant while still storing and returning scalar-label payload data when `payload_profile` is `scalar_label`, and scalar-label filters continue to use the scalar label field rather than the tenant routing field.
+
+TurboPuffer tenant namespace cache warmup is explicit. By default, `CloudMultiTenantSearchCase` does not warm tenant namespaces during `optimize()`; use `--multitenant-warmup-policy all` only when the benchmark should model proactively warmed tenant namespaces.
+
+Example: run 1,000-tenant IDs-only search on turbopuffer.
+
+```bash
+vectordbbench turbopuffer \
+  --case-type CloudMultiTenantSearchCase \
+  --dataset-with-size-type "Large Cohere (768dim, 10M)" \
+  --api-key "$TURBOPUFFER_API_KEY" \
+  --region aws-us-east-1 \
+  --namespace vdbbench_mt_seed \
+  --multitenant-namespace-prefix vdbbench_mt_ \
+  --tenant-count 1000 \
+  --tenant-prefix tenant_ \
+  --tenant-id-width 4 \
+  --payload-profile ids_only \
+  --num-concurrency 40,60,80 \
+  --concurrency-duration 30 \
+  --task-label cloud-multitenant-turbopuffer-1000t
+```
+
+### CloudColdLatencyCase
+
+**Purpose.** CloudColdLatencyCase measures first-query and cold-path latency that warm benchmark loops can hide. This matters for serverless products, storage-tiered products, idle workloads, and customer-facing applications where the first query after an idle period is visible to users.
+
+**How it works.** The case is intentionally search-only and must run against an existing collection that has already become cold according to the product's cache and storage behavior. It rejects `drop_old` and `load` stages because insert-then-immediately-search runs can leave caches, indexing paths, or vendor warmup APIs in an ambiguous state. `ColdWarmSearchRunner` runs serial searches in cold and warm passes. It records cold-latency details in `additional_parameters["cold_latency"]` and also records payload profile and estimated payload bytes per query.
+
+Example: run a pinned turbopuffer cold-latency test with scalar-label payloads.
+
+```bash
+vectordbbench turbopuffer \
+  --case-type CloudColdLatencyCase \
+  --skip-drop-old \
+  --skip-load \
+  --api-key "$TURBOPUFFER_API_KEY" \
+  --region aws-us-east-1 \
+  --namespace cloud_cold_latency_scalar_label \
+  --pin-namespace \
+  --pin-replicas 2 \
+  --payload-profile scalar_label \
+  --cloud-cold-query-count 1000 \
+  --skip-search-concurrent \
+  --task-label cloud-cold-latency-turbopuffer-pinned-scalar-label
+```
+
+## Caveats
+
+This release note introduces the new Cloud Leaderboard direction; it is not the full benchmark report. Detailed tables, raw JSON artifacts, pricing worksheets, and edge-case analysis should live in the benchmark report or external result artifact repository.
+
+Important caveats:
+
+- Pricing changes over time. Cost charts need a pricing date, region, and configuration assumptions.
+- Managed-service configuration can materially change results, especially for serverless scaling, pinned replicas, capacity units, and storage-tiering modes.
+- "Fully indexed" and "fully searchable" readiness may be exposed differently by each vendor, so the implementation must document how each status is detected or inferred.
+- The current multi-tenant case uses deterministic tenant assignment and uniform tenant routing. It does not represent every SaaS tenant distribution.
+- Multi-tenant routing labels or namespaces are not equivalent to scalar payload labels. Benchmark clients must keep those fields separate when a run combines tenant routing with scalar-label payload or filter behavior.
+- Cold latency depends on cache state, idle window, replica pinning, storage architecture, and service warmup behavior. The idle and warmup rules must stay strict between products.
+- Payload search rankings are workload-specific. IDs-only, scalar-label, vector-return, integer-filter, and label-filter runs can produce different winners.
+- Cost Pareto results must be read together with recall, latency, payload profile, and readiness constraints rather than as a standalone ranking.
diff --git a/docs/release/2026-06-full-text-search.md b/docs/release/2026-06-full-text-search.md
new file mode 100644
index 000000000..6c7a35a7b
--- /dev/null
+++ b/docs/release/2026-06-full-text-search.md
@@ -0,0 +1,118 @@
+# VectorDBBench Full Text Search Release Note
+
+June 2026
+
+Full text search support adds BM25-style text retrieval workloads to VectorDBBench. The goal is to compare database full text search paths with the same benchmark harness used for vector workloads: repeatable datasets, explicit load/search stages, structured result JSONs, and frontend result visualization.
+
+## Context
+
+VectorDBBench has historically focused on dense vector search and vector-oriented cloud cases. That leaves a gap for systems that also expose native text retrieval, sparse search, or BM25 ranking. Users evaluating retrieval systems often need to compare text-only retrieval before deciding whether to use dense vector, sparse vector, hybrid, or reranking layers.
+
+The Full Text Search benchmark covers that text-only layer. It measures end-to-end behavior for indexing raw text, optimizing the backend, searching with BM25-style ranking, and validating retrieval quality against semantic relevance labels from `ir_datasets`.
+
+The benchmark also records payload behavior. Some applications return only document IDs, while others return text fields in the search response. VectorDBBench models both paths through explicit payload profiles so throughput and latency can be interpreted together with the response shape.
+
+## Who we tested this round
+
+This round focuses on full text search support across the following backends:
+
+- Milvus, using its full text search BM25 path.
+- Zilliz Cloud, using the cloud full text search path.
+- Elasticsearch, using its BM25 text search path.
+- OSS OpenSearch, using its BM25 text search path.
+- Vespa, using BM25 ranking over indexed text fields.
+- turbopuffer, using its full text search namespace path.
+
+The benchmark is designed to keep the workload shape consistent while still recording backend-specific behavior. BM25 parameters and analyzer settings are controlled through the backend case config or CLI flags, so users can run either product-default comparisons or explicitly tuned comparisons.
+
+## The new tests we added
+
+### FullTextSearchPerformance
+
+**Purpose.** FullTextSearchPerformance measures BM25-style full text search as a first-class benchmark case. It answers the baseline question: after a backend indexes the same text corpus, what QPS, latency, recall, MRR, and NDCG does it deliver for text queries?
+
+**How it works.** The case loads raw text documents, builds the backend text index, runs the backend optimize path, executes optional serial quality checks, and then runs concurrent search. The result metric records load duration, insert duration, optimize duration, QPS, serial latency, concurrent latency, recall, MRR, NDCG, payload profile, inserted count, and explicit backend parameters.
+
+Example: run MS MARCO small on Milvus with IDs-only responses.
+
+```bash
+vectordbbench milvusfts \
+  --case-type FTSBm25Performance \
+  --dataset-with-size-type "MS MARCO Small (100K documents)" \
+  --uri "$MILVUS_URI" \
+  --payload-profile ids_only \
+  --load-concurrency 0 \
+  --num-concurrency 40,80 \
+  --task-label fts-milvus-msmarco-small-ids
+```
+
+### Full text search datasets and semantic ground truth
+
+**Purpose.** Full text search quality should measure whether a backend returns documents that are semantically relevant to the query, not only whether it reproduces one particular BM25 implementation's exact ranking. This aligns the benchmark with common IR evaluation practice and makes recall, MRR, and NDCG comparable across backend analyzers and ranking implementations.
+
+**How it works.** FTS datasets provide raw text for document insertion and query execution through `ir_datasets`. VectorDBBench loads positive qrels from the same source and uses them as semantic ground truth for serial quality checks. BM25 and analyzer settings are not loaded from dataset manifests; use backend config or CLI flags when a run needs explicit `k1`, `b`, analyzer, or backend-specific search parameters.
+
+Example: use a larger dataset while keeping the same FTS case type.
+
+```bash
+vectordbbench elasticcloudhnsw \
+  --case-type FTSBm25Performance \
+  --dataset-with-size-type "HotpotQA Medium (1M documents)" \
+  --host "$ELASTIC_HOST" \
+  --port "$ELASTIC_PORT" \
+  --password "$ELASTIC_PASSWORD" \
+  --payload-profile ids_only \
+  --load-concurrency 0 \
+  --num-concurrency 40,80 \
+  --task-label fts-elastic-hotpotqa-medium-ids
+```
+
+### Payload-aware full text search
+
+**Purpose.** Payload-aware FTS measures the cost of returning text fields, not only document IDs. This matters for applications where search results immediately include snippets, source text, or other fields needed by downstream ranking and display layers.
+
+**How it works.** The FTS case supports `payload_profile` values including `ids_only` and `text`. IDs-only runs are the recall baseline because they can run serial search against the ground truth without paying text-return overhead. Text payload runs measure concurrent search throughput and latency for larger response bodies; they can skip serial recall when the same indexed namespace or collection has already been validated by the IDs-only run.
+
+Example: run Vespa with text payload responses and concurrent search only.
+
+```bash
+vectordbbench vespa \
+  --case-type FTSBm25Performance \
+  --dataset-with-size-type "MS MARCO Medium (1M documents)" \
+  --uri "$VESPA_URI" \
+  --port "$VESPA_PORT" \
+  --payload-profile text \
+  --load-concurrency 0 \
+  --skip-search-serial \
+  --num-concurrency 40,80 \
+  --task-label fts-vespa-msmarco-medium-text
+```
+
+## Result artifacts
+
+Committed FTS result JSONs live under `vectordb_bench/results/FullTextSearch/<Backend>/`. Published artifacts are consolidated so each backend directory contains one backend-level JSON with multiple case results in its `results` list, instead of a pile of one-case files. That keeps the release payload readable while preserving the per-dataset and per-payload records needed by the dashboard.
+
+Use the result collector to consolidate split FTS run outputs before committing published examples:
+
+```bash
+python -m vectordb_bench.backend.result_collector \
+  vectordb_bench/results/FullTextSearch \
+  --merge-by-db \
+  --task-label fts_standard \
+  --replace
+```
+
+The default collector behavior still groups normal benchmark files by `run_id`. The `--merge-by-db` mode is intended for curated FTS result publication, where each backend should present the latest benchmark matrix as a single artifact. The Full Text Search frontend reads the latest backend-level result file from each backend directory and displays the committed matrix by backend, dataset, payload profile, load duration, QPS, recall, and latency.
+
+## Caveats
+
+This release note introduces the FTS benchmark path; it is not a complete benchmark report. Detailed raw outputs, machine setup, backend deployment scripts, and rerun notes should live in separate experiment reports when needed.
+
+Important caveats:
+
+- Semantic relevance labels are not the same as exact BM25 implementation correctness. A high quality score means the backend retrieved judged-relevant documents, not that it matched a specific analyzer or scorer bit-for-bit.
+- Analyzer behavior can materially affect recall and ranking. Tokenization, lowercase filters, stop words, stemming, token length limits, and field normalization should be recorded with the benchmark run when they are configured explicitly.
+- BM25 parameter support differs across products. Some backends expose `k1` and `b`, some expose average field length controls, and some compute or hide those values internally. Use backend config or CLI flags for explicit comparisons; otherwise the run represents product-default behavior.
+- IDs-only and text payload runs answer different questions. IDs-only is the cleanest recall and throughput baseline; text payload runs expose response-size overhead.
+- Load duration includes backend-specific insert and optimize behavior. Products may differ in whether optimize means force merge, compaction, warmup, or index deployment readiness.
+- Result JSONs under `vectordb_bench/results/FullTextSearch` are curated examples for the frontend. They should not be treated as the full historical experiment archive.
diff --git a/install/requirements_py3.11.txt b/install/requirements_py3.11.txt
index 5d5702492..a6f3f32cf 100644
--- a/install/requirements_py3.11.txt
+++ b/install/requirements_py3.11.txt
@@ -5,6 +5,8 @@ pinecone
 weaviate-client
 elasticsearch==8.16.0
 opensearch-py
+boto3
+requests-aws4auth
 pgvector
 pgvecto_rs[psycopg3]>=0.2.1
 sqlalchemy
@@ -14,17 +16,20 @@ pytz
 streamlit-autorefresh
 streamlit>=1.23.0
 streamlit_extras
+tornado>=6.0
 tqdm
 s3fs
 psutil
 polars
 plotly
 environs
-pydantic<v2
+pydantic>=2.0,<3
 scikit-learn
-pymilvus
+pymilvus>=2.6.15,<3.0.0
 clickhouse_connect
 pyvespa
 mysql-connector-python
+PyMySQL
 packaging
-hdrhistogram>=0.10.1
\ No newline at end of file
+hdrhistogram>=0.10.1
+ir_datasets
diff --git a/pyproject.toml b/pyproject.toml
index c240ae55a..ddbd3425b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,7 +8,7 @@ build-backend = "setuptools.build_meta"
 
 [tool.setuptools.packages.find]
 where = ["."]
-include = ["vectordb_bench", "vectordb_bench.cli"]
+include = ["vectordb_bench", "vectordb_bench.*"]
 
 [project]
 name = "vectordb-bench"
@@ -26,10 +26,10 @@ classifiers = [
 ]
 dependencies = [
     "click",
+    "pyyaml",
     "pytz",
-    "streamlit-autorefresh",
-    "streamlit<1.44,!=1.34.0", # There is a breaking change in 1.44 related to get_page https://discuss.streamlit.io/t/from-streamlit-source-util-import-get-pages-gone-in-v-1-44-0-need-urgent-help/98399
-    "streamlit_extras",
+    "streamlit>=1.61,<2",  # Stateful tabs and stretch widths used by the FTS page
+    "tornado>=6.0",
     "tqdm",
     "s3fs",
     "oss2",
@@ -37,11 +37,12 @@ dependencies = [
     "polars",
     "plotly",
     "environs",
-    "pydantic>=2.0",
+    "pydantic>=2.0,<3",
     "scikit-learn",
-    "pymilvus", # with pandas, numpy, ujson
-    "ujson",
+    "pymilvus>=2.6.15,<3.0.0", # with pandas, numpy
     "hdrhistogram>=0.10.1",
+    "ujson",
+    "ir_datasets",
 ]
 dynamic = ["version"]
 
@@ -51,41 +52,11 @@ test = [
     "ruff",
     "pytest",
 ]
-restful = [ "flask" ]
-
-all = [
-    "grpcio==1.53.0", # for qdrant-client and pymilvus
-    "grpcio-tools==1.53.0", # for qdrant-client and pymilvus
-    "qdrant-client",
-    "pinecone",
-    "weaviate-client",
-    "elasticsearch",
-    "sqlalchemy",
-    "redis",
-    "chromadb",
-    "pgvector",
-    "psycopg",
-    "psycopg-binary",
-    "pgvecto_rs[psycopg3]>=0.2.2",
-    "opensearch-dsl",
-    "opensearch-py",
-    "memorydb",
-    "alibabacloud_ha3engine_vector",
-    "mariadb",
-    "PyMySQL",
-    "clickhouse-connect",
-    "pyvespa",
-    "lancedb",
-    "mysql-connector-python",
-    "turbopuffer[fast]",
-    'zvec',
-    "endee==0.1.10", # TODO: check pydantic v2 compat
-]
-
+restful         = [ "flask" ]
 qdrant          = [ "qdrant-client" ]
 pinecone        = [ "pinecone" ]
-weaviate        = [ "weaviate-client" ]
-elastic         = [ "elasticsearch" ]
+weaviate        = [ "weaviate-client>=3.26.7,<4" ]
+elastic         = [ "elasticsearch>=8,<9" ]
 # For elastic and aliyun_elasticsearch
 
 pgvector        = [ "psycopg", "psycopg-binary", "pgvector" ]
@@ -95,7 +66,7 @@ pgvecto_rs      = [ "pgvecto_rs[psycopg3]>=0.2.2" ]
 redis           = [ "redis" ]
 memorydb        = [ "memorydb" ]
 chromadb        = [ "chromadb" ]
-opensearch      = [ "opensearch-py" ]
+opensearch      = [ "opensearch-py", "boto3", "requests-aws4auth" ]
 aliyun_opensearch = [ "alibabacloud_ha3engine_vector" ]
 mongodb         = [ "pymongo" ]
 mariadb         = [ "mariadb" ]
@@ -105,12 +76,17 @@ clickhouse      = [ "clickhouse-connect" ]
 vespa           = [ "pyvespa" ]
 lancedb         = [ "lancedb" ]
 oceanbase       = [ "mysql-connector-python" ]
-alisql          = [ "mysql-connector-python" ]
+alisql          = [ "mysqlclient" ]
+polardb         = [ "PyMySQL" ]
 doris           = [ "doris-vector-search" ]
 turbopuffer     = [ "turbopuffer" ]
 zvec            = [ "zvec" ]
 endee           = [ "endee==0.1.10" ]
 lindorm         = [ "opensearch-py" ]
+seekdb          = [ "mysql-connector-python" ]
+volc_mysql      = [ "mysql-connector-python" ]
+pinot           = [ "requests" ]
+adbpg           = [ "psycopg", "psycopg-binary", "pgvector" ]
 antfly          = [ "httpx" ]
 
 [project.urls]
@@ -157,7 +133,7 @@ lint.ignore = [
     "INP001", # TODO
     "TID252", # TODO
     "N801", "N802", "N815",
-    "S101", "S108", "S603", "S311",
+    "S101", "S108", "S603", "S311", "S608",
     "PLR2004",
     "RUF017",
     "C416",
@@ -241,4 +217,3 @@ builtins-ignorelist = [
 "vectordb_bench/backend/clients/*" = ["PLC0415"]
 "vectordb_bench/cli/batch_cli.py" = ["PLC0415"]
 "vectordb_bench/backend/data_source.py" = ["PLC0415"]
-
diff --git a/tests/pytest.ini b/tests/pytest.ini
index ad082137c..9f5751ee3 100644
--- a/tests/pytest.ini
+++ b/tests/pytest.ini
@@ -1,4 +1,8 @@
 [pytest]
 
-filterwarnings = 
+filterwarnings =
     ignore::UserWarning
+    ignore::DeprecationWarning
+
+markers =
+    integration: tests that require external services or network access (deselect with -m "not integration")
diff --git a/tests/test_adbpg.py b/tests/test_adbpg.py
new file mode 100644
index 000000000..465acf762
--- /dev/null
+++ b/tests/test_adbpg.py
@@ -0,0 +1,210 @@
+"""Unit tests for the ADB-PG Nova client config layer.
+
+These tests do not require a live database — they only exercise:
+  - AdbpgConfig defaults and connection-string assembly
+  - AdbpgIndexConfig.index_param() WITH-clause options (incl. raw auto_reduction)
+  - AdbpgIndexConfig.session_param() fastann GUC emission
+  - TestResult.read_file() round-trip when password is absent in saved JSON
+    (regression for the result-loading failure caused by polymorphic
+    serialization stripping subclass fields from DBConfig)
+
+Usage:
+  pytest tests/test_adbpg.py -v
+"""
+
+from __future__ import annotations
+
+import json
+from typing import TYPE_CHECKING
+
+import pytest
+from pydantic import SecretStr
+
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.adbpg.config import AdbpgConfig, AdbpgIndexConfig
+from vectordb_bench.backend.clients.api import MetricType
+from vectordb_bench.models import TestResult
+
+if TYPE_CHECKING:
+    from pathlib import Path
+
+
+def make_index_config(**overrides) -> AdbpgIndexConfig:
+    base = {
+        "metric_type": MetricType.COSINE,
+        "hnsw_m": 32,
+        "ef_search": 100,
+        "ef_construction": 200,
+        "nlist": 1024,
+        "algorithm": "novamr",
+        "rabitq_bits": 7,
+        "quantize_rescore_amp": 1.0,
+        "nova_adaptive_gamma": 0.0,
+        "max_scan_points": 2000,
+        "index_scan_mode": "snapshot",
+        "auto_reduction": False,
+        "nprobe": 5,
+    }
+    base.update(overrides)
+    return AdbpgIndexConfig(**base)
+
+
+class TestAdbpgConfig:
+    def test_defaults_allow_construction_without_password(self):
+        # Regression: result JSON only contains DBConfig parent fields
+        # (db_label/version/note) because of pydantic polymorphic serialization.
+        # AdbpgConfig must therefore be constructible from that minimal dict.
+        cfg = AdbpgConfig(db_label="", version="", note="")
+        assert cfg.host == "localhost"
+        assert cfg.port == 5432
+        assert cfg.db_name == "postgres"
+        assert cfg.password.get_secret_value() == ""
+
+    def test_to_dict_carries_utility_session_option(self):
+        cfg = AdbpgConfig(
+            user_name=SecretStr("u"),
+            password=SecretStr("pw"),
+            host="h.example.com",
+            port=5432,
+            db_name="postgres",
+        )
+        d = cfg.to_dict()
+        assert d["table_name"] == "vector"
+        cc = d["connect_config"]
+        assert cc["host"] == "h.example.com"
+        assert cc["user"] == "u"
+        assert cc["password"] == "pw"  # noqa: S105
+        assert cc["dbname"] == "postgres"
+        assert cc["options"] == "-c gp_session_role=utility"
+
+
+class TestAdbpgIndexConfigBuild:
+    def test_parse_metric(self):
+        assert make_index_config(metric_type=MetricType.L2).parse_metric() == "l2"
+        assert make_index_config(metric_type=MetricType.COSINE).parse_metric() == "cosine"
+        assert make_index_config(metric_type=MetricType.IP).parse_metric() == "ip"
+
+    def test_parse_metric_unsupported_raises(self):
+        with pytest.raises(ValueError, match="Metric type"):
+            make_index_config(metric_type=None).parse_metric()
+
+    def test_index_param_options_default(self):
+        params = make_index_config().index_param()
+        names = {opt["option_name"]: opt for opt in params["index_creation_with_options"]}
+        assert names["algorithm"]["val"] == "novamr"
+        assert names["hnsw_m"]["val"] == 32
+        assert names["hnsw_ef_construction"]["val"] == 200
+        assert names["nlist"]["val"] == 1024
+        assert names["rabitq_bits"]["val"] == 7
+        assert names["max_key_len"]["val"] == 1
+        # auto_reduction is omitted when False
+        assert "auto_reduction" not in names
+
+    def test_index_param_auto_reduction_emits_raw(self):
+        params = make_index_config(auto_reduction=True).index_param()
+        opt = next(o for o in params["index_creation_with_options"] if o["option_name"] == "auto_reduction")
+        # `raw=True` so the value is rendered as a bare SQL identifier (`on`)
+        # rather than a quoted literal.
+        assert opt["val"] == "on"
+        assert opt.get("raw") is True
+
+    def test_index_param_pca_dim_omitted_when_none(self):
+        params = make_index_config(pca_dim=None).index_param()
+        names = {opt["option_name"] for opt in params["index_creation_with_options"]}
+        assert "pca_dim" not in names
+
+    def test_index_param_pca_dim_emitted_when_set(self):
+        params = make_index_config(pca_dim=448).index_param()
+        opt = next(o for o in params["index_creation_with_options"] if o["option_name"] == "pca_dim")
+        assert opt["val"] == 448
+
+
+class TestAdbpgIndexConfigSession:
+    def test_session_param_emits_all_search_gucs(self):
+        cfg = make_index_config(
+            quantize_rescore_amp=0.6,
+            nova_adaptive_gamma=0.0,
+            ef_search=50,
+            max_scan_points=16000,
+            index_scan_mode="snapshot",
+            nprobe=64,
+        )
+        opts = cfg.session_param()["session_options"]
+        emitted = {o["parameter"]["setting_name"]: o["parameter"]["val"] for o in opts}
+        assert emitted["fastann.quantize_rescore_amp"] == "0.6"
+        assert emitted["fastann.nova_adaptive_gamma"] == "0.0"
+        assert emitted["fastann.hnsw_ef_search"] == "50"
+        assert emitted["fastann.hnsw_max_scan_points"] == "16000"
+        assert emitted["fastann.index_scan_mode"] == "snapshot"
+        # novad-specific GUC is always emitted (no-op for HNSW algorithms)
+        assert emitted["fastann.nova_nprobe"] == "64"
+
+    def test_session_param_emits_zero_values(self):
+        # Forcing 0 / 0.0 must still produce a SET command — callers rely on
+        # being able to pin a GUC to zero.
+        cfg = make_index_config(quantize_rescore_amp=0.0, nova_adaptive_gamma=0.0, nprobe=0)
+        opts = cfg.session_param()["session_options"]
+        emitted = {o["parameter"]["setting_name"]: o["parameter"]["val"] for o in opts}
+        assert emitted["fastann.quantize_rescore_amp"] == "0.0"
+        assert emitted["fastann.nova_adaptive_gamma"] == "0.0"
+        assert emitted["fastann.nova_nprobe"] == "0"
+
+
+class TestResultRoundTrip:
+    def test_read_file_with_minimal_db_config(self, tmp_path: Path):
+        """Saved result JSON keeps only DBConfig parent fields for db_config.
+
+        TestResult.read_file must still rehydrate the AdbpgConfig instance
+        without raising a Field-required pydantic ValidationError.
+        """
+        result_dir = tmp_path / "AnalyticDB for PostgreSQL"
+        result_dir.mkdir()
+        result_file = result_dir / "result_test_run.json"
+        payload = {
+            "run_id": "round-trip",
+            "task_label": "round-trip",
+            "results": [
+                {
+                    "metrics": {
+                        "max_load_count": 0,
+                        "insert_duration": 0.0,
+                        "optimize_duration": 0.0,
+                        "load_duration": 0.0,
+                        "qps": 1.0,
+                        "serial_latency_p99": 0.0,
+                        "serial_latency_p95": 0.0,
+                        "recall": 1.0,
+                        "ndcg": 1.0,
+                        "conc_num_list": [],
+                        "conc_qps_list": [],
+                        "conc_latency_p99_list": [],
+                        "conc_latency_p95_list": [],
+                        "conc_latency_avg_list": [],
+                    },
+                    "task_config": {
+                        "db": DB.Adbpg.value,
+                        "db_config": {"db_label": "", "version": "", "note": ""},
+                        "db_case_config": {
+                            "metric_type": "COSINE",
+                            "algorithm": "novamr",
+                            "hnsw_m": 16,
+                            "ef_search": 100,
+                            "ef_construction": 200,
+                            "nlist": 1024,
+                        },
+                        "case_config": {"case_id": 5, "custom_case": {}, "k": 10},
+                        "stages": ["search_serial"],
+                        "load_concurrency": 0,
+                    },
+                    "label": ":)",
+                }
+            ],
+            "timestamp": 0.0,
+        }
+        result_file.write_text(json.dumps(payload))
+
+        tr = TestResult.read_file(result_file, trans_unit=False)
+        assert len(tr.results) == 1
+        rehydrated = tr.results[0].task_config.db_config
+        assert isinstance(rehydrated, AdbpgConfig)
+        assert rehydrated.host == "localhost"  # came from default
diff --git a/tests/test_antfly.py b/tests/test_antfly.py
new file mode 100644
index 000000000..024a3b14f
--- /dev/null
+++ b/tests/test_antfly.py
@@ -0,0 +1,107 @@
+import base64
+import struct
+from typing import Any
+
+import pytest
+
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.antfly.antfly import Antfly
+from vectordb_bench.backend.clients.antfly.config import AntflyConfig, AntflyIndexConfig
+from vectordb_bench.backend.filter import IntFilter, LabelFilter, non_filter
+from vectordb_bench.backend.payload import PayloadProfile
+
+
+class _CaseConfig:
+    def index_param(self):
+        return {"distance_metric": "cosine"}
+
+    def search_param(self):
+        return {"search_effort": 0.6}
+
+
+class _Response:
+    is_success = True
+    status_code = 200
+    text = ""
+
+    def raise_for_status(self):
+        return None
+
+
+class _Client:
+    def __init__(self):
+        self.posts = []
+
+    def post(self, path: str, *, json: dict[str, Any]):
+        self.posts.append((path, json))
+        return _Response()
+
+
+def _adapter(*, with_scalar_labels: bool = False):
+    adapter = Antfly.__new__(Antfly)
+    adapter.case_config = _CaseConfig()
+    adapter.collection_name = "vdbbench"
+    adapter.with_scalar_labels = with_scalar_labels
+    adapter._filter_query = None
+    adapter._pack_query_vectors = True
+    adapter._legacy_api = False
+    adapter._write_sync_level = "write"
+    adapter._bench_status_last_log = 0.0
+    adapter.client = _Client()
+    return adapter
+
+
+def test_antfly_is_registered():
+    assert DB.Antfly.config_cls is AntflyConfig
+    assert DB.Antfly.case_config_cls() is AntflyIndexConfig
+    assert DB.Antfly.init_cls is Antfly
+
+
+def test_antfly_uses_native_cosine_and_ids_only_queries():
+    adapter = _adapter()
+
+    assert adapter.need_normalize_cosine() is False
+    body = adapter._metadata_query_body([1.0, 2.0], 10)
+    assert body["fields"] == []
+    assert body["search_effort"] == 0.6
+
+    adapter.prepare_filter(IntFilter(filter_rate=0.99, int_field="id", int_value=49_500))
+    assert adapter._metadata_query_body([1.0, 2.0], 10)["filter_query"] == {
+        "numeric_range": {"field": "id", "min": 49_500, "inclusive_min": True}
+    }
+
+    adapter.prepare_filter(LabelFilter(label_percentage=0.01))
+    assert adapter._filter_query == {"term": {"labels": "label_1p"}}
+    adapter.prepare_filter(non_filter)
+    assert "filter_query" not in adapter._metadata_query_body([1.0, 2.0], 10)
+
+    with pytest.raises(NotImplementedError):
+        adapter.search_embedding([1.0, 2.0], payload_profile=PayloadProfile.VECTOR)
+
+
+def test_antfly_insert_preserves_vectors_and_labels():
+    adapter = _adapter(with_scalar_labels=True)
+
+    inserted, error = adapter.insert_embeddings([[3.0, 4.0]], [7], labels_data=["bucket_007"])
+
+    assert error is None
+    assert inserted == 1
+    _, payload = adapter.client.posts[0]
+    row = payload["inserts"]["key:7"]
+    vector = base64.b64decode(row["_embeddings"]["vec"])
+    assert struct.unpack("<2f", vector) == pytest.approx((3.0, 4.0))
+    assert row["labels"] == "bucket_007"
+    assert payload["sync_level"] == "write"
+
+
+def test_write_readiness_probe_does_not_create_a_tombstone():
+    adapter = _adapter()
+    client = _Client()
+
+    adapter._wait_for_write_ready(client)
+
+    assert len(client.posts) == 1
+    assert client.posts[0][1] == {
+        "inserts": {"key:__circus_write_probe__": {"id": -1}},
+        "sync_level": "write",
+    }
diff --git a/tests/test_aws_opensearch.py b/tests/test_aws_opensearch.py
new file mode 100644
index 000000000..2e2930338
--- /dev/null
+++ b/tests/test_aws_opensearch.py
@@ -0,0 +1,46 @@
+from types import SimpleNamespace
+
+import pytest
+
+from vectordb_bench import config
+from vectordb_bench.backend.clients.aws_opensearch.aws_opensearch import AWSOpenSearch
+
+
+def test_serverless_insert_uses_configured_batch_size(monkeypatch) -> None:
+    bulk_requests = []
+
+    def bulk(*, body):
+        bulk_requests.append(body)
+
+    monkeypatch.setattr(config, "NUM_PER_BATCH", 2)
+
+    db = object.__new__(AWSOpenSearch)
+    db.client = SimpleNamespace(bulk=bulk)
+    db._is_serverless = True
+    db.index_name = "test-index"
+    db.vector_col_name = "embedding"
+    db.with_scalar_labels = False
+
+    inserted, error = db._insert_with_single_client(
+        embeddings=[[0.1], [0.2], [0.3], [0.4], [0.5]],
+        metadata=[1, 2, 3, 4, 5],
+    )
+
+    assert inserted == 5
+    assert error is None
+    assert [len(request) // 2 for request in bulk_requests] == [2, 2, 1]
+    assert [document["id"] for request in bulk_requests for document in request[1::2]] == [1, 2, 3, 4, 5]
+
+
+@pytest.mark.parametrize("batch_size", [0, -1])
+def test_serverless_insert_rejects_non_positive_batch_size(monkeypatch, batch_size: int) -> None:
+    monkeypatch.setattr(config, "NUM_PER_BATCH", batch_size)
+
+    db = object.__new__(AWSOpenSearch)
+    db._is_serverless = True
+
+    with pytest.raises(ValueError, match="NUM_PER_BATCH must be greater than 0"):
+        db._insert_with_single_client(
+            embeddings=[[0.1]],
+            metadata=[1],
+        )
diff --git a/tests/test_bench_runner.py b/tests/test_bench_runner.py
index e65c73887..7aff0c27e 100644
--- a/tests/test_bench_runner.py
+++ b/tests/test_bench_runner.py
@@ -1,5 +1,7 @@
 import time
 import logging
+
+import ujson
 from vectordb_bench.interface import BenchMarkRunner
 from vectordb_bench.models import (
     DB, IndexType, CaseType, TaskConfig, CaseConfig,
@@ -52,9 +54,8 @@ def test_performance_case_no_error(self):
         )
 
         t = task_config.copy()
-        d = t.model_dump_json(exclude={'db_config': {'password', 'api_key'}})
+        d = t.json(exclude={'db_config': {'password', 'api_key'}})
         log.info(f"{d}")
 
-        import ujson
         loads = ujson.loads(d)
         log.info(f"{loads}")
diff --git a/tests/test_case_runner_reuse.py b/tests/test_case_runner_reuse.py
new file mode 100644
index 000000000..55dfbda0b
--- /dev/null
+++ b/tests/test_case_runner_reuse.py
@@ -0,0 +1,171 @@
+from pydantic import SecretStr
+
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.api import EmptyDBCaseConfig, MetricType
+from vectordb_bench.backend.clients.doris.config import DorisCaseConfig, DorisConfig
+from vectordb_bench.backend.clients.pinecone.config import PineconeConfig
+from vectordb_bench.backend.clients.turbopuffer.config import TurboPufferConfig, TurboPufferIndexConfig
+from vectordb_bench.backend.data_source import DatasetSource
+from vectordb_bench.backend.dataset import DatasetWithSizeType
+from vectordb_bench.backend.task_runner import CaseRunner, RunningStatus, TaskRunner
+from vectordb_bench.interface import BenchMarkRunner
+from vectordb_bench.metric import Metric
+from vectordb_bench.models import CaseConfig, CaseType, TaskConfig, TaskStage, TestResult
+
+
+def make_runner(
+    *,
+    case_id: CaseType = CaseType.Performance1536D50K,
+    custom_case: dict | None = None,
+    db: DB = DB.TurboPuffer,
+    db_config=None,
+    db_case_config=None,
+    stages: list[TaskStage] | None = None,
+) -> CaseRunner:
+    if db_config is None:
+        if db == DB.TurboPuffer:
+            db_config = TurboPufferConfig(api_key="key", region="aws-us-east-1")
+        elif db == DB.Pinecone:
+            db_config = PineconeConfig(api_key="key", index_name="idx")
+        elif db == DB.Doris:
+            db_config = DorisConfig(password=SecretStr(""))
+        else:
+            db_config = DB.Test.config_cls()
+    if db_case_config is None:
+        if db == DB.TurboPuffer:
+            db_case_config = TurboPufferIndexConfig(metric_type=MetricType.COSINE)
+        elif db == DB.Doris:
+            db_case_config = DorisCaseConfig(metric_type=MetricType.COSINE)
+        else:
+            db_case_config = EmptyDBCaseConfig()
+
+    task = TaskConfig(
+        db=db,
+        db_config=db_config,
+        db_case_config=db_case_config,
+        case_config=CaseConfig(case_id=case_id, custom_case=custom_case or {}),
+        stages=stages or [TaskStage.DROP_OLD, TaskStage.LOAD, TaskStage.SEARCH_SERIAL],
+    )
+    return CaseRunner(
+        run_id="run-id",
+        config=task,
+        ca=task.case_config.case,
+        status=RunningStatus.PENDING,
+        dataset_source=DatasetSource.S3,
+    )
+
+
+def assert_not_reusable(left: CaseRunner, right: CaseRunner) -> None:
+    assert left != right
+    assert right != left
+    assert hash(left) != hash(right)
+
+
+def test_reuse_key_distinguishes_multitenant_routing_parameters():
+    base_case = {
+        "dataset_with_size_type": DatasetWithSizeType.CohereSmall.value,
+        "tenant_count": 2,
+        "tenant_prefix": "tenant_",
+        "tenant_id_width": 4,
+    }
+
+    assert_not_reusable(
+        make_runner(case_id=CaseType.CloudMultiTenantSearchCase, custom_case=base_case),
+        make_runner(case_id=CaseType.CloudMultiTenantSearchCase, custom_case={**base_case, "tenant_count": 3}),
+    )
+    assert_not_reusable(
+        make_runner(case_id=CaseType.CloudMultiTenantSearchCase, custom_case=base_case),
+        make_runner(case_id=CaseType.CloudMultiTenantSearchCase, custom_case={**base_case, "tenant_prefix": "org_"}),
+    )
+    assert_not_reusable(
+        make_runner(case_id=CaseType.CloudMultiTenantSearchCase, custom_case=base_case),
+        make_runner(case_id=CaseType.CloudMultiTenantSearchCase, custom_case={**base_case, "tenant_id_width": 2}),
+    )
+
+
+def test_reuse_key_distinguishes_single_layout_from_multitenant_layout():
+    dataset_case = {"dataset_with_size_type": DatasetWithSizeType.CohereSmall.value}
+
+    assert_not_reusable(
+        make_runner(case_id=CaseType.CloudPayloadSearchCase, custom_case=dataset_case),
+        make_runner(
+            case_id=CaseType.CloudMultiTenantSearchCase,
+            custom_case={**dataset_case, "tenant_count": 2},
+        ),
+    )
+
+
+def test_reuse_key_preserves_safe_payload_reuse():
+    dataset_case = {"dataset_with_size_type": DatasetWithSizeType.CohereSmall.value}
+
+    ids_only = make_runner(
+        case_id=CaseType.CloudPayloadSearchCase,
+        custom_case={**dataset_case, "payload_profile": "ids_only"},
+    )
+    vector = make_runner(
+        case_id=CaseType.CloudPayloadSearchCase,
+        custom_case={**dataset_case, "payload_profile": "vector"},
+    )
+
+    assert ids_only == vector
+    assert hash(ids_only) == hash(vector)
+
+
+def test_reuse_key_distinguishes_physical_db_targets():
+    assert_not_reusable(
+        make_runner(db_config=TurboPufferConfig(api_key="key", region="aws-us-east-1", namespace="namespace_a")),
+        make_runner(db_config=TurboPufferConfig(api_key="key", region="aws-us-east-1", namespace="namespace_b")),
+    )
+    assert_not_reusable(
+        make_runner(db=DB.Pinecone, db_config=PineconeConfig(api_key="key", index_name="index_a")),
+        make_runner(db=DB.Pinecone, db_config=PineconeConfig(api_key="key", index_name="index_b")),
+    )
+
+
+def test_reuse_key_distinguishes_doris_case_derived_table_names():
+    assert_not_reusable(
+        make_runner(db=DB.Doris, case_id=CaseType.Performance768D1M),
+        make_runner(
+            db=DB.Doris,
+            case_id=CaseType.NewIntFilterPerformanceCase,
+            custom_case={
+                "dataset_with_size_type": DatasetWithSizeType.CohereMedium.value,
+                "filter_rate": 0.01,
+            },
+        ),
+    )
+
+
+def test_search_only_runner_does_not_suppress_later_full_load(monkeypatch):
+    calls: list[bool] = []
+    search_only = make_runner(
+        stages=[TaskStage.SEARCH_SERIAL],
+    )
+    full_load = make_runner(
+        stages=[TaskStage.DROP_OLD, TaskStage.LOAD, TaskStage.SEARCH_SERIAL],
+    )
+
+    def fake_run(self: CaseRunner, drop_old: bool = True) -> Metric:
+        calls.append(drop_old)
+        return Metric()
+
+    class SendConn:
+        def __init__(self):
+            self.sent = []
+
+        def send(self, value):
+            self.sent.append(value)
+
+        def close(self):
+            return None
+
+    monkeypatch.setattr(CaseRunner, "run", fake_run)
+    monkeypatch.setattr(TestResult, "display", lambda self: None)
+    monkeypatch.setattr(TestResult, "flush", lambda self: None)
+
+    BenchMarkRunner()._async_task_v2(
+        TaskRunner(run_id="run-id", task_label="task", case_runners=[search_only, full_load]),
+        SendConn(),
+    )
+
+    assert calls == [False, True]
diff --git a/tests/test_cli_note.py b/tests/test_cli_note.py
new file mode 100644
index 000000000..1c601686d
--- /dev/null
+++ b/tests/test_cli_note.py
@@ -0,0 +1,125 @@
+import time
+from pathlib import Path
+
+from click.testing import CliRunner
+from pytest import MonkeyPatch
+
+from vectordb_bench.backend.clients.endee import cli as endee_cli
+from vectordb_bench.backend.clients.test import cli as test_cli
+from vectordb_bench.cli import cli as common_cli
+
+
+def invoke_test_command(monkeypatch: MonkeyPatch, args: list[str]):
+    captured = {}
+
+    def fake_run(tasks, task_label):
+        captured["task"] = tasks[0]
+        captured["task_label"] = task_label
+
+    monkeypatch.setattr(common_cli.benchmark_runner, "run", fake_run)
+    monkeypatch.setattr(common_cli.benchmark_runner, "has_running", lambda: False)
+    result = CliRunner().invoke(test_cli.Test, args)
+    return result, captured
+
+
+def invoke_endee_command(monkeypatch: MonkeyPatch, args: list[str]):
+    captured = {}
+
+    def fake_run(tasks, task_label):
+        captured["task"] = tasks[0]
+        captured["task_label"] = task_label
+
+    monkeypatch.setattr(endee_cli.benchmark_runner, "run", fake_run)
+    monkeypatch.setattr(endee_cli.benchmark_runner, "has_running", lambda: False)
+    monkeypatch.setattr(time, "sleep", lambda _: None)
+    result = CliRunner().invoke(
+        endee_cli.Endee,
+        ["--token", "secret", "--index-name", "test-index", *args],
+    )
+    return result, captured
+
+
+def test_common_cli_exposes_note_options() -> None:
+    result = CliRunner().invoke(test_cli.Test, ["--help"])
+
+    assert result.exit_code == 0, result.output
+    assert "--note TEXT" in result.output
+    assert "--note-file FILE" in result.output
+
+
+def test_common_cli_stores_inline_note_in_task_config(monkeypatch: MonkeyPatch) -> None:
+    note = '{"schema":"vdbbench-context/v1","deployment":"local"}'
+
+    result, captured = invoke_test_command(monkeypatch, ["--note", note])
+
+    assert result.exit_code == 0, result.output
+    assert captured["task"].db_config.note == note
+
+
+def test_common_cli_reads_note_file_into_task_config(monkeypatch: MonkeyPatch, tmp_path: Path) -> None:
+    note = '{\n  "schema": "vdbbench-context/v1",\n  "deployment": "managed"\n}'
+    note_file = tmp_path / "run-context.json"
+    note_file.write_text(note + "\n", encoding="utf-8")
+
+    result, captured = invoke_test_command(monkeypatch, ["--note-file", str(note_file)])
+
+    assert result.exit_code == 0, result.output
+    assert captured["task"].db_config.note == note
+
+
+def test_endee_cli_reads_note_file_into_task_config(monkeypatch: MonkeyPatch, tmp_path: Path) -> None:
+    note = '{"schema":"vdbbench-context/v1","deployment":"endee-local"}'
+    note_file = tmp_path / "endee-run-context.json"
+    note_file.write_text(note, encoding="utf-8")
+
+    result, captured = invoke_endee_command(monkeypatch, ["--note-file", str(note_file)])
+
+    assert result.exit_code == 0, result.output
+    assert captured["task"].db_config.note == note
+
+
+def test_common_cli_rejects_inline_note_with_note_file(monkeypatch: MonkeyPatch, tmp_path: Path) -> None:
+    note_file = tmp_path / "run-context.json"
+    note_file.write_text("context", encoding="utf-8")
+
+    result, _ = invoke_test_command(
+        monkeypatch,
+        ["--note", "inline", "--note-file", str(note_file)],
+    )
+
+    assert result.exit_code != 0
+    assert "--note and --note-file cannot be used together" in result.output
+
+
+def test_common_cli_rejects_empty_inline_note_with_note_file(
+    monkeypatch: MonkeyPatch,
+    tmp_path: Path,
+) -> None:
+    note_file = tmp_path / "run-context.json"
+    note_file.write_text("context", encoding="utf-8")
+
+    result, _ = invoke_test_command(monkeypatch, ["--note", "", "--note-file", str(note_file)])
+
+    assert result.exit_code != 0
+    assert "--note and --note-file cannot be used together" in result.output
+
+
+def test_common_cli_rejects_empty_note_file(monkeypatch: MonkeyPatch, tmp_path: Path) -> None:
+    note_file = tmp_path / "empty.txt"
+    note_file.write_text("", encoding="utf-8")
+
+    result, _ = invoke_test_command(monkeypatch, ["--note-file", str(note_file)])
+
+    assert result.exit_code != 0
+    assert "Note file is empty" in result.output
+
+
+def test_common_cli_rejects_non_utf8_note_file(monkeypatch: MonkeyPatch, tmp_path: Path) -> None:
+    note_file = tmp_path / "invalid.txt"
+    with note_file.open("wb") as output:
+        output.write(b"\xff")
+
+    result, _ = invoke_test_command(monkeypatch, ["--note-file", str(note_file)])
+
+    assert result.exit_code != 0
+    assert "Note file is not valid UTF-8" in result.output
diff --git a/tests/test_cloud_cold_latency_case.py b/tests/test_cloud_cold_latency_case.py
new file mode 100644
index 000000000..55f610b38
--- /dev/null
+++ b/tests/test_cloud_cold_latency_case.py
@@ -0,0 +1,470 @@
+import json
+from contextlib import contextmanager
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from vectordb_bench.backend.assembler import Assembler
+from vectordb_bench.backend.cases import CaseLabel, CaseType, CloudColdLatencyCase
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.api import EmptyDBCaseConfig
+from vectordb_bench.backend.clients.pinecone.config import PineconeConfig
+from vectordb_bench.backend.data_source import DatasetSource
+from vectordb_bench.backend.dataset import DatasetWithSizeType
+from vectordb_bench.backend.filter import Filter, FilterOp
+from vectordb_bench.backend.payload import PayloadProfile
+from vectordb_bench.backend.result_collector import ResultCollector
+from vectordb_bench.backend.runner.cold_warm_runner import ColdWarmSearchRunner
+from vectordb_bench.backend.task_runner import CaseRunner, RunningStatus
+from vectordb_bench.cli.cli import get_custom_case_config
+from vectordb_bench.metric import Metric
+from vectordb_bench.models import CaseConfig, CaseResult, TaskConfig, TaskStage, TestResult
+
+
+def test_cloud_cold_latency_case_defaults_to_laion_100m():
+    case = CloudColdLatencyCase()
+
+    assert case.case_id == CaseType.CloudColdLatencyCase
+    assert case.label == CaseLabel.CloudColdLatency
+    assert case.dataset.data.name == "LAION"
+    assert case.dataset.data.size == 100_000_000
+    assert case.dataset.data.dim == 768
+    assert case.payload_profile == PayloadProfile.IDS_ONLY
+    assert case.query_count == 1000
+    assert case.filters.type == FilterOp.NonFilter
+
+
+def test_cloud_cold_latency_case_accepts_payload_dataset_and_int_filter():
+    case = CloudColdLatencyCase(
+        dataset_with_size_type=DatasetWithSizeType.CohereSmall.value,
+        payload_profile="vector",
+        filter_rate=0.9,
+        query_count=10,
+    )
+
+    assert case.dataset_with_size_type == DatasetWithSizeType.CohereSmall
+    assert case.dataset.data.name == "Cohere"
+    assert case.payload_profile == PayloadProfile.VECTOR
+    assert case.filter_rate == 0.9
+    assert case.query_count == 10
+    assert case.filters.type == FilterOp.NumGE
+
+
+def test_cloud_cold_latency_case_accepts_label_filter():
+    case = CloudColdLatencyCase(label_percentage=0.9)
+
+    assert case.label_percentage == 0.9
+    assert case.filters.type == FilterOp.StrEqual
+
+
+def test_cloud_cold_latency_case_rejects_two_filter_types():
+    with pytest.raises(ValueError, match="supports only one filter type"):
+        CloudColdLatencyCase(filter_rate=0.9, label_percentage=0.9)
+
+
+def test_cloud_cold_latency_case_rejects_invalid_query_count():
+    with pytest.raises(ValueError, match="query_count must be positive"):
+        CloudColdLatencyCase(query_count=0)
+
+
+def test_case_config_builds_cloud_cold_latency_case_from_custom_case():
+    case = CaseConfig(
+        case_id=CaseType.CloudColdLatencyCase,
+        custom_case={
+            "payload_profile": "scalar_label",
+            "label_percentage": 0.9,
+            "query_count": 12,
+        },
+    ).case
+
+    assert isinstance(case, CloudColdLatencyCase)
+    assert case.payload_profile == PayloadProfile.SCALAR_LABEL
+    assert case.label_percentage == 0.9
+    assert case.query_count == 12
+
+
+def test_cli_builds_cloud_cold_latency_custom_case_config():
+    params = {
+        "case_type": "CloudColdLatencyCase",
+        "payload_profile": "vector",
+        "cloud_filter_rate": 0.9,
+        "cloud_label_percentage": None,
+        "cloud_cold_query_count": 1000,
+        "dataset_with_size_type": DatasetWithSizeType.CohereMedium.value,
+    }
+
+    assert get_custom_case_config(params) == {
+        "payload_profile": "vector",
+        "filter_rate": 0.9,
+        "query_count": 1000,
+        "dataset_with_size_type": DatasetWithSizeType.CohereMedium.value,
+    }
+
+
+def test_cli_keeps_cloud_cold_latency_default_dataset_as_laion():
+    params = {
+        "case_type": "CloudColdLatencyCase",
+        "payload_profile": "ids_only",
+        "cloud_filter_rate": None,
+        "cloud_label_percentage": None,
+        "cloud_cold_query_count": 1000,
+        "dataset_with_size_type": None,
+    }
+
+    custom_case = get_custom_case_config(params)
+    case = CaseConfig(case_id=CaseType.CloudColdLatencyCase, custom_case=custom_case).case
+
+    assert custom_case == {
+        "payload_profile": "ids_only",
+        "query_count": 1000,
+    }
+    assert case.dataset.data.name == "LAION"
+    assert case.dataset.data.size == 100_000_000
+
+
+def test_cloud_cold_latency_result_file_uses_cold_latency_metrics(tmp_path: Path):
+    cold_latency = {
+        "cold_stats": {
+            "first_query_latency": 0.2,
+            "p99_latency": 0.3,
+            "p95_latency": 0.25,
+            "avg_latency": 0.21,
+        },
+        "warm_stats": {
+            "first_query_latency": 0.1,
+            "p99_latency": 0.15,
+            "p95_latency": 0.12,
+            "avg_latency": 0.11,
+        },
+        "ratios": {
+            "first_query_latency": 2.0,
+            "p99_latency": 2.0,
+            "p95_latency": 2.0833,
+            "avg_latency": 1.9091,
+        },
+    }
+    result = CaseResult(
+        task_config=TaskConfig(
+            db=DB.Pinecone,
+            db_config=PineconeConfig(
+                db_label="pinecone_cloud_cold_latency",
+                api_key="secret-key",
+                index_name="laion100m",
+            ),
+            db_case_config=EmptyDBCaseConfig(),
+            case_config=CaseConfig(
+                case_id=CaseType.CloudColdLatencyCase,
+                custom_case={"payload_profile": "vector", "query_count": 1000},
+            ),
+            stages=[TaskStage.SEARCH_SERIAL],
+            load_concurrency=0,
+        ),
+        metrics=Metric(
+            insert_duration=0.0,
+            optimize_duration=0.0,
+            load_duration=0.0,
+            payload_profile="vector",
+            payload_estimated_bytes_per_query=309200,
+            additional_parameters={"cold_latency": cold_latency},
+        ),
+    )
+    test_result = TestResult(run_id="run-id", task_label="cloud_cold_latency_pinecone", results=[result])
+
+    test_result.write_db_file(tmp_path, test_result, "pinecone")
+
+    result_file = next(tmp_path.glob("result_*_pinecone.json"))
+    raw_output = result_file.read_text()
+    assert raw_output.startswith('{\n  "run_id"')
+    written = json.loads(raw_output)
+    assert written["results"][0]["metrics"] == {
+        "insert_duration": 0.0,
+        "optimize_duration": 0.0,
+        "load_duration": 0.0,
+        "payload_profile": "vector",
+        "payload_estimated_bytes_per_query": 309200,
+        "cold_latency": cold_latency,
+    }
+    assert written["results"][0]["task_config"]["db_config"]["api_key"] == "**********"
+    assert written["results"][0]["task_config"]["db_config"]["index_name"] == "laion100m"
+    assert written["results"][0]["task_config"]["case_config"] == {
+        "case_id": 700,
+        "custom_case": {"payload_profile": "vector", "query_count": 1000},
+    }
+
+    read_back = TestResult.read_file(result_file)
+    assert read_back.results[0].task_config.case_config.case_id == CaseType.CloudColdLatencyCase
+    assert read_back.results[0].task_config.case_config.custom_case == {
+        "payload_profile": "vector",
+        "query_count": 1000,
+    }
+    assert read_back.results[0].metrics.additional_parameters["cold_latency"] == cold_latency
+
+    collected = ResultCollector.collect(tmp_path)
+    assert len(collected) == 1
+    assert collected[0].results[0].metrics.additional_parameters["cold_latency"] == cold_latency
+
+
+class FakeColdWarmDB:
+    name = "FakeColdWarmDB"
+
+    def __init__(self, supported_payload_profiles: set[PayloadProfile] | None = None):
+        self.supported_payload_profiles = supported_payload_profiles or {PayloadProfile.IDS_ONLY}
+        self.calls = []
+        self.prepare_filter_calls = []
+        self.init_enter_count = 0
+
+    def supports_payload_profile(self, payload_profile: PayloadProfile) -> bool:
+        return payload_profile in self.supported_payload_profiles
+
+    def need_normalize_cosine(self) -> bool:
+        return False
+
+    @contextmanager
+    def init(self):
+        self.init_enter_count += 1
+        yield
+
+    def prepare_filter(self, filters: Filter):
+        self.prepare_filter_calls.append(filters)
+
+    def search_embedding(self, query: list[float], k: int = 100, **kwargs) -> list[int]:
+        self.calls.append((query, k, kwargs))
+        return list(range(k))
+
+
+def test_cold_warm_runner_computes_stats_and_ratios(monkeypatch: pytest.MonkeyPatch):
+    db = FakeColdWarmDB()
+    # Cold latencies: 0.2, 0.2, 0.2. Warm latencies: 0.1, 0.1, 0.1.
+    perf_values = iter([0.0, 0.2, 0.2, 0.4, 0.4, 0.6, 0.6, 0.7, 0.7, 0.8, 0.8, 0.9])
+    monkeypatch.setattr("vectordb_bench.backend.runner.cold_warm_runner.time.perf_counter", lambda: next(perf_values))
+
+    runner = ColdWarmSearchRunner(
+        db=db,
+        test_data=[[0.1], [0.2], [0.3]],
+        k=3,
+        query_count=3,
+    )
+
+    result = runner.run()
+
+    assert result == {
+        "cold_stats": {
+            "first_query_latency": 0.2,
+            "p99_latency": 0.2,
+            "p95_latency": 0.2,
+            "avg_latency": 0.2,
+        },
+        "warm_stats": {
+            "first_query_latency": 0.1,
+            "p99_latency": 0.1,
+            "p95_latency": 0.1,
+            "avg_latency": 0.1,
+        },
+        "cold_warm_ratio": {
+            "first_query_latency_ratio": 2.0,
+            "p99_latency_ratio": 2.0,
+            "p95_latency_ratio": 2.0,
+            "avg_latency_ratio": 2.0,
+        },
+    }
+    assert db.init_enter_count == 1
+    assert len(db.prepare_filter_calls) == 1
+    assert [call[0] for call in db.calls] == [[0.1], [0.2], [0.3], [0.1], [0.2], [0.3]]
+
+
+def test_cold_warm_runner_passes_payload_profile_in_both_passes(monkeypatch: pytest.MonkeyPatch):
+    db = FakeColdWarmDB(supported_payload_profiles={PayloadProfile.IDS_ONLY, PayloadProfile.VECTOR})
+    perf_values = iter([0.0, 0.1, 0.1, 0.2])
+    monkeypatch.setattr("vectordb_bench.backend.runner.cold_warm_runner.time.perf_counter", lambda: next(perf_values))
+
+    runner = ColdWarmSearchRunner(
+        db=db,
+        test_data=[np.array([0.1])],
+        k=3,
+        payload_profile=PayloadProfile.VECTOR,
+        query_count=1,
+    )
+
+    runner.run()
+
+    assert db.calls == [
+        ([0.1], 3, {"payload_profile": PayloadProfile.VECTOR}),
+        ([0.1], 3, {"payload_profile": PayloadProfile.VECTOR}),
+    ]
+
+
+def test_cold_warm_runner_omits_payload_profile_for_ids_only(monkeypatch: pytest.MonkeyPatch):
+    db = FakeColdWarmDB()
+    perf_values = iter([0.0, 0.1, 0.1, 0.2])
+    monkeypatch.setattr("vectordb_bench.backend.runner.cold_warm_runner.time.perf_counter", lambda: next(perf_values))
+
+    runner = ColdWarmSearchRunner(
+        db=db,
+        test_data=[[0.1]],
+        k=3,
+        payload_profile=PayloadProfile.IDS_ONLY,
+        query_count=1,
+    )
+
+    runner.run()
+
+    assert db.calls == [
+        ([0.1], 3, {}),
+        ([0.1], 3, {}),
+    ]
+
+
+def test_cold_warm_runner_fails_for_unsupported_payload_profile():
+    db = FakeColdWarmDB()
+
+    with pytest.raises(NotImplementedError, match="payload_profile=vector"):
+        ColdWarmSearchRunner(
+            db=db,
+            test_data=[[0.1]],
+            payload_profile=PayloadProfile.VECTOR,
+            query_count=1,
+        )
+
+
+def test_cold_warm_runner_rejects_too_few_queries():
+    db = FakeColdWarmDB()
+
+    with pytest.raises(ValueError, match="query_count=2 exceeds test_data size=1"):
+        ColdWarmSearchRunner(db=db, test_data=[[0.1]], query_count=2)
+
+
+def test_assembler_schedules_cloud_cold_latency_case():
+    task = TaskConfig(
+        db=DB.Test,
+        db_config=DB.Test.config_cls(),
+        db_case_config=EmptyDBCaseConfig(),
+        case_config=CaseConfig(
+            case_id=CaseType.CloudColdLatencyCase,
+            custom_case={"query_count": 1},
+        ),
+        stages=[TaskStage.SEARCH_SERIAL],
+    )
+
+    runner = Assembler.assemble_all("run-id", "task-label", [task], DatasetSource.S3)
+
+    assert len(runner.case_runners) == 1
+    assert runner.case_runners[0].ca.label == CaseLabel.CloudColdLatency
+
+
+def test_case_runner_stores_cloud_cold_latency_metric(monkeypatch: pytest.MonkeyPatch):
+    case = CloudColdLatencyCase(query_count=1)
+    case.dataset.test_data = [[0.1]]
+    task = TaskConfig(
+        db=DB.Test,
+        db_config=DB.Test.config_cls(),
+        db_case_config=EmptyDBCaseConfig(),
+        case_config=CaseConfig(
+            case_id=CaseType.CloudColdLatencyCase,
+            custom_case={"query_count": 1},
+        ),
+        stages=[TaskStage.SEARCH_SERIAL],
+    )
+    runner = CaseRunner(
+        run_id="run-id",
+        config=task,
+        ca=case,
+        status=RunningStatus.PENDING,
+        dataset_source=DatasetSource.S3,
+    )
+    runner.db = FakeColdWarmDB()
+
+    expected = {
+        "cold_stats": {
+            "first_query_latency": 0.2,
+            "p99_latency": 0.2,
+            "p95_latency": 0.2,
+            "avg_latency": 0.2,
+        },
+        "warm_stats": {
+            "first_query_latency": 0.1,
+            "p99_latency": 0.1,
+            "p95_latency": 0.1,
+            "avg_latency": 0.1,
+        },
+        "cold_warm_ratio": {
+            "first_query_latency_ratio": 2.0,
+            "p99_latency_ratio": 2.0,
+            "p95_latency_ratio": 2.0,
+            "avg_latency_ratio": 2.0,
+        },
+    }
+    captured_kwargs = {}
+
+    class FakeRunner:
+        def __init__(self, **kwargs):
+            captured_kwargs.update(kwargs)
+
+        def run(self):
+            return expected
+
+    monkeypatch.setattr("vectordb_bench.backend.task_runner.ColdWarmSearchRunner", FakeRunner)
+
+    metric = runner._run_cloud_cold_latency_case(drop_old=False)
+
+    assert metric.additional_parameters["cold_latency"] == expected
+    assert metric.payload_profile == "ids_only"
+    assert metric.payload_estimated_bytes_per_query == case.estimated_payload_bytes_per_query(task.case_config.k)
+    assert captured_kwargs == {
+        "db": runner.db,
+        "test_data": [[0.1]],
+        "filters": case.filters,
+        "k": task.case_config.k,
+        "payload_profile": case.payload_profile,
+        "query_count": case.query_count,
+    }
+
+
+def test_cloud_cold_latency_case_rejects_drop_old():
+    case = CloudColdLatencyCase(query_count=1)
+    case.dataset.test_data = [[0.1]]
+    task = TaskConfig(
+        db=DB.Test,
+        db_config=DB.Test.config_cls(),
+        db_case_config=EmptyDBCaseConfig(),
+        case_config=CaseConfig(
+            case_id=CaseType.CloudColdLatencyCase,
+            custom_case={"query_count": 1},
+        ),
+        stages=[TaskStage.SEARCH_SERIAL],
+    )
+    runner = CaseRunner(
+        run_id="run-id",
+        config=task,
+        ca=case,
+        status=RunningStatus.PENDING,
+        dataset_source=DatasetSource.S3,
+    )
+
+    with pytest.raises(ValueError, match="requires an existing cold collection"):
+        runner._run_cloud_cold_latency_case(drop_old=True)
+
+
+def test_cloud_cold_latency_case_rejects_load_stage():
+    case = CloudColdLatencyCase(query_count=1)
+    case.dataset.test_data = [[0.1]]
+    task = TaskConfig(
+        db=DB.Test,
+        db_config=DB.Test.config_cls(),
+        db_case_config=EmptyDBCaseConfig(),
+        case_config=CaseConfig(
+            case_id=CaseType.CloudColdLatencyCase,
+            custom_case={"query_count": 1},
+        ),
+        stages=[TaskStage.LOAD, TaskStage.SEARCH_SERIAL],
+    )
+    runner = CaseRunner(
+        run_id="run-id",
+        config=task,
+        ca=case,
+        status=RunningStatus.PENDING,
+        dataset_source=DatasetSource.S3,
+    )
+
+    with pytest.raises(ValueError, match="search-only"):
+        runner._run_cloud_cold_latency_case(drop_old=False)
diff --git a/tests/test_cloud_insert_case.py b/tests/test_cloud_insert_case.py
new file mode 100644
index 000000000..28e70f7bd
--- /dev/null
+++ b/tests/test_cloud_insert_case.py
@@ -0,0 +1,1108 @@
+import json
+from contextlib import contextmanager
+from pathlib import Path
+from unittest.mock import MagicMock
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from vectordb_bench import config
+from vectordb_bench.backend.assembler import Assembler
+from vectordb_bench.backend.cases import CaseLabel, CaseType, CloudInsertCase
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.api import EmptyDBCaseConfig, VectorDB
+from vectordb_bench.backend.clients.milvus.milvus import Milvus
+from vectordb_bench.backend.clients.pinecone.config import PineconeConfig
+from vectordb_bench.backend.clients.pinecone.pinecone import Pinecone
+from vectordb_bench.backend.clients.turbopuffer.config import TurboPufferIndexConfig
+from vectordb_bench.backend.clients.turbopuffer.turbopuffer import TurboPuffer
+from vectordb_bench.backend.clients.zilliz_cloud.config import AutoIndexConfig, ZillizCloudConfig
+from vectordb_bench.backend.data_source import DatasetSource
+from vectordb_bench.backend.dataset import DatasetWithSizeType
+from vectordb_bench.backend.payload import PayloadProfile
+from vectordb_bench.backend.result_collector import ResultCollector
+from vectordb_bench.backend.runner.concurrent_runner import ConcurrentInsertRunner
+from vectordb_bench.backend.runner.executor import TaskResult
+from vectordb_bench.backend.task_runner import CaseRunner
+from vectordb_bench.cli.cli import get_custom_case_config
+from vectordb_bench.metric import Metric
+from vectordb_bench.models import CaseConfig, CaseResult, TaskConfig, TaskStage, TestResult
+
+
+class _SerialTaskExecutor:
+    def __init__(self):
+        self.submitted = []
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        return False
+
+    def submit(self, fn):
+        self.submitted.append(fn)
+
+    def wait_all(self):
+        results = []
+        for fn in self.submitted:
+            try:
+                results.append(TaskResult(value=fn()))
+            except Exception as e:
+                results.append(TaskResult(error=e))
+        return results
+
+
+class _ConcurrentRunnerData:
+    train_id_field = "id"
+    train_vector_field = "vector"
+
+
+class _ConcurrentRunnerDataset:
+    data = _ConcurrentRunnerData()
+
+    def __init__(self, ids):
+        self.ids = ids
+
+    def iter_batches(self, batch_size):
+        return iter([pd.DataFrame({"id": [row_id], "vector": [np.array([row_id / 10 + 0.1])]}) for row_id in self.ids])
+
+
+def test_cloud_insert_case_defaults_to_laion_100m():
+    case = CloudInsertCase(batch_size=1000)
+
+    assert case.case_id == CaseType.CloudInsertCase
+    assert case.label == CaseLabel.CloudInsert
+    assert case.dataset.data.name == "LAION"
+    assert case.dataset.data.size == 100_000_000
+    assert case.batch_size == 1000
+    assert case.duration is None
+    assert case.readiness_timeout is None
+
+
+def test_case_config_builds_cloud_insert_case_from_custom_case():
+    case = CaseConfig(
+        case_id=CaseType.CloudInsertCase,
+        custom_case={
+            "batch_size": 5000,
+            "duration": 1800,
+            "dataset_with_size_type": DatasetWithSizeType.CohereMedium.value,
+        },
+    ).case
+
+    assert isinstance(case, CloudInsertCase)
+    assert case.batch_size == 5000
+    assert case.duration == 1800
+    assert case.dataset.data.name == "Cohere"
+    assert case.dataset.data.size == 1_000_000
+
+
+def test_case_config_builds_cloud_insert_case_from_laion_100m_dataset_option():
+    case = CaseConfig(
+        case_id=CaseType.CloudInsertCase,
+        custom_case={
+            "batch_size": 10_000,
+            "dataset_with_size_type": "Large LAION (768dim, 100M)",
+        },
+    ).case
+
+    assert isinstance(case, CloudInsertCase)
+    assert case.batch_size == 10_000
+    assert case.dataset_with_size_type == DatasetWithSizeType.LAIONLarge
+    assert case.dataset.data.name == "LAION"
+    assert case.dataset.data.size == 100_000_000
+    assert case.dataset.data.dim == 768
+
+
+def test_laion_100m_dataset_option_uses_100m_timeouts():
+    assert DatasetWithSizeType.LAIONLarge.get_load_timeout() == config.LOAD_TIMEOUT_768D_100M
+    assert DatasetWithSizeType.LAIONLarge.get_optimize_timeout() == config.OPTIMIZE_TIMEOUT_768D_100M
+
+
+def test_cli_builds_cloud_insert_custom_case_config():
+    params = {
+        "case_type": "CloudInsertCase",
+        "cloud_insert_batch_size": 10_000,
+        "cloud_insert_duration": 1800,
+        "cloud_insert_readiness_timeout": 7200,
+        "cloud_insert_readiness_poll_interval": 10,
+        "dataset_with_size_type": DatasetWithSizeType.CohereMedium.value,
+    }
+
+    assert get_custom_case_config(params) == {
+        "batch_size": 10_000,
+        "duration": 1800,
+        "readiness_timeout": 7200,
+        "readiness_poll_interval": 10,
+        "dataset_with_size_type": DatasetWithSizeType.CohereMedium.value,
+    }
+
+
+def test_cli_builds_cloud_insert_custom_case_config_with_laion_100m_dataset():
+    cfg = get_custom_case_config(
+        {
+            "case_type": "CloudInsertCase",
+            "cloud_insert_batch_size": 10_000,
+            "cloud_insert_duration": None,
+            "cloud_insert_readiness_timeout": None,
+            "cloud_insert_readiness_poll_interval": None,
+            "dataset_with_size_type": DatasetWithSizeType.LAIONLarge.value,
+        }
+    )
+
+    assert cfg == {
+        "batch_size": 10_000,
+        "duration": None,
+        "dataset_with_size_type": DatasetWithSizeType.LAIONLarge.value,
+    }
+
+    case = CaseConfig(case_id=CaseType.CloudInsertCase, custom_case=cfg).case
+    assert case.dataset_with_size_type == DatasetWithSizeType.LAIONLarge
+    assert case.dataset.data.name == "LAION"
+    assert case.dataset.data.size == 100_000_000
+
+
+def test_cli_builds_cloud_insert_custom_case_config_with_default_dataset():
+    cfg = get_custom_case_config(
+        {
+            "case_type": "CloudInsertCase",
+            "cloud_insert_batch_size": 10_000,
+            "cloud_insert_duration": None,
+            "cloud_insert_readiness_timeout": None,
+            "cloud_insert_readiness_poll_interval": None,
+            "dataset_with_size_type": None,
+        }
+    )
+
+    assert cfg == {
+        "batch_size": 10_000,
+        "duration": None,
+        "dataset_with_size_type": DatasetWithSizeType.CohereMedium.value,
+    }
+
+    case = CaseConfig(case_id=CaseType.CloudInsertCase, custom_case=cfg).case
+    assert case.dataset_with_size_type == DatasetWithSizeType.CohereMedium
+    assert case.dataset.data.size == 1_000_000
+
+
+def test_cli_builds_multitenant_custom_case_config():
+    cfg = get_custom_case_config(
+        {
+            "case_type": "CloudMultiTenantSearchCase",
+            "dataset_with_size_type": "Small Cohere (768dim, 100K)",
+            "tenant_count": 13,
+            "tenant_prefix": "acct_",
+            "tenant_id_width": 3,
+            "payload_profile": "vector",
+            "cloud_filter_rate": 0.01,
+            "cloud_label_percentage": None,
+        }
+    )
+
+    assert cfg == {
+        "dataset_with_size_type": "Small Cohere (768dim, 100K)",
+        "tenant_count": 13,
+        "tenant_prefix": "acct_",
+        "tenant_id_width": 3,
+        "payload_profile": "vector",
+        "filter_rate": 0.01,
+    }
+
+
+def test_cli_omits_multitenant_dataset_when_not_selected():
+    cfg = get_custom_case_config(
+        {
+            "case_type": "CloudMultiTenantSearchCase",
+            "dataset_with_size_type": None,
+            "tenant_count": 13,
+            "tenant_prefix": "acct_",
+            "tenant_id_width": 3,
+            "payload_profile": "ids_only",
+            "cloud_filter_rate": None,
+            "cloud_label_percentage": None,
+        }
+    )
+
+    assert cfg == {
+        "tenant_count": 13,
+        "tenant_prefix": "acct_",
+        "tenant_id_width": 3,
+        "payload_profile": "ids_only",
+    }
+
+    case = CaseConfig(case_id=CaseType.CloudMultiTenantSearchCase, custom_case=cfg).case
+    assert case.dataset_with_size_type == DatasetWithSizeType.CohereLarge
+    assert case.dataset.data.size == 10_000_000
+
+
+def test_assembler_schedules_cloud_insert_case():
+    task = TaskConfig(
+        db=DB.ZillizCloud,
+        db_config=ZillizCloudConfig(uri="https://example.com", user="db_admin"),
+        db_case_config=AutoIndexConfig(),
+        case_config=CaseConfig(
+            case_id=CaseType.CloudInsertCase,
+            custom_case={
+                "batch_size": 1000,
+                "dataset_with_size_type": DatasetWithSizeType.CohereMedium.value,
+            },
+        ),
+        stages=[TaskStage.DROP_OLD, TaskStage.LOAD],
+    )
+
+    runner = Assembler.assemble_all("run-id", "task-label", [task], DatasetSource.S3)
+
+    assert len(runner.case_runners) == 1
+    assert runner.case_runners[0].ca.label == CaseLabel.CloudInsert
+
+
+def test_default_insert_readiness_is_immediately_ready():
+    class FakeVectorDB(VectorDB):
+        def __init__(self, dim, db_config, db_case_config, collection_name="", drop_old=False):
+            pass
+
+        @contextmanager
+        def init(self):
+            yield
+
+        def insert_embeddings(self, embeddings, metadata, labels_data=None, **kwargs):
+            return len(embeddings), None
+
+        def search_embedding(self, query, k=100, payload_profile=None):
+            return []
+
+        def optimize(self, data_size=None):
+            pass
+
+    assert FakeVectorDB(1, {}, EmptyDBCaseConfig()).poll_insert_readiness(10) == {
+        "fully_searchable": True,
+        "fully_indexed": True,
+        "additional_parameters": {},
+    }
+
+
+def test_metric_contains_cloud_insert_output_fields():
+    metric = Metric(
+        inserted_count=100,
+        insert_rows_per_second=83.33,
+        insert_completion_seconds=1.2,
+        searchable_after_insert_seconds=3.4,
+        indexed_after_searchable_seconds=5.6,
+        additional_parameters={"disable_backpressure": True},
+    )
+
+    assert metric.inserted_count == 100
+    assert metric.insert_rows_per_second == 83.33
+    assert metric.insert_completion_seconds == 1.2
+    assert metric.searchable_after_insert_seconds == 3.4
+    assert metric.indexed_after_searchable_seconds == 5.6
+    assert metric.additional_parameters == {"disable_backpressure": True}
+
+
+def test_cloud_insert_result_file_uses_insert_only_metrics(tmp_path: Path):
+    result = CaseResult(
+        task_config=TaskConfig(
+            db=DB.Pinecone,
+            db_config=PineconeConfig(
+                db_label="pinecone_cloud_insert_laion100m_bs1k",
+                api_key="secret-key",
+                index_name="laion100m",
+            ),
+            db_case_config=EmptyDBCaseConfig(),
+            case_config=CaseConfig(
+                case_id=CaseType.CloudInsertCase,
+                custom_case={"batch_size": 1000, "duration": None},
+            ),
+            stages=[TaskStage.DROP_OLD, TaskStage.LOAD],
+            load_concurrency=0,
+        ),
+        metrics=Metric(
+            inserted_count=100_000_000,
+            insert_rows_per_second=3919.9296,
+            insert_completion_seconds=255.1066,
+            searchable_after_insert_seconds=0.0,
+            indexed_after_searchable_seconds=28.5956,
+            additional_parameters={},
+        ),
+    )
+    test_result = TestResult(run_id="run-id", task_label="cloud_insert_pinecone_laion100m_bs1k", results=[result])
+
+    test_result.write_db_file(tmp_path, test_result, "pinecone")
+
+    result_file = next(tmp_path.glob("result_*_pinecone.json"))
+    raw_output = result_file.read_text()
+    assert raw_output.startswith('{\n  "run_id"')
+    written = json.loads(raw_output)
+    assert written["results"][0]["metrics"] == {
+        "inserted_count": 100_000_000,
+        "insert_rows_per_second": 3919.9296,
+        "insert_completion_seconds": 255.1066,
+        "searchable_after_insert_seconds": 0.0,
+        "indexed_after_searchable_seconds": 28.5956,
+        "additional_parameters": {},
+    }
+    assert written["results"][0]["task_config"]["db_config"]["api_key"] == "**********"
+    assert written["results"][0]["task_config"]["db_config"]["index_name"] == "laion100m"
+    assert written["results"][0]["task_config"]["case_config"] == {
+        "case_id": 600,
+        "custom_case": {"batch_size": 1000, "duration": None},
+    }
+
+    read_back = TestResult.read_file(result_file)
+    assert read_back.results[0].task_config.case_config.case_id == CaseType.CloudInsertCase
+    assert read_back.results[0].task_config.case_config.custom_case == {"batch_size": 1000, "duration": None}
+
+    collected = ResultCollector.collect(tmp_path)
+    assert len(collected) == 1
+    assert collected[0].results[0].metrics.inserted_count == 100_000_000
+
+
+def test_turbopuffer_insert_can_disable_backpressure():
+    db = TurboPuffer.__new__(TurboPuffer)
+    db.with_scalar_labels = False
+    db._scalar_id_field = "id"
+    db._vector_field = "vector"
+    db.metric = "cosine_distance"
+    db.db_case_config = TurboPufferIndexConfig(disable_backpressure=True)
+
+    class Namespace:
+        kwargs = None
+
+        def write(self, **kwargs):
+            self.kwargs = kwargs
+
+    db.ns = Namespace()
+
+    assert db.insert_embeddings([[0.1]], [1]) == (1, None)
+    assert db.ns.kwargs["disable_backpressure"] is True
+
+
+def test_turbopuffer_insert_serializes_numpy_vectors():
+    db = TurboPuffer.__new__(TurboPuffer)
+    db.with_scalar_labels = False
+    db._scalar_id_field = "id"
+    db._vector_field = "vector"
+    db.metric = "cosine_distance"
+    db.db_case_config = TurboPufferIndexConfig(disable_backpressure=False)
+
+    class Namespace:
+        kwargs = None
+
+        def write(self, **kwargs):
+            self.kwargs = kwargs
+
+    db.ns = Namespace()
+
+    insert_count, error = db.insert_embeddings([np.array([0.1, 0.2])], [1])
+
+    assert error is None
+    assert insert_count == 1
+    assert db.ns.kwargs["upsert_columns"]["vector"] == [[0.1, 0.2]]
+
+
+def test_turbopuffer_insert_returns_write_error():
+    db = TurboPuffer.__new__(TurboPuffer)
+    db.with_scalar_labels = False
+    db._scalar_id_field = "id"
+    db._vector_field = "vector"
+    db.metric = "cosine_distance"
+    db.db_case_config = TurboPufferIndexConfig(disable_backpressure=False)
+
+    class Namespace:
+        def write(self, **kwargs):
+            raise RuntimeError("write failed")
+
+    db.ns = Namespace()
+
+    insert_count, error = db.insert_embeddings([[0.1]], [1])
+
+    assert insert_count == 0
+    assert isinstance(error, RuntimeError)
+
+
+def test_milvus_insert_readiness_uses_entity_count_and_index_progress():
+    db = Milvus.__new__(Milvus)
+    db.collection_name = "c"
+    db._vector_index_name = "vector_idx"
+    db.client = type(
+        "Client",
+        (),
+        {
+            "flush": lambda self, collection_name: None,
+            "get_collection_stats": lambda self, collection_name: {"row_count": "10"},
+            "describe_index": lambda self, collection_name, index_name: {"pending_index_rows": 0},
+        },
+    )()
+
+    assert db.poll_insert_readiness(10) == {
+        "fully_searchable": True,
+        "fully_indexed": True,
+        "additional_parameters": {},
+    }
+
+
+def test_pinecone_insert_readiness_uses_vector_count():
+    db = Pinecone.__new__(Pinecone)
+    db.index = type("Index", (), {"describe_index_stats": lambda self: {"total_vector_count": 9}})()
+
+    assert db.poll_insert_readiness(10)["fully_searchable"] is False
+    assert db.poll_insert_readiness(9)["fully_indexed"] is True
+
+
+def test_pinecone_supports_cloud_payload_profiles():
+    db = Pinecone.__new__(Pinecone)
+
+    assert db.supports_payload_profile(PayloadProfile.IDS_ONLY)
+    assert db.supports_payload_profile(PayloadProfile.SCALAR_LABEL)
+    assert db.supports_payload_profile(PayloadProfile.VECTOR)
+
+
+def test_pinecone_search_requests_metadata_for_scalar_label_payload():
+    db = Pinecone.__new__(Pinecone)
+    db.expr = None
+
+    class Index:
+        kwargs = None
+
+        def query(self, **kwargs):
+            self.kwargs = kwargs
+            return {"matches": [{"id": "1"}]}
+
+    db.index = Index()
+
+    assert db.search_embedding([0.1], payload_profile=PayloadProfile.SCALAR_LABEL) == [1]
+    assert db.index.kwargs["include_metadata"] is True
+    assert db.index.kwargs["include_values"] is False
+
+
+def test_pinecone_search_requests_values_for_vector_payload():
+    db = Pinecone.__new__(Pinecone)
+    db.expr = {"meta": {"$gte": 10}}
+
+    class Index:
+        kwargs = None
+
+        def query(self, **kwargs):
+            self.kwargs = kwargs
+            return {"matches": [{"id": "2"}]}
+
+    db.index = Index()
+
+    assert db.search_embedding([0.1], payload_profile=PayloadProfile.VECTOR) == [2]
+    assert db.index.kwargs["include_metadata"] is False
+    assert db.index.kwargs["include_values"] is True
+    assert db.index.kwargs["filter"] == {"meta": {"$gte": 10}}
+
+
+def test_pinecone_search_retries_rate_limited_queries(monkeypatch):
+    db = Pinecone.__new__(Pinecone)
+    db.expr = None
+    monkeypatch.setenv("PINECONE_QUERY_RETRY_SLEEP_SECONDS", "0.25")
+    sleeps = []
+    monkeypatch.setattr("vectordb_bench.backend.clients.pinecone.pinecone.time.sleep", sleeps.append)
+
+    class RateLimitError(Exception):
+        status = 429
+
+    class Index:
+        calls = 0
+
+        def query(self, **kwargs):
+            self.calls += 1
+            if self.calls < 3:
+                raise RateLimitError("too many requests")
+            return {"matches": [{"id": "3"}]}
+
+    db.index = Index()
+
+    assert db.search_embedding([0.1]) == [3]
+    assert db.index.calls == 3
+    assert sleeps == [0.25, 0.25]
+
+
+def test_pinecone_search_stops_after_rate_limit_retry_budget(monkeypatch):
+    db = Pinecone.__new__(Pinecone)
+    db.expr = None
+    monkeypatch.setenv("PINECONE_QUERY_MAX_RETRIES", "1")
+    monkeypatch.setattr("vectordb_bench.backend.clients.pinecone.pinecone.time.sleep", lambda _: None)
+
+    class RateLimitError(Exception):
+        status = 429
+
+    class Index:
+        def query(self, **kwargs):
+            raise RateLimitError("too many requests")
+
+    db.index = Index()
+
+    try:
+        db.search_embedding([0.1])
+    except RateLimitError:
+        pass
+    else:
+        raise AssertionError("expected Pinecone rate limit error")
+
+
+def test_pinecone_insert_tracks_last_write_lsn():
+    db = Pinecone.__new__(Pinecone)
+    db.batch_size = 1000
+    db.with_scalar_labels = False
+    db._scalar_id_field = "meta"
+
+    class UpsertResponse:
+        _response_info = {"raw_headers": {"x-pinecone-request-lsn": "123"}}
+
+    class Index:
+        def upsert(self, records):
+            return UpsertResponse()
+
+    db.index = Index()
+
+    insert_count, error = db.insert_embeddings([[0.1]], [1])
+
+    assert error is None
+    assert insert_count == 1
+    assert db._last_write_lsn == 123
+
+
+def test_pinecone_insert_keeps_highest_write_lsn():
+    db = Pinecone.__new__(Pinecone)
+    db.batch_size = 1
+    db.with_scalar_labels = False
+    db._scalar_id_field = "meta"
+    responses = iter(["123", "122"])
+
+    class UpsertResponse:
+        def __init__(self, lsn):
+            self._response_info = {"raw_headers": {"x-pinecone-request-lsn": lsn}}
+
+    class Index:
+        def upsert(self, records):
+            return UpsertResponse(next(responses))
+
+    db.index = Index()
+
+    insert_count, error = db.insert_embeddings([[0.1], [0.2]], [1, 2])
+
+    assert error is None
+    assert insert_count == 2
+    assert db._last_write_lsn == 123
+
+
+def test_pinecone_record_write_lsn_keeps_highest_value():
+    db = Pinecone.__new__(Pinecone)
+
+    db._record_write_lsn(123)
+    db._record_write_lsn(122)
+
+    assert db._last_write_lsn == 123
+
+
+def test_pinecone_insert_readiness_uses_lsn_when_available():
+    db = Pinecone.__new__(Pinecone)
+    db._last_write_lsn = 123
+    db._readiness_probe_vector = [0.0]
+
+    class QueryResponse(dict):
+        def __init__(self, indexed_lsn):
+            super().__init__({"matches": []})
+            self._response_info = {"raw_headers": {"x-pinecone-max-indexed-lsn": str(indexed_lsn)}}
+
+    class Index:
+        indexed_lsn = 122
+
+        def describe_index_stats(self):
+            return {"total_vector_count": 10}
+
+        def query(self, **kwargs):
+            return QueryResponse(self.indexed_lsn)
+
+    db.index = Index()
+
+    assert db.poll_insert_readiness(10)["fully_searchable"] is False
+    db.index.indexed_lsn = 123
+    assert db.poll_insert_readiness(10)["fully_indexed"] is True
+
+
+def test_turbopuffer_insert_readiness_uses_unindexed_bytes():
+    db = TurboPuffer.__new__(TurboPuffer)
+    db.db_case_config = TurboPufferIndexConfig(disable_backpressure=True)
+    db.ns = type("Namespace", (), {"metadata": lambda self: {"unindexed_bytes": 1}})()
+
+    status = db.poll_insert_readiness(10)
+
+    assert status["fully_searchable"] is True
+    assert status["fully_indexed"] is False
+    assert status["additional_parameters"] == {"disable_backpressure": True}
+
+
+def test_turbopuffer_insert_readiness_uses_nested_unindexed_bytes():
+    db = TurboPuffer.__new__(TurboPuffer)
+    db.db_case_config = TurboPufferIndexConfig(disable_backpressure=False)
+    db.ns = type(
+        "Namespace",
+        (),
+        {"metadata": lambda self: {"index": {"status": "updating", "unindexed_bytes": 1}}},
+    )()
+
+    status = db.poll_insert_readiness(10)
+
+    assert status["fully_searchable"] is True
+    assert status["fully_indexed"] is False
+    assert status["additional_parameters"] == {"disable_backpressure": False}
+
+
+def test_cloud_insert_runner_records_insert_and_readiness_metrics(monkeypatch):
+    class Data:
+        train_id_field = "id"
+        train_vector_field = "vector"
+        metric_type = "L2"
+
+    class Dataset:
+        data = Data()
+
+        def iter_batches(self, batch_size):
+            assert batch_size == 2
+            return iter(
+                [
+                    pd.DataFrame({"id": [1, 2], "vector": [np.array([0.1]), np.array([0.2])]}),
+                    pd.DataFrame({"id": [3], "vector": [np.array([0.3])]}),
+                ]
+            )
+
+    class DB:
+        thread_safe = True
+        name = "fake"
+        inserts = []
+        readiness_calls = 0
+
+        @contextmanager
+        def init(self):
+            yield
+
+        def need_normalize_cosine(self):
+            return False
+
+        def insert_embeddings(self, embeddings, metadata, labels_data=None):
+            self.inserts.append((embeddings, metadata))
+            return len(metadata), None
+
+        def poll_insert_readiness(self, expected_count):
+            self.readiness_calls += 1
+            return {
+                "fully_searchable": self.readiness_calls >= 2,
+                "fully_indexed": self.readiness_calls >= 3,
+                "additional_parameters": {"example": "value"},
+            }
+
+    db = DB()
+    monkeypatch.setattr("vectordb_bench.backend.task_runner.time.sleep", lambda _: None)
+    case = CloudInsertCase(batch_size=2)
+    case.dataset = Dataset()
+    config = type("Config", (), {"load_concurrency": 1})()
+    runner = CaseRunner.construct(ca=case, db=db, config=config)
+
+    metric = runner._run_cloud_insert_case()
+
+    assert [metadata for _, metadata in db.inserts] == [[1, 2], [3]]
+    assert metric.inserted_count == 3
+    assert metric.insert_rows_per_second > 0
+    assert metric.insert_completion_seconds >= 0
+    assert metric.searchable_after_insert_seconds >= 0
+    assert metric.indexed_after_searchable_seconds >= 0
+    assert metric.additional_parameters == {"example": "value"}
+
+
+def test_cloud_insert_runner_times_out_when_readiness_never_completes(monkeypatch):
+    class Data:
+        train_id_field = "id"
+        train_vector_field = "vector"
+        metric_type = "L2"
+
+    class Dataset:
+        data = Data()
+
+        def iter_batches(self, batch_size):
+            return iter([pd.DataFrame({"id": [1], "vector": [np.array([0.1])]})])
+
+    class DB:
+        @contextmanager
+        def init(self):
+            yield
+
+        def need_normalize_cosine(self):
+            return False
+
+        def poll_insert_readiness(self, expected_count):
+            return {
+                "fully_searchable": False,
+                "fully_indexed": False,
+                "additional_parameters": {"reason": "stalled"},
+            }
+
+    class FakeConcurrentInsertRunner:
+        def __init__(self, *args, **kwargs):
+            pass
+
+        def task(self):
+            return 1
+
+    def fail_on_sleep(_seconds):
+        raise AssertionError("readiness polling did not time out before sleeping")
+
+    monkeypatch.setattr("vectordb_bench.backend.task_runner.ConcurrentInsertRunner", FakeConcurrentInsertRunner)
+    monkeypatch.setattr("vectordb_bench.backend.task_runner.time.sleep", fail_on_sleep)
+    case = CloudInsertCase(batch_size=1, readiness_timeout=0, readiness_poll_interval=0)
+    case.dataset = Dataset()
+    runner = CaseRunner.construct(ca=case, db=DB(), config=type("Config", (), {"load_concurrency": 1})())
+
+    with pytest.raises(TimeoutError, match="fully_searchable.*last_status.*stalled"):
+        runner._run_cloud_insert_case()
+
+
+def test_cloud_insert_runner_uses_concurrent_insert_runner(monkeypatch):
+    created = {}
+
+    class Data:
+        metric_type = "L2"
+
+    class Dataset:
+        data = Data()
+
+    class FakeConcurrentInsertRunner:
+        def __init__(self, db, dataset, normalize, filters, max_workers, batch_size, duration):
+            created.update(
+                {
+                    "db": db,
+                    "dataset": dataset,
+                    "normalize": normalize,
+                    "filters": filters,
+                    "max_workers": max_workers,
+                    "batch_size": batch_size,
+                    "duration": duration,
+                }
+            )
+
+        def task(self):
+            return 3
+
+    class DB:
+        @contextmanager
+        def init(self):
+            yield
+
+        def need_normalize_cosine(self):
+            return False
+
+        def poll_insert_readiness(self, expected_count):
+            return {"fully_searchable": True, "fully_indexed": True, "additional_parameters": {}}
+
+    monkeypatch.setattr("vectordb_bench.backend.task_runner.ConcurrentInsertRunner", FakeConcurrentInsertRunner)
+    case = CloudInsertCase(batch_size=1000, duration=60)
+    case.dataset = Dataset()
+    config = type("Config", (), {"load_concurrency": 7})()
+    runner = CaseRunner.construct(ca=case, db=DB(), config=config)
+
+    metric = runner._run_cloud_insert_case()
+
+    assert metric.inserted_count == 3
+    assert created["batch_size"] == 1000
+    assert created["duration"] == 60
+    assert created["max_workers"] == 7
+    assert created["dataset"] is case.dataset
+
+
+def test_concurrent_insert_runner_does_not_retry_non_retryable_insert_errors(monkeypatch):
+    from vectordb_bench.backend.runner import concurrent_runner as concurrent_runner_module
+
+    class NonRetryableInsertError(RuntimeError):
+        non_retryable = True
+
+    class FakeDB:
+        def __init__(self):
+            self.calls = 0
+
+        def insert_embeddings(self, **kwargs):
+            self.calls += 1
+            return 2, NonRetryableInsertError("partial tenant insert")
+
+    monkeypatch.setattr(concurrent_runner_module.time, "sleep", lambda _seconds: None)
+
+    runner = ConcurrentInsertRunner.__new__(ConcurrentInsertRunner)
+    db = FakeDB()
+
+    with pytest.raises(RuntimeError, match="Non-retryable insert failure"):
+        runner._insert_batch_with_retry(
+            db,
+            embeddings=[[1.0], [2.0], [3.0]],
+            metadata=[0, 1, 2],
+            tenant_labels_data=["tenant_0000", "tenant_0001", "tenant_0000"],
+        )
+
+    assert db.calls == 1
+
+
+def test_concurrent_insert_runner_stops_handing_out_batches_after_non_retryable_error():
+    class NonRetryableInsertError(RuntimeError):
+        non_retryable = True
+
+    class DB:
+        thread_safe = True
+        name = "fake"
+
+        def __init__(self):
+            self.calls = []
+
+        @contextmanager
+        def init(self):
+            yield
+
+        def insert_embeddings(self, embeddings, metadata, labels_data=None):
+            self.calls.append(metadata)
+            if len(self.calls) == 1:
+                return 0, NonRetryableInsertError("partial tenant insert")
+            return len(metadata), None
+
+    db = DB()
+    runner = ConcurrentInsertRunner(
+        db, _ConcurrentRunnerDataset([0, 1, 2]), normalize=False, max_workers=2, batch_size=1
+    )
+    runner._create_executor = lambda: _SerialTaskExecutor()
+
+    with pytest.raises(RuntimeError, match="Non-retryable insert failure"):
+        runner.task()
+
+    assert db.calls == [[0]]
+
+
+def test_concurrent_insert_runner_stops_handing_out_batches_after_retry_exhaustion(monkeypatch):
+    from vectordb_bench.backend.runner import concurrent_runner as concurrent_runner_module
+
+    class DB:
+        thread_safe = True
+        name = "fake"
+
+        def __init__(self):
+            self.calls = []
+
+        @contextmanager
+        def init(self):
+            yield
+
+        def insert_embeddings(self, embeddings, metadata, labels_data=None):
+            self.calls.append(metadata)
+            return 0, RuntimeError("insert failed")
+
+    monkeypatch.setattr(concurrent_runner_module.config, "MAX_INSERT_RETRY", 0)
+
+    db = DB()
+    runner = ConcurrentInsertRunner(db, _ConcurrentRunnerDataset([0, 1]), normalize=False, max_workers=2, batch_size=1)
+    runner._create_executor = lambda: _SerialTaskExecutor()
+
+    with pytest.raises(RuntimeError, match="Insert failed and retried more than 0 times"):
+        runner.task()
+
+    assert db.calls == [[0]]
+
+
+def test_concurrent_insert_runner_uses_custom_batch_size_iterator():
+    class Data:
+        train_id_field = "id"
+        train_vector_field = "vector"
+
+    class Dataset:
+        data = Data()
+        requested_batch_size = None
+
+        def iter_batches(self, batch_size):
+            self.requested_batch_size = batch_size
+            return iter(
+                [
+                    pd.DataFrame(
+                        {
+                            "id": [1, 2],
+                            "vector": [np.array([0.1]), np.array([0.2])],
+                        }
+                    )
+                ]
+            )
+
+        def __iter__(self):
+            raise AssertionError("ConcurrentInsertRunner should request an explicit batch size")
+
+    class DB:
+        thread_safe = True
+        name = "fake"
+
+        def __init__(self):
+            self.inserts = []
+
+        @contextmanager
+        def init(self):
+            yield
+
+        def insert_embeddings(self, embeddings, metadata, labels_data=None):
+            self.inserts.append((embeddings, metadata))
+            return len(metadata), None
+
+    dataset = Dataset()
+    db = DB()
+    runner = ConcurrentInsertRunner(db, dataset, normalize=False, max_workers=1, batch_size=1000)
+
+    assert runner.task() == 2
+    assert dataset.requested_batch_size == 1000
+    assert db.inserts == [([[0.1], [0.2]], [1, 2])]
+
+
+class TenantInsertProbeDB:
+    name = "TenantInsertProbeDB"
+    thread_safe = True
+
+    def __init__(self):
+        self.calls = []
+
+    @contextmanager
+    def init(self):
+        yield
+
+    def insert_embeddings(self, embeddings, metadata, labels_data=None, tenant_labels_data=None):
+        self.calls.append(
+            {
+                "embeddings": embeddings,
+                "metadata": metadata,
+                "labels_data": labels_data,
+                "tenant_labels_data": tenant_labels_data,
+            }
+        )
+        return len(embeddings), None
+
+
+class TenantAwareCase:
+    is_multitenant = True
+
+    def tenant_labels_for_ids(self, row_ids):
+        return [f"tenant_{int(row_id) % 3:04d}" for row_id in row_ids]
+
+
+def test_concurrent_insert_runner_passes_tenant_labels():
+    db = TenantInsertProbeDB()
+    dataset = MagicMock()
+    dataset.data.train_id_field = "id"
+    dataset.data.train_vector_field = "emb"
+    dataset.iter_batches.return_value = iter(
+        [
+            pd.DataFrame(
+                {
+                    "id": [0, 1, 5],
+                    "emb": [[1.0, 0.0], [0.0, 1.0], [0.5, 0.5]],
+                }
+            )
+        ]
+    )
+
+    runner = ConcurrentInsertRunner(
+        db=db,
+        dataset=dataset,
+        normalize=False,
+        max_workers=1,
+        batch_size=10,
+        tenant_case=TenantAwareCase(),
+    )
+
+    count = runner.task()
+
+    assert count == 3
+    assert db.calls[0]["metadata"] == [0, 1, 5]
+    assert db.calls[0]["tenant_labels_data"] == ["tenant_0000", "tenant_0001", "tenant_0002"]
+
+
+def test_concurrent_insert_runner_passes_scalar_labels_for_scalar_payload_without_filter():
+    db = TenantInsertProbeDB()
+    dataset = MagicMock()
+    dataset.data.train_id_field = "id"
+    dataset.data.train_vector_field = "emb"
+    dataset.data.scalar_labels_file_separated = False
+    dataset.iter_batches.return_value = iter(
+        [
+            pd.DataFrame(
+                {
+                    "id": [0, 1, 5],
+                    "emb": [[1.0, 0.0], [0.0, 1.0], [0.5, 0.5]],
+                    "labels": ["label_a", "label_b", "label_c"],
+                }
+            )
+        ]
+    )
+
+    runner = ConcurrentInsertRunner(
+        db=db,
+        dataset=dataset,
+        normalize=False,
+        max_workers=1,
+        batch_size=10,
+        with_scalar_labels=True,
+    )
+
+    count = runner.task()
+
+    assert count == 3
+    assert db.calls[0]["metadata"] == [0, 1, 5]
+    assert db.calls[0]["labels_data"] == ["label_a", "label_b", "label_c"]
+    assert db.calls[0]["tenant_labels_data"] is None
+
+
+def test_pre_run_prepares_separated_scalar_labels_for_scalar_payload():
+    class Dataset:
+        def __init__(self):
+            self.prepare_kwargs = None
+            self.data = type("Data", (), {"dim": 2, "metric_type": "L2"})()
+
+        def prepare(self, source, filters, with_train_files, with_scalar_labels=False):
+            self.prepare_kwargs = {
+                "source": source,
+                "filters": filters,
+                "with_train_files": with_train_files,
+                "with_scalar_labels": with_scalar_labels,
+            }
+            return True
+
+    class Case:
+        is_multitenant = False
+        with_scalar_labels = True
+        filters = MagicMock()
+        dataset = Dataset()
+
+    class DB:
+        init_args = None
+
+        @classmethod
+        def init_cls(cls, **kwargs):
+            cls.init_args = kwargs
+            return cls()
+
+        def need_normalize_cosine(self):
+            return False
+
+    class DBConfig:
+        def to_dict(self):
+            return {}
+
+    config = type(
+        "Config",
+        (),
+        {
+            "db": DB,
+            "db_config": DBConfig(),
+            "db_case_config": EmptyDBCaseConfig(),
+            "stages": [TaskStage.LOAD],
+        },
+    )()
+    runner = CaseRunner.construct(
+        ca=Case(),
+        config=config,
+        dataset_source=DatasetSource.S3,
+    )
+
+    runner._pre_run()
+
+    assert Case.dataset.prepare_kwargs["with_scalar_labels"] is True
diff --git a/tests/test_cloud_payload_case.py b/tests/test_cloud_payload_case.py
new file mode 100644
index 000000000..d3d97fb60
--- /dev/null
+++ b/tests/test_cloud_payload_case.py
@@ -0,0 +1,181 @@
+import pytest
+
+from vectordb_bench import config
+from vectordb_bench.backend.cases import CaseType, CloudPayloadSearchCase
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.api import EmptyDBCaseConfig
+from vectordb_bench.backend.data_source import DatasetSource
+from vectordb_bench.backend.dataset import DatasetWithSizeType
+from vectordb_bench.backend.payload import PayloadProfile
+from vectordb_bench.backend.runner.mp_runner import MultiProcessingSearchRunner
+from vectordb_bench.backend.runner.serial_runner import SerialSearchRunner
+from vectordb_bench.backend.task_runner import CaseRunner, RunningStatus
+from vectordb_bench.cli.cli import get_custom_case_config
+from vectordb_bench.models import CaseConfig, TaskConfig
+
+
+class FakeDB:
+    name = "FakeDB"
+
+    def __init__(self, supported_payload_profiles=None):
+        self.supported_payload_profiles = supported_payload_profiles or {PayloadProfile.IDS_ONLY}
+        self.calls = []
+
+    def supports_payload_profile(self, payload_profile: PayloadProfile) -> bool:
+        return payload_profile in self.supported_payload_profiles
+
+    def search_embedding(self, query: list[float], k: int = 100, **kwargs) -> list[int]:
+        self.calls.append((query, k, kwargs))
+        return list(range(k))
+
+
+def test_payload_profile_estimates_response_bytes():
+    assert PayloadProfile.IDS_ONLY.estimated_bytes_per_query(k=10, dim=768) == 200
+    assert PayloadProfile.VECTOR.estimated_bytes_per_query(k=10, dim=768) == 30_920
+    assert PayloadProfile.TEXT.estimated_bytes_per_query(k=10, dim=0) == 5_320
+
+
+def test_cloud_payload_case_defaults_to_laion_100m():
+    case = CloudPayloadSearchCase(payload_profile="vector")
+
+    assert case.case_id == CaseType.CloudPayloadSearchCase
+    assert case.dataset.data.name == "LAION"
+    assert case.dataset.data.size == 100_000_000
+    assert case.dataset.data.dim == 768
+    assert case.payload_profile == PayloadProfile.VECTOR
+    assert case.estimated_payload_bytes_per_query(config.K_DEFAULT) == 309_200
+
+
+def test_cloud_payload_case_accepts_sized_dataset():
+    case = CloudPayloadSearchCase(dataset_with_size_type=DatasetWithSizeType.CohereSmall.value)
+
+    assert case.dataset_with_size_type == DatasetWithSizeType.CohereSmall
+    assert case.dataset.data.name == "Cohere"
+    assert case.dataset.data.size == 100_000
+
+
+def test_case_config_builds_cloud_payload_case_from_custom_case():
+    case = CaseConfig(
+        case_id=CaseType.CloudPayloadSearchCase,
+        custom_case={"payload_profile": "vector"},
+    ).case
+
+    assert isinstance(case, CloudPayloadSearchCase)
+    assert case.payload_profile == PayloadProfile.VECTOR
+
+
+def test_case_runner_reuse_key_distinguishes_scalar_label_schema_requirement():
+    ids_only_case = CloudPayloadSearchCase(
+        dataset_with_size_type=DatasetWithSizeType.CohereSmall.value,
+        payload_profile=PayloadProfile.IDS_ONLY,
+    )
+    scalar_label_case = CloudPayloadSearchCase(
+        dataset_with_size_type=DatasetWithSizeType.CohereSmall.value,
+        payload_profile=PayloadProfile.SCALAR_LABEL,
+    )
+    task = TaskConfig(
+        db=DB.Test,
+        db_config=DB.Test.config_cls(),
+        db_case_config=EmptyDBCaseConfig(),
+        case_config=CaseConfig(case_id=CaseType.CloudPayloadSearchCase),
+    )
+
+    ids_only_runner = CaseRunner(
+        run_id="run-id",
+        config=task,
+        ca=ids_only_case,
+        status=RunningStatus.PENDING,
+        dataset_source=DatasetSource.S3,
+    )
+    scalar_label_runner = CaseRunner(
+        run_id="run-id",
+        config=task,
+        ca=scalar_label_case,
+        status=RunningStatus.PENDING,
+        dataset_source=DatasetSource.S3,
+    )
+
+    assert ids_only_case.with_scalar_labels is False
+    assert scalar_label_case.with_scalar_labels is True
+    assert ids_only_runner != scalar_label_runner
+    assert hash(ids_only_runner) != hash(scalar_label_runner)
+
+
+def test_cli_propagates_cloud_payload_dataset_selection():
+    custom_case = get_custom_case_config(
+        {
+            "case_type": "CloudPayloadSearchCase",
+            "dataset_with_size_type": DatasetWithSizeType.CohereSmall.value,
+            "payload_profile": "vector",
+            "cloud_filter_rate": None,
+            "cloud_label_percentage": None,
+        }
+    )
+
+    assert custom_case["dataset_with_size_type"] == DatasetWithSizeType.CohereSmall.value
+
+    case = CaseConfig(case_id=CaseType.CloudPayloadSearchCase, custom_case=custom_case).case
+    assert case.dataset_with_size_type == DatasetWithSizeType.CohereSmall
+    assert case.dataset.data.size == 100_000
+
+
+def test_cli_omits_cloud_payload_dataset_when_not_selected():
+    custom_case = get_custom_case_config(
+        {
+            "case_type": "CloudPayloadSearchCase",
+            "dataset_with_size_type": None,
+            "payload_profile": "ids_only",
+            "cloud_filter_rate": None,
+            "cloud_label_percentage": None,
+        }
+    )
+
+    assert "dataset_with_size_type" not in custom_case
+
+    case = CaseConfig(case_id=CaseType.CloudPayloadSearchCase, custom_case=custom_case).case
+    assert case.dataset_with_size_type is None
+    assert case.dataset.data.name == "LAION"
+    assert case.dataset.data.size == 100_000_000
+
+
+def test_serial_runner_omits_payload_argument_for_ids_only():
+    db = FakeDB()
+    runner = SerialSearchRunner(db=db, test_data=[[0.1]], ground_truth=[[0]], k=3)
+
+    assert runner._get_db_search_res([0.1]) == [0, 1, 2]
+    assert db.calls == [([0.1], 3, {})]
+
+
+def test_serial_runner_passes_payload_argument_for_vector_profile():
+    db = FakeDB(supported_payload_profiles={PayloadProfile.IDS_ONLY, PayloadProfile.VECTOR})
+    runner = SerialSearchRunner(
+        db=db,
+        test_data=[[0.1]],
+        ground_truth=[[0]],
+        k=3,
+        payload_profile=PayloadProfile.VECTOR,
+    )
+
+    assert runner._get_db_search_res([0.1]) == [0, 1, 2]
+    assert db.calls == [([0.1], 3, {"payload_profile": PayloadProfile.VECTOR})]
+
+
+def test_search_runners_fail_fast_for_unsupported_payload_profile():
+    db = FakeDB()
+
+    with pytest.raises(NotImplementedError, match="payload_profile=vector"):
+        SerialSearchRunner(
+            db=db,
+            test_data=[[0.1]],
+            ground_truth=[[0]],
+            k=3,
+            payload_profile=PayloadProfile.VECTOR,
+        )
+
+    with pytest.raises(NotImplementedError, match="payload_profile=vector"):
+        MultiProcessingSearchRunner(
+            db=db,
+            test_data=[[0.1]],
+            k=3,
+            payload_profile=PayloadProfile.VECTOR,
+        )
diff --git a/tests/test_cloud_payload_search.py b/tests/test_cloud_payload_search.py
new file mode 100644
index 000000000..cda8f63ba
--- /dev/null
+++ b/tests/test_cloud_payload_search.py
@@ -0,0 +1,128 @@
+from contextlib import contextmanager
+
+import polars as pl
+
+from vectordb_bench.backend.cases import CloudPayloadSearchCase
+from vectordb_bench.backend.clients.milvus.milvus import Milvus
+from vectordb_bench.backend.data_source import DatasetSource
+from vectordb_bench.backend.dataset import Dataset
+from vectordb_bench.backend.filter import FilterOp, non_filter
+from vectordb_bench.backend.payload import PayloadProfile
+from vectordb_bench.backend.runner.serial_runner import SerialSearchRunner
+
+
+class _Client:
+    def __init__(self):
+        self.search_kwargs = None
+
+    def search(self, **kwargs):
+        self.search_kwargs = kwargs
+        return [[{"pk": 1}]]
+
+
+def test_laion_100m_declares_scalar_label_assets():
+    laion = Dataset.LAION.get(100_000_000)
+
+    assert laion.with_scalar_labels is True
+    assert laion.scalar_labels_file == "scalar_labels.parquet"
+    assert 0.01 in laion.scalar_label_percentages
+
+
+def test_scalar_label_payload_profile_estimates_small_string_payload():
+    assert PayloadProfile.SCALAR_LABEL.estimated_bytes_per_query(k=100, dim=768) == 3600
+
+
+def test_scalar_label_payload_profile_requires_scalar_label_materialization_without_filter():
+    case = CloudPayloadSearchCase(payload_profile="scalar_label")
+
+    assert case.filters.type == FilterOp.NonFilter
+    assert case.with_scalar_labels is True
+
+
+def test_dataset_prepare_loads_separated_scalar_labels_for_scalar_payload(monkeypatch):
+    dataset = Dataset.LAION.manager(100_000_000)
+    dataset.data.with_remote_resource = False
+    loaded_scalar_labels = object()
+
+    def fake_read_file(file_name):
+        if file_name == dataset.data.scalar_labels_file:
+            return loaded_scalar_labels
+        return pl.DataFrame({dataset.data.test_vector_field: [], dataset.data.gt_neighbors_field: []})
+
+    monkeypatch.setattr(dataset, "_read_file", fake_read_file)
+
+    dataset.prepare(
+        source=DatasetSource.S3,
+        filters=non_filter,
+        with_train_files=False,
+        with_scalar_labels=True,
+    )
+
+    assert dataset.scalar_labels is loaded_scalar_labels
+
+
+def test_cloud_payload_case_can_combine_label_filter_with_scalar_label_payload():
+    case = CloudPayloadSearchCase(
+        payload_profile="scalar_label",
+        label_percentage=0.01,
+    )
+
+    assert case.payload_profile == PayloadProfile.SCALAR_LABEL
+    assert case.filters.type == FilterOp.StrEqual
+    assert case.filters.label_value == "label_1p"
+
+
+def test_milvus_scalar_label_payload_requests_label_output_field():
+    db = Milvus.__new__(Milvus)
+    db.client = _Client()
+    db.case_config = type("CaseConfig", (), {"search_param": lambda self: {}})()
+    db.collection_name = "collection"
+    db.expr = "label == 'label_1p'"
+    db._primary_field = "pk"
+    db._vector_field = "vector"
+    db._scalar_label_field = "label"
+
+    assert db.supports_payload_profile(PayloadProfile.SCALAR_LABEL)
+    assert db.search_embedding([0.1, 0.2], payload_profile=PayloadProfile.SCALAR_LABEL) == [1]
+    assert db.client.search_kwargs["output_fields"] == ["label"]
+
+
+class TenantSearchProbeDB:
+    name = "TenantSearchProbeDB"
+
+    def __init__(self):
+        self.tenants = []
+
+    def supports_payload_profile(self, payload_profile):
+        return True
+
+    @contextmanager
+    def init(self):
+        yield
+
+    def prepare_filter(self, filters):
+        return None
+
+    def search_embedding(self, query, k=100, payload_profile=None, tenant=None):
+        self.tenants.append(tenant)
+        return []
+
+
+def test_serial_search_runner_passes_tenant_and_skips_recall():
+    db = TenantSearchProbeDB()
+    runner = SerialSearchRunner(
+        db=db,
+        test_data=[[1.0, 0.0], [0.0, 1.0]],
+        ground_truth=None,
+        tenant_labels=["tenant_0000", "tenant_0001"],
+        measure_recall=False,
+    )
+
+    recall, ndcg, p99, p95 = runner.search((runner.test_data, runner.ground_truth))
+
+    assert recall == 0
+    assert ndcg == 0
+    assert p99 >= 0
+    assert p95 >= 0
+    assert set(db.tenants).issubset({"tenant_0000", "tenant_0001"})
+    assert db.tenants
diff --git a/tests/test_concurrent_runner.py b/tests/test_concurrent_runner.py
new file mode 100644
index 000000000..c9e5d9267
--- /dev/null
+++ b/tests/test_concurrent_runner.py
@@ -0,0 +1,159 @@
+"""Tests for ConcurrentInsertRunner against a running Milvus instance.
+
+Includes:
+  - Correctness tests (threading & async backends)
+  - Parameterized benchmark: serial vs concurrent across (batch_size, workers) matrix
+
+NUM_PER_BATCH is set via os.environ before each run. Since runners execute
+task() in a spawn subprocess that re-imports config, the env var takes effect.
+
+Requires:
+  - Milvus running at localhost:19530
+  - Network access to download OpenAI 50K dataset
+
+Usage:
+  pytest tests/test_concurrent_runner.py -v -s     # correctness tests only
+  python tests/test_concurrent_runner.py             # full benchmark matrix
+"""
+
+# ruff: noqa: T201
+
+from __future__ import annotations
+
+import logging
+import os
+import time
+
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.milvus.config import FLATConfig
+from vectordb_bench.backend.dataset import Dataset, DatasetSource
+from vectordb_bench.backend.runner.concurrent_runner import ConcurrentInsertRunner, ExecutorBackend
+from vectordb_bench.backend.runner.serial_runner import SerialInsertRunner
+
+log = logging.getLogger("vectordb_bench")
+log.setLevel(logging.INFO)
+
+DATASET_SIZE = 50_000
+
+
+# ── Shared helpers ──────────────────────────────────────────────────────
+
+
+def get_milvus_db(collection_name: str):
+    return DB.Milvus.init_cls(
+        dim=1536,
+        db_config={"uri": "http://localhost:19530", "user": "", "password": ""},
+        db_case_config=FLATConfig(metric_type="COSINE"),
+        collection_name=collection_name,
+        drop_old=True,
+    )
+
+
+def prepare_dataset():
+    dataset = Dataset.OPENAI.manager(DATASET_SIZE)
+    dataset.prepare(DatasetSource.AliyunOSS)
+    return dataset
+
+
+def set_batch_size(batch_size: int) -> None:
+    os.environ["NUM_PER_BATCH"] = str(batch_size)
+
+
+def timed_run(runner: SerialInsertRunner | ConcurrentInsertRunner) -> tuple[int, float]:
+    start = time.perf_counter()
+    count = runner.run()
+    return count, time.perf_counter() - start
+
+
+# ── Correctness tests (pytest) ──────────────────────────────────────────
+
+
+def test_concurrent_insert_threading():
+    """Test concurrent insert with threading backend."""
+    db = get_milvus_db("test_conc_threading")
+    runner = ConcurrentInsertRunner(
+        db=db,
+        dataset=prepare_dataset(),
+        normalize=False,
+        max_workers=4,
+        backend=ExecutorBackend.THREADING,
+    )
+    count = runner.run()
+    assert count == DATASET_SIZE, f"Expected {DATASET_SIZE}, got {count}"
+
+
+def test_concurrent_insert_async():
+    """Test concurrent insert with async backend."""
+    db = get_milvus_db("test_conc_async")
+    runner = ConcurrentInsertRunner(
+        db=db,
+        dataset=prepare_dataset(),
+        normalize=False,
+        max_workers=4,
+        backend=ExecutorBackend.ASYNC,
+    )
+    count = runner.run()
+    assert count == DATASET_SIZE, f"Expected {DATASET_SIZE}, got {count}"
+
+
+# ── Parameterized benchmark ────────────────────────────────────────────
+
+
+def run_serial(batch_size: int) -> tuple[int, float]:
+    set_batch_size(batch_size)
+    runner = SerialInsertRunner(
+        db=get_milvus_db(f"bench_serial_b{batch_size}"),
+        dataset=prepare_dataset(),
+        normalize=False,
+    )
+    return timed_run(runner)
+
+
+def run_concurrent(batch_size: int, workers: int) -> tuple[int, float]:
+    set_batch_size(batch_size)
+    runner = ConcurrentInsertRunner(
+        db=get_milvus_db(f"bench_conc_b{batch_size}_w{workers}"),
+        dataset=prepare_dataset(),
+        normalize=False,
+        max_workers=workers,
+        backend=ExecutorBackend.THREADING,
+    )
+    return timed_run(runner)
+
+
+def bench_matrix():
+    batch_sizes = [100, 500, 1000, 5000]
+    worker_counts = [1, 2, 4, 8]
+
+    conc_headers = [f"conc({w}w)" for w in worker_counts]
+    speedup_headers = [f"speedup({w}w)" for w in worker_counts]
+    print(f"\n{'Batch':>6} {'#Bat':>5} {'serial':>8}", end="")
+    for h in conc_headers:
+        print(f" {h:>10}", end="")
+    for h in speedup_headers:
+        print(f" {h:>12}", end="")
+    print()
+    print("-" * (22 + 10 * len(worker_counts) + 12 * len(worker_counts)))
+
+    for bs in batch_sizes:
+        n_batches = DATASET_SIZE // bs
+        _, dur_s = run_serial(bs)
+
+        conc_durs = []
+        for w in worker_counts:
+            _, dur_c = run_concurrent(bs, w)
+            conc_durs.append(dur_c)
+
+        print(f"{bs:>6} {n_batches:>5} {dur_s:>7.2f}s", end="")
+        for dur_c in conc_durs:
+            print(f" {dur_c:>9.2f}s", end="")
+        for dur_c in conc_durs:
+            print(f" {dur_s / dur_c:>11.2f}x", end="")
+        print()
+
+    # restore default
+    set_batch_size(100)
+
+
+if __name__ == "__main__":
+    bench_matrix()
diff --git a/tests/test_db_client_resolution.py b/tests/test_db_client_resolution.py
new file mode 100644
index 000000000..ce0278eb0
--- /dev/null
+++ b/tests/test_db_client_resolution.py
@@ -0,0 +1,17 @@
+"""Every DB must resolve config_cls/init_cls or raise ModuleNotFoundError.
+Anything else breaks the Run Test page's missing-optional-dep hint path.
+"""
+
+import pytest
+
+from vectordb_bench.backend.clients import DB
+
+
+@pytest.mark.parametrize("db", list(DB), ids=lambda d: d.name)
+def test_db_resolves_or_missing_module(db):
+    for attr in ("config_cls", "init_cls"):
+        try:
+            getattr(db, attr)
+        except ModuleNotFoundError as e:
+            assert e.name, f"{db.name}.{attr}: ModuleNotFoundError has no .name"
+            return
diff --git a/tests/test_filtered_adapters.py b/tests/test_filtered_adapters.py
new file mode 100644
index 000000000..a5d0d713f
--- /dev/null
+++ b/tests/test_filtered_adapters.py
@@ -0,0 +1,79 @@
+from importlib import import_module
+from typing import Any
+
+import pytest
+
+from vectordb_bench.backend.filter import FilterOp, IntFilter, non_filter
+
+
+@pytest.fixture
+def numeric_filter() -> IntFilter:
+    return IntFilter(filter_rate=0.99, int_field="id", int_value=49_500)
+
+
+def _adapter_class(dependency: str, module: str, name: str) -> Any:
+    pytest.importorskip(dependency)
+    return getattr(import_module(module), name)
+
+
+def test_qdrant_numeric_filter_is_inclusive(numeric_filter: IntFilter):
+    adapter_cls = _adapter_class(
+        "qdrant_client",
+        "vectordb_bench.backend.clients.qdrant_local.qdrant_local",
+        "QdrantLocal",
+    )
+
+    assert FilterOp.NumGE in adapter_cls.supported_filter_types
+    adapter = adapter_cls.__new__(adapter_cls)
+    adapter._primary_field = "pk"
+    adapter.prepare_filter(numeric_filter)
+    numeric_range = adapter._query_filter.must[0].range
+    assert numeric_range.gte == 49_500
+    assert numeric_range.gt is None
+    adapter.prepare_filter(non_filter)
+    assert adapter._query_filter is None
+
+
+def test_weaviate_numeric_filter_is_inclusive(numeric_filter: IntFilter):
+    adapter_cls = _adapter_class(
+        "weaviate",
+        "vectordb_bench.backend.clients.weaviate_cloud.weaviate_cloud",
+        "WeaviateCloud",
+    )
+
+    assert FilterOp.NumGE in adapter_cls.supported_filter_types
+    adapter = adapter_cls.__new__(adapter_cls)
+    adapter._scalar_field = "key"
+    adapter.prepare_filter(numeric_filter)
+    assert adapter._where_filter == {
+        "path": ["key"],
+        "operator": "GreaterThanEqual",
+        "valueInt": 49_500,
+    }
+
+
+def test_chroma_numeric_filter_is_inclusive(numeric_filter: IntFilter):
+    adapter_cls = _adapter_class(
+        "chromadb",
+        "vectordb_bench.backend.clients.chroma.chroma",
+        "ChromaClient",
+    )
+
+    assert FilterOp.NumGE in adapter_cls.supported_filter_types
+    adapter = adapter_cls.__new__(adapter_cls)
+    adapter.prepare_filter(numeric_filter)
+    assert adapter._where_filter == {"index": {"$gte": 49_500}}
+
+
+def test_elasticsearch_numeric_filter_is_inclusive(numeric_filter: IntFilter):
+    adapter_cls = _adapter_class(
+        "elasticsearch",
+        "vectordb_bench.backend.clients.elastic_cloud.elastic_cloud",
+        "ElasticCloud",
+    )
+
+    assert FilterOp.NumGE in adapter_cls.supported_filter_types
+    adapter = adapter_cls.__new__(adapter_cls)
+    adapter.id_col_name = "id"
+    adapter.prepare_filter(numeric_filter)
+    assert adapter.filter == {"range": {"id": {"gte": 49_500}}}
diff --git a/tests/test_fts_backend_filters.py b/tests/test_fts_backend_filters.py
new file mode 100644
index 000000000..830fa9c60
--- /dev/null
+++ b/tests/test_fts_backend_filters.py
@@ -0,0 +1,177 @@
+# ruff: noqa: ANN001, ARG001, E402
+
+import io
+import sys
+import threading
+import types
+
+import pytest
+
+sys.modules.setdefault("turbopuffer", types.SimpleNamespace(Turbopuffer=object))
+
+from vectordb_bench.backend.clients.elastic_cloud.config import ElasticCloudFtsConfig
+from vectordb_bench.backend.clients.elastic_cloud.elastic_cloud import ElasticCloud
+from vectordb_bench.backend.clients.milvus.config import MilvusFtsConfig
+from vectordb_bench.backend.clients.milvus.milvus import Milvus
+from vectordb_bench.backend.clients.turbopuffer.turbopuffer import TurboPuffer
+from vectordb_bench.backend.clients.vespa.config import VespaFtsConfig
+from vectordb_bench.backend.clients.vespa.vespa import Vespa
+from vectordb_bench.backend.filter import NewIntFilter
+
+
+def test_milvus_fts_insert_and_search_use_filter_id():
+    db = Milvus.__new__(Milvus)
+    db._is_fts = True
+    db.name = "Milvus"
+    db.collection_name = "col"
+    db.batch_size = 1000
+    db.with_scalar_labels = False
+    db._primary_field = "doc_id"
+    db._text_field = "text"
+    db._filter_id_field = "filter_id"
+    db._sparse_field = "sparse"
+    db.case_config = MilvusFtsConfig()
+    calls = {}
+
+    class Client:
+        def insert(self, collection_name, rows):
+            calls["insert"] = (collection_name, rows)
+            return {"insert_count": len(rows)}
+
+        def search(self, **kwargs):
+            calls["search"] = kwargs
+            return [[{"entity": {"doc_id": "d1"}}]]
+
+    db.client = Client()
+
+    assert db.insert_documents(["alpha"], ["d1"], filter_ids=[12]) == (1, None)
+    db.prepare_filter(NewIntFilter(filter_rate=0.5, int_field="filter_id", int_value=10))
+    assert db.search_documents("alpha") == ["d1"]
+
+    assert calls["insert"] == ("col", [{"doc_id": "d1", "text": "alpha", "filter_id": 12}])
+    assert calls["search"]["filter"] == "filter_id >= 10"
+
+
+def test_elastic_cloud_fts_insert_and_search_use_filter_id(monkeypatch):
+    db = ElasticCloud.__new__(ElasticCloud)
+    db._is_fts = True
+    db.indice = "idx"
+    db.id_col_name = "doc_id"
+    db.text_col_name = "text"
+    db.filter_id_col_name = "filter_id"
+    db.filter = None
+    db.client = object()
+    bulk_calls = []
+
+    def fake_bulk(client, actions):
+        bulk_calls.append(actions)
+        return len(actions), []
+
+    monkeypatch.setattr("vectordb_bench.backend.clients.elastic_cloud.elastic_cloud.bulk", fake_bulk)
+    assert db.insert_documents(["alpha"], ["d1"], filter_ids=[3]) == (1, None)
+    assert bulk_calls == [
+        [
+            {
+                "_index": "idx",
+                "_id": "d1",
+                "_source": {"doc_id": "d1", "text": "alpha", "filter_id": 3},
+            }
+        ]
+    ]
+
+    search_calls = {}
+
+    class Client:
+        def search(self, **kwargs):
+            search_calls.update(kwargs)
+            return {"hits": {"hits": [{"fields": {"doc_id": ["d1"]}}]}}
+
+    db.client = Client()
+    db.prepare_filter(NewIntFilter(filter_rate=0.5, int_field="filter_id", int_value=3))
+    assert db.search_documents("alpha") == ["d1"]
+    assert search_calls["query"] == {
+        "bool": {
+            "must": {"match": {"text": "alpha"}},
+            "filter": {"range": {"filter_id": {"gte": 3}}},
+        }
+    }
+
+
+def test_elastic_cloud_fts_mapping_has_filter_id():
+    assert ElasticCloudFtsConfig().index_param()["properties"]["filter_id"] == {"type": "long"}
+
+
+def test_vespa_fts_feed_and_search_use_filter_id():
+    db = Vespa.__new__(Vespa)
+    db._is_fts = True
+    db.schema_name = "schema"
+    db._text_field = "text"
+    db._filter_id_field = "filter_id"
+    db._filter_expr = None
+    db.case_config = VespaFtsConfig()
+    db._feed_lock = threading.Lock()
+    db._feed_written_count = 0
+    output = io.StringIO()
+
+    db._ensure_fts_feed_client = lambda: types.SimpleNamespace(
+        poll=lambda: None,
+        stdin=types.SimpleNamespace(write=output.write, flush=lambda: None),
+    )
+
+    db._write_fts_feed_batch(["alpha"], ["d1"], [9])
+    assert '"filter_id":9' in output.getvalue()
+
+    calls = {}
+
+    class Result:
+        def get_json(self):
+            return {"root": {"children": [{"fields": {"id": "d1"}}]}}
+
+    def query(payload):
+        calls["payload"] = payload
+        return Result()
+
+    db.client = types.SimpleNamespace(query=query)
+    db.prepare_filter(NewIntFilter(filter_rate=0.5, int_field="filter_id", int_value=9))
+    assert db.search_documents("alpha") == ["d1"]
+    assert calls["payload"]["yql"] == "select id from schema where userQuery() and filter_id >= 9"
+
+
+def test_turbopuffer_fts_insert_and_search_use_filter_id():
+    db = TurboPuffer.__new__(TurboPuffer)
+    db._is_fts = True
+    db._text_field = "text"
+    db._scalar_id_field = "id"
+    db._filter_id_field = "filter_id"
+    db.expr = None
+    db.db_case_config = types.SimpleNamespace(disable_backpressure=False)
+    write_calls = {}
+    query_calls = {}
+
+    class Namespace:
+        def write(self, **kwargs):
+            write_calls.update(kwargs)
+
+        def query(self, **kwargs):
+            query_calls.update(kwargs)
+            return types.SimpleNamespace(rows=[types.SimpleNamespace(id="d1")])
+
+    db.ns = Namespace()
+
+    assert db.insert_documents(["alpha"], ["d1"], filter_ids=[5]) == (1, None)
+    db.prepare_filter(NewIntFilter(filter_rate=0.5, int_field="filter_id", int_value=5))
+    assert db.search_documents("alpha") == ["d1"]
+
+    assert write_calls["upsert_columns"]["filter_id"] == [5]
+    assert query_calls["filters"] == ("filter_id", "Gte", 5)
+
+
+@pytest.mark.parametrize("db_cls", [Milvus, ElasticCloud, Vespa, TurboPuffer])
+def test_fts_filter_rejects_non_filter_id_field(db_cls):
+    db = db_cls.__new__(db_cls)
+    db._is_fts = True
+    db._filter_id_field = "filter_id"
+    db.filter_id_col_name = "filter_id"
+
+    with pytest.raises(ValueError, match="filter_id"):
+        db.prepare_filter(NewIntFilter(filter_rate=0.5, int_field="id", int_value=1))
diff --git a/tests/test_fts_cases.py b/tests/test_fts_cases.py
new file mode 100644
index 000000000..848bc3b45
--- /dev/null
+++ b/tests/test_fts_cases.py
@@ -0,0 +1,47 @@
+import pytest
+
+from vectordb_bench.backend.cases import FTS_FILTER_ID_FIELD, FTSBm25Performance
+from vectordb_bench.backend.dataset import FtsDatasetWithSizeType
+from vectordb_bench.backend.filter import FilterOp
+
+
+def test_fts_filter_case_uses_filter_id_for_large_dataset():
+    case = FTSBm25Performance(
+        dataset_with_size_type=FtsDatasetWithSizeType.MSMarcoLarge,
+        filter_rate=0.95,
+    )
+
+    filters = case.filters
+
+    assert filters.type == FilterOp.NumGE
+    assert filters.int_field == FTS_FILTER_ID_FIELD
+    assert filters.int_value == int(8_841_823 * 0.95)
+    assert "Filter 95%" in case.name
+
+
+def test_fts_filter_case_rejects_small_and_medium_datasets():
+    with pytest.raises(ValueError, match="only supported"):
+        FTSBm25Performance(
+            dataset_with_size_type=FtsDatasetWithSizeType.MSMarcoSmall,
+            filter_rate=0.5,
+        )
+
+    with pytest.raises(ValueError, match="only supported"):
+        FTSBm25Performance(
+            dataset_with_size_type=FtsDatasetWithSizeType.HotpotQAMedium,
+            filter_rate=0.5,
+        )
+
+
+def test_fts_filter_case_rejects_unsupported_filter_rate():
+    with pytest.raises(ValueError, match="must be one of"):
+        FTSBm25Performance(
+            dataset_with_size_type=FtsDatasetWithSizeType.HotpotQALarge,
+            filter_rate=0.8,
+        )
+
+
+def test_fts_filter_case_has_no_distribution_switch():
+    case = FTSBm25Performance(dataset_with_size_type=FtsDatasetWithSizeType.MSMarcoLarge)
+
+    assert not hasattr(case, "filter_id_distribution")
diff --git a/tests/test_fts_cli_user_control.py b/tests/test_fts_cli_user_control.py
new file mode 100644
index 000000000..1496b3bc8
--- /dev/null
+++ b/tests/test_fts_cli_user_control.py
@@ -0,0 +1,94 @@
+from typing import get_type_hints
+
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.api import IndexType
+from vectordb_bench.backend.clients.elastic_cloud.config import ElasticCloudFtsConfig, ElasticCloudIndexConfig
+from vectordb_bench.backend.clients.milvus.config import MilvusFtsConfig
+from vectordb_bench.backend.clients.vespa.config import VespaFtsConfig, VespaHNSWConfig
+from vectordb_bench.backend.dataset import FtsDatasetWithSizeType
+from vectordb_bench.cli.cli import CommonTypedDict, get_custom_case_config, select_cli_db_case_config
+
+
+def test_common_cli_exposes_optional_fts_bm25_overrides():
+    hints = get_type_hints(CommonTypedDict, include_extras=True)
+
+    assert "bm25_k1" in hints
+    assert "bm25_b" in hints
+    assert "fts_filter_rate" in hints
+    assert "fts_filter_id_distribution" not in hints
+
+
+def test_cli_builds_fts_filter_case_config():
+    custom_case = get_custom_case_config(
+        {
+            "case_type": "FTSBm25Performance",
+            "dataset_with_size_type": FtsDatasetWithSizeType.MSMarcoLarge.value,
+            "payload_profile": "ids_only",
+            "fts_filter_rate": 0.95,
+        }
+    )
+
+    assert custom_case == {
+        "dataset_with_size_type": FtsDatasetWithSizeType.MSMarcoLarge.value,
+        "payload_profile": "ids_only",
+        "filter_rate": 0.95,
+    }
+
+
+def test_cli_applies_bm25_overrides_to_existing_fts_config():
+    config = MilvusFtsConfig(drop_ratio_search=0.1)
+
+    selected = select_cli_db_case_config(
+        DB.Milvus,
+        config,
+        "FTSBm25Performance",
+        {"bm25_k1": 1.4, "bm25_b": 0.6},
+    )
+
+    assert isinstance(selected, MilvusFtsConfig)
+    assert selected.bm25_k1 == 1.4
+    assert selected.bm25_b == 0.6
+    assert selected.drop_ratio_search == 0.1
+    assert selected.sparse_index_param()["params"] == {
+        "inverted_index_algo": "DAAT_MAXSCORE",
+        "bm25_k1": 1.4,
+        "bm25_b": 0.6,
+    }
+
+
+def test_cli_applies_bm25_overrides_after_routing_vector_config_to_fts():
+    selected = select_cli_db_case_config(
+        DB.ElasticCloud,
+        ElasticCloudIndexConfig(index=IndexType.ES_HNSW, number_of_shards=3),
+        "FTSBm25Performance",
+        {"bm25_k1": 1.7, "bm25_b": 0.2},
+    )
+
+    assert isinstance(selected, ElasticCloudFtsConfig)
+    assert selected.number_of_shards == 3
+    assert selected.bm25_k1 == 1.7
+    assert selected.bm25_b == 0.2
+    assert selected.index_param()["properties"]["text"]["similarity"] == "vdbbench_bm25"
+    assert selected.similarity_settings() == {
+        "similarity": {
+            "vdbbench_bm25": {
+                "type": "BM25",
+                "k1": 1.7,
+                "b": 0.2,
+            }
+        }
+    }
+
+
+def test_cli_leaves_fts_bm25_defaults_when_options_are_omitted():
+    selected = select_cli_db_case_config(
+        DB.Vespa,
+        VespaHNSWConfig(),
+        "FTSBm25Performance",
+        {"bm25_k1": None, "bm25_b": None},
+    )
+
+    assert isinstance(selected, VespaFtsConfig)
+    assert selected.bm25_k1 is None
+    assert selected.bm25_b is None
+    assert selected.rank_properties() == []
diff --git a/tests/test_fts_dataset.py b/tests/test_fts_dataset.py
new file mode 100644
index 000000000..a5eaf9791
--- /dev/null
+++ b/tests/test_fts_dataset.py
@@ -0,0 +1,301 @@
+import math
+from dataclasses import dataclass
+from typing import ClassVar
+
+import pytest
+
+from vectordb_bench.backend.dataset import (
+    FtsDataset,
+    FtsDatasetManager,
+    FtsDatasetWithSizeType,
+    FtsFilterIdPermutation,
+    HotpotQAFts,
+    HotpotQATranslator,
+    MSMarcoFts,
+    MSMarcoTranslator,
+    SizeLabel,
+)
+from vectordb_bench.backend.filter import NewIntFilter
+
+
+@dataclass
+class Query:
+    query_id: str
+    text: str
+
+
+@dataclass
+class Doc:
+    doc_id: str
+    text: str
+    title: str = ""
+
+
+@dataclass
+class Qrel:
+    query_id: str
+    doc_id: str
+    relevance: int
+
+
+class FakeDataset:
+    def __init__(self):
+        self.queries = [Query("q1", "alpha query"), Query("q2", "beta query")]
+        self.docs = [
+            Doc("d1", "alpha document", "A"),
+            Doc("d2", "beta document", "B"),
+            Doc("d3", "gamma document", "C"),
+            Doc("d4", "delta document", "D"),
+        ]
+        self.qrels = [Qrel("q1", "d3", 1), Qrel("q1", "d2", 0), Qrel("q2", "d1", 2)]
+
+    def queries_iter(self):
+        yield from self.queries
+
+    def docs_iter(self):
+        yield from self.docs
+
+    def qrels_iter(self):
+        yield from self.qrels
+
+
+class FakeDatasetWithMissingQrel(FakeDataset):
+    def __init__(self):
+        super().__init__()
+        self.qrels = [Qrel("q1", "missing", 1)]
+
+
+class FakeDatasetWithLowPermutationQrels(FakeDataset):
+    def __init__(self):
+        super().__init__()
+        self.qrels = [Qrel("q1", "d3", 1), Qrel("q2", "d4", 2)]
+
+
+def make_tiny_msmarco_manager(size: int = 3) -> FtsDatasetManager:
+    small_label = MSMarcoFts._size_label[100_000]
+
+    class TinyMSMarcoFts(MSMarcoFts):
+        _size_label: ClassVar[dict[int, SizeLabel]] = {
+            **MSMarcoFts._size_label,
+            size: small_label._replace(size=size),
+        }
+
+    return FtsDatasetManager(data=TinyMSMarcoFts(size=size))
+
+
+def test_msmarco_translator_uses_string_ids_and_qrels():
+    translator = MSMarcoTranslator()
+    dataset = FakeDataset()
+
+    query = translator.translate_query(Query("10", "hello"))
+    document = translator.translate_document(Doc("20", "hello\tworld\nagain"))
+    ground_truth = translator.load_ground_truth(dataset)
+
+    assert query.query_id == "10"
+    assert document.doc_id == "20"
+    assert document.text == "hello world again"
+    assert ground_truth == {"q1": {"d3": 1}, "q2": {"d1": 2}}
+
+
+def test_hotpotqa_translator_combines_title_and_text_and_uses_qrels():
+    translator = HotpotQATranslator()
+    dataset = FakeDataset()
+
+    document = translator.translate_document(Doc("doc-a", "body", "Title"))
+    ground_truth = translator.load_ground_truth(dataset)
+
+    assert document.doc_id == "doc-a"
+    assert document.text == "Title body"
+    assert ground_truth == {"q1": {"d3": 1}, "q2": {"d1": 2}}
+
+
+def test_fts_filter_id_permutation_has_stable_sequence():
+    permutation = FtsFilterIdPermutation.for_size(8)
+
+    assert permutation.algorithm == "affine_permutation_v1"
+    assert permutation.multiplier == 5
+    assert permutation.offset == 3
+    assert [permutation.map(i) for i in range(8)] == [3, 0, 5, 2, 7, 4, 1, 6]
+    assert permutation == FtsFilterIdPermutation.for_size(8)
+
+
+@pytest.mark.parametrize("size", [1, 2, 3, 4, 8, 10, 97, 100, 1_000, 100_000, 1_000_000, 5_233_329, 8_841_823])
+def test_fts_filter_id_permutation_is_bijective(size: int):
+    permutation = FtsFilterIdPermutation.for_size(size)
+
+    assert math.gcd(permutation.multiplier, size) == 1
+    assert 0 <= permutation.offset < size
+    if size <= 1_000:
+        assert sorted(permutation.map(i) for i in range(size)) == list(range(size))
+
+
+@pytest.mark.parametrize(("size", "filter_rate"), [(8, 0.5), (100, 0.99), (101, 0.75)])
+def test_fts_filter_id_permutation_preserves_exact_selectivity(size: int, filter_rate: float):
+    permutation = FtsFilterIdPermutation.for_size(size)
+    filter_value = int(size * filter_rate)
+
+    matched = sum(permutation.map(i) >= filter_value for i in range(size))
+
+    assert matched == size - filter_value
+
+
+def test_fts_filter_id_permutation_scatters_matches_across_corpus_order():
+    permutation = FtsFilterIdPermutation.for_size(100)
+
+    matching_ordinals = [ordinal for ordinal in range(100) if permutation.map(ordinal) >= 90]
+
+    assert matching_ordinals != list(range(90, 100))
+    assert len({ordinal // 10 for ordinal in matching_ordinals}) >= 6
+
+
+def test_fts_filter_id_permutation_validates_bounds():
+    with pytest.raises(ValueError, match="size must be positive"):
+        FtsFilterIdPermutation.for_size(0)
+
+    permutation = FtsFilterIdPermutation.for_size(3)
+    with pytest.raises(ValueError, match="ordinal must be in"):
+        permutation.map(3)
+
+
+def test_fts_iterator_preserves_qrel_docs_before_filler():
+    manager = make_tiny_msmarco_manager()
+    manager._ir_dataset = FakeDataset()
+    manager.required_doc_ids = {"d4"}
+    manager.selected_doc_ids = manager._build_selected_doc_ids()
+
+    assert manager.selected_doc_ids == {"d1", "d2", "d4"}
+
+    batches = list(manager)
+    docs = [(doc.doc_id, doc.filter_id) for batch in batches for doc in batch]
+
+    assert len(docs) == 3
+    assert ("d4", 1) in docs
+    assert all(not doc_id.isdecimal() for doc_id, _ in docs)
+    assert [filter_id for _, filter_id in docs] == [2, 0, 1]
+
+
+def test_fts_qrel_filter_ids_match_sparse_emitted_documents_when_one_is_skipped():
+    class UnassignableDocument:
+        doc_id = "d3"
+        text = "malformed"
+
+        @property
+        def filter_id(self) -> int | None:
+            return None
+
+        @filter_id.setter
+        def filter_id(self, value: int | None) -> None:  # noqa: ARG002
+            raise ValueError("cannot assign filter_id")
+
+    class SparseTranslator:
+        def iter_documents(self, dataset):  # noqa: ARG002
+            yield Doc("d1", "one")
+            yield Doc("d2", "two")
+            yield UnassignableDocument()
+            yield Doc("d4", "four")
+            yield Doc("d5", "five")
+
+    manager = make_tiny_msmarco_manager(size=3)
+    manager._ir_dataset = object()
+    manager._translator = SparseTranslator()
+    manager.required_doc_ids = {"d1", "d5"}
+    manager.selected_doc_ids = {"d1", "d3", "d5"}
+
+    qrel_filter_ids = manager._build_qrel_filter_ids()
+    emitted_filter_ids = {doc.doc_id: doc.filter_id for batch in manager for doc in batch}
+
+    assert emitted_filter_ids == {"d1": 2, "d5": 0}
+    assert qrel_filter_ids == emitted_filter_ids
+
+
+def test_fts_prepare_integer_filter_derives_filtered_qrels(monkeypatch: pytest.MonkeyPatch):
+    manager = make_tiny_msmarco_manager(size=4)
+    monkeypatch.setattr(manager._translator, "load", FakeDataset)
+
+    filters = NewIntFilter(filter_rate=0.5, int_field="filter_id", int_value=2)
+    assert manager.prepare(source=None, filters=filters)
+
+    assert [query.query_id for query in manager.queries_data] == ["q1", "q2"]
+    assert manager.gt_data == [{"d3": 1}, {"d1": 2}]
+    assert [query.query_id for query in manager.recall_queries_data] == ["q2"]
+    assert manager.recall_gt_data == [{"d1": 2}]
+    assert manager.recall_skipped is False
+    assert manager.recall_skip_reason is None
+    assert manager.qrel_filter_ids == {"d1": 3, "d3": 1}
+    assert manager.filter_stats == {
+        "filter_type": "NumGE",
+        "filter_field": "filter_id",
+        "filter_value": 2,
+        "filter_rate": 0.5,
+        "filter_id_distribution": "affine_permutation_v1",
+        "filter_id_multiplier": 3,
+        "filter_id_offset": 3,
+        "matched_doc_count": 2,
+        "matched_doc_ratio": 0.5,
+        "original_query_count": 2,
+        "filtered_query_count": 1,
+        "filtered_query_ratio": 0.5,
+        "original_relevant_doc_count": 2,
+        "filtered_relevant_doc_count": 1,
+    }
+
+
+def test_fts_prepare_integer_filter_skips_empty_filtered_qrels(monkeypatch: pytest.MonkeyPatch):
+    manager = make_tiny_msmarco_manager(size=4)
+    monkeypatch.setattr(manager._translator, "load", FakeDatasetWithLowPermutationQrels)
+
+    filters = NewIntFilter(filter_rate=0.75, int_field="filter_id", int_value=3)
+    assert manager.prepare(source=None, filters=filters)
+
+    assert [query.query_id for query in manager.queries_data] == ["q1", "q2"]
+    assert manager.gt_data == [{"d3": 1}, {"d4": 2}]
+    assert manager.recall_queries_data == []
+    assert manager.recall_gt_data == []
+    assert manager.recall_skipped is True
+    assert manager.recall_skip_reason == "no_positive_qrels_after_filter"
+    assert manager.filter_stats["filtered_query_count"] == 0
+    assert manager.filter_stats["filtered_relevant_doc_count"] == 0
+
+
+def test_fts_prepare_integer_filter_requires_filter_id_field(monkeypatch: pytest.MonkeyPatch):
+    manager = make_tiny_msmarco_manager(size=4)
+    monkeypatch.setattr(manager._translator, "load", FakeDataset)
+
+    filters = NewIntFilter(filter_rate=0.5, int_field="id", int_value=2)
+    with pytest.raises(ValueError, match="int_field='filter_id'"):
+        manager.prepare(source=None, filters=filters)
+
+
+def test_fts_cap_rejects_required_qrel_docs_missing_from_corpus():
+    manager = make_tiny_msmarco_manager()
+    manager._ir_dataset = FakeDataset()
+    manager.required_doc_ids = {"missing"}
+
+    with pytest.raises(ValueError, match="missing from corpus"):
+        manager._build_selected_doc_ids()
+
+
+def test_fts_prepare_propagates_missing_required_qrel_docs(monkeypatch: pytest.MonkeyPatch):
+    manager = FtsDatasetManager(data=MSMarcoFts(size=100_000))
+    monkeypatch.setattr(manager._translator, "load", FakeDatasetWithMissingQrel)
+
+    with pytest.raises(ValueError, match="missing from corpus"):
+        manager.prepare(source=None)
+
+
+def test_fts_dataset_size_registry():
+    assert FtsDataset.MSMARCO.manager(100_000).data.full_name == "MS MARCO FTS (SMALL)"
+    assert FtsDataset.MSMARCO.manager(1_000_000).data.full_name == "MS MARCO FTS (MEDIUM)"
+    assert FtsDataset.HOTPOTQA.manager(5_233_329).data.full_name == "HotpotQA FTS (LARGE)"
+    assert FtsDatasetWithSizeType.MSMarcoSmall.get_manager().data.size == 100_000
+    assert FtsDatasetWithSizeType.HotpotQAMedium.get_manager().data.size == 1_000_000
+    with pytest.raises(ValueError, match="not supported"):
+        FtsDataset.MSMARCO.manager(3)
+
+
+def test_cap_smaller_than_required_qrels_is_invalid():
+    manager = FtsDatasetManager(data=HotpotQAFts(size=100_000))
+    manager.qrels_data = {"q1": {"a": 1, "b": 1, "c": 1}}
+
+    with pytest.raises(ValueError, match="requires 3 qrel documents"):
+        manager._validate_cap(required_doc_ids={"a", "b", "c"}, target_size=2)
diff --git a/tests/test_fts_filter_runner.py b/tests/test_fts_filter_runner.py
new file mode 100644
index 000000000..fe7179334
--- /dev/null
+++ b/tests/test_fts_filter_runner.py
@@ -0,0 +1,182 @@
+import threading
+
+from vectordb_bench.backend.cases import CaseLabel
+from vectordb_bench.backend.data_source import DatasetSource
+from vectordb_bench.backend.dataset import FtsDocument, FtsQuery
+from vectordb_bench.backend.filter import non_filter
+from vectordb_bench.backend.payload import PayloadProfile
+from vectordb_bench.backend.runner.concurrent_runner import ConcurrentInsertRunner
+from vectordb_bench.backend.task_runner import CaseRunner
+from vectordb_bench.models import TaskStage
+
+
+def test_concurrent_insert_runner_fts_batch_includes_filter_ids():
+    runner = ConcurrentInsertRunner.__new__(ConcurrentInsertRunner)
+    runner._deadline = None
+    runner._stop_event = None
+    runner._iter_lock = threading.Lock()
+    runner._prefetched_fts_batch = None
+    runner._dataset_iter = iter(
+        [
+            [
+                FtsDocument(doc_id="d1", text="alpha", filter_id=0),
+                FtsDocument(doc_id="d2", text="beta", filter_id=5),
+            ]
+        ]
+    )
+
+    assert runner._next_fts_batch() == {
+        "texts": ["alpha", "beta"],
+        "doc_ids": ["d1", "d2"],
+        "filter_ids": [0, 5],
+    }
+
+
+def test_fts_pre_run_passes_filters_to_dataset(monkeypatch):
+    filter_obj = object()
+
+    class Dataset:
+        def __init__(self):
+            self.calls = []
+
+        def prepare(self, source, filters=None):
+            self.calls.append((source, filters))
+
+    class Case:
+        label = CaseLabel.FullTextSearchPerformance
+        is_multitenant = False
+        dataset = Dataset()
+        filters = filter_obj
+
+    config_obj = type("Config", (), {"stages": [TaskStage.LOAD]})()
+    runner = CaseRunner.construct(ca=Case(), config=config_obj, dataset_source=DatasetSource.S3)
+    init_calls = []
+    monkeypatch.setattr(CaseRunner, "init_db", lambda self, drop_old=True: init_calls.append(drop_old))
+
+    runner._pre_run(drop_old=False)
+
+    assert runner.ca.dataset.calls == [(DatasetSource.S3, filter_obj)]
+    assert init_calls == [False]
+
+
+def test_fts_perf_metric_includes_dataset_filter_stats():
+    class Dataset:
+        filter_stats = {
+            "filter_field": "filter_id",
+            "filter_value": 90,
+            "filtered_query_count": 12,
+        }
+
+    class Case:
+        label = CaseLabel.FullTextSearchPerformance
+        dataset = Dataset()
+
+    config_obj = type("Config", (), {"stages": []})()
+    runner = CaseRunner.construct(ca=Case(), config=config_obj)
+
+    metric = runner._run_perf_case(drop_old=False)
+
+    assert metric.additional_parameters["fts_filter"] == Dataset.filter_stats
+
+
+class DummyDb:
+    name = "dummy"
+
+    def supports_payload_profile(self, payload_profile):
+        return True
+
+    def supports_document_payload_profile(self, payload_profile):
+        return True
+
+    def search_documents(self, query, k, payload_profile=None):
+        return []
+
+
+class FtsConcurrencyConfig:
+    num_concurrency = [60, 80]
+    concurrency_duration = 30
+    concurrency_timeout = 300
+    serial_cooldown = 0
+
+
+class FtsCaseConfig:
+    k = 10
+    concurrency_search_config = FtsConcurrencyConfig()
+
+
+def test_fts_search_runners_use_full_queries_for_concurrency_and_filtered_queries_for_recall():
+    class Dataset:
+        queries_data = [
+            FtsQuery(query_id="q1", text="full query one"),
+            FtsQuery(query_id="q2", text="full query two"),
+        ]
+        gt_data = [{"d1": 1}, {"d2": 1}]
+        recall_queries_data = [FtsQuery(query_id="q2", text="full query two")]
+        recall_gt_data = [{"d2": 1}]
+        recall_skipped = False
+        recall_skip_reason = None
+
+    class Case:
+        label = CaseLabel.FullTextSearchPerformance
+        dataset = Dataset()
+        filters = non_filter
+        payload_profile = PayloadProfile.IDS_ONLY
+
+    config_obj = type(
+        "Config",
+        (),
+        {
+            "stages": [TaskStage.SEARCH_SERIAL, TaskStage.SEARCH_CONCURRENT],
+            "case_config": FtsCaseConfig(),
+        },
+    )()
+    runner = CaseRunner.construct(ca=Case(), config=config_obj, db=DummyDb())
+
+    runner._init_fts_search_runner()
+
+    assert runner.search_runner.test_data == ["full query one", "full query two"]
+    assert runner.serial_search_runner.test_data == ["full query two"]
+    assert runner.serial_search_runner.ground_truth == [{"d2": 1}]
+
+
+def test_fts_perf_metric_marks_recall_skipped_without_blocking_case(monkeypatch):
+    class Dataset:
+        filter_stats = {
+            "filter_field": "filter_id",
+            "filter_value": 99,
+            "filtered_query_count": 0,
+        }
+        queries_data = [
+            FtsQuery(query_id="q1", text="full query one"),
+            FtsQuery(query_id="q2", text="full query two"),
+        ]
+        recall_queries_data = []
+        recall_skipped = True
+        recall_skip_reason = "no_positive_qrels_after_filter"
+
+    class Case:
+        label = CaseLabel.FullTextSearchPerformance
+        dataset = Dataset()
+
+    config_obj = type(
+        "Config",
+        (),
+        {
+            "stages": [TaskStage.SEARCH_SERIAL],
+            "case_config": FtsCaseConfig(),
+        },
+    )()
+    runner = CaseRunner.construct(ca=Case(), config=config_obj)
+    monkeypatch.setattr(CaseRunner, "_init_search_runners", lambda self: None)
+
+    metric = runner._run_perf_case(drop_old=False)
+
+    assert metric.recall == 0.0
+    assert metric.ndcg == 0.0
+    assert metric.mrr == 0.0
+    assert metric.additional_parameters["fts_recall"] == {
+        "skipped": True,
+        "reason": "no_positive_qrels_after_filter",
+        "serial_query_count": 0,
+        "full_query_count": 2,
+    }
diff --git a/tests/test_fts_format_results.py b/tests/test_fts_format_results.py
new file mode 100644
index 000000000..e46c5670a
--- /dev/null
+++ b/tests/test_fts_format_results.py
@@ -0,0 +1,43 @@
+from vectordb_bench.backend.cases import CaseType
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.milvus.config import MilvusConfig, MilvusFtsConfig
+from vectordb_bench.metric import Metric
+from vectordb_bench.models import CaseConfig, CaseResult, TaskConfig, TestResult
+from vectordb_bench.restful.format_res import format_results
+
+
+def test_format_results_supports_fts_dataset_and_metrics():
+    test_result = TestResult(
+        run_id="run-1",
+        task_label="fts-task",
+        timestamp=123,
+        results=[
+            CaseResult(
+                task_config=TaskConfig(
+                    db=DB.Milvus,
+                    db_config=MilvusConfig(uri="http://localhost:19530"),
+                    db_case_config=MilvusFtsConfig(),
+                    case_config=CaseConfig(case_id=CaseType.FTSBm25Performance, k=10),
+                ),
+                metrics=Metric(
+                    qps=12.5,
+                    recall=0.7,
+                    ndcg=0.8,
+                    mrr=0.9,
+                    serial_latency_p99=0.11,
+                    serial_latency_p95=0.1,
+                    conc_latency_p99_list=[0.2],
+                    conc_latency_p95_list=[0.15],
+                    conc_latency_avg_list=[0.12],
+                ),
+            )
+        ],
+    )
+
+    [formatted] = format_results([test_result], "fts-task")
+
+    assert formatted["dataset"] == "MS MARCO FTS (SMALL)"
+    assert formatted["dim"] == 0
+    assert formatted["mrr"] == 0.9
+    assert formatted["serial_latency_p95"] == 0.1
+    assert formatted["conc_latency_p95_list"] == [0.15]
diff --git a/tests/test_fts_frontend_results.py b/tests/test_fts_frontend_results.py
new file mode 100644
index 000000000..5b802356b
--- /dev/null
+++ b/tests/test_fts_frontend_results.py
@@ -0,0 +1,282 @@
+import json
+import tomllib
+from inspect import signature
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+import streamlit as st
+
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.frontend.config.dbCaseConfigs import get_fts_case_items
+from vectordb_bench.frontend.pages.full_text_search import (
+    _concurrency_rows,
+    _draw_filtered_qps_tab,
+    _peak_filtered_qps_rows,
+    load_full_text_search_rows,
+)
+
+
+def test_oss_opensearch_fts_cases_are_selectable_in_run_test():
+    assert all(item.supports_dbs([DB.OSSOpenSearch]) for item in get_fts_case_items())
+
+
+def test_streamlit_dependency_floor_supports_fts_tab_state_api():
+    repo_root = Path(__file__).resolve().parents[1]
+    dependencies = tomllib.loads((repo_root / "pyproject.toml").read_text())["project"]["dependencies"]
+
+    assert "streamlit>=1.61,<2" in dependencies
+    assert {"default", "key", "on_change"}.issubset(signature(st.tabs).parameters)
+
+
+def test_frontend_separates_filtered_results_and_expands_concurrency_qps(tmp_path: Path):
+    result_file = tmp_path / "result_fts.json"
+    result_file.write_text(
+        json.dumps(
+            {
+                "task_label": "fts-results",
+                "results": [
+                    {
+                        "metrics": {
+                            "qps": 100.0,
+                            "recall": 0.9,
+                            "ndcg": 0.8,
+                            "mrr": 0.7,
+                        },
+                        "task_config": {
+                            "db": "ZillizCloud",
+                            "case_config": {
+                                "custom_case": {
+                                    "dataset_with_size_type": "HotpotQA Large (5.2M documents)",
+                                    "payload_profile": "ids_only",
+                                }
+                            },
+                        },
+                    },
+                    {
+                        "metrics": {
+                            "qps": 200.0,
+                            "recall": 0.95,
+                            "ndcg": 0.85,
+                            "mrr": 0.75,
+                            "additional_parameters": {
+                                "fts_filter": {
+                                    "filter_rate": 0.5,
+                                    "filter_id_distribution": "affine_permutation_v1",
+                                }
+                            },
+                            "conc_num_list": [60, 80],
+                            "conc_qps_list": [1200.0, 1500.0],
+                        },
+                        "task_config": {
+                            "db": "ZillizCloud",
+                            "case_config": {
+                                "custom_case": {
+                                    "dataset_with_size_type": "HotpotQA Large (5.2M documents)",
+                                    "payload_profile": "ids_only",
+                                    "filter_rate": 0.5,
+                                }
+                            },
+                        },
+                    },
+                ],
+            }
+        )
+    )
+
+    data = load_full_text_search_rows(tmp_path)
+    standard_data = data[~data["is_filtered"]]
+    filtered_data = data[data["is_filtered"]]
+    concurrency_data = _concurrency_rows(filtered_data)
+    peak_data = _peak_filtered_qps_rows(filtered_data)
+
+    assert len(standard_data) == 1
+    assert standard_data.iloc[0]["qps"] == 100.0
+    assert len(filtered_data) == 1
+    assert filtered_data.iloc[0]["filter_rate_label"] == "50%"
+    assert "filter_distribution" not in filtered_data.columns
+    assert concurrency_data[["concurrency", "qps"]].to_dict("records") == [
+        {"concurrency": 60, "qps": 1200.0},
+        {"concurrency": 80, "qps": 1500.0},
+    ]
+    assert peak_data[["filter_rate_label", "concurrency", "qps"]].to_dict("records") == [
+        {"filter_rate_label": "50%", "concurrency": 80, "qps": 1500.0}
+    ]
+
+
+def test_checked_in_consolidated_permuted_results_expose_concurrency_qps_for_all_backends():
+    repo_root = Path(__file__).resolve().parents[1]
+    result_dir = repo_root / "vectordb_bench" / "results" / "FullTextSearch"
+
+    data = load_full_text_search_rows(result_dir)
+    filtered_data = data[data["is_filtered"]]
+    concurrency_data = _concurrency_rows(filtered_data)
+    peak_data = _peak_filtered_qps_rows(filtered_data)
+
+    assert len(data) == 64
+    assert len(filtered_data) == 40
+    assert len(concurrency_data) == 90
+    assert (pd.to_numeric(filtered_data["p99_s"]) > 0).all()
+    assert (pd.to_numeric(filtered_data["p95_s"]) > 0).all()
+    assert (pd.to_numeric(filtered_data["recall"]) > 0).all()
+    assert (pd.to_numeric(filtered_data["ndcg"]) > 0).all()
+    assert (pd.to_numeric(filtered_data["mrr"]) > 0).all()
+    assert len(peak_data) == 20
+    assert set(filtered_data["backend"].astype(str)) == {
+        "ElasticSearch",
+        "OSSOpenSearch",
+        "TurboPuffer",
+        "ZillizCloud",
+    }
+    assert set(concurrency_data["concurrency"]) == {40, 60, 80}
+    assert set(peak_data["filter_rate_label"]) == {"50%", "75%", "90%", "95%", "99%"}
+    assert set(peak_data["concurrency"]).issubset({60, 80})
+    assert set(pd.to_numeric(filtered_data["filter_rate"])) == {0.5, 0.75, 0.9, 0.95, 0.99}
+    assert not (result_dir / "ZillizCloud" / "result_20260709_fts_filtered_zillizcloud.json").exists()
+
+    expected_result_files = {
+        "ElasticCloud": "result_20260626_fts_standard_elasticcloud.json",
+        "OpenSearch": "result_20260708_fts_standard_opensearch.json",
+        "TurboPuffer": "result_20260626_fts_standard_turbopuffer.json",
+        "ZillizCloud": "result_20260626_fts_standard_zillizcloud.json",
+    }
+    expected_result_counts = {
+        "ElasticCloud": 16,
+        "OpenSearch": 16,
+        "TurboPuffer": 16,
+        "ZillizCloud": 16,
+    }
+    result_files = sorted(result_dir.glob("*/result_*.json"))
+    assert len(result_files) == 4
+    assert {path.parent.name: path.name for path in result_files} == expected_result_files
+
+    filtered_results = []
+    result_counts = {}
+    for result_file in result_files:
+        results = json.loads(result_file.read_text())["results"]
+        result_counts[result_file.parent.name] = len(results)
+        if result_file.parent.name == "ZillizCloud":
+            assert all(
+                "one persistent segment"
+                in json.loads(case_result["task_config"]["db_config"]["note"])["evidence"]["source"]
+                for case_result in results
+            )
+        for case_result in results:
+            custom_case = case_result["task_config"]["case_config"].get("custom_case") or {}
+            fts_filter = case_result["metrics"].get("additional_parameters", {}).get("fts_filter") or {}
+            filter_rate = custom_case.get("filter_rate", fts_filter.get("filter_rate"))
+            if filter_rate is not None:
+                assert "filter_id_distribution" not in custom_case
+                assert fts_filter["filter_id_distribution"] == "affine_permutation_v1"
+                filtered_results.append(case_result)
+
+    assert result_counts == expected_result_counts
+    assert len(filtered_results) == 40
+    expected_serial_fields = {
+        "serial_latency_p99",
+        "serial_latency_p95",
+        "recall",
+        "ndcg",
+        "mrr",
+    }
+    for case_result in filtered_results:
+        stages = set(case_result["task_config"]["stages"])
+        provenance = case_result["metrics"]["additional_parameters"].get("serial_measurement")
+
+        assert {"search_concurrent", "search_serial"}.issubset(stages)
+        if provenance is None:
+            assert case_result["task_config"]["db"] == "ZillizCloud"
+            assert all(case_result["metrics"][field] > 0 for field in expected_serial_fields)
+            continue
+        assert provenance["composed_from_separate_run"] is True
+        assert set(provenance["metric_fields"]) == expected_serial_fields
+        assert provenance["source_file"].startswith("result_")
+        assert len(provenance["source_sha256"]) == 64
+        assert "search_serial" in provenance["source_stages"]
+        assert provenance["source_note"]
+
+
+def test_filtered_qps_chart_groups_backend_bars_by_filter_rate():
+    repo_root = Path(__file__).resolve().parents[1]
+    result_dir = repo_root / "vectordb_bench" / "results" / "FullTextSearch"
+    data = load_full_text_search_rows(result_dir)
+    filtered_data = data[data["is_filtered"] & (data["dataset_family"].astype(str) == "MS MARCO")]
+
+    class PlotRecorder:
+        figure = None
+
+        def info(self, _message: str) -> None:
+            raise AssertionError("Expected filtered QPS chart data")
+
+        def plotly_chart(self, figure: Any, **_kwargs) -> None:
+            self.figure = figure
+
+    recorder = PlotRecorder()
+    _draw_filtered_qps_tab(recorder, filtered_data)
+
+    assert recorder.figure is not None
+    assert recorder.figure.layout.barmode == "group"
+    assert {trace.type for trace in recorder.figure.data} == {"bar"}
+    assert {trace.name for trace in recorder.figure.data} == {
+        "ElasticSearch",
+        "OSSOpenSearch",
+        "TurboPuffer",
+        "ZillizCloud",
+    }
+    expected_filter_rates = {"50%", "75%", "90%", "95%", "99%"}
+    assert all(set(trace.x) == expected_filter_rates for trace in recorder.figure.data)
+    assert recorder.figure.layout.xaxis.type == "category"
+    assert tuple(recorder.figure.layout.xaxis.categoryarray) == ("50%", "75%", "90%", "95%", "99%")
+
+
+def test_checked_in_zilliz_standard_results_use_semantic_metrics():
+    repo_root = Path(__file__).resolve().parents[1]
+    result_dir = repo_root / "vectordb_bench" / "results" / "FullTextSearch"
+
+    data = load_full_text_search_rows(result_dir)
+    zilliz_data = data[
+        (~data["is_filtered"]) & (data["backend"].astype(str) == "ZillizCloud") & (data["payload"] == "ids_only")
+    ]
+    expected = {
+        "MS MARCO Small": (0.9157, 0.7206, 0.6713),
+        "MS MARCO Medium": (0.8261, 0.5298, 0.4572),
+        "MS MARCO Large": (0.6283, 0.2763, 0.1889),
+        "HotpotQA Small": (0.9225, 0.8459, 0.9485),
+        "HotpotQA Medium": (0.8437, 0.7322, 0.8636),
+        "HotpotQA Large": (0.7674, 0.6265, 0.7574),
+    }
+
+    assert len(zilliz_data) == len(expected)
+    for row in zilliz_data.to_dict("records"):
+        assert (row["recall"], row["ndcg"], row["mrr"]) == expected[str(row["dataset"])]
+
+
+def test_checked_in_zilliz_large_standard_results_retain_load_metrics():
+    repo_root = Path(__file__).resolve().parents[1]
+    result_file = (
+        repo_root
+        / "vectordb_bench"
+        / "results"
+        / "FullTextSearch"
+        / "ZillizCloud"
+        / "result_20260626_fts_standard_zillizcloud.json"
+    )
+    expected = {
+        "HotpotQA Large (5.2M documents)": (5_233_329, 92.5443, 81.9515, 174.4958),
+        "MS MARCO Large (8.8M documents)": (8_841_823, 183.6571, 98.8113, 282.4684),
+    }
+
+    large_results = {}
+    for case_result in json.loads(result_file.read_text())["results"]:
+        custom_case = case_result["task_config"]["case_config"].get("custom_case") or {}
+        dataset = custom_case.get("dataset_with_size_type")
+        if dataset in expected and custom_case.get("filter_rate") is None:
+            metrics = case_result["metrics"]
+            large_results[dataset] = (
+                metrics["inserted_count"],
+                metrics["insert_duration"],
+                metrics["optimize_duration"],
+                metrics["load_duration"],
+            )
+
+    assert large_results == expected
diff --git a/tests/test_fts_metrics.py b/tests/test_fts_metrics.py
new file mode 100644
index 000000000..fbbb8552a
--- /dev/null
+++ b/tests/test_fts_metrics.py
@@ -0,0 +1,69 @@
+from types import TracebackType
+from typing import Any
+
+import pytest
+
+from vectordb_bench.backend.payload import PayloadProfile
+from vectordb_bench.backend.runner.serial_runner import SerialSearchRunner
+from vectordb_bench.backend.workload import WorkloadKind
+from vectordb_bench.metric import calc_mrr_fts, calc_ndcg_fts, calc_recall_fts
+
+
+class FakeSearchDB:
+    name = "FakeSearchDB"
+
+    def init(self):
+        return self
+
+    def __enter__(self):
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc: BaseException | None,
+        tb: TracebackType | None,
+    ) -> bool:
+        return False
+
+    def prepare_filter(self, filters: Any) -> None:
+        return None
+
+    def supports_document_payload_profile(self, payload_profile: PayloadProfile) -> bool:
+        return payload_profile == PayloadProfile.IDS_ONLY
+
+    def supports_payload_profile(self, payload_profile: PayloadProfile) -> bool:
+        return True
+
+    def search_documents(self, query: str, k: int) -> list[str]:
+        assert query == "query"
+        assert k == 3
+        return ["missing", "d2", "d1"]
+
+
+def test_semantic_fts_metrics_use_relevance_grades():
+    qrels = {"d1": 3, "d2": 1}
+    got = ["missing", "d2", "d1"]
+
+    assert calc_recall_fts(2, qrels, got) == 0.5
+    assert calc_recall_fts(3, qrels, got) == 1.0
+    assert calc_mrr_fts(3, qrels, got) == 0.5
+    assert calc_ndcg_fts(3, qrels, got) == pytest.approx(0.5869, abs=1e-4)
+
+
+def test_serial_runner_returns_semantic_fts_metrics():
+    runner = SerialSearchRunner(
+        db=FakeSearchDB(),
+        test_data=["query"],
+        ground_truth=[{"d1": 3, "d2": 1}],
+        k=3,
+        workload_kind=WorkloadKind.FULL_TEXT,
+    )
+
+    recall, ndcg, mrr, p99, p95 = runner.search((runner.test_data, runner.ground_truth))
+
+    assert recall == 1.0
+    assert ndcg == 0.5869
+    assert mrr == 0.5
+    assert p99 >= 0
+    assert p95 >= 0
diff --git a/tests/test_lancedb_config.py b/tests/test_lancedb_config.py
new file mode 100644
index 000000000..cf2d6e12f
--- /dev/null
+++ b/tests/test_lancedb_config.py
@@ -0,0 +1,280 @@
+"""Offline unit tests for LanceDB config objects.
+
+These tests don't require a running LanceDB instance. They freeze the
+contract for the three IVF-family indexes (IVF_PQ / IVF_HNSW_SQ /
+IVF_HNSW_PQ) so that any future refactor that accidentally diverges their
+code paths (e.g. introduces index-type-specific branches) will fail CI.
+
+Background: IVF_HNSW_SQ / IVF_HNSW_PQ share the same code path as IVF_PQ
+and the CLI is wired up for all three. These tests encode that claim.
+"""
+
+import typing
+from typing import Annotated, get_type_hints
+
+from vectordb_bench.backend.clients.api import IndexType, MetricType
+from vectordb_bench.backend.clients.lancedb.config import (
+    LanceDBAutoIndexConfig,
+    LanceDBIndexConfig,
+    LanceDBIVFHNSWPQConfig,
+    LanceDBIVFHNSWSQConfig,
+    LanceDBNoIndexConfig,
+    _lancedb_case_config,
+)
+
+# ---------------------------------------------------------------------------
+# Registry mapping
+# ---------------------------------------------------------------------------
+
+
+def test_registry_covers_all_lancedb_index_types():
+    """Every LanceDB-supported IndexType must resolve to a config class."""
+    required = {
+        IndexType.IVFPQ,
+        IndexType.AUTOINDEX,
+        IndexType.IVF_HNSW_SQ,
+        IndexType.IVF_HNSW_PQ,
+        IndexType.NONE,
+    }
+    assert required.issubset(_lancedb_case_config.keys())
+
+    # HNSW is kept for backwards compatibility and must map to IVF_HNSW_SQ.
+    assert _lancedb_case_config[IndexType.HNSW] is LanceDBIVFHNSWSQConfig
+
+
+# ---------------------------------------------------------------------------
+# index_param() / search_param() contract
+# ---------------------------------------------------------------------------
+
+
+def test_ivfpq_default_params_are_minimal():
+    cfg = LanceDBIndexConfig()
+    assert cfg.index == IndexType.IVFPQ
+    p = cfg.index_param()
+    # Always present
+    assert p["metric"] == "cosine" or p["metric"] == "l2"
+    assert "num_bits" in p
+    # Zero-valued optionals stay out of the param dict so LanceDB uses its
+    # own defaults.
+    assert "num_partitions" not in p
+    assert "num_sub_vectors" not in p
+    # search_param() is empty when all tunables are zero.
+    assert cfg.search_param() == {}
+
+
+def test_ivfpq_tuned_params_are_forwarded():
+    cfg = LanceDBIndexConfig(
+        metric_type=MetricType.COSINE,
+        num_partitions=256,
+        num_sub_vectors=96,
+        nbits=8,
+        nprobes=20,
+        refine_factor=10,
+    )
+    p = cfg.index_param()
+    assert p == {
+        "metric": "cosine",
+        "num_bits": 8,
+        "sample_rate": 256,
+        "max_iterations": 50,
+        "num_partitions": 256,
+        "num_sub_vectors": 96,
+    }
+    assert cfg.search_param() == {"nprobes": 20, "refine_factor": 10}
+
+
+def test_ivf_hnsw_sq_params_are_forwarded():
+    cfg = LanceDBIVFHNSWSQConfig(
+        metric_type=MetricType.COSINE,
+        num_partitions=256,
+        m=16,
+        ef_construction=200,
+        ef=128,
+        nprobes=20,
+        refine_factor=10,
+    )
+    p = cfg.index_param()
+    assert p["index_type"] == "IVF_HNSW_SQ"
+    assert p["num_partitions"] == 256
+    assert p["m"] == 16
+    assert p["ef_construction"] == 200
+    assert cfg.search_param() == {
+        "ef": 128,
+        "nprobes": 20,
+        "refine_factor": 10,
+    }
+
+
+def test_ivf_hnsw_pq_params_are_forwarded():
+    cfg = LanceDBIVFHNSWPQConfig(
+        metric_type=MetricType.COSINE,
+        num_partitions=256,
+        num_sub_vectors=96,
+        m=16,
+        ef_construction=200,
+        ef=128,
+        nprobes=20,
+        refine_factor=10,
+    )
+    p = cfg.index_param()
+    assert p["index_type"] == "IVF_HNSW_PQ"
+    assert p["num_partitions"] == 256
+    assert p["num_sub_vectors"] == 96
+    assert p["m"] == 16
+    assert p["ef_construction"] == 200
+    assert cfg.search_param() == {
+        "ef": 128,
+        "nprobes": 20,
+        "refine_factor": 10,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Code-path unification
+# ---------------------------------------------------------------------------
+
+
+def test_ivf_family_shares_search_knobs():
+    """IVF_PQ exposes nprobes+refine_factor; IVF_HNSW_SQ/PQ additionally
+    expose ef. These are the only keys lancedb.py's search_embedding knows
+    how to forward, so any new index type must stay inside this union.
+    """
+    allowed = {"nprobes", "ef", "refine_factor"}
+
+    ivfpq = LanceDBIndexConfig(nprobes=10, refine_factor=5)
+    sq = LanceDBIVFHNSWSQConfig(nprobes=10, ef=64, refine_factor=5)
+    pq = LanceDBIVFHNSWPQConfig(nprobes=10, ef=64, refine_factor=5)
+
+    for cfg in (ivfpq, sq, pq):
+        assert set(cfg.search_param().keys()).issubset(allowed)
+
+
+def test_no_index_and_autoindex_are_well_formed():
+    none_cfg = LanceDBNoIndexConfig()
+    assert none_cfg.index == IndexType.NONE
+    assert none_cfg.index_param() == {}
+
+    auto_cfg = LanceDBAutoIndexConfig()
+    assert auto_cfg.index == IndexType.AUTOINDEX
+    assert "metric" in auto_cfg.index_param()
+
+
+# ---------------------------------------------------------------------------
+# CLI wiring — validate via static TypedDict introspection (no heavy runtime
+# imports needed, avoids hdrh / streamlit / etc. dependency chains)
+# ---------------------------------------------------------------------------
+
+
+def _extract_click_option_names(typed_dict_cls: type) -> set[str]:
+    """Extract ``--option-name`` strings from a Click-annotated TypedDict.
+
+    Each field is ``Annotated[T, click.option("--name", ...)]``.
+    We pull the option strings from the ``click.Option`` metadata.
+    """
+    hints = get_type_hints(typed_dict_cls, include_extras=True)
+    names: set[str] = set()
+    for _field, hint in hints.items():
+        if typing.get_origin(hint) is Annotated:
+            for meta in hint.__metadata__:
+                # click.option(...) produces a click.core.Decorator / functools.partial
+                # but in this project it's stored as a click.Argument or a
+                # ``functools.partial`` wrapping ``click.option``. Extract the
+                # first positional string that starts with "--".
+                if hasattr(meta, "name") and isinstance(meta.name, str):
+                    names.add(meta.name)
+                # click.option() returns a decorator whose .args[0] is the
+                # option flag(s). We try a few accessor patterns.
+                for attr in ("args", "decls"):
+                    for val in getattr(meta, attr, ()):
+                        if isinstance(val, str) and val.startswith("--"):
+                            names.add(val)
+                # For click.option stored as click.core.Option or Decorator
+                if hasattr(meta, "opts"):
+                    for opt in meta.opts:
+                        if isinstance(opt, str) and opt.startswith("--"):
+                            names.add(opt)
+    return names
+
+
+def test_cli_typed_dicts_define_all_expected_commands():
+    """Every expected CLI TypedDict class must be importable from lancedb/cli.py."""
+    # We mock the heavy module to avoid pulling the entire runtime.
+    # lancedb/cli.py imports ....cli.cli which triggers hdrh etc.
+    # Instead we just verify the TypedDict definitions exist in source.
+    import ast
+    from pathlib import Path
+
+    cli_path = Path("vectordb_bench/backend/clients/lancedb/cli.py")
+    tree = ast.parse(cli_path.read_text())
+
+    class_names: set[str] = set()
+    func_names: set[str] = set()
+    for node in ast.walk(tree):
+        if isinstance(node, ast.ClassDef):
+            class_names.add(node.name)
+        elif isinstance(node, ast.FunctionDef):
+            func_names.add(node.name)
+
+    # TypedDicts that drive CLI options
+    assert {
+        "LanceDBTypedDict",
+        "LanceDBIVFPQTypedDict",
+        "LanceDBIVFHNSWSQTypedDict",
+        "LanceDBIVFHNSWPQTypedDict",
+    }.issubset(class_names)
+
+    # Command functions registered via @cli.command()
+    assert {"LanceDB", "LanceDBAutoIndex", "LanceDBIVFPQ", "LanceDBIVFHNSWSQ", "LanceDBIVFHNSWPQ"}.issubset(func_names)
+
+
+def test_cli_typeddict_ivfpq_has_search_knobs():
+    """IVF_PQ TypedDict must define nprobes / refine_factor / num_partitions."""
+    import ast
+    from pathlib import Path
+
+    cli_src = Path("vectordb_bench/backend/clients/lancedb/cli.py").read_text()
+    tree = ast.parse(cli_src)
+
+    def _fields_of(cls_name: str) -> set[str]:
+        for node in ast.walk(tree):
+            if isinstance(node, ast.ClassDef) and node.name == cls_name:
+                return {
+                    item.target.id
+                    for item in node.body
+                    if isinstance(item, ast.AnnAssign) and isinstance(item.target, ast.Name)
+                }
+        return set()
+
+    ivfpq = _fields_of("LanceDBIVFPQTypedDict")
+    assert {"nprobes", "refine_factor", "num_partitions", "num_sub_vectors", "nbits"}.issubset(ivfpq)
+
+
+def test_cli_typeddict_hnsw_variants_are_superset_of_ivfpq():
+    """Both HNSW TypedDicts must have nprobes + refine_factor + graph knobs."""
+    import ast
+    from pathlib import Path
+
+    cli_src = Path("vectordb_bench/backend/clients/lancedb/cli.py").read_text()
+    tree = ast.parse(cli_src)
+
+    def _fields_of(cls_name: str) -> set[str]:
+        for node in ast.walk(tree):
+            if isinstance(node, ast.ClassDef) and node.name == cls_name:
+                return {
+                    item.target.id
+                    for item in node.body
+                    if isinstance(item, ast.AnnAssign) and isinstance(item.target, ast.Name)
+                }
+        return set()
+
+    sq_fields = _fields_of("LanceDBIVFHNSWSQTypedDict")
+    pq_fields = _fields_of("LanceDBIVFHNSWPQTypedDict")
+
+    # Both must have shared search knobs
+    shared_knobs = {"nprobes", "refine_factor", "num_partitions", "m", "ef", "ef_construction"}
+    assert shared_knobs.issubset(sq_fields), f"SQ missing: {shared_knobs - sq_fields}"
+    assert shared_knobs.issubset(pq_fields), f"PQ missing: {shared_knobs - pq_fields}"
+
+    # PQ variant has num_sub_vectors, SQ does not
+    assert "num_sub_vectors" in pq_fields
+    assert "num_sub_vectors" not in sq_fields
diff --git a/tests/test_milvus.py b/tests/test_milvus.py
new file mode 100644
index 000000000..ef131ef87
--- /dev/null
+++ b/tests/test_milvus.py
@@ -0,0 +1,467 @@
+"""E2E test for Milvus client using MilvusClient API.
+
+Requires a running Milvus instance at localhost:19530.
+"""
+
+import logging
+from types import SimpleNamespace
+from unittest.mock import MagicMock, call
+
+import pytest
+from pydantic import SecretStr
+
+from vectordb_bench.backend.cases import CaseType
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.api import IndexType
+from vectordb_bench.backend.clients.milvus.config import MilvusConfig
+from vectordb_bench.backend.clients.milvus.milvus import MILVUS_FORCE_MERGE_TARGET_SIZE_MB, Milvus
+from vectordb_bench.backend.payload import PayloadProfile
+from vectordb_bench.interface import BenchMarkRunner
+from vectordb_bench.models import CaseConfig, TaskConfig
+
+log = logging.getLogger(__name__)
+
+
+class TestMilvusOptimize:
+    def _milvus(
+        self,
+        *,
+        compact_side_effect: Exception | None = None,
+        is_fts: bool = False,
+        is_gpu_index: bool = False,
+    ):
+        milvus = Milvus.__new__(Milvus)
+        milvus.name = "Milvus"
+        milvus.collection_name = "test_collection"
+        milvus._is_fts = is_fts
+        milvus.case_config = SimpleNamespace(is_gpu_index=is_gpu_index)
+        milvus.client = MagicMock()
+        milvus.client.compact.side_effect = compact_side_effect
+        milvus.client.compact.return_value = 42
+        milvus._wait_for_segments_sorted = MagicMock()
+        milvus._wait_for_index = MagicMock()
+        milvus._wait_for_compaction = MagicMock()
+        return milvus
+
+    def test_optimize_compact_uses_safe_force_merge_target_size(self):
+        milvus = self._milvus()
+
+        milvus._optimize()
+
+        milvus.client.compact.assert_any_call("test_collection", target_size=MILVUS_FORCE_MERGE_TARGET_SIZE_MB)
+        milvus.client.refresh_load.assert_called_once_with("test_collection")
+
+    def test_optimize_compacts_fts_collections(self):
+        milvus = self._milvus(is_fts=True)
+
+        milvus._optimize()
+
+        milvus.client.compact.assert_any_call("test_collection", target_size=MILVUS_FORCE_MERGE_TARGET_SIZE_MB)
+        milvus.client.refresh_load.assert_called_once_with("test_collection")
+
+    def test_optimize_flushes_and_runs_normal_compaction_before_force_merge(self):
+        milvus = self._milvus(compact_side_effect=[41, 42])
+
+        milvus._optimize()
+
+        milvus.client.flush.assert_called_once_with("test_collection")
+        assert milvus.client.compact.call_args_list == [
+            call("test_collection"),
+            call("test_collection", target_size=MILVUS_FORCE_MERGE_TARGET_SIZE_MB),
+        ]
+        assert milvus._wait_for_segments_sorted.call_count == 2
+        assert milvus._wait_for_index.call_count == 3
+        assert milvus._wait_for_compaction.call_args_list == [call(41), call(42)]
+        milvus.client.refresh_load.assert_called_once_with("test_collection")
+
+    def test_optimize_retries_when_compacting_segments_are_missing_from_force_merge_plan(
+        self, monkeypatch: pytest.MonkeyPatch
+    ):
+        class FakeMilvusClient:
+            def __init__(self):
+                self.segment_ids = [1, 2, 3]
+                self.force_merge_completed = False
+
+            def flush(self, _collection_name: str):
+                pass
+
+            def list_persistent_segments(self, _collection_name: str):
+                return [
+                    SimpleNamespace(
+                        segment_id=segment_id,
+                        is_sorted=True,
+                        state_name="Flushed",
+                        level_name="L1",
+                    )
+                    for segment_id in self.segment_ids
+                ]
+
+            def describe_index(self, _collection_name: str, _index_name: str):
+                return {"pending_index_rows": 0}
+
+            def compact(self, _collection_name: str, *, target_size: int | None = None):
+                if target_size is None:
+                    return 90
+                assert target_size == MILVUS_FORCE_MERGE_TARGET_SIZE_MB
+                if self.segment_ids == [1, 2, 3]:
+                    return 100
+                if self.segment_ids == [10, 20]:
+                    return 101
+                message = f"unexpected force merge input: {self.segment_ids}"
+                raise AssertionError(message)
+
+            def get_compaction_state(self, compaction_id: int):
+                if compaction_id == 90:
+                    pass
+                elif compaction_id == 100:
+                    self.segment_ids = [10, 20]
+                elif compaction_id == 101:
+                    self.segment_ids = [30]
+                    self.force_merge_completed = True
+                else:
+                    message = f"unexpected compaction id: {compaction_id}"
+                    raise AssertionError(message)
+                return "Completed"
+
+            def get_compaction_plans(self, compaction_id: int):
+                sources = [1] if compaction_id == 100 else [10, 20]
+                return SimpleNamespace(plans=[SimpleNamespace(sources=sources)])
+
+            def refresh_load(self, _collection_name: str):
+                assert self.force_merge_completed, "refresh started after only a partial force merge"
+
+        milvus = Milvus.__new__(Milvus)
+        milvus.name = "Milvus"
+        milvus.collection_name = "test_collection"
+        milvus._is_fts = False
+        milvus._main_index_name = "vector_idx"
+        milvus.case_config = SimpleNamespace(is_gpu_index=False)
+        milvus.client = FakeMilvusClient()
+        monkeypatch.setattr("vectordb_bench.backend.clients.milvus.milvus.time.sleep", lambda _seconds: None)
+
+        milvus.optimize(data_size=500_000)
+
+        assert milvus.client.force_merge_completed
+
+    def test_optimize_retries_when_all_segments_are_compacting(self, monkeypatch: pytest.MonkeyPatch):
+        class FakeMilvusClient:
+            def __init__(self):
+                self.segment_ids = [1, 2]
+                self.compact_attempts = 0
+                self.force_merge_completed = False
+
+            def flush(self, _collection_name: str):
+                pass
+
+            def list_persistent_segments(self, _collection_name: str):
+                return [
+                    SimpleNamespace(
+                        segment_id=segment_id,
+                        is_sorted=True,
+                        state_name="Flushed",
+                        level_name="L1",
+                    )
+                    for segment_id in self.segment_ids
+                ]
+
+            def describe_index(self, _collection_name: str, _index_name: str):
+                return {"pending_index_rows": 0}
+
+            def compact(self, _collection_name: str, *, target_size: int | None = None):
+                if target_size is None:
+                    return 90
+                assert target_size == MILVUS_FORCE_MERGE_TARGET_SIZE_MB
+                self.compact_attempts += 1
+                if self.compact_attempts == 1:
+                    self.segment_ids = [10]
+                    return -1
+                return 101
+
+            def get_compaction_state(self, compaction_id: int):
+                if compaction_id == 90:
+                    return "Completed"
+                assert compaction_id == 101
+                self.segment_ids = [20]
+                self.force_merge_completed = True
+                return "Completed"
+
+            def get_compaction_plans(self, compaction_id: int):
+                assert compaction_id == 101
+                return SimpleNamespace(plans=[SimpleNamespace(sources=[10])])
+
+            def refresh_load(self, _collection_name: str):
+                assert self.force_merge_completed, "refresh started without a force merge job"
+
+        milvus = Milvus.__new__(Milvus)
+        milvus.name = "Milvus"
+        milvus.collection_name = "test_collection"
+        milvus._is_fts = False
+        milvus._main_index_name = "vector_idx"
+        milvus.case_config = SimpleNamespace(is_gpu_index=False)
+        milvus.client = FakeMilvusClient()
+        monkeypatch.setattr("vectordb_bench.backend.clients.milvus.milvus.time.sleep", lambda _seconds: None)
+
+        milvus.optimize(data_size=500_000)
+
+        assert milvus.client.force_merge_completed
+
+    def test_force_merge_uses_fresh_snapshots_and_stops_after_max_attempts(self, monkeypatch: pytest.MonkeyPatch):
+        class FakeMilvusClient:
+            def __init__(self):
+                self.segment_ids = [1, 2]
+                self.compact_attempts = 0
+
+            def list_persistent_segments(self, _collection_name: str):
+                return [
+                    SimpleNamespace(
+                        segment_id=segment_id,
+                        is_sorted=True,
+                        state_name="Flushed",
+                        level_name="L1",
+                    )
+                    for segment_id in self.segment_ids
+                ]
+
+            def describe_index(self, _collection_name: str, _index_name: str):
+                return {"pending_index_rows": 0}
+
+            def compact(self, _collection_name: str, *, target_size: int):
+                assert target_size == MILVUS_FORCE_MERGE_TARGET_SIZE_MB
+                self.compact_attempts += 1
+                return 100 + self.compact_attempts
+
+            def get_compaction_state(self, _compaction_id: int):
+                next_segment_ids = {
+                    1: [10, 20],
+                    2: [30, 40],
+                    3: [50, 60],
+                }
+                self.segment_ids = next_segment_ids[self.compact_attempts]
+                return "Completed"
+
+            def get_compaction_plans(self, _compaction_id: int):
+                planned_source = {1: 1, 2: 10, 3: 30}[self.compact_attempts]
+                return SimpleNamespace(plans=[SimpleNamespace(sources=[planned_source])])
+
+        milvus = Milvus.__new__(Milvus)
+        milvus.name = "Milvus"
+        milvus.collection_name = "test_collection"
+        milvus._main_index_name = "vector_idx"
+        milvus.client = FakeMilvusClient()
+        monkeypatch.setattr("vectordb_bench.backend.clients.milvus.milvus.time.sleep", lambda _seconds: None)
+
+        with pytest.raises(RuntimeError, match=r"after 3 attempts.*missing segments: \[40\]"):
+            milvus._force_merge(max_attempts=3)
+
+        assert milvus.client.compact_attempts == 3
+
+    def test_optimize_skips_gpu_index_compaction(self):
+        milvus = self._milvus(is_gpu_index=True)
+
+        milvus._optimize()
+
+        milvus.client.compact.assert_not_called()
+        milvus.client.refresh_load.assert_called_once_with("test_collection")
+
+    def test_optimize_skips_property_style_permission_denied(self):
+        error = RuntimeError("permission denied")
+        error.code = SimpleNamespace(name="PERMISSION_DENIED")
+        milvus = self._milvus(compact_side_effect=error)
+
+        milvus._optimize()
+
+        milvus.client.refresh_load.assert_called_once_with("test_collection")
+
+    def test_optimize_reraises_non_permission_error(self):
+        error = RuntimeError("boom")
+        error.code = SimpleNamespace(name="UNAVAILABLE")
+        milvus = self._milvus(compact_side_effect=error)
+
+        with pytest.raises(RuntimeError, match="boom") as exc_info:
+            milvus._optimize()
+
+        assert exc_info.value is error
+        milvus.client.refresh_load.assert_not_called()
+
+
+@pytest.mark.integration
+class TestMilvus:
+    """E2E test for Milvus using Performance1536D50K (OpenAI 50K dataset)."""
+
+    def test_performance_1536d_50k(self):
+        """Full benchmark: download dataset, insert, optimize (force merge), search."""
+        runner = BenchMarkRunner()
+
+        task_config = TaskConfig(
+            db=DB.Milvus,
+            db_config=MilvusConfig(uri=SecretStr("http://localhost:19530")),
+            db_case_config=DB.Milvus.case_config_cls(index_type=IndexType.Flat)(),
+            case_config=CaseConfig(case_id=CaseType.Performance1536D50K),
+        )
+
+        runner.run([task_config])
+        runner._sync_running_task()
+        result = runner.get_results()
+        log.info(f"test result: {result}")
+        assert len(result) > 0
+
+
+def test_milvus_multitenant_search_uses_tenant_label_filter():
+    captured = {}
+
+    def search(**kwargs):
+        captured.update(kwargs)
+        return [[{"pk": 1}]]
+
+    db = object.__new__(Milvus)
+    db.client = SimpleNamespace(search=search)
+    db.collection_name = "test_collection"
+    db._vector_field = "vector"
+    db._primary_field = "pk"
+    db._scalar_label_field = "label"
+    db.case_config = SimpleNamespace(search_param=lambda: {"metric_type": "COSINE"})
+    db.expr = ""
+
+    result = db.search_embedding([0.1, 0.2], k=3, payload_profile=PayloadProfile.IDS_ONLY, tenant="tenant_0003")
+
+    assert result == [1]
+    assert captured["filter"] == "label == 'tenant_0003'"
+
+
+def test_milvus_validate_multitenant_schema_accepts_partition_key_label(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    closed = []
+
+    class FakeMilvusClient:
+        def __init__(self, **_kwargs: object) -> None:
+            pass
+
+        def describe_collection(self, _collection_name: str) -> dict:
+            return {
+                "fields": [
+                    {"name": "pk", "is_primary": True},
+                    {"name": "label", "is_partition_key": True},
+                ]
+            }
+
+        def close(self) -> None:
+            closed.append(True)
+
+    monkeypatch.setattr("vectordb_bench.backend.clients.milvus.milvus.MilvusClient", FakeMilvusClient)
+
+    db = object.__new__(Milvus)
+    db.name = "Milvus"
+    db.db_config = {"uri": "http://example.invalid", "user": None, "password": None, "token": ""}
+    db.collection_name = "existing"
+    db._scalar_label_field = "label"
+
+    db.validate_multitenant_schema()
+
+    assert closed == [True]
+
+
+def test_milvus_validate_multitenant_schema_rejects_non_partition_key_label(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    class FakeMilvusClient:
+        def __init__(self, **_kwargs: object) -> None:
+            pass
+
+        def describe_collection(self, _collection_name: str) -> dict:
+            return {"fields": [{"name": "label", "is_partition_key": False}]}
+
+        def close(self) -> None:
+            pass
+
+    monkeypatch.setattr("vectordb_bench.backend.clients.milvus.milvus.MilvusClient", FakeMilvusClient)
+
+    db = object.__new__(Milvus)
+    db.name = "Milvus"
+    db.db_config = {"uri": "http://example.invalid", "user": None, "password": None, "token": ""}
+    db.collection_name = "existing"
+    db._scalar_label_field = "label"
+
+    with pytest.raises(ValueError, match="label field is not a partition key"):
+        db.validate_multitenant_schema()
+
+
+def test_milvus_validate_multitenant_schema_uses_existing_labels_partition_key(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    captured = {}
+
+    class FakeMilvusClient:
+        def __init__(self, **_kwargs: object) -> None:
+            pass
+
+        def describe_collection(self, _collection_name: str) -> dict:
+            return {
+                "fields": [
+                    {"name": "pk", "is_primary": True},
+                    {"name": "labels", "is_partition_key": True},
+                    {"name": "scalar_label", "nullable": True},
+                ]
+            }
+
+        def close(self) -> None:
+            pass
+
+    def search(**kwargs):
+        captured.update(kwargs)
+        return [[{"pk": 1}]]
+
+    monkeypatch.setattr("vectordb_bench.backend.clients.milvus.milvus.MilvusClient", FakeMilvusClient)
+
+    db = object.__new__(Milvus)
+    db.name = "Milvus"
+    db.db_config = {"uri": "http://example.invalid", "user": None, "password": None, "token": ""}
+    db.collection_name = "existing"
+    db._vector_field = "vector"
+    db._primary_field = "pk"
+    db._scalar_label_field = "label"
+    db.case_config = SimpleNamespace(search_param=lambda: {"metric_type": "COSINE"})
+    db.expr = ""
+
+    db.validate_multitenant_schema()
+    db.client = SimpleNamespace(search=search)
+
+    db.search_embedding([0.1, 0.2], payload_profile=PayloadProfile.SCALAR_LABEL, tenant="tenant_0003")
+
+    assert captured["filter"] == "labels == 'tenant_0003'"
+    assert captured["output_fields"] == ["scalar_label"]
+
+
+def test_milvus_multitenant_insert_writes_tenant_and_scalar_payload_labels() -> None:
+    inserted = {}
+
+    def insert(collection_name, batch_data):
+        inserted["collection_name"] = collection_name
+        inserted["batch_data"] = batch_data
+        return {"insert_count": len(batch_data)}
+
+    db = object.__new__(Milvus)
+    db.client = SimpleNamespace(insert=insert)
+    db.collection_name = "test_collection"
+    db.batch_size = 100
+    db._primary_field = "pk"
+    db._scalar_id_field = "id"
+    db._vector_field = "vector"
+    db._scalar_label_field = "label"
+    db._scalar_payload_label_field = "scalar_label"
+    db._multitenant_partition_key_field = "labels"
+    db.with_scalar_labels = True
+
+    count, err = db.insert_embeddings(
+        embeddings=[[0.1, 0.2], [0.3, 0.4]],
+        metadata=[1, 2],
+        labels_data=["label_a", "label_b"],
+        tenant_labels_data=["tenant_0001", "tenant_0002"],
+    )
+
+    assert count == 2
+    assert err is None
+    assert inserted["batch_data"] == [
+        {"pk": 1, "id": 1, "vector": [0.1, 0.2], "labels": "tenant_0001", "scalar_label": "label_a"},
+        {"pk": 2, "id": 2, "vector": [0.3, 0.4], "labels": "tenant_0002", "scalar_label": "label_b"},
+    ]
diff --git a/tests/test_milvus_zilliz_cli.py b/tests/test_milvus_zilliz_cli.py
new file mode 100644
index 000000000..4177c8898
--- /dev/null
+++ b/tests/test_milvus_zilliz_cli.py
@@ -0,0 +1,82 @@
+from click.testing import CliRunner
+from pytest import MonkeyPatch
+
+from vectordb_bench.backend.clients.milvus import cli as milvus_cli
+from vectordb_bench.backend.clients.zilliz_cloud import cli as zilliz_cli
+
+
+def test_milvus_cli_builds_shared_connection_config() -> None:
+    parameters = {
+        "db_label": "milvus-test",
+        "uri": "http://localhost:19530",
+        "user_name": "root",
+        "password": "secret",
+        "num_shards": "2",
+        "replica_number": "3",
+        "collection_name": "bench_collection",
+    }
+
+    config = milvus_cli._build_milvus_config(parameters)
+
+    assert config.db_label == "milvus-test"
+    assert config.uri.get_secret_value() == "http://localhost:19530"
+    assert config.user == "root"
+    assert config.password.get_secret_value() == "secret"
+    assert config.num_shards == 2
+    assert config.replica_number == 3
+    assert config.collection_name == "bench_collection"
+
+    parameters["password"] = None
+    assert milvus_cli._build_milvus_config(parameters).password is None
+
+
+def test_milvus_autoindex_cli_enables_partition_key_for_multitenant_case(
+    monkeypatch: MonkeyPatch,
+) -> None:
+    captured = {}
+
+    def fake_run(**kwargs):
+        captured.update(kwargs)
+
+    monkeypatch.setattr(milvus_cli, "run", fake_run)
+
+    result = CliRunner().invoke(
+        milvus_cli.MilvusAutoIndex,
+        [
+            "--case-type",
+            "CloudMultiTenantSearchCase",
+            "--uri",
+            "http://localhost:19530",
+            "--dry-run",
+        ],
+    )
+
+    assert result.exit_code == 0, result.output
+    assert captured["db_case_config"].use_partition_key is True
+
+
+def test_zilliz_autoindex_cli_enables_partition_key_for_multitenant_case(
+    monkeypatch: MonkeyPatch,
+) -> None:
+    captured = {}
+
+    def fake_run(**kwargs):
+        captured.update(kwargs)
+
+    monkeypatch.setattr(zilliz_cli, "run", fake_run)
+
+    result = CliRunner().invoke(
+        zilliz_cli.ZillizAutoIndex,
+        [
+            "--case-type",
+            "CloudMultiTenantSearchCase",
+            "--uri",
+            "https://example.api.gcp-us-west1.zillizcloud.com",
+            "--token",
+            "secret",
+            "--dry-run",
+        ],
+    )
+
+    assert result.exit_code == 0, result.output
+    assert captured["db_case_config"].use_partition_key is True
diff --git a/tests/test_multitenant_case.py b/tests/test_multitenant_case.py
new file mode 100644
index 000000000..45939c32a
--- /dev/null
+++ b/tests/test_multitenant_case.py
@@ -0,0 +1,400 @@
+from contextlib import contextmanager
+from types import SimpleNamespace
+
+import pytest
+
+from vectordb_bench.backend.cases import CaseType, CloudMultiTenantSearchCase
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.api import EmptyDBCaseConfig, VectorDB
+from vectordb_bench.backend.clients.zilliz_cloud.config import AutoIndexConfig, ZillizCloudConfig
+from vectordb_bench.backend.data_source import DatasetSource
+from vectordb_bench.backend.dataset import DatasetManager, DatasetWithSizeType
+from vectordb_bench.backend.filter import FilterOp
+from vectordb_bench.backend.payload import PayloadProfile
+from vectordb_bench.backend.task_runner import CaseRunner, RunningStatus
+from vectordb_bench.models import CaseConfig, TaskConfig, TaskStage
+
+
+def test_multitenant_case_defaults_to_cohere_large_1000_tenants():
+    case = CloudMultiTenantSearchCase()
+
+    assert case.case_id == CaseType.CloudMultiTenantSearchCase
+    assert case.dataset_with_size_type == DatasetWithSizeType.CohereLarge
+    assert case.dataset.data.size == 10_000_000
+    assert case.tenant_count == 1000
+    assert case.tenant_prefix == "tenant_"
+    assert case.tenant_id_width == 4
+    assert case.measure_recall is False
+    assert case.is_multitenant is True
+    assert case.filters.type == FilterOp.NonFilter
+
+
+def test_multitenant_case_accepts_dataset_and_tenant_count():
+    case = CloudMultiTenantSearchCase(
+        dataset_with_size_type=DatasetWithSizeType.CohereSmall.value,
+        tenant_count=7,
+        tenant_prefix="acct_",
+        tenant_id_width=2,
+        payload_profile=PayloadProfile.VECTOR.value,
+        filter_rate=0.01,
+    )
+
+    assert case.dataset_with_size_type == DatasetWithSizeType.CohereSmall
+    assert case.dataset.data.size == 100_000
+    assert case.payload_profile == PayloadProfile.VECTOR
+    assert case.estimated_payload_bytes_per_query(k=10) == PayloadProfile.VECTOR.estimated_bytes_per_query(
+        k=10,
+        dim=case.dataset.data.dim,
+    )
+    assert case.filters.type == FilterOp.NumGE
+    assert case.tenant_count == 7
+    assert case.tenant_for_id(0) == "acct_00"
+    assert case.tenant_for_id(8) == "acct_01"
+    assert case.tenant_labels_for_ids([0, 1, 8, 13]) == ["acct_00", "acct_01", "acct_01", "acct_06"]
+
+
+def test_case_config_constructs_multitenant_case():
+    case = CaseType.CloudMultiTenantSearchCase.case_cls(
+        {
+            "dataset_with_size_type": DatasetWithSizeType.CohereSmall.value,
+            "tenant_count": 5,
+            "payload_profile": PayloadProfile.SCALAR_LABEL.value,
+            "label_percentage": 0.01,
+        }
+    )
+
+    assert isinstance(case, CloudMultiTenantSearchCase)
+    assert case.payload_profile == PayloadProfile.SCALAR_LABEL
+    assert case.filters.type == FilterOp.StrEqual
+    assert case.tenant_labels() == ["tenant_0000", "tenant_0001", "tenant_0002", "tenant_0003", "tenant_0004"]
+
+
+class TenantApiProbeDB(VectorDB):
+    name = "TenantApiProbeDB"
+
+    def __init__(self, dim=2, db_config=None, db_case_config=None, collection_name="test", drop_old=False, **kwargs):
+        self.insert_calls = []
+        self.search_calls = []
+
+    @contextmanager
+    def init(self):
+        yield
+
+    def insert_embeddings(self, embeddings, metadata, labels_data=None, tenant_labels_data=None, **kwargs):
+        self.insert_calls.append((embeddings, metadata, labels_data, tenant_labels_data))
+        return len(embeddings), None
+
+    def search_embedding(self, query, k=100, payload_profile=None, tenant=None):
+        self.search_calls.append((query, k, payload_profile, tenant))
+        return []
+
+    def optimize(self, data_size=None):
+        return None
+
+
+def test_vector_db_accepts_optional_tenant_context():
+    db = TenantApiProbeDB(db_case_config=EmptyDBCaseConfig())
+
+    count, err = db.insert_embeddings([[0.1, 0.2]], [42], tenant_labels_data=["tenant_0002"])
+    result = db.search_embedding([0.1, 0.2], tenant="tenant_0002")
+
+    assert count == 1
+    assert err is None
+    assert result == []
+    assert db.insert_calls[0][3] == ["tenant_0002"]
+    assert db.search_calls[0][3] == "tenant_0002"
+
+
+def test_search_only_zilliz_multitenant_validates_existing_partition_key_schema(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    case = CloudMultiTenantSearchCase()
+    task = TaskConfig(
+        db=DB.ZillizCloud,
+        db_config=ZillizCloudConfig(uri="http://example.invalid", collection_name="existing"),
+        db_case_config=AutoIndexConfig(use_partition_key=False),
+        case_config=CaseConfig(case_id=CaseType.CloudMultiTenantSearchCase),
+        stages=[TaskStage.SEARCH_CONCURRENT],
+    )
+    runner = CaseRunner(
+        run_id="run-id",
+        config=task,
+        ca=case,
+        status=RunningStatus.PENDING,
+        dataset_source=DatasetSource.S3,
+    )
+    calls: list[tuple[str, object]] = []
+
+    class ExistingCollectionDB:
+        def supports_multitenant(self) -> bool:
+            return True
+
+        def set_multitenant_context(self, tenant_labels: list[str]) -> None:
+            calls.append(("set_context", tenant_labels))
+
+        def validate_multitenant_schema(self) -> None:
+            calls.append(("validate_schema", None))
+
+    def fake_init_db(self: CaseRunner, _drop_old: bool = True) -> None:
+        self.db = ExistingCollectionDB()
+
+    def fake_prepare(*_args: object, **_kwargs: object) -> None:
+        calls.append(("prepare", None))
+
+    monkeypatch.setattr(CaseRunner, "init_db", fake_init_db)
+    monkeypatch.setattr(DatasetManager, "prepare", fake_prepare)
+
+    runner._pre_run(drop_old=False)
+
+    assert ("validate_schema", None) in calls
+    assert calls[0][0] == "set_context"
+
+
+def test_zilliz_multitenant_create_still_requires_partition_key() -> None:
+    case = CloudMultiTenantSearchCase()
+    task = TaskConfig(
+        db=DB.ZillizCloud,
+        db_config=ZillizCloudConfig(uri="http://example.invalid", collection_name="new_collection"),
+        db_case_config=AutoIndexConfig(use_partition_key=False),
+        case_config=CaseConfig(case_id=CaseType.CloudMultiTenantSearchCase),
+        stages=[TaskStage.DROP_OLD, TaskStage.LOAD],
+    )
+    runner = CaseRunner(
+        run_id="run-id",
+        config=task,
+        ca=case,
+        status=RunningStatus.PENDING,
+        dataset_source=DatasetSource.S3,
+    )
+
+    with pytest.raises(ValueError, match="requires use_partition_key=True"):
+        runner._pre_run(drop_old=True)
+
+
+class FakeTurboNamespace:
+    def __init__(self):
+        self.write_calls = []
+        self.query_calls = []
+
+    def write(self, **kwargs):
+        self.write_calls.append(kwargs)
+
+    def query(self, **kwargs):
+        self.query_calls.append(kwargs)
+        return SimpleNamespace(rows=[SimpleNamespace(id="10")])
+
+    def metadata(self):
+        return {"index": {"unindexed_bytes": 0}}
+
+
+class FakeTurboClient:
+    def __init__(self):
+        self.namespaces = {}
+
+    def namespace(self, name):
+        self.namespaces.setdefault(name, FakeTurboNamespace())
+        return self.namespaces[name]
+
+
+def test_turbopuffer_groups_multitenant_insert_and_search(monkeypatch):
+    from vectordb_bench.backend.clients.api import MetricType
+    from vectordb_bench.backend.clients.turbopuffer.config import TurboPufferIndexConfig
+    from vectordb_bench.backend.clients.turbopuffer.turbopuffer import TurboPuffer
+
+    fake_client = FakeTurboClient()
+    monkeypatch.setattr(TurboPuffer, "_create_client", lambda self: fake_client)
+
+    db = TurboPuffer(
+        dim=2,
+        db_config={
+            "api_key": "k",
+            "region": "r",
+            "api_base_url": None,
+            "namespace": "single",
+            "multitenant_namespace_prefix": "mt_",
+        },
+        db_case_config=TurboPufferIndexConfig(metric_type=MetricType.COSINE),
+        drop_old=False,
+    )
+    db.set_multitenant_context(["tenant_0000", "tenant_0001"])
+
+    with db.init():
+        count, err = db.insert_embeddings(
+            embeddings=[[1.0, 0.0], [0.0, 1.0], [0.5, 0.5]],
+            metadata=[0, 1, 2],
+            tenant_labels_data=["tenant_0000", "tenant_0001", "tenant_0000"],
+        )
+        result = db.search_embedding([1.0, 0.0], k=1, tenant="tenant_0001")
+
+    assert count == 3
+    assert err is None
+    assert result == [10]
+    assert fake_client.namespaces["mt_tenant_0000"].write_calls[0]["upsert_columns"]["id"] == [0, 2]
+    assert fake_client.namespaces["mt_tenant_0001"].write_calls[0]["upsert_columns"]["id"] == [1]
+    assert fake_client.namespaces["mt_tenant_0001"].query_calls
+
+
+def test_turbopuffer_multitenant_insert_preserves_scalar_payload_labels(monkeypatch):
+    from vectordb_bench.backend.clients.api import MetricType
+    from vectordb_bench.backend.clients.turbopuffer.config import TurboPufferIndexConfig
+    from vectordb_bench.backend.clients.turbopuffer.turbopuffer import TurboPuffer
+
+    fake_client = FakeTurboClient()
+    monkeypatch.setattr(TurboPuffer, "_create_client", lambda self: fake_client)
+
+    db = TurboPuffer(
+        dim=2,
+        db_config={
+            "api_key": "k",
+            "region": "r",
+            "api_base_url": None,
+            "namespace": "single",
+            "multitenant_namespace_prefix": "mt_",
+            "scalar_payload_label_field": "scalar_label",
+        },
+        db_case_config=TurboPufferIndexConfig(metric_type=MetricType.COSINE),
+        drop_old=False,
+        with_scalar_labels=True,
+    )
+
+    with db.init():
+        count, err = db.insert_embeddings(
+            embeddings=[[1.0, 0.0], [0.0, 1.0]],
+            metadata=[0, 1],
+            labels_data=["label_a", "label_b"],
+            tenant_labels_data=["tenant_0000", "tenant_0001"],
+        )
+
+    assert count == 2
+    assert err is None
+    assert fake_client.namespaces["mt_tenant_0000"].write_calls[0]["upsert_columns"]["scalar_label"] == ["label_a"]
+    assert fake_client.namespaces["mt_tenant_0001"].write_calls[0]["upsert_columns"]["scalar_label"] == ["label_b"]
+
+
+def test_turbopuffer_multitenant_partial_insert_failure_is_explicit(monkeypatch):
+    from vectordb_bench.backend.clients.api import MetricType
+    from vectordb_bench.backend.clients.turbopuffer.config import TurboPufferIndexConfig
+    from vectordb_bench.backend.clients.turbopuffer.turbopuffer import TurboPuffer
+
+    class FailingNamespace(FakeTurboNamespace):
+        def __init__(self, fail_write: bool = False):
+            super().__init__()
+            self.fail_write = fail_write
+
+        def write(self, **kwargs):
+            self.write_calls.append(kwargs)
+            if self.fail_write:
+                raise RuntimeError("tenant write failed")
+
+    class FailingTurboClient:
+        def __init__(self):
+            self.namespaces = {}
+
+        def namespace(self, name):
+            self.namespaces.setdefault(name, FailingNamespace(fail_write=name == "mt_tenant_0001"))
+            return self.namespaces[name]
+
+    fake_client = FailingTurboClient()
+    monkeypatch.setattr(TurboPuffer, "_create_client", lambda self: fake_client)
+
+    db = TurboPuffer(
+        dim=2,
+        db_config={
+            "api_key": "k",
+            "region": "r",
+            "api_base_url": None,
+            "namespace": "single",
+            "multitenant_namespace_prefix": "mt_",
+        },
+        db_case_config=TurboPufferIndexConfig(metric_type=MetricType.COSINE),
+        drop_old=False,
+    )
+
+    with db.init():
+        count, err = db.insert_embeddings(
+            embeddings=[[1.0, 0.0], [0.0, 1.0], [0.5, 0.5]],
+            metadata=[0, 1, 2],
+            tenant_labels_data=["tenant_0000", "tenant_0001", "tenant_0000"],
+        )
+
+    assert count == 2
+    assert getattr(err, "non_retryable", False) is True
+    assert getattr(err, "inserted_count") == 2
+    assert getattr(err, "successful_tenants") == {"tenant_0000": 2}
+    assert getattr(err, "failed_tenant") == "tenant_0001"
+    assert "tenant_0001" in str(err)
+    assert fake_client.namespaces["mt_tenant_0000"].write_calls
+    assert fake_client.namespaces["mt_tenant_0001"].write_calls
+
+
+def test_turbopuffer_pins_multitenant_namespaces_on_init(monkeypatch):
+    from vectordb_bench.backend.clients.api import MetricType
+    from vectordb_bench.backend.clients.turbopuffer import turbopuffer as turbopuffer_module
+    from vectordb_bench.backend.clients.turbopuffer.config import TurboPufferIndexConfig
+    from vectordb_bench.backend.clients.turbopuffer.turbopuffer import TurboPuffer
+
+    fake_client = FakeTurboClient()
+    calls = []
+
+    def fake_metadata_request(api_key, region, namespace, method, payload=None, api_base_url=None):
+        calls.append((method, namespace, payload, api_base_url))
+        return {}
+
+    def fake_wait_for_pinning(api_key, region, namespace, replicas, api_base_url=None, timeout=None):
+        calls.append(("WAIT", namespace, replicas, timeout))
+        return {"pinning": {"replicas": replicas, "status": {"ready_replicas": replicas}}}
+
+    monkeypatch.setattr(TurboPuffer, "_create_client", lambda self: fake_client)
+    monkeypatch.setattr(turbopuffer_module, "namespace_metadata_request", fake_metadata_request)
+    monkeypatch.setattr(turbopuffer_module, "wait_for_namespace_pinning", fake_wait_for_pinning)
+
+    db = TurboPuffer(
+        dim=2,
+        db_config={
+            "api_key": "k",
+            "region": "r",
+            "api_base_url": "https://tpuf.example",
+            "namespace": "single",
+            "multitenant_namespace_prefix": "mt_",
+            "pin_namespace": True,
+            "pin_replicas": 2,
+            "pin_timeout": 30,
+        },
+        db_case_config=TurboPufferIndexConfig(metric_type=MetricType.COSINE),
+        drop_old=False,
+    )
+    db.set_multitenant_context(["tenant_0000", "tenant_0001"])
+
+    with db.init():
+        pass
+
+    assert calls == [
+        ("PATCH", "mt_tenant_0000", {"pinning": {"replicas": 2}}, "https://tpuf.example"),
+        ("WAIT", "mt_tenant_0000", 2, 30),
+        ("PATCH", "mt_tenant_0001", {"pinning": {"replicas": 2}}, "https://tpuf.example"),
+        ("WAIT", "mt_tenant_0001", 2, 30),
+    ]
+
+
+def test_turbopuffer_supports_scalar_label_payload_for_multitenant_search() -> None:
+    from vectordb_bench.backend.clients.turbopuffer.turbopuffer import TurboPuffer
+
+    fake_client = FakeTurboClient()
+    db = TurboPuffer.__new__(TurboPuffer)
+    db.client = fake_client
+    db.namespace = "single"
+    db.multitenant_namespace_prefix = "mt_"
+    db._ns_cache = {}
+    db._vector_field = "vector"
+    db._scalar_label_field = "label"
+    db._scalar_payload_label_field = "scalar_label"
+    db.expr = None
+
+    assert db.supports_payload_profile(PayloadProfile.SCALAR_LABEL)
+    assert db.search_embedding(
+        [1.0, 0.0],
+        k=50,
+        payload_profile=PayloadProfile.SCALAR_LABEL,
+        tenant="tenant_0001",
+    ) == [10]
+    assert fake_client.namespaces["mt_tenant_0001"].query_calls[0]["include_attributes"] == ["scalar_label"]
diff --git a/tests/test_oss_opensearch_fts.py b/tests/test_oss_opensearch_fts.py
new file mode 100644
index 000000000..25b5b295c
--- /dev/null
+++ b/tests/test_oss_opensearch_fts.py
@@ -0,0 +1,340 @@
+# ruff: noqa: E402
+
+import sys
+import types
+
+import pytest
+from click.testing import CliRunner
+
+
+class _FakeOpenSearch:
+    pass
+
+
+sys.modules.setdefault("opensearchpy", types.SimpleNamespace(OpenSearch=_FakeOpenSearch))
+
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.api import IndexType
+from vectordb_bench.backend.clients.elastic_cloud.config import ElasticCloudFtsConfig
+from vectordb_bench.backend.clients.oss_opensearch import cli as oss_opensearch_cli
+from vectordb_bench.backend.clients.oss_opensearch.config import OSSOpenSearchFtsConfig, OSSOpenSearchIndexConfig
+from vectordb_bench.backend.clients.oss_opensearch.oss_opensearch import OSSOpenSearch, OpenSearchError
+from vectordb_bench.backend.filter import NewIntFilter
+from vectordb_bench.backend.payload import PayloadProfile
+from vectordb_bench.cli.cli import select_cli_db_case_config
+
+
+def make_fts_db():
+    db = OSSOpenSearch.__new__(OSSOpenSearch)
+    db.index_name = "idx"
+    db.id_col_name = "doc_id"
+    db.text_col_name = "text"
+    db.filter_id_col_name = "filter_id"
+    db.filter = None
+    db._is_fts = True
+    db.client = object()
+    return db
+
+
+def test_oss_opensearch_fts_config_defaults():
+    config = OSSOpenSearchFtsConfig()
+
+    assert config.index_param() == ElasticCloudFtsConfig().index_param()
+    assert config.index_param()["properties"]["doc_id"] == {"type": "keyword"}
+    assert config.index_param()["properties"]["filter_id"] == {"type": "long"}
+    assert config.index_param()["properties"]["text"] == {"type": "text"}
+    assert config.search_param() == {}
+
+
+def test_oss_opensearch_fts_config_supports_bm25_similarity():
+    config = OSSOpenSearchFtsConfig(bm25_k1=1.2, bm25_b=0.75)
+    elastic_config = ElasticCloudFtsConfig(bm25_k1=1.2, bm25_b=0.75)
+
+    assert config.similarity_settings() == elastic_config.similarity_settings()
+    assert config.index_param()["properties"]["text"]["similarity"] == "vdbbench_bm25"
+    assert config.similarity_settings() == {
+        "similarity": {
+            "vdbbench_bm25": {
+                "type": "BM25",
+                "k1": 1.2,
+                "b": 0.75,
+            }
+        }
+    }
+
+
+def test_oss_opensearch_declares_full_text_support():
+    assert OSSOpenSearch.supports_full_text_search() is True
+    assert DB.OSSOpenSearch.case_config_cls(IndexType.FTS) is OSSOpenSearchFtsConfig
+
+
+def test_oss_opensearch_fts_cli_does_not_require_vector_index_options(monkeypatch: pytest.MonkeyPatch):
+    captured = {}
+    monkeypatch.setattr(oss_opensearch_cli, "run", lambda **kwargs: captured.update(kwargs))
+
+    result = CliRunner().invoke(
+        oss_opensearch_cli.OSSOpenSearch,
+        ["--host", "localhost", "--case-type", "FTSBm25Performance", "--dry-run"],
+    )
+
+    assert result.exit_code == 0, result.output
+    assert isinstance(captured["db_case_config"], OSSOpenSearchFtsConfig)
+
+
+def test_oss_opensearch_fts_cli_uses_single_node_defaults_and_force_merge_flag(monkeypatch: pytest.MonkeyPatch):
+    captured = {}
+    monkeypatch.setattr(oss_opensearch_cli, "run", lambda **kwargs: captured.update(kwargs))
+
+    result = CliRunner().invoke(
+        oss_opensearch_cli.OSSOpenSearch,
+        [
+            "--host",
+            "localhost",
+            "--case-type",
+            "FTSBm25Performance",
+            "--force-merge-enabled",
+            "false",
+            "--dry-run",
+        ],
+    )
+
+    assert result.exit_code == 0, result.output
+    assert captured["db_case_config"].number_of_replicas == 0
+    assert captured["db_case_config"].force_merge_enabled is False
+
+
+def test_oss_opensearch_generic_fts_routing_preserves_force_merge():
+    selected = select_cli_db_case_config(
+        DB.OSSOpenSearch,
+        OSSOpenSearchIndexConfig(force_merge_enabled=False),
+        "FTSBm25Performance",
+    )
+
+    assert isinstance(selected, OSSOpenSearchFtsConfig)
+    assert selected.force_merge_enabled is False
+
+
+def test_oss_opensearch_green_health_wait_is_bounded_and_diagnostic():
+    db = make_fts_db()
+    db.case_config = OSSOpenSearchFtsConfig(number_of_replicas=1)
+    calls = {}
+
+    class Cluster:
+        def health(self, **kwargs):
+            calls.update(kwargs)
+            return {
+                "status": "yellow",
+                "timed_out": True,
+                "unassigned_shards": 1,
+                "number_of_nodes": 1,
+            }
+
+    db.client = types.SimpleNamespace(cluster=Cluster())
+
+    with pytest.raises(OpenSearchError, match="configured_replicas=1.*unassigned_shards=1.*number_of_nodes=1"):
+        db._wait_till_green()
+
+    assert calls == {
+        "index": "idx",
+        "wait_for_status": "green",
+        "timeout": "30m",
+    }
+
+
+def test_oss_opensearch_create_index_fts_uses_text_mappings_and_settings():
+    db = OSSOpenSearch.__new__(OSSOpenSearch)
+    db._is_fts = True
+    db.case_config = OSSOpenSearchFtsConfig(
+        number_of_shards=2,
+        number_of_replicas=1,
+        refresh_interval="10s",
+    )
+    db.index_name = "idx"
+    calls = {}
+
+    class Indices:
+        def create(self, **kwargs):
+            calls.update(kwargs)
+
+    class Client:
+        indices = Indices()
+
+    db._create_index(Client())
+
+    assert calls == {
+        "index": "idx",
+        "body": {
+            "settings": {
+                "index": {
+                    "number_of_shards": 2,
+                    "number_of_replicas": 1,
+                    "refresh_interval": "10s",
+                }
+            },
+            "mappings": {
+                "properties": {
+                    "doc_id": {"type": "keyword"},
+                    "filter_id": {"type": "long"},
+                    "text": {"type": "text"},
+                }
+            },
+        },
+    }
+
+
+def test_oss_opensearch_insert_documents_builds_bulk_body():
+    db = make_fts_db()
+    captured = {}
+
+    class Client:
+        def bulk(self, **kwargs):
+            captured.update(kwargs)
+            return {"errors": False}
+
+    db.client = Client()
+
+    assert db.insert_documents(["alpha", "beta"], ["d1", "d2"], filter_ids=[0, 7]) == (2, None)
+    assert captured["body"] == [
+        {"index": {"_index": "idx", "_id": "d1"}},
+        {"doc_id": "d1", "text": "alpha", "filter_id": 0},
+        {"index": {"_index": "idx", "_id": "d2"}},
+        {"doc_id": "d2", "text": "beta", "filter_id": 7},
+    ]
+
+
+def test_oss_opensearch_insert_documents_reports_partial_bulk_failure():
+    db = make_fts_db()
+
+    class Client:
+        def bulk(self, **kwargs):
+            return {
+                "errors": True,
+                "items": [
+                    {"index": {"_id": "d1", "status": 201}},
+                    {
+                        "index": {
+                            "_id": "d2",
+                            "status": 429,
+                            "error": {
+                                "type": "rejected_execution_exception",
+                                "reason": "indexing queue is full",
+                            },
+                        }
+                    },
+                ],
+            }
+
+    db.client = Client()
+
+    insert_count, error = db.insert_documents(["alpha", "beta"], ["d1", "d2"])
+
+    assert insert_count == 1
+    assert isinstance(error, RuntimeError)
+    assert "failed for 1/2 documents" in str(error)
+    assert "successful=1" in str(error)
+    assert "id=d2" in str(error)
+    assert "rejected_execution_exception: indexing queue is full" in str(error)
+
+
+def test_oss_opensearch_insert_documents_rejects_malformed_bulk_error_response():
+    db = make_fts_db()
+
+    class Client:
+        def bulk(self, **kwargs):
+            return {"errors": True}
+
+    db.client = Client()
+
+    insert_count, error = db.insert_documents(["alpha"], ["d1"])
+
+    assert insert_count == 0
+    assert isinstance(error, RuntimeError)
+    assert str(error) == "OpenSearch FTS bulk response reported errors without an items list"
+
+
+def test_oss_opensearch_insert_documents_validates_lengths():
+    db = make_fts_db()
+
+    class Client:
+        def bulk(self, **kwargs):
+            raise AssertionError("bulk should not be called")
+
+    db.client = Client()
+
+    with pytest.raises(ValueError, match=r"Mismatch between texts .* and doc_ids .* lengths"):
+        db.insert_documents(["alpha", "beta"], ["d1"])
+
+
+def test_oss_opensearch_search_documents_builds_match_query():
+    db = make_fts_db()
+    calls = {}
+
+    class Client:
+        def search(self, **kwargs):
+            calls.update(kwargs)
+            return {"hits": {"hits": [{"fields": {"doc_id": ["d1"]}}]}}
+
+    db.client = Client()
+
+    assert db.search_documents("hello world", k=3) == ["d1"]
+    assert calls["index"] == "idx"
+    assert calls["body"] == {"query": {"match": {"text": "hello world"}}}
+    assert calls["size"] == 3
+    assert calls["stored_fields"] == "_none_"
+    assert calls["filter_path"] == ["hits.hits._id", "hits.hits.fields.doc_id"]
+
+
+def test_oss_opensearch_search_documents_applies_fts_filter():
+    db = make_fts_db()
+    calls = {}
+
+    class Client:
+        def search(self, **kwargs):
+            calls.update(kwargs)
+            return {"hits": {"hits": [{"fields": {"doc_id": ["d1"]}}]}}
+
+    db.client = Client()
+    db.prepare_filter(NewIntFilter(filter_rate=0.5, int_field="filter_id", int_value=10))
+
+    assert db.search_documents("hello world", k=3) == ["d1"]
+    assert calls["body"] == {
+        "query": {
+            "bool": {
+                "must": {"match": {"text": "hello world"}},
+                "filter": {"range": {"filter_id": {"gte": 10}}},
+            }
+        }
+    }
+
+
+def test_oss_opensearch_search_documents_requests_text_payload():
+    db = make_fts_db()
+    calls = {}
+
+    class Client:
+        def search(self, **kwargs):
+            calls.update(kwargs)
+            return {"hits": {"hits": [{"_id": "d1", "_source": {"text": "hello"}}]}}
+
+    db.client = Client()
+
+    assert db.search_documents("hello world", k=3, payload_profile=PayloadProfile.TEXT) == ["d1"]
+    assert calls["_source"] == ["text"]
+    assert "stored_fields" not in calls
+    assert calls["filter_path"] == [
+        "hits.hits._id",
+        "hits.hits.fields.doc_id",
+        "hits.hits._source.text",
+    ]
+
+
+def test_oss_opensearch_document_methods_guard_non_fts_mode():
+    db = OSSOpenSearch.__new__(OSSOpenSearch)
+    db._is_fts = False
+    db.client = object()
+
+    with pytest.raises(RuntimeError, match="OSSOpenSearch full-text insert requires OSSOpenSearchFtsConfig"):
+        db.insert_documents(["alpha"], ["d1"])
+
+    with pytest.raises(RuntimeError, match="OSSOpenSearch full-text search requires OSSOpenSearchFtsConfig"):
+        db.search_documents("alpha")
diff --git a/tests/test_pgvector.py b/tests/test_pgvector.py
new file mode 100644
index 000000000..cdd9461a9
--- /dev/null
+++ b/tests/test_pgvector.py
@@ -0,0 +1,180 @@
+"""Tests for PgVector client and ConcurrentInsertRunner.
+
+Reproduces issue #756: insert fails with
+  TypeError: no default __reduce__ due to non-trivial __cinit__
+when ConcurrentInsertRunner deep-copies a PgVector instance that has a live
+psycopg connection open (the connection is opened by `with self.db.init():`
+inside task() before the deepcopy in _get_thread_db()).
+
+Requires:
+  docker run -d --name pgvector-test \
+    -e POSTGRES_USER=vectordb -e POSTGRES_PASSWORD=vectordb \
+    -e POSTGRES_DB=vectordb -p 5432:5432 \
+    pgvector/pgvector:pg17
+
+Usage:
+  pytest tests/test_pgvector.py -v -s
+"""
+
+from __future__ import annotations
+
+import logging
+import pickle
+from unittest.mock import MagicMock
+
+import numpy as np
+import pytest
+
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.clients.pgvector.config import PgVectorHNSWConfig
+from vectordb_bench.backend.dataset import Dataset, DatasetSource
+from vectordb_bench.backend.filter import Filter, FilterOp, non_filter
+from vectordb_bench.backend.runner.concurrent_runner import ConcurrentInsertRunner
+
+log = logging.getLogger(__name__)
+
+# ── Connection config ────────────────────────────────────────────────────────
+
+DB_CONFIG = {
+    "connect_config": {
+        "host": "localhost",
+        "port": 5432,
+        "dbname": "vectordb",
+        "user": "vectordb",
+        "password": "vectordb",
+    },
+    "table_name": "test_pgvector",
+}
+
+DIM = 128
+COUNT = 500
+RNG = np.random.default_rng(42)
+
+
+# ── Helpers ──────────────────────────────────────────────────────────────────
+
+
+def make_hnsw_config(**kwargs) -> PgVectorHNSWConfig:
+    return PgVectorHNSWConfig(
+        metric_type="COSINE",
+        m=16,
+        ef_construction=64,
+        ef_search=64,
+        **kwargs,
+    )
+
+
+def make_db(table_name: str = "test_pgvector", drop_old: bool = True) -> DB.PgVector.init_cls:
+    cfg = dict(DB_CONFIG)
+    cfg["table_name"] = table_name
+    return DB.PgVector.init_cls(
+        dim=DIM,
+        db_config=cfg,
+        db_case_config=make_hnsw_config(),
+        drop_old=drop_old,
+    )
+
+
+def random_embeddings(n: int = COUNT, d: int = DIM) -> list[list[float]]:
+    return RNG.random((n, d)).tolist()
+
+
+# ── Basic client tests ────────────────────────────────────────────────────────
+
+
+class TestPgVectorBasic:
+    """Unit tests for the PgVector client (no subprocess)."""
+
+    def test_insert_and_search(self):
+        db = make_db("test_basic")
+        embeddings = random_embeddings()
+        metadata = list(range(COUNT))
+
+        with db.init():
+            count, err = db.insert_embeddings(embeddings=embeddings, metadata=metadata)
+        assert err is None, f"Insert error: {err}"
+        assert count == COUNT
+
+        with db.init():
+            db.optimize()
+
+        with db.init():
+            db.prepare_filter(Filter(type=FilterOp.NonFilter))
+            results = db.search_embedding(query=embeddings[0], k=10)
+        assert len(results) > 0
+
+    def test_db_is_not_thread_safe(self):
+        db = make_db("test_thread_safe")
+        assert db.thread_safe is False
+
+    def test_db_picklable_after_init(self):
+        """PgVector instance must be picklable after __init__ (conn/cursor are None).
+
+        This is required for ConcurrentInsertRunner which spawns a subprocess
+        and pickles self (which includes self.db).
+        """
+        db = make_db("test_pickle")
+        data = pickle.dumps(db)
+        db2 = pickle.loads(data)  # noqa: S301
+        assert db2.dim == DIM
+
+    def test_get_thread_db_with_open_connection(self):
+        """Regression test for issue #756.
+
+        ConcurrentInsertRunner.task() opens `with self.db.init()` before calling
+        workers. For non-thread-safe DBs the original _get_thread_db() then called
+        deepcopy(self.db) — but the live psycopg C-extension Connection is not
+        deep-copyable, causing TypeError.
+
+        Fixed code returns self.db directly (no deepcopy), so this test must pass
+        without raising.
+        """
+        db = make_db("test_get_thread_db")
+        runner = ConcurrentInsertRunner(db=db, dataset=MagicMock(), normalize=False)
+
+        with db.init():
+            assert db.conn is not None
+            result = runner._get_thread_db()  # TypeError here on original code
+
+        assert result is db
+
+
+# ── ConcurrentInsertRunner tests ──────────────────────────────────────────────
+
+
+class TestPgVectorConcurrentInsert:
+    """Tests for ConcurrentInsertRunner with PgVector (reproduces issue #756)."""
+
+    @pytest.mark.integration
+    def test_concurrent_insert_e2e(self):
+        """E2E regression test for issue #756 using the OpenAI 50K dataset.
+
+        Exercises the full pipeline:
+          ProcessPoolExecutor(spawn) → pickle runner → subprocess task()
+          → with self.db.init() → worker _get_thread_db() → insert batches
+
+        FAILS on original code (TypeError: deepcopy of live psycopg connection).
+        PASSES on fixed code.
+        """
+        dataset = Dataset.OPENAI.manager(50_000)
+        dataset.prepare(DatasetSource.AliyunOSS)
+
+        cfg = dict(DB_CONFIG)
+        cfg["table_name"] = "test_e2e_insert"
+        db = DB.PgVector.init_cls(
+            dim=dataset.data.dim,
+            db_config=cfg,
+            db_case_config=PgVectorHNSWConfig(
+                metric_type="COSINE",
+                m=16,
+                ef_construction=64,
+                ef_search=64,
+            ),
+            drop_old=True,
+        )
+
+        runner = ConcurrentInsertRunner(db=db, dataset=dataset, normalize=True, filters=non_filter)
+        count = runner.run()
+
+        assert count == 50_000, f"Expected 50000 rows, got {count}"
+        log.info(f"E2E insert completed: {count} rows")
diff --git a/tests/test_pinecone_multitenant.py b/tests/test_pinecone_multitenant.py
new file mode 100644
index 000000000..1a81d55ff
--- /dev/null
+++ b/tests/test_pinecone_multitenant.py
@@ -0,0 +1,214 @@
+import threading
+from types import SimpleNamespace
+
+
+class FakePineconeIndex:
+    def __init__(self):
+        self.upserts = []
+        self.queries = []
+        self.deletes = []
+
+    def describe_index_stats(self):
+        return {"dimension": 2, "total_vector_count": 3, "namespaces": {"mt_tenant_0000": {}, "mt_tenant_0001": {}}}
+
+    def upsert(self, vectors, namespace=None):
+        self.upserts.append((vectors, namespace))
+        return SimpleNamespace(_response_info={"raw_headers": {"x-pinecone-request-lsn": "7"}})
+
+    def query(self, **kwargs):
+        self.queries.append(kwargs)
+        return SimpleNamespace(
+            matches=[{"id": "11"}],
+            _response_info={"raw_headers": {"x-pinecone-max-indexed-lsn": "7"}},
+        )
+
+    def delete(self, delete_all=False, namespace=None):
+        self.deletes.append((delete_all, namespace))
+
+
+class FailingPineconeIndex(FakePineconeIndex):
+    def upsert(self, vectors, namespace=None):
+        self.upserts.append((vectors, namespace))
+        if namespace == "mt_tenant_0001":
+            raise RuntimeError("tenant upsert failed")
+        return SimpleNamespace(_response_info={"raw_headers": {"x-pinecone-request-lsn": "7"}})
+
+
+class FakePineconeClient:
+    def __init__(self, index):
+        self.index = index
+
+    def Index(self, name):
+        return self.index
+
+
+def test_pinecone_groups_multitenant_upsert_and_query(monkeypatch):
+    from vectordb_bench.backend.clients.pinecone import pinecone as pinecone_module
+    from vectordb_bench.backend.clients.pinecone.pinecone import Pinecone
+
+    fake_index = FakePineconeIndex()
+    monkeypatch.setattr(
+        pinecone_module.pinecone,
+        "Pinecone",
+        lambda api_key: FakePineconeClient(fake_index),
+    )
+
+    db = Pinecone(
+        dim=2,
+        db_config={
+            "api_key": "k",
+            "index_name": "idx",
+            "multitenant_namespace_prefix": "mt_",
+        },
+        db_case_config=None,
+        drop_old=False,
+    )
+    db.set_multitenant_context(["tenant_0000", "tenant_0001"])
+
+    with db.init():
+        count, err = db.insert_embeddings(
+            embeddings=[[1.0, 0.0], [0.0, 1.0], [0.5, 0.5]],
+            metadata=[0, 1, 2],
+            tenant_labels_data=["tenant_0000", "tenant_0001", "tenant_0000"],
+        )
+        result = db.search_embedding([1.0, 0.0], k=1, tenant="tenant_0001")
+
+    assert count == 3
+    assert err is None
+    assert result == [11]
+    assert fake_index.upserts[0][1] == "mt_tenant_0000"
+    assert fake_index.upserts[1][1] == "mt_tenant_0001"
+    assert fake_index.queries[-1]["namespace"] == "mt_tenant_0001"
+
+
+def test_pinecone_multitenant_upsert_preserves_scalar_payload_labels(monkeypatch):
+    from vectordb_bench.backend.clients.pinecone import pinecone as pinecone_module
+    from vectordb_bench.backend.clients.pinecone.pinecone import Pinecone
+
+    fake_index = FakePineconeIndex()
+    monkeypatch.setattr(
+        pinecone_module.pinecone,
+        "Pinecone",
+        lambda api_key: FakePineconeClient(fake_index),
+    )
+
+    db = Pinecone(
+        dim=2,
+        db_config={
+            "api_key": "k",
+            "index_name": "idx",
+            "multitenant_namespace_prefix": "mt_",
+        },
+        db_case_config=None,
+        drop_old=False,
+        with_scalar_labels=True,
+    )
+
+    with db.init():
+        count, err = db.insert_embeddings(
+            embeddings=[[1.0, 0.0], [0.0, 1.0]],
+            metadata=[0, 1],
+            labels_data=["label_a", "label_b"],
+            tenant_labels_data=["tenant_0000", "tenant_0001"],
+        )
+
+    assert count == 2
+    assert err is None
+    assert fake_index.upserts[0][0][0][2]["label"] == "label_a"
+    assert fake_index.upserts[1][0][0][2]["label"] == "label_b"
+
+
+def test_pinecone_multitenant_partial_insert_failure_is_explicit(monkeypatch):
+    from vectordb_bench.backend.clients.pinecone import pinecone as pinecone_module
+    from vectordb_bench.backend.clients.pinecone.pinecone import Pinecone
+
+    fake_index = FailingPineconeIndex()
+    monkeypatch.setattr(
+        pinecone_module.pinecone,
+        "Pinecone",
+        lambda api_key: FakePineconeClient(fake_index),
+    )
+
+    db = Pinecone(
+        dim=2,
+        db_config={
+            "api_key": "k",
+            "index_name": "idx",
+            "multitenant_namespace_prefix": "mt_",
+        },
+        db_case_config=None,
+        drop_old=False,
+    )
+
+    with db.init():
+        count, err = db.insert_embeddings(
+            embeddings=[[1.0, 0.0], [0.0, 1.0], [0.5, 0.5]],
+            metadata=[0, 1, 2],
+            tenant_labels_data=["tenant_0000", "tenant_0001", "tenant_0000"],
+        )
+
+    assert count == 2
+    assert getattr(err, "non_retryable", False) is True
+    assert getattr(err, "inserted_count") == 2
+    assert getattr(err, "successful_tenants") == {"tenant_0000": 2}
+    assert getattr(err, "failed_tenant") == "tenant_0001"
+    assert getattr(err, "failed_tenant_count") == 1
+    assert db._multitenant_insert_counts == {"tenant_0000": 2}
+
+
+def test_pinecone_multitenant_insert_counts_are_thread_safe(monkeypatch):
+    from vectordb_bench.backend.clients.pinecone import pinecone as pinecone_module
+    from vectordb_bench.backend.clients.pinecone.pinecone import Pinecone
+
+    class RacingCounts(dict):
+        def __init__(self, lock_getter):
+            super().__init__()
+            self.barrier = threading.Barrier(2)
+            self.lock_getter = lock_getter
+
+        def get(self, key, default=None):
+            value = super().get(key, default)
+            lock = self.lock_getter()
+            if key == "tenant_0000" and (lock is None or not lock.locked()):
+                self.barrier.wait(timeout=5)
+            return value
+
+    fake_index = FakePineconeIndex()
+    monkeypatch.setattr(
+        pinecone_module.pinecone,
+        "Pinecone",
+        lambda api_key: FakePineconeClient(fake_index),
+    )
+
+    db = Pinecone(
+        dim=2,
+        db_config={
+            "api_key": "k",
+            "index_name": "idx",
+            "multitenant_namespace_prefix": "mt_",
+        },
+        db_case_config=None,
+        drop_old=False,
+    )
+    db._multitenant_insert_counts = RacingCounts(lambda: getattr(db, "_multitenant_insert_counts_lock", None))
+    results = []
+
+    def insert_one(row_id):
+        results.append(
+            db.insert_embeddings(
+                embeddings=[[float(row_id), 0.0]],
+                metadata=[row_id],
+                tenant_labels_data=["tenant_0000"],
+            )
+        )
+
+    with db.init():
+        threads = [threading.Thread(target=insert_one, args=(row_id,)) for row_id in (1, 2)]
+        for thread in threads:
+            thread.start()
+        for thread in threads:
+            thread.join()
+
+    assert len(fake_index.upserts) == 2
+    assert sorted(results) == [(1, None), (1, None)]
+    assert dict(db._multitenant_insert_counts) == {"tenant_0000": 2}
diff --git a/tests/test_turbopuffer_cli.py b/tests/test_turbopuffer_cli.py
new file mode 100644
index 000000000..46db473ca
--- /dev/null
+++ b/tests/test_turbopuffer_cli.py
@@ -0,0 +1,338 @@
+from types import SimpleNamespace
+
+from click.testing import CliRunner
+from pytest import MonkeyPatch
+
+from vectordb_bench.backend.clients.api import MetricType
+from vectordb_bench.backend.clients.turbopuffer import cli as turbopuffer_cli
+from vectordb_bench.backend.clients.turbopuffer import turbopuffer as turbopuffer_client
+from vectordb_bench.backend.clients.turbopuffer.turbopuffer import TurboPuffer
+
+
+def test_turbopuffer_cli_accepts_multitenant_namespace_prefix_and_metric_type(
+    monkeypatch: MonkeyPatch,
+) -> None:
+    captured = {}
+
+    def fake_run(**kwargs):
+        captured.update(kwargs)
+
+    monkeypatch.setattr(turbopuffer_cli, "run", fake_run)
+
+    result = CliRunner().invoke(
+        turbopuffer_cli.TurboPuffer,
+        [
+            "--skip-drop-old",
+            "--skip-load",
+            "--skip-search-serial",
+            "--search-concurrent",
+            "--case-type",
+            "CloudMultiTenantSearchCase",
+            "--api-key",
+            "secret",
+            "--region",
+            "aws-us-west-2",
+            "--namespace",
+            "cohere10m_multitenant",
+            "--multitenant-namespace-prefix",
+            "cohere10m_",
+            "--scalar-payload-label-field",
+            "scalar_label",
+            "--metric-type",
+            "COSINE",
+            "--dry-run",
+        ],
+    )
+
+    assert result.exit_code == 0
+    assert captured["db_config"].multitenant_namespace_prefix == "cohere10m_"
+    assert captured["db_config"].scalar_payload_label_field == "scalar_label"
+    assert captured["db_case_config"].metric_type == MetricType.COSINE
+    assert captured["db_case_config"].multitenant_warmup_policy == "none"
+
+
+def test_turbopuffer_cli_skips_pin_namespace_during_dry_run(monkeypatch: MonkeyPatch) -> None:
+    calls = []
+    captured = {}
+
+    def fake_metadata_request(api_key, region, namespace, method, payload=None, api_base_url=None):
+        calls.append((method, payload, api_key, region, namespace, api_base_url))
+        return {}
+
+    def fake_wait_for_pinning(api_key, region, namespace, replicas, api_base_url=None, timeout=None):
+        calls.append(("WAIT", replicas, api_key, region, namespace, api_base_url, timeout))
+        return {"pinning": {"replicas": replicas, "status": {"ready_replicas": replicas}}}
+
+    def fake_run(**kwargs):
+        captured.update(kwargs)
+
+    monkeypatch.setattr(turbopuffer_client, "namespace_metadata_request", fake_metadata_request)
+    monkeypatch.setattr(turbopuffer_client, "wait_for_namespace_pinning", fake_wait_for_pinning)
+    monkeypatch.setattr(turbopuffer_cli, "run", fake_run)
+
+    result = CliRunner().invoke(
+        turbopuffer_cli.TurboPuffer,
+        [
+            "--skip-drop-old",
+            "--skip-load",
+            "--skip-search-serial",
+            "--search-concurrent",
+            "--case-type",
+            "CloudPayloadSearchCase",
+            "--api-key",
+            "secret",
+            "--region",
+            "aws-us-west-2",
+            "--namespace",
+            "laion100m",
+            "--pin-namespace",
+            "--pin-replicas",
+            "2",
+            "--pin-timeout",
+            "7200",
+            "--metric-type",
+            "COSINE",
+            "--disable-backpressure",
+            "--dry-run",
+        ],
+    )
+
+    assert result.exit_code == 0, result.output
+    assert calls == []
+    assert captured["db_config"].pin_namespace is False
+    assert captured["db_config"].pin_namespace_requested is True
+    assert captured["db_config"].pin_replicas == 2
+    assert captured["db_config"].pin_timeout == 7200
+    assert captured["db_config"].pin_target_namespace_count == 1
+    assert captured["db_case_config"].metric_type == MetricType.COSINE
+    assert captured["db_case_config"].disable_backpressure is True
+
+
+def test_turbopuffer_cli_accepts_multitenant_warmup_policy(
+    monkeypatch: MonkeyPatch,
+) -> None:
+    captured = {}
+
+    def fake_run(**kwargs):
+        captured.update(kwargs)
+
+    monkeypatch.setattr(turbopuffer_cli, "run", fake_run)
+
+    result = CliRunner().invoke(
+        turbopuffer_cli.TurboPuffer,
+        [
+            "--case-type",
+            "CloudMultiTenantSearchCase",
+            "--api-key",
+            "secret",
+            "--region",
+            "aws-us-west-2",
+            "--multitenant-warmup-policy",
+            "all",
+            "--dry-run",
+        ],
+    )
+
+    assert result.exit_code == 0, result.output
+    assert captured["db_case_config"].multitenant_warmup_policy == "all"
+
+
+def test_turbopuffer_multitenant_optimize_skips_base_namespace_by_default(
+    monkeypatch: MonkeyPatch,
+) -> None:
+    warmed = []
+
+    class FakeNamespace:
+        def __init__(self, name: str):
+            self.name = name
+
+        def hint_cache_warm(self):
+            warmed.append(self.name)
+
+    class FakeClient:
+        def namespace(self, name: str):
+            return FakeNamespace(name)
+
+    monkeypatch.setattr(turbopuffer_client.time, "sleep", lambda _seconds: None)
+    db = object.__new__(TurboPuffer)
+    db.client = FakeClient()
+    db.ns = FakeNamespace("base")
+    db.namespace = "base"
+    db.multitenant_namespace_prefix = "mt_"
+    db.multitenant_tenant_labels = ["tenant_0000", "tenant_0001"]
+    db._ns_cache = {}
+    db.db_case_config = SimpleNamespace(time_wait_warmup=1, multitenant_warmup_policy="none")
+
+    db.optimize()
+
+    assert warmed == []
+
+
+def test_turbopuffer_multitenant_optimize_can_warm_all_tenant_namespaces(
+    monkeypatch: MonkeyPatch,
+) -> None:
+    warmed = []
+
+    class FakeNamespace:
+        def __init__(self, name: str):
+            self.name = name
+
+        def hint_cache_warm(self):
+            warmed.append(self.name)
+
+    class FakeClient:
+        def namespace(self, name: str):
+            return FakeNamespace(name)
+
+    monkeypatch.setattr(turbopuffer_client.time, "sleep", lambda _seconds: None)
+    db = object.__new__(TurboPuffer)
+    db.client = FakeClient()
+    db.ns = FakeNamespace("base")
+    db.namespace = "base"
+    db.multitenant_namespace_prefix = "mt_"
+    db.multitenant_tenant_labels = ["tenant_0000", "tenant_0001"]
+    db._ns_cache = {}
+    db.db_case_config = SimpleNamespace(time_wait_warmup=1, multitenant_warmup_policy="all")
+
+    db.optimize()
+
+    assert warmed == ["mt_tenant_0000", "mt_tenant_0001"]
+
+
+def test_turbopuffer_cli_skips_multitenant_pin_namespaces_during_dry_run(
+    monkeypatch: MonkeyPatch,
+) -> None:
+    calls = []
+    captured = {}
+
+    def fake_metadata_request(api_key, region, namespace, method, payload=None, api_base_url=None):
+        calls.append((method, namespace, payload))
+        return {}
+
+    def fake_wait_for_pinning(api_key, region, namespace, replicas, api_base_url=None, timeout=None):
+        calls.append(("WAIT", namespace, replicas))
+        return {"pinning": {"replicas": replicas, "status": {"ready_replicas": replicas}}}
+
+    def fake_run(**kwargs):
+        captured.update(kwargs)
+
+    monkeypatch.setattr(turbopuffer_client, "namespace_metadata_request", fake_metadata_request)
+    monkeypatch.setattr(turbopuffer_client, "wait_for_namespace_pinning", fake_wait_for_pinning)
+    monkeypatch.setattr(turbopuffer_cli, "run", fake_run)
+
+    result = CliRunner().invoke(
+        turbopuffer_cli.TurboPuffer,
+        [
+            "--skip-drop-old",
+            "--skip-load",
+            "--skip-search-serial",
+            "--search-concurrent",
+            "--case-type",
+            "CloudMultiTenantSearchCase",
+            "--tenant-count",
+            "2",
+            "--tenant-prefix",
+            "tenant_",
+            "--tenant-id-width",
+            "4",
+            "--api-key",
+            "secret",
+            "--region",
+            "aws-us-west-2",
+            "--namespace",
+            "unused_single_namespace",
+            "--multitenant-namespace-prefix",
+            "cohere10m_",
+            "--pin-namespace",
+            "--pin-replicas",
+            "1",
+            "--dry-run",
+        ],
+    )
+
+    assert result.exit_code == 0, result.output
+    assert calls == []
+    assert captured["db_config"].pin_namespace is False
+    assert captured["db_config"].pin_namespace_requested is True
+    assert captured["db_config"].pin_target_namespace_count == 2
+    assert captured["db_config"].multitenant_namespace_prefix == "cohere10m_"
+
+
+def test_turbopuffer_unpin_namespace_uses_pin_timeout(monkeypatch: MonkeyPatch) -> None:
+    calls = []
+
+    def fake_metadata_request(api_key, region, namespace, method, payload=None, api_base_url=None):
+        calls.append((method, payload, api_key, region, namespace, api_base_url))
+        return {}
+
+    def fake_wait_for_pinning(api_key, region, namespace, replicas, api_base_url=None, timeout=None):
+        calls.append(("WAIT", replicas, api_key, region, namespace, api_base_url, timeout))
+        return {"pinning": None}
+
+    monkeypatch.setattr(turbopuffer_client, "namespace_metadata_request", fake_metadata_request)
+    monkeypatch.setattr(turbopuffer_client, "wait_for_namespace_pinning", fake_wait_for_pinning)
+
+    result = CliRunner().invoke(
+        turbopuffer_cli.TurboPufferUnpin,
+        [
+            "--api-key",
+            "secret",
+            "--region",
+            "aws-us-west-2",
+            "--namespace",
+            "laion100m",
+            "--pin-timeout",
+            "7200",
+        ],
+    )
+
+    assert result.exit_code == 0, result.output
+    assert calls == [
+        ("PATCH", {"pinning": None}, "secret", "aws-us-west-2", "laion100m", None),
+        ("WAIT", None, "secret", "aws-us-west-2", "laion100m", None, 7200),
+    ]
+
+
+def test_turbopuffer_fts_insert_declares_filter_id_filterable() -> None:
+    writes = []
+
+    class FakeNamespace:
+        def write(self, **kwargs):
+            writes.append(kwargs)
+
+    db = object.__new__(TurboPuffer)
+    db.ns = FakeNamespace()
+    db._is_fts = True
+    db._text_field = "text"
+    db._scalar_id_field = "id"
+    db._filter_id_field = "filter_id"
+    db.db_case_config = SimpleNamespace(disable_backpressure=False)
+
+    count, error = db.insert_documents(
+        texts=["alpha", "beta"],
+        doc_ids=["doc-1", "doc-2"],
+        filter_ids=[10, 20],
+    )
+
+    assert error is None
+    assert count == 2
+    assert writes == [
+        {
+            "upsert_columns": {
+                "id": ["doc-1", "doc-2"],
+                "text": ["alpha", "beta"],
+                "filter_id": [10, 20],
+            },
+            "schema": {
+                "text": {
+                    "type": "string",
+                    "full_text_search": True,
+                },
+                "filter_id": {
+                    "type": "int",
+                    "filterable": True,
+                },
+            },
+            "disable_backpressure": False,
+        }
+    ]
diff --git a/tests/test_volc_mysql_client.py b/tests/test_volc_mysql_client.py
new file mode 100644
index 000000000..ab602be9d
--- /dev/null
+++ b/tests/test_volc_mysql_client.py
@@ -0,0 +1,236 @@
+from vectordb_bench.backend.clients.volc_mysql.volc_mysql import VolcMySQL
+
+
+def test_volc_mysql_is_not_thread_safe():
+    # mysql.connector is not thread-safe; ConcurrentInsertRunner relies on
+    # this class attribute to clamp max_workers=1.
+    assert VolcMySQL.thread_safe is False
+
+
+def test_volc_mysql_ready_to_load_does_not_return_falsy_none():
+    # The base VectorDB has no ready_to_load() method to override here;
+    # the previous stub returned None, which any `if not db.ready_to_load()`
+    # caller would misread as "not ready".
+    assert not hasattr(VolcMySQL, "ready_to_load") or VolcMySQL.ready_to_load.__qualname__.startswith("VectorDB")
+
+
+from vectordb_bench.backend.filter import FilterOp
+
+
+def test_volc_mysql_supports_numge_filter():
+    # Matches the WHERE id >= %s prepared SQL the client already builds in init().
+    assert FilterOp.NumGE in VolcMySQL.supported_filter_types
+    assert FilterOp.NonFilter in VolcMySQL.supported_filter_types
+
+
+from pydantic import SecretStr
+
+from vectordb_bench.backend.clients.volc_mysql.config import VolcMySQLConfig
+
+
+def test_volc_mysql_config_to_dict_has_only_driver_kwargs():
+    cfg = VolcMySQLConfig(password=SecretStr("pw"))
+    d = cfg.to_dict()
+    # mysql.connector.connect accepts these; the previously-declared
+    # read_timeout/write_timeout are not valid kwargs for that driver.
+    assert set(d.keys()) == {"host", "port", "user", "password"}
+    assert d["user"] == "root"
+    assert d["password"] == "pw"
+
+
+from vectordb_bench.backend.clients.volc_mysql.config import VolcMySQLHNSWConfig
+
+
+def test_volc_mysql_hnsw_config_optional_fields_default_to_none():
+    cfg = VolcMySQLHNSWConfig()
+    assert cfg.M is None
+    assert cfg.ef_search is None
+    assert cfg.ef_construction is None
+    assert cfg.quant_algorithm is None
+    assert cfg.quant_type is None
+
+
+from vectordb_bench.backend.clients.volc_mysql.volc_mysql import _build_index_attrs_json
+
+
+def test_build_index_attrs_json_drops_none_values():
+    j = _build_index_attrs_json({
+        "metric_type": "l2",
+        "M": 16,
+        "ef_construction": 128,
+        "quant_algorithm": None,
+        "quant_type": None,
+    })
+    # Compact JSON, deterministic key order (insertion order from helper).
+    assert j == '{"algorithm":"hnsw","distance":"l2","m":16,"ef_construction":128}'
+
+
+def test_build_index_attrs_json_includes_quantization_when_set():
+    j = _build_index_attrs_json({
+        "metric_type": "cosine",
+        "M": 32,
+        "ef_construction": 200,
+        "quant_algorithm": "SQ",
+        "quant_type": "8_bit",
+    })
+    assert j == (
+        '{"algorithm":"hnsw","distance":"cosine","m":32,"ef_construction":200,'
+        '"quant_algorithm":"SQ","quant_type":"8_bit"}'
+    )
+
+
+from unittest.mock import MagicMock
+
+from pydantic import SecretStr as _SecretStr
+
+from vectordb_bench.backend.filter import IntFilter, NonFilter
+
+
+def _make_client_with_mock_cursor():
+    """Build a VolcMySQL without touching a DB (drop_old=False does not connect),
+    then wire up the prebuilt SQL templates and a mock cursor so search_embedding
+    can be exercised offline.
+    """
+    cfg = VolcMySQLConfig(password=_SecretStr("pw")).to_dict()
+    client = VolcMySQL(dim=4, db_config=cfg, db_case_config=VolcMySQLHNSWConfig(), drop_old=False)
+    client.select_sql = "SELECT id FROM t ORDER BY d LIMIT %s"
+    client.select_sql_with_filter = "SELECT id FROM t WHERE id >= %s ORDER BY d LIMIT %s"
+    client._binary_vec = False
+    cursor = MagicMock()
+    cursor.fetchall.return_value = [(1,), (2,)]
+    client.conn = MagicMock()
+    client.cursor = cursor
+    return client, cursor
+
+
+def test_search_embedding_uses_unfiltered_sql_by_default():
+    client, cursor = _make_client_with_mock_cursor()
+    client.search_embedding([0.1, 0.2, 0.3, 0.4], k=10)
+    sql, params = cursor.execute.call_args[0]
+    assert sql == client.select_sql
+    assert params == ("[0.1, 0.2, 0.3, 0.4]", 10)
+
+
+def test_search_embedding_applies_numge_filter_after_prepare_filter():
+    # Regression guard: runners apply filters via prepare_filter(), never by
+    # passing filters to search_embedding. Without prepare_filter wiring the
+    # client silently ran an unfiltered search and reported wrong recall.
+    client, cursor = _make_client_with_mock_cursor()
+    client.prepare_filter(IntFilter(int_value=500, filter_rate=0.99))
+    client.search_embedding([0.1, 0.2, 0.3, 0.4], k=10)
+    sql, params = cursor.execute.call_args[0]
+    assert sql == client.select_sql_with_filter
+    assert params == (500, "[0.1, 0.2, 0.3, 0.4]", 10)
+
+
+def test_prepare_filter_nonfilter_resets_to_unfiltered():
+    client, cursor = _make_client_with_mock_cursor()
+    client.prepare_filter(IntFilter(int_value=500, filter_rate=0.99))
+    client.prepare_filter(NonFilter())
+    client.search_embedding([0.1, 0.2, 0.3, 0.4], k=10)
+    sql, _ = cursor.execute.call_args[0]
+    assert sql == client.select_sql
+
+
+import tempfile
+
+import pytest
+
+from vectordb_bench.backend.clients.volc_mysql import volc_mysql as volc_mysql_module
+
+
+def _make_uninitialized_client():
+    cfg = VolcMySQLConfig(password=_SecretStr("pw")).to_dict()
+    return VolcMySQL(dim=4, db_config=cfg, db_case_config=VolcMySQLHNSWConfig(), drop_old=False)
+
+
+def test_init_closes_connection_when_create_database_fails(monkeypatch):
+    """If admin_cursor.execute(CREATE DATABASE ...) raises inside init(),
+    the open connection and both cursors must still be closed and the
+    instance attributes reset to None. Prior to the fix this leaked because
+    setup ran outside the try/finally.
+    """
+    client = _make_uninitialized_client()
+    conn = MagicMock(name="conn")
+    cursor = MagicMock(name="cursor")
+    admin_cursor = MagicMock(name="admin_cursor")
+    admin_cursor.execute.side_effect = RuntimeError("CREATE DATABASE failed")
+    monkeypatch.setattr(client, "_create_connection", lambda: (conn, cursor, admin_cursor))
+
+    with pytest.raises(RuntimeError, match="CREATE DATABASE failed"):
+        with client.init():
+            pass
+
+    cursor.close.assert_called_once()
+    admin_cursor.close.assert_called_once()
+    conn.close.assert_called_once()
+    assert client.conn is None
+    assert client.cursor is None
+    assert client.admin_cursor is None
+
+
+def test_select_sql_does_not_hardcode_force_index(monkeypatch):
+    """Streaming/read_write cases search before optimize() creates idx_v,
+    so FORCE INDEX(idx_v) in the prebuilt SQL would fail every pre-optimize
+    search with "Key 'idx_v' doesn't exist in table".
+    """
+    from vectordb_bench.backend.clients.api import MetricType
+
+    cfg = VolcMySQLConfig(password=_SecretStr("pw")).to_dict()
+    case_cfg = VolcMySQLHNSWConfig(metric_type=MetricType.COSINE)
+    client = VolcMySQL(dim=4, db_config=cfg, db_case_config=case_cfg, drop_old=False)
+    conn = MagicMock()
+    cursor = MagicMock()
+    admin_cursor = MagicMock()
+    monkeypatch.setattr(client, "_create_connection", lambda: (conn, cursor, admin_cursor))
+    monkeypatch.setattr(client, "_probe_binary_support", lambda: False)
+
+    with client.init():
+        assert "FORCE INDEX" not in client.select_sql
+        assert "FORCE INDEX" not in client.select_sql_with_filter
+        assert "idx_v" not in client.select_sql
+        assert "idx_v" not in client.select_sql_with_filter
+
+
+def test_insert_embeddings_returns_actual_rowcount_on_partial_load(tmp_path, monkeypatch):
+    """LOAD DATA is already committed when the rowcount mismatch is detected;
+    reporting (0, err) corrupts the serial runner's already_insert_count and
+    sends its retry into a PK-collision wall. Return (actual, err) so the
+    runner slices past the already-loaded prefix on retry.
+    """
+    client = _make_uninitialized_client()
+    client._batch_counter = 0
+    client._binary_vec = False
+    cursor = MagicMock()
+    cursor.rowcount = 7
+    client.cursor = cursor
+    client.conn = MagicMock()
+    monkeypatch.setattr(volc_mysql_module.tempfile, "gettempdir", lambda: str(tmp_path))
+
+    embeddings = [[float(i)] * 4 for i in range(10)]
+    metadata = list(range(10))
+    actual, err = client.insert_embeddings(embeddings, metadata)
+
+    assert actual == 7
+    assert isinstance(err, RuntimeError)
+    assert "wrote 7 rows, expected 10" in str(err)
+
+
+def test_insert_embeddings_returns_n_on_full_load(tmp_path, monkeypatch):
+    """Sanity: when rowcount matches, return (n, None). Guards against the
+    partial-load fix regressing the happy path.
+    """
+    client = _make_uninitialized_client()
+    client._batch_counter = 0
+    client._binary_vec = False
+    cursor = MagicMock()
+    cursor.rowcount = 10
+    client.cursor = cursor
+    client.conn = MagicMock()
+    monkeypatch.setattr(volc_mysql_module.tempfile, "gettempdir", lambda: str(tmp_path))
+
+    embeddings = [[float(i)] * 4 for i in range(10)]
+    metadata = list(range(10))
+    actual, err = client.insert_embeddings(embeddings, metadata)
+    assert actual == 10
+    assert err is None
diff --git a/tests/test_volc_mysql_encoder.py b/tests/test_volc_mysql_encoder.py
new file mode 100644
index 000000000..fe656bf29
--- /dev/null
+++ b/tests/test_volc_mysql_encoder.py
@@ -0,0 +1,74 @@
+import struct
+
+from vectordb_bench.backend.clients.volc_mysql.volc_mysql import _encode_batch_to_tsv
+
+
+def test_encode_batch_to_tsv_sorts_by_id_and_hex_encodes(tmp_path):
+    tsv = tmp_path / "batch.tsv"
+    _encode_batch_to_tsv(
+        metadata=[42, 7, 99],
+        embeddings=[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],
+        dim=2,
+        tsv_path=str(tsv),
+    )
+
+    lines = tsv.read_text().splitlines()
+    assert len(lines) == 3
+
+    # Sorted by id ascending: 7, 42, 99
+    assert lines[0].startswith("7\t")
+    assert lines[1].startswith("42\t")
+    assert lines[2].startswith("99\t")
+
+    # The (3.0, 4.0) embedding was paired with id=7
+    _, hex_str = lines[0].split("\t")
+    floats = struct.unpack("<2f", bytes.fromhex(hex_str))
+    assert floats == (3.0, 4.0)
+
+
+def test_encode_batch_to_tsv_writes_dim_floats_per_row(tmp_path):
+    tsv = tmp_path / "batch.tsv"
+    _encode_batch_to_tsv(
+        metadata=[1],
+        embeddings=[[0.5] * 1536],
+        dim=1536,
+        tsv_path=str(tsv),
+    )
+
+    line = tsv.read_text().rstrip("\n")
+    id_str, hex_str = line.split("\t")
+    assert id_str == "1"
+    # 1536 floats * 4 bytes * 2 hex chars = 12288 chars
+    assert len(hex_str) == 12288
+    assert bytes.fromhex(hex_str) == struct.pack("<1536f", *([0.5] * 1536))
+
+
+def test_encode_batch_to_tsv_empty_batch(tmp_path):
+    tsv = tmp_path / "batch.tsv"
+    _encode_batch_to_tsv(metadata=[], embeddings=[], dim=4, tsv_path=str(tsv))
+    assert tsv.read_text() == ""
+
+
+def test_encode_batch_to_tsv_text_mode_sorts_and_formats(tmp_path):
+    # binary=False (to_vector fallback): rows sorted by id, vector written as a
+    # delimiter-safe "[f1,f2,...]" literal with no tab/newline inside the field.
+    tsv = tmp_path / "batch.tsv"
+    _encode_batch_to_tsv(
+        metadata=[42, 7, 99],
+        embeddings=[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],
+        dim=2,
+        tsv_path=str(tsv),
+        binary=False,
+    )
+
+    lines = tsv.read_text().splitlines()
+    assert len(lines) == 3
+    # Sorted by id ascending: 7, 42, 99; id=7 was paired with (3.0, 4.0)
+    assert lines[0] == "7\t[3.0,4.0]"
+    assert lines[1] == "42\t[1.0,2.0]"
+    assert lines[2] == "99\t[5.0,6.0]"
+    # field is delimiter-safe: no tab/newline inside the bracketed literal
+    for line in lines:
+        _id, vec = line.split("\t")
+        assert vec.startswith("[") and vec.endswith("]")
+        assert "\t" not in vec
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deleted file mode 100644
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diff --git a/vectordb_bench/__init__.py b/vectordb_bench/__init__.py
index 07f77bb02..1491630e0 100644
--- a/vectordb_bench/__init__.py
+++ b/vectordb_bench/__init__.py
@@ -17,9 +17,10 @@ class config:
     LOG_FILE = env.str("LOG_FILE", "logs/vectordb_bench.log")
 
     DEFAULT_DATASET_URL = env.str("DEFAULT_DATASET_URL", AWS_S3_URL)
-    DATASET_SOURCE = env.str("DATASET_SOURCE", "S3")  # Options "S3" or "AliyunOSS"
+    DATASET_SOURCE = env.str("DATASET_SOURCE", "S3")  # Options "S3", "AliyunOSS", or "IR_DATASETS"
     DATASET_LOCAL_DIR = env.path("DATASET_LOCAL_DIR", "/tmp/vectordb_bench/dataset")
     NUM_PER_BATCH = env.int("NUM_PER_BATCH", 100)
+    LOAD_CONCURRENCY = env.int("LOAD_CONCURRENCY", 0)  # 0 = cpu_count
     TIME_PER_BATCH = 1  # 1s. for streaming insertion.
     MAX_INSERT_RETRY = 5
     MAX_SEARCH_RETRY = 5
@@ -34,6 +35,9 @@ class config:
     CONCURRENCY_DURATION = 30
 
     CONCURRENCY_TIMEOUT = 3600
+    SERIAL_COOLDOWN = 0
+    CLOUD_INSERT_READINESS_TIMEOUT = env.float("CLOUD_INSERT_READINESS_TIMEOUT", None)
+    CLOUD_INSERT_READINESS_POLL_INTERVAL = env.float("CLOUD_INSERT_READINESS_POLL_INTERVAL", 5.0)
 
     RESULTS_LOCAL_DIR = env.path(
         "RESULTS_LOCAL_DIR",
diff --git a/vectordb_bench/backend/assembler.py b/vectordb_bench/backend/assembler.py
index 3268cde35..fe7f0ddc6 100644
--- a/vectordb_bench/backend/assembler.py
+++ b/vectordb_bench/backend/assembler.py
@@ -6,6 +6,7 @@
 from vectordb_bench.models import TaskConfig
 
 from .cases import CaseLabel
+from .dataset import FtsDatasetManager
 from .task_runner import CaseRunner, RunningStatus, TaskRunner
 
 log = logging.getLogger(__name__)
@@ -24,7 +25,18 @@ def assemble(cls, run_id: str, task: TaskConfig, source: DatasetSource) -> CaseR
         c_cls = task.case_config.case_id.case_cls
 
         c = c_cls(task.case_config.custom_case)
-        if type(task.db_case_config) is not EmptyDBCaseConfig:
+        if c.label == CaseLabel.FullTextSearchPerformance and not task.db.init_cls.supports_full_text_search():
+            msg = f"{task.db.value} does not support full-text search"
+            raise ValueError(msg)
+
+        # Auto-select data source based on dataset type
+        actual_source = DatasetSource.IR_DATASETS if isinstance(c.dataset, FtsDatasetManager) else source
+
+        if (
+            type(task.db_case_config) is not EmptyDBCaseConfig
+            and not isinstance(c.dataset, FtsDatasetManager)
+            and hasattr(c.dataset.data, "metric_type")
+        ):
             task.db_case_config.metric_type = c.dataset.data.metric_type
 
         return CaseRunner(
@@ -32,7 +44,7 @@ def assemble(cls, run_id: str, task: TaskConfig, source: DatasetSource) -> CaseR
             config=task,
             ca=c,
             status=RunningStatus.PENDING,
-            dataset_source=source,
+            dataset_source=actual_source,
         )
 
     @classmethod
@@ -48,10 +60,15 @@ def assemble_all(
         load_runners = [r for r in runners if r.ca.label == CaseLabel.Load]
         perf_runners = [r for r in runners if r.ca.label == CaseLabel.Performance]
         streaming_runners = [r for r in runners if r.ca.label == CaseLabel.Streaming]
+        cloud_insert_runners = [r for r in runners if r.ca.label == CaseLabel.CloudInsert]
+        cloud_cold_latency_runners = [r for r in runners if r.ca.label == CaseLabel.CloudColdLatency]
+        fts_runners = [r for r in runners if r.ca.label == CaseLabel.FullTextSearchPerformance]
+
+        search_filter_runners = [*perf_runners, *cloud_cold_latency_runners]
 
         # group by db
         db2runner: dict[DB, list[CaseRunner]] = {}
-        for r in perf_runners:
+        for r in search_filter_runners:
             db = r.config.db
             if db not in db2runner:
                 db2runner[db] = []
@@ -71,8 +88,10 @@ def assemble_all(
         all_runners = []
         all_runners.extend(load_runners)
         all_runners.extend(streaming_runners)
+        all_runners.extend(cloud_insert_runners)
         for v in db2runner.values():
             all_runners.extend(v)
+        all_runners.extend(fts_runners)
 
         return TaskRunner(
             run_id=run_id,
diff --git a/vectordb_bench/backend/cases.py b/vectordb_bench/backend/cases.py
index edfcdea19..24eaf768e 100644
--- a/vectordb_bench/backend/cases.py
+++ b/vectordb_bench/backend/cases.py
@@ -5,13 +5,32 @@
 from vectordb_bench import config
 from vectordb_bench.backend.clients.api import MetricType
 from vectordb_bench.backend.filter import Filter, FilterOp, IntFilter, LabelFilter, NewIntFilter, NonFilter, non_filter
+from vectordb_bench.backend.payload import PayloadProfile
 from vectordb_bench.base import BaseModel
 from vectordb_bench.frontend.components.custom.getCustomConfig import CustomDatasetConfig
 
-from .dataset import CustomDataset, Dataset, DatasetManager, DatasetWithSizeType
+from .dataset import (
+    CustomDataset,
+    Dataset,
+    DatasetManager,
+    DatasetWithSizeType,
+    FtsDatasetManager,
+    FtsDatasetWithSizeType,
+)
 
 log = logging.getLogger(__name__)
 
+FTS_FILTER_ID_FIELD = "filter_id"
+FTS_FILTER_RATES = (0.5, 0.75, 0.9, 0.95, 0.99)
+
+
+def _format_filter_rate(filter_rate: float) -> str:
+    return f"{filter_rate * 100:g}%"
+
+
+def _is_supported_fts_filter_rate(filter_rate: float) -> bool:
+    return any(abs(filter_rate - supported_rate) < 1e-9 for supported_rate in FTS_FILTER_RATES)
+
 
 class CaseType(Enum):
     """
@@ -56,6 +75,11 @@ class CaseType(Enum):
     LabelFilterPerformanceCase = 300
 
     NewIntFilterPerformanceCase = 400
+    CloudPayloadSearchCase = 500
+    FTSBm25Performance = 503
+    CloudInsertCase = 600
+    CloudColdLatencyCase = 700
+    CloudMultiTenantSearchCase = 800
 
     def case_cls(self, custom_configs: dict | None = None) -> type["Case"]:
         if custom_configs is None:
@@ -79,6 +103,9 @@ class CaseLabel(Enum):
     Load = auto()
     Performance = auto()
     Streaming = auto()
+    CloudInsert = auto()
+    CloudColdLatency = auto()
+    FullTextSearchPerformance = auto()
 
 
 class Case(BaseModel):
@@ -102,14 +129,24 @@ class Case(BaseModel):
     optimize_timeout: float | int | None = None
 
     filter_rate: float | None = None
+    payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY
 
     @property
     def filters(self) -> Filter:
         return non_filter
 
+    def estimated_payload_bytes_per_query(self, k: int | None) -> int:
+        if k is None:
+            k = config.K_DEFAULT
+        return self.payload_profile.estimated_bytes_per_query(k=k, dim=self.dataset.data.dim)
+
+    @property
+    def is_multitenant(self) -> bool:
+        return False
+
     @property
     def with_scalar_labels(self) -> bool:
-        return self.filters.type == FilterOp.StrEqual
+        return self.filters.type == FilterOp.StrEqual or self.payload_profile == PayloadProfile.SCALAR_LABEL
 
     def check_scalar_labels(self) -> None:
         if self.with_scalar_labels and not self.dataset.data.with_scalar_labels:
@@ -594,6 +631,259 @@ def filters(self) -> Filter:
         return NewIntFilter(filter_rate=self.filter_rate, int_field=int_field, int_value=int_value)
 
 
+class CloudPayloadSearchCase(PerformanceCase):
+    case_id: CaseType = CaseType.CloudPayloadSearchCase
+    dataset_with_size_type: DatasetWithSizeType | None = None
+    payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY
+    filter_rate: float | None = None
+    label_percentage: float | None = None
+
+    def __init__(
+        self,
+        dataset_with_size_type: DatasetWithSizeType | str | None = None,
+        payload_profile: PayloadProfile | str = PayloadProfile.IDS_ONLY,
+        filter_rate: float | None = None,
+        label_percentage: float | None = None,
+        **kwargs,
+    ):
+        if filter_rate is not None and label_percentage is not None:
+            msg = "CloudPayloadSearchCase supports only one filter type per run"
+            raise ValueError(msg)
+        if dataset_with_size_type is not None and not isinstance(dataset_with_size_type, DatasetWithSizeType):
+            dataset_with_size_type = DatasetWithSizeType(dataset_with_size_type)
+        if not isinstance(payload_profile, PayloadProfile):
+            payload_profile = PayloadProfile(payload_profile)
+
+        if dataset_with_size_type is None:
+            dataset = Dataset.LAION.manager(100_000_000)
+            load_timeout = config.LOAD_TIMEOUT_768D_100M
+            optimize_timeout = config.OPTIMIZE_TIMEOUT_768D_100M
+            dataset_name = "LAION 100M (768dim)"
+        else:
+            dataset = dataset_with_size_type.get_manager()
+            load_timeout = dataset_with_size_type.get_load_timeout()
+            optimize_timeout = dataset_with_size_type.get_optimize_timeout()
+            dataset_name = dataset_with_size_type.value
+
+        name = f"Cloud Payload Search - {payload_profile.value} - {dataset_name}"
+        description = (
+            "Cloud leaderboard search envelope case with explicit response payload profile. "
+            f"Payload profile: {payload_profile.value}; dataset: {dataset_name}."
+        )
+        super().__init__(
+            name=name,
+            description=description,
+            dataset=dataset,
+            load_timeout=load_timeout,
+            optimize_timeout=optimize_timeout,
+            dataset_with_size_type=dataset_with_size_type,
+            payload_profile=payload_profile,
+            filter_rate=filter_rate,
+            label_percentage=label_percentage,
+            **kwargs,
+        )
+
+    @property
+    def filters(self) -> Filter:
+        if self.label_percentage is not None:
+            return LabelFilter(label_percentage=self.label_percentage)
+        if self.filter_rate is None:
+            return non_filter
+        int_field = self.dataset.data.train_id_field
+        int_value = int(self.dataset.data.size * self.filter_rate)
+        return NewIntFilter(filter_rate=self.filter_rate, int_field=int_field, int_value=int_value)
+
+
+class CloudColdLatencyCase(Case):
+    case_id: CaseType = CaseType.CloudColdLatencyCase
+    label: CaseLabel = CaseLabel.CloudColdLatency
+    dataset_with_size_type: DatasetWithSizeType | None = None
+    payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY
+    filter_rate: float | None = None
+    label_percentage: float | None = None
+    query_count: int = 1000
+
+    def __init__(
+        self,
+        dataset_with_size_type: DatasetWithSizeType | str | None = None,
+        payload_profile: PayloadProfile | str = PayloadProfile.IDS_ONLY,
+        filter_rate: float | None = None,
+        label_percentage: float | None = None,
+        query_count: int = 1000,
+        **kwargs,
+    ):
+        if filter_rate is not None and label_percentage is not None:
+            msg = "CloudColdLatencyCase supports only one filter type per run"
+            raise ValueError(msg)
+        if query_count <= 0:
+            msg = "query_count must be positive"
+            raise ValueError(msg)
+        if dataset_with_size_type is not None and not isinstance(dataset_with_size_type, DatasetWithSizeType):
+            dataset_with_size_type = DatasetWithSizeType(dataset_with_size_type)
+        if not isinstance(payload_profile, PayloadProfile):
+            payload_profile = PayloadProfile(payload_profile)
+
+        if dataset_with_size_type is None:
+            dataset = Dataset.LAION.manager(100_000_000)
+            load_timeout = config.LOAD_TIMEOUT_768D_100M
+            optimize_timeout = config.OPTIMIZE_TIMEOUT_768D_100M
+            dataset_name = "LAION 100M (768dim)"
+        else:
+            dataset = dataset_with_size_type.get_manager()
+            load_timeout = dataset_with_size_type.get_load_timeout()
+            optimize_timeout = dataset_with_size_type.get_optimize_timeout()
+            dataset_name = dataset_with_size_type.value
+
+        name = f"Cloud Cold Latency - {payload_profile.value} - {dataset_name}"
+        description = (
+            "Cloud leaderboard cold/warm serial latency case with explicit response payload profile. "
+            f"Payload profile: {payload_profile.value}; dataset: {dataset_name}; query count: {query_count}."
+        )
+        super().__init__(
+            name=name,
+            description=description,
+            dataset=dataset,
+            load_timeout=load_timeout,
+            optimize_timeout=optimize_timeout,
+            dataset_with_size_type=dataset_with_size_type,
+            payload_profile=payload_profile,
+            filter_rate=filter_rate,
+            label_percentage=label_percentage,
+            query_count=query_count,
+            **kwargs,
+        )
+
+    @property
+    def filters(self) -> Filter:
+        if self.label_percentage is not None:
+            return LabelFilter(label_percentage=self.label_percentage)
+        if self.filter_rate is None:
+            return non_filter
+        int_field = self.dataset.data.train_id_field
+        int_value = int(self.dataset.data.size * self.filter_rate)
+        return NewIntFilter(filter_rate=self.filter_rate, int_field=int_field, int_value=int_value)
+
+
+class CloudInsertCase(Case):
+    case_id: CaseType = CaseType.CloudInsertCase
+    label: CaseLabel = CaseLabel.CloudInsert
+    batch_size: int
+    duration: float | None = None
+    readiness_timeout: float | None = config.CLOUD_INSERT_READINESS_TIMEOUT
+    readiness_poll_interval: float = config.CLOUD_INSERT_READINESS_POLL_INTERVAL
+    dataset_with_size_type: DatasetWithSizeType | None = None
+
+    def __init__(
+        self,
+        batch_size: int,
+        duration: float | None = None,
+        readiness_timeout: float | None = config.CLOUD_INSERT_READINESS_TIMEOUT,
+        readiness_poll_interval: float = config.CLOUD_INSERT_READINESS_POLL_INTERVAL,
+        dataset_with_size_type: DatasetWithSizeType | str | None = None,
+        **kwargs,
+    ):
+        if dataset_with_size_type is not None and not isinstance(dataset_with_size_type, DatasetWithSizeType):
+            dataset_with_size_type = DatasetWithSizeType(dataset_with_size_type)
+        dataset = (
+            Dataset.LAION.manager(100_000_000)
+            if dataset_with_size_type is None
+            else dataset_with_size_type.get_manager()
+        )
+        super().__init__(
+            name=f"Cloud Insert - batch {batch_size}",
+            description="Cloud leaderboard insert-only case with readiness polling.",
+            dataset=dataset,
+            batch_size=batch_size,
+            duration=duration,
+            readiness_timeout=readiness_timeout,
+            readiness_poll_interval=readiness_poll_interval,
+            dataset_with_size_type=dataset_with_size_type,
+            **kwargs,
+        )
+
+
+class CloudMultiTenantSearchCase(PerformanceCase):
+    case_id: CaseType = CaseType.CloudMultiTenantSearchCase
+    dataset_with_size_type: DatasetWithSizeType = DatasetWithSizeType.CohereLarge
+    tenant_count: int = 1000
+    tenant_prefix: str = "tenant_"
+    tenant_id_width: int = 4
+    tenant_distribution: str = "uniform_by_id_mod"
+    measure_recall: bool = False
+    payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY
+    filter_rate: float | None = None
+    label_percentage: float | None = None
+
+    def __init__(
+        self,
+        dataset_with_size_type: DatasetWithSizeType | str = DatasetWithSizeType.CohereLarge,
+        tenant_count: int = 1000,
+        tenant_prefix: str = "tenant_",
+        tenant_id_width: int = 4,
+        payload_profile: PayloadProfile | str = PayloadProfile.IDS_ONLY,
+        filter_rate: float | None = None,
+        label_percentage: float | None = None,
+        **kwargs,
+    ):
+        if filter_rate is not None and label_percentage is not None:
+            msg = "CloudMultiTenantSearchCase supports only one filter type per run"
+            raise ValueError(msg)
+        if not isinstance(dataset_with_size_type, DatasetWithSizeType):
+            dataset_with_size_type = DatasetWithSizeType(dataset_with_size_type)
+        if not isinstance(payload_profile, PayloadProfile):
+            payload_profile = PayloadProfile(payload_profile)
+        if tenant_count <= 0:
+            msg = "tenant_count must be greater than 0"
+            raise ValueError(msg)
+        if tenant_id_width <= 0:
+            msg = "tenant_id_width must be greater than 0"
+            raise ValueError(msg)
+
+        dataset = dataset_with_size_type.get_manager()
+        super().__init__(
+            name=f"Cloud Multi-Tenant Search - {dataset_with_size_type.value}, {tenant_count} tenants",
+            description=(
+                "Multi-tenant QPS/latency benchmark with deterministic tenant routing "
+                f"({dataset_with_size_type.value}, {tenant_count} tenants)."
+            ),
+            dataset=dataset,
+            load_timeout=dataset_with_size_type.get_load_timeout(),
+            optimize_timeout=dataset_with_size_type.get_optimize_timeout(),
+            dataset_with_size_type=dataset_with_size_type,
+            tenant_count=tenant_count,
+            tenant_prefix=tenant_prefix,
+            tenant_id_width=tenant_id_width,
+            payload_profile=payload_profile,
+            filter_rate=filter_rate,
+            label_percentage=label_percentage,
+            **kwargs,
+        )
+
+    @property
+    def is_multitenant(self) -> bool:
+        return True
+
+    def tenant_for_id(self, row_id: int) -> str:
+        tenant_id = int(row_id) % self.tenant_count
+        return f"{self.tenant_prefix}{tenant_id:0{self.tenant_id_width}d}"
+
+    def tenant_labels_for_ids(self, row_ids: list[int]) -> list[str]:
+        return [self.tenant_for_id(row_id) for row_id in row_ids]
+
+    def tenant_labels(self) -> list[str]:
+        return [f"{self.tenant_prefix}{tenant_id:0{self.tenant_id_width}d}" for tenant_id in range(self.tenant_count)]
+
+    @property
+    def filters(self) -> Filter:
+        if self.label_percentage is not None:
+            return LabelFilter(label_percentage=self.label_percentage)
+        if self.filter_rate is None:
+            return non_filter
+        int_field = self.dataset.data.train_id_field
+        int_value = int(self.dataset.data.size * self.filter_rate)
+        return NewIntFilter(filter_rate=self.filter_rate, int_field=int_field, int_value=int_value)
+
+
 class LabelFilterPerformanceCase(PerformanceCase):
     case_id: CaseType = CaseType.LabelFilterPerformanceCase
     dataset_with_size_type: DatasetWithSizeType
@@ -631,6 +921,71 @@ def filters(self) -> Filter:
         return LabelFilter(label_percentage=self.label_percentage)
 
 
+class FtsPerformanceCase(Case):
+    """Base class for full-text search BM25 performance cases."""
+
+    label: CaseLabel = CaseLabel.FullTextSearchPerformance
+    dataset: FtsDatasetManager
+
+    filter_rate: float | None = None
+
+    @property
+    def filters(self) -> Filter:
+        if self.filter_rate is None:
+            return non_filter
+        int_value = int(self.dataset.data.size * self.filter_rate)
+        return NewIntFilter(filter_rate=self.filter_rate, int_field=FTS_FILTER_ID_FIELD, int_value=int_value)
+
+    def estimated_payload_bytes_per_query(self, k: int | None) -> int:
+        if k is None:
+            k = config.K_DEFAULT
+        return self.payload_profile.estimated_bytes_per_query(k=k, dim=0)
+
+
+class FTSBm25Performance(FtsPerformanceCase):
+    case_id: CaseType = CaseType.FTSBm25Performance
+    dataset_with_size_type: FtsDatasetWithSizeType = FtsDatasetWithSizeType.MSMarcoSmall
+
+    def __init__(
+        self,
+        dataset_with_size_type: FtsDatasetWithSizeType | str = FtsDatasetWithSizeType.MSMarcoSmall,
+        filter_rate: float | None = None,
+        **kwargs,
+    ):
+        if not isinstance(dataset_with_size_type, FtsDatasetWithSizeType):
+            dataset_with_size_type = FtsDatasetWithSizeType(dataset_with_size_type)
+        if filter_rate is not None:
+            if not dataset_with_size_type.is_advanced:
+                msg = "FTS filter cases are only supported for MS MARCO Large and HotpotQA Large"
+                raise ValueError(msg)
+            if not _is_supported_fts_filter_rate(filter_rate):
+                supported_rates = ", ".join(_format_filter_rate(rate) for rate in FTS_FILTER_RATES)
+                msg = f"FTS filter_rate must be one of: {supported_rates}"
+                raise ValueError(msg)
+        dataset = dataset_with_size_type.get_manager()
+        filter_suffix = f", Filter {_format_filter_rate(filter_rate)}" if filter_rate is not None else ""
+        name = f"FTS BM25 Performance - {dataset_with_size_type.value}{filter_suffix}"
+        description = (
+            f"This case tests native BM25 full-text search performance on {dataset_with_size_type.value}. "
+            "It measures index building time, recall, serial latency, and search QPS."
+        )
+        if filter_rate is not None:
+            description += (
+                f" The FTS filter case searches only documents with {FTS_FILTER_ID_FIELD} >= "
+                f"int(dataset_size * {filter_rate})."
+            )
+        super().__init__(
+            name=name,
+            description=description,
+            dataset=dataset,
+            dataset_with_size_type=dataset_with_size_type,
+            filter_rate=filter_rate,
+            load_timeout=dataset_with_size_type.get_load_timeout(),
+            optimize_timeout=dataset_with_size_type.get_optimize_timeout(),
+            **kwargs,
+        )
+
+
 type2case = {
     CaseType.CapacityDim960: CapacityDim960,
     CaseType.CapacityDim128: CapacityDim128,
@@ -655,4 +1010,9 @@ def filters(self) -> Filter:
     CaseType.StreamingCustomDataset: StreamingCustomDataset,
     CaseType.NewIntFilterPerformanceCase: NewIntFilterPerformanceCase,
     CaseType.LabelFilterPerformanceCase: LabelFilterPerformanceCase,
+    CaseType.CloudPayloadSearchCase: CloudPayloadSearchCase,
+    CaseType.FTSBm25Performance: FTSBm25Performance,
+    CaseType.CloudInsertCase: CloudInsertCase,
+    CaseType.CloudColdLatencyCase: CloudColdLatencyCase,
+    CaseType.CloudMultiTenantSearchCase: CloudMultiTenantSearchCase,
 }
diff --git a/vectordb_bench/backend/clients/__init__.py b/vectordb_bench/backend/clients/__init__.py
index ebb3b3b13..81c65c4e0 100644
--- a/vectordb_bench/backend/clients/__init__.py
+++ b/vectordb_bench/backend/clients/__init__.py
@@ -59,6 +59,12 @@ class DB(Enum):
     Zvec = "Zvec"
     Endee = "Endee"
     Lindorm = "Lindorm"
+    VectorChord = "VectorChord"
+    PolarDB = "PolarDB"
+    Pinot = "Pinot"
+    SeekDB = "SeekDB"
+    VolcMySQL = "VolcMySQL"
+    Adbpg = "AnalyticDB for PostgreSQL"
     Antfly = "Antfly"
 
     @property
@@ -247,6 +253,35 @@ def init_cls(self) -> type[VectorDB]:  # noqa: PLR0911, PLR0912, C901, PLR0915
 
             return LindormVector
 
+        if self == DB.VectorChord:
+            from .vectorchord.vectorchord import VectorChord
+
+            return VectorChord
+        if self == DB.PolarDB:
+            from .polardb.polardb import PolarDB
+
+            return PolarDB
+
+        if self == DB.Pinot:
+            from .pinot.pinot import Pinot
+
+            return Pinot
+
+        if self == DB.SeekDB:
+            from .seekdb.seekdb import SeekDB
+
+            return SeekDB
+
+        if self == DB.VolcMySQL:
+            from .volc_mysql.volc_mysql import VolcMySQL
+
+            return VolcMySQL
+
+        if self == DB.Adbpg:
+            from .adbpg.adbpg import Adbpg
+
+            return Adbpg
+
         if self == DB.Antfly:
             from .antfly.antfly import Antfly
 
@@ -441,6 +476,35 @@ def config_cls(self) -> type[DBConfig]:  # noqa: PLR0911, PLR0912, C901, PLR0915
 
             return LindormConfig
 
+        if self == DB.VectorChord:
+            from .vectorchord.config import VectorChordConfig
+
+            return VectorChordConfig
+        if self == DB.PolarDB:
+            from .polardb.config import PolarDBConfig
+
+            return PolarDBConfig
+
+        if self == DB.Pinot:
+            from .pinot.config import PinotConfig
+
+            return PinotConfig
+
+        if self == DB.SeekDB:
+            from .seekdb.config import SeekDBConfig
+
+            return SeekDBConfig
+
+        if self == DB.VolcMySQL:
+            from .volc_mysql.config import VolcMySQLConfig
+
+            return VolcMySQLConfig
+
+        if self == DB.Adbpg:
+            from .adbpg.config import AdbpgConfig
+
+            return AdbpgConfig
+
         if self == DB.Antfly:
             from .antfly.config import AntflyConfig
 
@@ -459,11 +523,19 @@ def case_config_cls(  # noqa: C901, PLR0911, PLR0912, PLR0915
             return _milvus_case_config.get(index_type)
 
         if self == DB.ZillizCloud:
+            if index_type == IndexType.FTS:
+                from .zilliz_cloud.config import ZillizCloudFtsConfig
+
+                return ZillizCloudFtsConfig
             from .zilliz_cloud.config import AutoIndexConfig
 
             return AutoIndexConfig
 
         if self == DB.ElasticCloud:
+            if index_type == IndexType.FTS:
+                from .elastic_cloud.config import ElasticCloudFtsConfig
+
+                return ElasticCloudFtsConfig
             from .elastic_cloud.config import ElasticCloudIndexConfig
 
             return ElasticCloudIndexConfig
@@ -499,6 +571,10 @@ def case_config_cls(  # noqa: C901, PLR0911, PLR0912, PLR0915
             return AWSOpenSearchIndexConfig
 
         if self == DB.OSSOpenSearch:
+            if index_type == IndexType.FTS:
+                from .oss_opensearch.config import OSSOpenSearchFtsConfig
+
+                return OSSOpenSearchFtsConfig
             from .oss_opensearch.config import OSSOpenSearchIndexConfig
 
             return OSSOpenSearchIndexConfig
@@ -559,6 +635,10 @@ def case_config_cls(  # noqa: C901, PLR0911, PLR0912, PLR0915
             return _cockroachdb_case_config.get(index_type)
 
         if self == DB.Vespa:
+            if index_type == IndexType.FTS:
+                from .vespa.config import VespaFtsConfig
+
+                return VespaFtsConfig
             from .vespa.config import VespaHNSWConfig
 
             return VespaHNSWConfig
@@ -592,12 +672,21 @@ def case_config_cls(  # noqa: C901, PLR0911, PLR0912, PLR0915
 
             return AliSQLIndexConfig
 
+        if self == DB.PolarDB:
+            from .polardb.config import _polardb_case_config
+
+            return _polardb_case_config.get(index_type)
+
         if self == DB.Doris:
             from .doris.config import DorisCaseConfig
 
             return DorisCaseConfig
 
         if self == DB.TurboPuffer:
+            if index_type == IndexType.FTS:
+                from .turbopuffer.config import TurboPufferFtsConfig
+
+                return TurboPufferFtsConfig
             from .turbopuffer.config import TurboPufferIndexConfig
 
             return TurboPufferIndexConfig
@@ -612,6 +701,35 @@ def case_config_cls(  # noqa: C901, PLR0911, PLR0912, PLR0915
 
             return _lindorm_vector_case_config.get(index_type)
 
+        if self == DB.VectorChord:
+            from .vectorchord.config import _vectorchord_case_config
+
+            return _vectorchord_case_config.get(index_type)
+
+        if self == DB.Pinot:
+            from .pinot.config import PinotHNSWConfig, PinotIVFFlatConfig, PinotIVFPQConfig
+
+            return {
+                IndexType.HNSW: PinotHNSWConfig,
+                IndexType.IVFFlat: PinotIVFFlatConfig,
+                IndexType.IVFPQ: PinotIVFPQConfig,
+            }.get(index_type, PinotHNSWConfig)
+
+        if self == DB.SeekDB:
+            from .seekdb.config import _seekdb_case_config
+
+            return _seekdb_case_config.get(index_type)
+
+        if self == DB.VolcMySQL:
+            from .volc_mysql.config import _volcmysql_case_config
+
+            return _volcmysql_case_config.get(index_type)
+
+        if self == DB.Adbpg:
+            from .adbpg.config import AdbpgIndexConfig
+
+            return AdbpgIndexConfig
+
         if self == DB.Antfly:
             from .antfly.config import AntflyIndexConfig
 
diff --git a/vectordb_bench/backend/clients/adbpg/__init__.py b/vectordb_bench/backend/clients/adbpg/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/vectordb_bench/backend/clients/adbpg/adbpg.py b/vectordb_bench/backend/clients/adbpg/adbpg.py
new file mode 100644
index 000000000..bc5ac3486
--- /dev/null
+++ b/vectordb_bench/backend/clients/adbpg/adbpg.py
@@ -0,0 +1,373 @@
+"""Wrapper around the Aliyun ADBPG (AnalyticDB for PostgreSQL) vector database."""
+
+import logging
+from collections.abc import Generator, Sequence
+from contextlib import contextmanager
+from typing import Any
+
+import numpy as np
+import psycopg
+from pgvector.psycopg import register_vector
+from psycopg import Connection, Cursor, sql
+
+from vectordb_bench.backend.filter import Filter, FilterOp
+
+from ..api import VectorDB
+from .config import AdbpgConfigDict, AdbpgIndexConfig
+
+log = logging.getLogger(__name__)
+
+
+class Adbpg(VectorDB):
+    """ADBPG vector database client, using psycopg."""
+
+    # psycopg Cursor is not thread-safe and the COPY protocol cannot be
+    # interleaved on a shared connection. Match PgVector/VectorChord and
+    # let ConcurrentInsertRunner clamp max_workers=1.
+    thread_safe: bool = False
+
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+        FilterOp.StrEqual,
+    ]
+
+    conn: psycopg.Connection[Any] | None = None
+    cursor: psycopg.Cursor[Any] | None = None
+
+    _search: sql.Composed
+
+    def __init__(
+        self,
+        dim: int,
+        db_config: AdbpgConfigDict,
+        db_case_config: AdbpgIndexConfig,
+        drop_old: bool = False,
+        with_scalar_labels: bool = False,
+        **kwargs,
+    ):
+        self.name = "Adbpg"
+        self.case_config = db_case_config
+        # Allow the framework layer (task_runner) to inject a case-specific table
+        # name via the `collection_name` kwarg (see Doris for the same pattern).
+        override_name = kwargs.get("collection_name")
+        self.table_name = override_name if override_name else db_config["table_name"]
+        self.connect_config = db_config["connect_config"]
+        self.dim = dim
+        self.with_scalar_labels = with_scalar_labels
+
+        self._primary_field = "id"
+        self._vector_field = "embedding"
+        self._scalar_label_field = "label"
+        # Index name derives from the table name + algorithm, e.g. vector_1024d_10m_novamr_index.
+        self._index_name = f"{self.table_name}_{self.case_config.algorithm}_index"
+
+        self.where_clause = ""
+
+        # construct basic units
+        self.conn, self.cursor = self._create_connection(**self.connect_config)
+
+        log.info(f"{self.name} config values: {self.connect_config}\n{self.case_config}")
+        if not any(
+            (
+                self.case_config.create_index_before_load,
+                self.case_config.create_index_after_load,
+            ),
+        ):
+            msg = (
+                f"{self.name} config must create an index using create_index_before_load or create_index_after_load"
+                f"{self.name} config values: {self.connect_config}\n{self.case_config}"
+            )
+            log.error(msg)
+            raise RuntimeError(msg)
+
+        if drop_old:
+            self._drop_index()
+            self._drop_table()
+            self._create_table(dim)
+            if self.case_config.create_index_before_load:
+                self._create_index()
+
+        self.cursor.close()
+        self.conn.close()
+        self.cursor = None
+        self.conn = None
+
+    @staticmethod
+    def _create_connection(**kwargs) -> tuple[Connection, Cursor]:
+        conn = psycopg.connect(**kwargs)
+        register_vector(conn)
+        conn.autocommit = False
+        cursor = conn.cursor()
+
+        assert conn is not None, "Connection is not initialized"
+        assert cursor is not None, "Cursor is not initialized"
+
+        return conn, cursor
+
+    def _generate_search_query(self) -> sql.Composed:
+        search_param = self.case_config.search_param()
+        distance_operator = {
+            "l2": "<->",
+            "ip": "<#>",
+            "cosine": "<=>",
+        }.get(search_param["metric"], "<->")
+
+        where_clause = sql.SQL(self.where_clause) if self.where_clause else sql.SQL("")
+
+        return sql.Composed(
+            [
+                sql.SQL(
+                    "SELECT {primary_field} FROM public.{table_name} {where_clause} ORDER BY {vector_field} ",
+                ).format(
+                    table_name=sql.Identifier(self.table_name),
+                    primary_field=sql.Identifier(self._primary_field),
+                    where_clause=where_clause,
+                    vector_field=sql.Identifier(self._vector_field),
+                ),
+                sql.SQL(distance_operator),
+                sql.SQL(" {search_vector}::vector({dim}) LIMIT %s::int").format(
+                    search_vector=sql.Placeholder(),
+                    dim=self.dim,
+                ),
+            ],
+        )
+
+    @contextmanager
+    def init(self) -> Generator[None, None, None]:
+        """Open a session, apply GUCs, yield, then close."""
+        self.conn, self.cursor = self._create_connection(**self.connect_config)
+
+        session_options: Sequence[dict[str, Any]] = self.case_config.session_param()["session_options"]
+
+        if len(session_options) > 0:
+            for setting in session_options:
+                command = sql.SQL("SET {setting_name} = {val};").format(
+                    setting_name=sql.Identifier(setting["parameter"]["setting_name"]),
+                    val=sql.Identifier(str(setting["parameter"]["val"])),
+                )
+                log.debug(command.as_string(self.cursor))
+                self.cursor.execute(command)
+            self.conn.commit()
+
+        try:
+            yield
+        finally:
+            self.cursor.close()
+            self.conn.close()
+            self.cursor = None
+            self.conn = None
+
+    def _drop_table(self):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+        log.info(f"{self.name} client drop table : {self.table_name}")
+
+        self.cursor.execute(
+            sql.SQL("DROP TABLE IF EXISTS public.{table_name}").format(
+                table_name=sql.Identifier(self.table_name),
+            ),
+        )
+        self.conn.commit()
+
+    def optimize(self, data_size: int | None = None):
+        self._post_insert()
+
+    def _post_insert(self):
+        log.info(f"{self.name} post insert before optimize")
+        if self.case_config.create_index_after_load:
+            self._drop_index()
+            self._create_index()
+
+    def _drop_index(self):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+        log.info(f"{self.name} client drop index : {self._index_name}")
+
+        drop_index_sql = sql.SQL("DROP INDEX IF EXISTS {index_name}").format(
+            index_name=sql.Identifier(self._index_name),
+        )
+        log.debug(drop_index_sql.as_string(self.cursor))
+        self.cursor.execute(drop_index_sql)
+        self.conn.commit()
+
+    def _set_parallel_index_build_param(self):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        index_param = self.case_config.index_param()
+
+        if index_param["build_parallel_processes"] is not None:
+            self.cursor.execute(
+                sql.SQL("SET fastann.build_parallel_processes TO {};").format(
+                    index_param["build_parallel_processes"],
+                ),
+            )
+            self.conn.commit()
+
+        results = self.cursor.execute(sql.SQL("SHOW fastann.build_parallel_processes;")).fetchall()
+        log.info(f"{self.name} parallel index creation parameters: {results}")
+
+    def _create_index(self):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+        log.info(f"{self.name} client create index : {self._index_name}")
+
+        index_param = self.case_config.index_param()
+        self._set_parallel_index_build_param()
+
+        # Pre-build GUC: raise optimizer level before creating the ANN index.
+        self.cursor.execute(sql.SQL("SET fastann.nova_build_optimize_level = 3;"))
+        self.conn.commit()
+
+        options = []
+        options.append(sql.SQL("dim = {dim}").format(dim=sql.Literal(self.dim)))
+        options.append(
+            sql.SQL("distancemeasure = {measure}").format(
+                measure=sql.Identifier(index_param["metric"]),
+            ),
+        )
+
+        for option in index_param["index_creation_with_options"]:
+            if option["val"] is not None:
+                # When `raw` is set, emit the value as a bare SQL token
+                # (e.g. auto_reduction=on) instead of a quoted literal.
+                rendered_val = sql.SQL(str(option["val"])) if option.get("raw") else sql.Literal(option["val"])
+                options.append(
+                    sql.SQL("{option_name} = {val}").format(
+                        option_name=sql.Identifier(option["option_name"]),
+                        val=rendered_val,
+                    ),
+                )
+
+        with_clause = sql.SQL("WITH ({});").format(sql.SQL(", ").join(options)) if options else sql.Composed(())
+
+        # Covering index: always INCLUDE the primary field (e.g. id).
+        index_create_sql = sql.SQL(
+            """
+            CREATE INDEX IF NOT EXISTS {index_name} ON public.{table_name}
+            USING ann ({vector_field}) INCLUDE ({primary_field})
+            """,
+        ).format(
+            index_name=sql.Identifier(self._index_name),
+            table_name=sql.Identifier(self.table_name),
+            vector_field=sql.Identifier(self._vector_field),
+            primary_field=sql.Identifier(self._primary_field),
+        )
+
+        full_sql = (index_create_sql + with_clause).join(" ")
+        log.debug(full_sql.as_string(self.cursor))
+        self.cursor.execute(full_sql)
+        self.conn.commit()
+
+    def _create_table(self, dim: int):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        try:
+            log.info(f"{self.name} client create table : {self.table_name}")
+
+            if self.with_scalar_labels:
+                self.cursor.execute(
+                    sql.SQL(
+                        """
+                        CREATE TABLE IF NOT EXISTS public.{table_name}
+                        ({primary_field} BIGINT PRIMARY KEY, embedding vector({dim}), {label_field} VARCHAR(64));
+                        """,
+                    ).format(
+                        table_name=sql.Identifier(self.table_name),
+                        primary_field=sql.Identifier(self._primary_field),
+                        dim=dim,
+                        label_field=sql.Identifier(self._scalar_label_field),
+                    ),
+                )
+            else:
+                self.cursor.execute(
+                    sql.SQL(
+                        """
+                        CREATE TABLE IF NOT EXISTS public.{table_name}
+                        ({primary_field} BIGINT PRIMARY KEY, embedding vector({dim}));
+                        """,
+                    ).format(
+                        table_name=sql.Identifier(self.table_name),
+                        primary_field=sql.Identifier(self._primary_field),
+                        dim=dim,
+                    ),
+                )
+
+            self.cursor.execute(
+                sql.SQL(
+                    "ALTER TABLE public.{table_name} ALTER COLUMN embedding SET STORAGE PLAIN;",
+                ).format(table_name=sql.Identifier(self.table_name)),
+            )
+            self.conn.commit()
+        except Exception as e:
+            log.warning(f"Failed to create adbpg table: {self.table_name} error: {e}")
+            raise e from None
+
+    def insert_embeddings(
+        self,
+        embeddings: list[list[float]],
+        metadata: list[int],
+        labels_data: list[str] | None = None,
+        **kwargs: Any,
+    ) -> tuple[int, Exception | None]:
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+        if self.with_scalar_labels:
+            assert labels_data is not None, "labels_data should be provided if with_scalar_labels is set to True"
+
+        try:
+            metadata_arr = np.array(metadata)
+            embeddings_arr = np.array(embeddings)
+
+            with self.cursor.copy(
+                sql.SQL("COPY public.{table_name} FROM STDIN (FORMAT BINARY)").format(
+                    table_name=sql.Identifier(self.table_name),
+                ),
+            ) as copy:
+                for i, row in enumerate(metadata_arr):
+                    if self.with_scalar_labels:
+                        copy.set_types(["bigint", "vector", "varchar"])
+                        copy.write_row((row, embeddings_arr[i], labels_data[i]))
+                    else:
+                        copy.set_types(["bigint", "vector"])
+                        copy.write_row((row, embeddings_arr[i]))
+            self.conn.commit()
+
+            return len(metadata), None
+        except Exception as e:
+            log.warning(f"Failed to insert data into adbpg table ({self.table_name}), error: {e}")
+            return 0, e
+
+    def prepare_filter(self, filters: Filter):
+        if filters.type == FilterOp.NonFilter:
+            self.where_clause = ""
+        elif filters.type == FilterOp.NumGE:
+            self.where_clause = f"WHERE {self._primary_field} >= {filters.int_value}"
+        elif filters.type == FilterOp.StrEqual:
+            self.where_clause = f"WHERE {self._scalar_label_field} = '{filters.label_value}'"
+        else:
+            msg = f"Not support Filter for Adbpg - {filters}"
+            raise ValueError(msg)
+
+        self._search = self._generate_search_query()
+
+    def search_embedding(
+        self,
+        query: list[float],
+        k: int = 100,
+        timeout: int | None = None,
+        **kwargs: Any,
+    ) -> list[int]:
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        q = np.asarray(query)
+        result = self.cursor.execute(
+            self._search,
+            (q, k),
+            prepare=True,
+            binary=True,
+        )
+        return [int(i[0]) for i in result.fetchall()]
diff --git a/vectordb_bench/backend/clients/adbpg/cli.py b/vectordb_bench/backend/clients/adbpg/cli.py
new file mode 100644
index 000000000..e579f9028
--- /dev/null
+++ b/vectordb_bench/backend/clients/adbpg/cli.py
@@ -0,0 +1,205 @@
+import os
+from typing import Annotated, Unpack
+
+import click
+from pydantic import SecretStr
+
+from vectordb_bench.backend.clients import DB
+
+from ....cli.cli import (
+    CommonTypedDict,
+    cli,
+    click_parameter_decorators_from_typed_dict,
+    get_custom_case_config,
+    run,
+)
+
+
+class AdbpgTypedDict(CommonTypedDict):
+    user_name: Annotated[
+        str,
+        click.option("--user-name", type=str, help="Db username", required=True),
+    ]
+    password: Annotated[
+        str,
+        click.option(
+            "--password",
+            type=str,
+            help="Postgres database password",
+            default=lambda: os.environ.get("POSTGRES_PASSWORD", ""),
+            show_default="$POSTGRES_PASSWORD",
+        ),
+    ]
+    host: Annotated[str, click.option("--host", type=str, help="Db host", required=True)]
+    port: Annotated[
+        int,
+        click.option(
+            "--port",
+            type=int,
+            help="Postgres database port",
+            default=5432,
+            show_default=True,
+            required=False,
+        ),
+    ]
+    db_name: Annotated[str, click.option("--db-name", type=str, help="Db name", required=True)]
+    hnsw_m: Annotated[
+        int,
+        click.option("--hnsw-m", type=int, help="hnsw_m", default=48, show_default=True, required=False),
+    ]
+    ef_search: Annotated[
+        int,
+        click.option("--ef-search", type=int, help="ef_search", default=150, show_default=True, required=False),
+    ]
+    ef_construction: Annotated[
+        int,
+        click.option(
+            "--ef-construction",
+            type=int,
+            help="ef_construction",
+            default=600,
+            show_default=True,
+            required=False,
+        ),
+    ]
+    nlist: Annotated[
+        int,
+        click.option("--nlist", type=int, help="nlist", default=1024, show_default=True, required=False),
+    ]
+    rabitq_bits: Annotated[
+        int,
+        click.option("--rabitq-bits", type=int, help="rabitq_bits", default=7, show_default=True, required=False),
+    ]
+    quantize_rescore_amp: Annotated[
+        float,
+        click.option(
+            "--quantize-rescore-amp",
+            type=float,
+            help="fastann.quantize_rescore_amp",
+            default=0.0,
+            show_default=True,
+            required=False,
+        ),
+    ]
+    nova_adaptive_gamma: Annotated[
+        float,
+        click.option(
+            "--nova-adaptive-gamma",
+            type=float,
+            help="fastann.nova_adaptive_gamma",
+            default=0.0,
+            show_default=True,
+            required=False,
+        ),
+    ]
+    auto_reduction: Annotated[
+        bool,
+        click.option(
+            "--auto-reduction/--no-auto-reduction",
+            "auto_reduction",
+            type=bool,
+            help="Index WITH auto_reduction=on when enabled",
+            default=False,
+            show_default=True,
+            required=False,
+        ),
+    ]
+    max_scan_points: Annotated[
+        int,
+        click.option(
+            "--max-scan-points",
+            type=int,
+            help="max_scan_points",
+            default=20000,
+            show_default=True,
+            required=False,
+        ),
+    ]
+    index_scan_mode: Annotated[
+        str,
+        click.option(
+            "--index-scan-mode",
+            type=str,
+            help="fastann.index_scan_mode",
+            default="snapshot",
+            show_default=True,
+            required=False,
+        ),
+    ]
+    algorithm: Annotated[
+        str,
+        click.option(
+            "--algorithm",
+            type=str,
+            help="algorithm",
+            default="novamr",
+            show_default=True,
+            required=False,
+        ),
+    ]
+    build_parallel_processes: Annotated[
+        int,
+        click.option(
+            "--build-parallel-processes",
+            type=int,
+            help="Sets the maximum process to build index",
+            required=False,
+        ),
+    ]
+    pca_dim: Annotated[
+        int | None,
+        click.option(
+            "--pca-dim",
+            type=int,
+            help="PCA dimension for index dimensionality reduction",
+            default=None,
+            show_default=True,
+            required=False,
+        ),
+    ]
+    nprobe: Annotated[
+        int,
+        click.option(
+            "--nprobe",
+            type=int,
+            help="fastann.nova_nprobe (novad search)",
+            default=5,
+            show_default=True,
+            required=False,
+        ),
+    ]
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(AdbpgTypedDict)
+def AdbpgNova(**parameters: Unpack[AdbpgTypedDict]):
+    from .config import AdbpgConfig, AdbpgIndexConfig
+
+    parameters["custom_case"] = get_custom_case_config(parameters)
+    run(
+        db=DB.Adbpg,
+        db_config=AdbpgConfig(
+            user_name=SecretStr(parameters["user_name"]),
+            password=SecretStr(parameters["password"]),
+            host=parameters["host"],
+            port=parameters["port"],
+            db_name=parameters["db_name"],
+        ),
+        db_case_config=AdbpgIndexConfig(
+            hnsw_m=parameters["hnsw_m"],
+            ef_search=parameters["ef_search"],
+            ef_construction=parameters["ef_construction"],
+            nlist=parameters["nlist"],
+            algorithm=parameters["algorithm"],
+            build_parallel_processes=parameters["build_parallel_processes"],
+            rabitq_bits=parameters["rabitq_bits"],
+            quantize_rescore_amp=parameters["quantize_rescore_amp"],
+            nova_adaptive_gamma=parameters["nova_adaptive_gamma"],
+            auto_reduction=parameters["auto_reduction"],
+            pca_dim=parameters["pca_dim"],
+            max_scan_points=parameters["max_scan_points"],
+            index_scan_mode=parameters["index_scan_mode"],
+            nprobe=parameters["nprobe"],
+        ),
+        **parameters,
+    )
diff --git a/vectordb_bench/backend/clients/adbpg/config.py b/vectordb_bench/backend/clients/adbpg/config.py
new file mode 100644
index 000000000..cc69085bc
--- /dev/null
+++ b/vectordb_bench/backend/clients/adbpg/config.py
@@ -0,0 +1,137 @@
+from collections.abc import Mapping, Sequence
+from typing import Any, TypedDict
+
+from pydantic import BaseModel, SecretStr
+
+from ..api import DBCaseConfig, DBConfig, MetricType
+
+
+class AdbpgSessionCommands(TypedDict):
+    session_options: Sequence[dict[str, Any]]
+
+
+class AdbpgConfigDict(TypedDict):
+    """These keys will be directly used as kwargs in psycopg connection string,
+    so the names must match exactly psycopg API."""
+
+    user: str
+    password: str
+    host: str
+    port: int
+    dbname: str
+
+
+class AdbpgConfig(DBConfig):
+    user_name: SecretStr = SecretStr("tester")
+    password: SecretStr = SecretStr("")
+    host: str = "localhost"
+    port: int = 5432
+    db_name: str = "postgres"
+
+    def to_dict(self) -> dict:
+        user_str = self.user_name.get_secret_value() if isinstance(self.user_name, SecretStr) else self.user_name
+        pwd_str = self.password.get_secret_value()
+        return {
+            "table_name": "vector",
+            "connect_config": {
+                "host": self.host,
+                "port": self.port,
+                "dbname": self.db_name,
+                "user": user_str,
+                "password": pwd_str,
+                "options": "-c gp_session_role=utility",
+            },
+        }
+
+
+class AdbpgIndexConfig(BaseModel, DBCaseConfig):
+    metric_type: MetricType | None = None
+    create_index_before_load: bool = False
+    create_index_after_load: bool = True
+
+    # ADB PG specific parameters
+    hnsw_m: int = 48
+    ef_search: int = 150
+    ef_construction: int = 600
+    nlist: int = 1024
+    algorithm: str = "novamr"
+    build_parallel_processes: int | None = None
+    # rabitq quantization params
+    rabitq_bits: int = 7
+    quantize_rescore_amp: float = 0.0
+    nova_adaptive_gamma: float = 0.0
+    max_scan_points: int = 20000
+    index_scan_mode: str = "snapshot"
+    auto_reduction: bool = False
+    pca_dim: int | None = None
+    # novad-specific search param (no-op for novamr/HNSW algorithms)
+    nprobe: int = 5
+
+    def parse_metric(self) -> str:
+        if self.metric_type == MetricType.L2:
+            return "l2"
+        if self.metric_type == MetricType.COSINE:
+            return "cosine"
+        if self.metric_type == MetricType.IP:
+            return "ip"
+        msg = f"Metric type {self.metric_type} is not supported!"
+        raise ValueError(msg)
+
+    @staticmethod
+    def _build_forced_set_options(set_mapping: Mapping[str, Any]) -> Sequence[dict[str, Any]]:
+        """Always emit SET commands regardless of value (including 0 / 0.0)."""
+        return [
+            {
+                "parameter": {
+                    "setting_name": name,
+                    "val": str(value),
+                },
+            }
+            for name, value in set_mapping.items()
+        ]
+
+    def index_param(self) -> dict:
+        with_options = [
+            {"option_name": "algorithm", "val": self.algorithm},
+            {"option_name": "hnsw_m", "val": self.hnsw_m},
+            {"option_name": "hnsw_ef_construction", "val": self.ef_construction},
+            {"option_name": "nlist", "val": self.nlist},
+            {"option_name": "rabitq_bits", "val": self.rabitq_bits},
+            # Covering index key length.
+            {"option_name": "max_key_len", "val": 1},
+        ]
+        # Optional: auto_reduction=on — only include when True.
+        # Uses raw=True so the value 'on' is emitted as a bare identifier
+        # instead of a quoted string literal.
+        if self.auto_reduction:
+            with_options.append({"option_name": "auto_reduction", "val": "on", "raw": True})
+        if self.pca_dim is not None:
+            with_options.append({"option_name": "pca_dim", "val": self.pca_dim})
+
+        return {
+            "metric": self.parse_metric(),
+            "build_parallel_processes": self.build_parallel_processes,
+            "create_index_before_load": self.create_index_before_load,
+            "create_index_after_load": self.create_index_after_load,
+            "index_creation_with_options": with_options,
+        }
+
+    def search_param(self) -> dict:
+        return {
+            "metric": self.parse_metric(),
+        }
+
+    def session_param(self) -> AdbpgSessionCommands:
+        # All CLI-driven search GUCs are always sent, regardless of value,
+        # so that callers can explicitly tune any parameter — including to 0.
+        session_parameters = {
+            "fastann.quantize_rescore_amp": self.quantize_rescore_amp,
+            "fastann.nova_adaptive_gamma": self.nova_adaptive_gamma,
+            "fastann.hnsw_ef_search": self.ef_search,
+            "fastann.hnsw_max_scan_points": self.max_scan_points,
+            "fastann.index_scan_mode": self.index_scan_mode,
+            "fastann.nova_nprobe": self.nprobe,
+            "optimizer": "off",
+            "elog_process_parameters": "off",
+        }
+        return {"session_options": self._build_forced_set_options(session_parameters)}
diff --git a/vectordb_bench/backend/clients/alisql/alisql.py b/vectordb_bench/backend/clients/alisql/alisql.py
index f88cf9d88..196d88731 100644
--- a/vectordb_bench/backend/clients/alisql/alisql.py
+++ b/vectordb_bench/backend/clients/alisql/alisql.py
@@ -1,7 +1,9 @@
+import array
 import logging
-from contextlib import contextmanager
+import queue
+from contextlib import contextmanager, suppress
 
-import mysql.connector as mysql
+import MySQLdb
 import numpy as np
 
 from ..api import VectorDB
@@ -11,6 +13,8 @@
 
 
 class AliSQL(VectorDB):
+    thread_safe = True
+
     def __init__(
         self,
         dim: int,
@@ -25,8 +29,9 @@ def __init__(
         self.case_config = db_case_config
         self.table_name = collection_name
         self.dim = dim
+        # Pool of extra connections used to parallelize inserts; built in init().
+        self._insert_pool: queue.SimpleQueue | None = None
 
-        # construct basic units
         self.conn, self.cursor = self._create_connection()
 
         if drop_old:
@@ -39,12 +44,11 @@ def __init__(
         self.conn = None
 
     def _create_connection(self):
-        conn = mysql.connect(
+        conn = MySQLdb.connect(
             host=self.db_config["host"],
             user=self.db_config["user"],
             port=self.db_config["port"],
             password=self.db_config["password"],
-            buffered=True,
         )
         cursor = conn.cursor()
 
@@ -53,13 +57,40 @@ def _create_connection(self):
 
         return conn, cursor
 
+    def _acquire_insert_conn(self):
+        """Borrow a connection from the insert pool, opening a new one if empty.
+
+        The pool grows lazily to the number of concurrent insert workers: with N
+        worker threads at most N connections are checked out at once.
+        """
+        try:
+            return self._insert_pool.get_nowait()
+        except queue.Empty:
+            conn, cursor = self._create_connection()
+            cursor.execute("SET sql_mode = ''")
+            return conn, cursor
+
+    def _drain_insert_pool(self):
+        """Close every pooled insert connection. Called from init()'s finally, after
+        all insert workers have joined, so nothing is checked out at this point.
+        """
+        pool, self._insert_pool = self._insert_pool, None
+        if pool is None:
+            return
+        while True:
+            try:
+                conn, cursor = pool.get_nowait()
+            except queue.Empty:
+                break
+            with suppress(Exception):
+                cursor.close()
+                conn.close()
+
     def _drop_db(self):
         assert self.conn is not None, "Connection is not initialized"
         assert self.cursor is not None, "Cursor is not initialized"
         log.info(f'{self.name} client drop db : {self.db_config["database"]}')
 
-        # flush tables before dropping database to avoid some locking issue
-        self.cursor.execute("FLUSH TABLES")
         self.cursor.execute(f'DROP DATABASE IF EXISTS {self.db_config["database"]}')
         self.cursor.execute("COMMIT")
         self.cursor.execute("FLUSH TABLES")
@@ -107,25 +138,28 @@ def init(self):
                 self.cursor.execute(f"SET SESSION vidx_hnsw_ef_search = {search_param['ef_search']}")
             self.cursor.execute("COMMIT")
 
-        self.insert_sql = (
-            f'INSERT INTO {self.db_config["database"]}.{self.table_name} (id, v) VALUES (%s, %s)'  # noqa: S608
-        )
+        self.insert_sql = f'INSERT INTO {self.db_config["database"]}.{self.table_name} (id, v) VALUES (%s, %s)'
         self.select_sql = (
-            f'SELECT id FROM {self.db_config["database"]}.{self.table_name} '  # noqa: S608
+            f'SELECT id FROM {self.db_config["database"]}.{self.table_name} '
             f"ORDER by vec_distance_{search_param['metric_type']}(v, %s) LIMIT %s"
         )
         self.select_sql_with_filter = (
-            f'SELECT id FROM {self.db_config["database"]}.{self.table_name} WHERE id >= %s '  # noqa: S608
+            f'SELECT id FROM {self.db_config["database"]}.{self.table_name} WHERE id >= %s '
             f"ORDER by vec_distance_{search_param['metric_type']}(v, %s) LIMIT %s"
         )
 
+        self._search_cursor = self.cursor
+        self._insert_pool = queue.SimpleQueue()
+
         try:
             yield
         finally:
+            self._drain_insert_pool()
             self.cursor.close()
             self.conn.close()
             self.cursor = None
             self.conn = None
+            self._search_cursor = None
 
     def ready_to_load(self) -> bool:
         pass
@@ -140,6 +174,10 @@ def optimize(self, data_size: int) -> None:
             index_options = f"DISTANCE={index_param['metric_type']}"
             if index_param["index_type"] == "HNSW" and index_param["M"] is not None:
                 index_options += f" M={index_param['M']}"
+            if index_param.get("shards") is not None:
+                index_options += f" SHARDS={index_param['shards']}"
+            if index_param.get("quantization") is not None:
+                index_options += f" QUANTIZATION={index_param['quantization']}"
 
             self.cursor.execute(f"""
               ALTER TABLE {self.db_config["database"]}.{self.table_name}
@@ -153,7 +191,7 @@ def optimize(self, data_size: int) -> None:
 
     @staticmethod
     def vector_to_hex(v):  # noqa: ANN001
-        return np.array(v, "float32").tobytes()
+        return array.array("f", v).tobytes()
 
     def insert_embeddings(
         self,
@@ -161,28 +199,29 @@ def insert_embeddings(
         metadata: list[int],
         **kwargs,
     ) -> tuple[int, Exception]:
-        """Insert embeddings into the database.
-        Should call self.init() first.
-        """
-        assert self.conn is not None, "Connection is not initialized"
-        assert self.cursor is not None, "Cursor is not initialized"
+        """Insert one batch of embeddings. Should call self.init() first.
 
+        thread_safe=True: the concurrent insert runner may call this from several
+        worker threads sharing this instance. Each call borrows its own connection
+        from self._insert_pool, so the workers drive parallel insert streams.
+        """
+        conn, cursor = self._acquire_insert_conn()
         try:
-            metadata_arr = np.array(metadata)
-            embeddings_arr = np.array(embeddings)
+            embeddings_f32 = np.asarray(embeddings, dtype=np.float32)
+            batch_data = [(int(metadata[i]), embeddings_f32[i].tobytes()) for i in range(len(metadata))]
 
-            batch_data = []
-            for i, row in enumerate(metadata_arr):
-                batch_data.append((int(row), self.vector_to_hex(embeddings_arr[i])))
-
-            self.cursor.executemany(self.insert_sql, batch_data)
-            self.cursor.execute("COMMIT")
-            self.cursor.execute("FLUSH TABLES")
-
-            return len(metadata), None
+            cursor.executemany(self.insert_sql, batch_data)
+            cursor.execute("COMMIT")
         except Exception as e:
             log.warning(f"Failed to insert data into Vector table ({self.table_name}), error: {e}")
+            # the connection may be left in a bad state; drop it instead of reusing
+            with suppress(Exception):
+                cursor.close()
+                conn.close()
             return 0, e
+        else:
+            self._insert_pool.put((conn, cursor))
+            return len(metadata), None
 
     def search_embedding(
         self,
@@ -193,17 +232,17 @@ def search_embedding(
         **kwargs,
     ) -> list[int]:
         assert self.conn is not None, "Connection is not initialized"
-        assert self.cursor is not None, "Cursor is not initialized"
+        assert self._search_cursor is not None, "Cursor is not initialized"
 
-        search_param = self.case_config.search_param()  # noqa: F841
+        query_bytes = self.vector_to_hex(query)
 
         try:
             if filters:
-                self.cursor.execute(self.select_sql_with_filter, (filters.get("id"), self.vector_to_hex(query), k))
+                self._search_cursor.execute(self.select_sql_with_filter, (filters.get("id"), query_bytes, k))
             else:
-                self.cursor.execute(self.select_sql, (self.vector_to_hex(query), k))
-            return [row[0] for row in self.cursor.fetchall()]
+                self._search_cursor.execute(self.select_sql, (query_bytes, k))
+            return [row[0] for row in self._search_cursor.fetchall()]
 
-        except mysql.Error:
+        except MySQLdb.Error:
             log.exception("Failed to execute search query")
             raise
diff --git a/vectordb_bench/backend/clients/alisql/cli.py b/vectordb_bench/backend/clients/alisql/cli.py
index aee3ec2b2..55eedaf97 100644
--- a/vectordb_bench/backend/clients/alisql/cli.py
+++ b/vectordb_bench/backend/clients/alisql/cli.py
@@ -85,6 +85,26 @@ class AliSQLHNSWTypedDict(AliSQLTypedDict):
         ),
     ]
 
+    shards: Annotated[
+        int | None,
+        click.option(
+            "--shards",
+            type=int,
+            help="Number of shards for the vector index",
+            required=False,
+        ),
+    ]
+
+    quantization: Annotated[
+        str | None,
+        click.option(
+            "--quantization",
+            type=click.Choice(["SQ8", "SQ16"], case_sensitive=False),
+            help="Quantization algorithm for the vector index",
+            required=False,
+        ),
+    ]
+
 
 @cli.command()
 @click_parameter_decorators_from_typed_dict(AliSQLHNSWTypedDict)
@@ -106,6 +126,8 @@ def AliSQLHNSW(
         db_case_config=AliSQLHNSWConfig(
             M=parameters["m"],
             ef_search=parameters["ef_search"],
+            shards=parameters["shards"],
+            quantization=parameters["quantization"].upper() if parameters["quantization"] is not None else None,
         ),
         **parameters,
     )
diff --git a/vectordb_bench/backend/clients/alisql/config.py b/vectordb_bench/backend/clients/alisql/config.py
index 16c56e90f..7d384c981 100644
--- a/vectordb_bench/backend/clients/alisql/config.py
+++ b/vectordb_bench/backend/clients/alisql/config.py
@@ -51,6 +51,8 @@ def parse_metric(self) -> str:
 class AliSQLHNSWConfig(AliSQLIndexConfig, DBCaseConfig):
     M: int | None = None
     ef_search: int | None = None
+    shards: int | None = None
+    quantization: str | None = None
     index: IndexType = IndexType.HNSW
 
     def index_param(self) -> dict:
@@ -58,6 +60,8 @@ def index_param(self) -> dict:
             "metric_type": self.parse_metric(),
             "index_type": self.index.value,
             "M": self.M,
+            "shards": self.shards,
+            "quantization": self.quantization,
         }
 
     def search_param(self) -> dict:
diff --git a/vectordb_bench/backend/clients/antfly/antfly.py b/vectordb_bench/backend/clients/antfly/antfly.py
index 9523e417e..be9a99c5b 100644
--- a/vectordb_bench/backend/clients/antfly/antfly.py
+++ b/vectordb_bench/backend/clients/antfly/antfly.py
@@ -1,7 +1,6 @@
 import base64
 import json
 import logging
-import math
 import os
 import struct
 import time
@@ -10,7 +9,9 @@
 
 import httpx
 
-from ..api import DBCaseConfig, MetricType, VectorDB
+from ...filter import Filter, FilterOp
+from ...payload import PayloadProfile
+from ..api import DBCaseConfig, VectorDB
 
 log = logging.getLogger(__name__)
 
@@ -35,6 +36,12 @@ def _pack_dense_f32(values: list[float]) -> str:
 
 
 def _make_client(base_url: str, timeout: float) -> httpx.Client:
+    raw_timeout = os.environ.get("ANTFLY_VDBBENCH_HTTP_TIMEOUT")
+    if raw_timeout:
+        try:
+            timeout = float(raw_timeout)
+        except ValueError:
+            log.warning("Ignoring invalid ANTFLY_VDBBENCH_HTTP_TIMEOUT=%r", raw_timeout)
     return httpx.Client(
         base_url=base_url,
         timeout=timeout,
@@ -58,6 +65,12 @@ def _detect_api_root(host: str, port: int) -> str:
 
 
 class Antfly(VectorDB):
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+        FilterOp.StrEqual,
+    ]
+
     def __init__(
         self,
         dim: int,
@@ -65,12 +78,15 @@ def __init__(
         db_case_config: DBCaseConfig,
         collection_name: str = "vdbbench",
         drop_old: bool = False,
+        with_scalar_labels: bool = False,
         **kwargs,
     ):
         self.db_config = db_config
         self.case_config = db_case_config
         self.collection_name = collection_name
         self.dim = dim
+        self.with_scalar_labels = with_scalar_labels
+        self._filter_query: dict[str, Any] | None = None
 
         # Antfly v0.1 used /api/v1; current antfly-zig serves the public DB API
         # at /db/v1. Auto-detect unless ANTFLY_API_ROOT pins it explicitly.
@@ -84,6 +100,7 @@ def __init__(
         self._store_port = db_config.get("store_port")
         self._use_direct_store_search = bool(db_config.get("use_direct_store_search"))
         self._pack_query_vectors = bool(db_config.get("pack_query_vectors"))
+        self._write_sync_level = os.environ.get("ANTFLY_VDBBENCH_SYNC_LEVEL", "write")
         self._direct_shard_id: str | None = None
         self._bench_status_last_log = 0.0
         num_shards = db_config.get("num_shards", 1)
@@ -192,7 +209,10 @@ def _wait_for_write_ready(self, client: httpx.Client):
         # legacy (Go) binaries return 500 "shard is still initializing" for a
         # few seconds after table creation. Probe with a throwaway document
         # (no embedding, so it never lands in the vector index) until a batch
-        # write succeeds, then delete it.
+        # write succeeds. The probe doc is intentionally left in place: deleting
+        # it would leave a tombstone, and a table that has ever deleted a doc
+        # loses the "all docs visible" fast path. Every dense query would then
+        # materialize the complete live-doc set as a positive filter.
         probe_key = "key:__circus_write_probe__"
         deadline = time.monotonic() + TABLE_READY_TIMEOUT
         last_error = None
@@ -203,10 +223,6 @@ def _wait_for_write_ready(self, client: httpx.Client):
                     json={"inserts": {probe_key: {"id": -1}}, "sync_level": "write"},
                 )
                 if r.is_success:
-                    client.post(
-                        f"/tables/{self.collection_name}/batch",
-                        json={"deletes": [probe_key], "sync_level": "write"},
-                    )
                     log.info("Write path is ready")
                     return
                 last_error = f"{r.status_code}: {r.text[:120]}"
@@ -425,20 +441,9 @@ def _store_base_url(self) -> str:
         return f"http://{self._store_host}:{self._store_port}"
 
     def need_normalize_cosine(self) -> bool:
-        return True
-
-    def _uses_cosine_distance(self) -> bool:
-        try:
-            return self.case_config.index_param().get("distance_metric") == "cosine"
-        except Exception:
-            return getattr(self.case_config, "metric_type", None) == MetricType.COSINE
-
-    @staticmethod
-    def _normalize_vector(vector: list[float]) -> list[float]:
-        norm = math.sqrt(sum(value * value for value in vector))
-        if norm == 0:
-            return vector
-        return [value / norm for value in vector]
+        # Antfly computes cosine norms natively. Returning True would make the
+        # shared runner normalize every vector before the adapter sees it.
+        return False
 
     @staticmethod
     def _pack_vector(vector: list[float]) -> str:
@@ -459,11 +464,15 @@ def _serialize_insert_vector(self, vector: list[float]) -> str | list[float]:
         return self._pack_vector(vector)
 
     def _metadata_query_body(self, query: list[float], k: int) -> dict[str, Any]:
-        return {
+        body = {
             "embeddings": {"vec": self._serialize_query_vector(query)},
             "limit": k,
+            "fields": [],
             **self.case_config.search_param(),
         }
+        if getattr(self, "_filter_query", None) is not None:
+            body["filter_query"] = self._filter_query
+        return body
 
     def _store_query_body(self, query: list[float], k: int) -> dict[str, Any]:
         search_params = self.case_config.search_param()
@@ -525,31 +534,49 @@ def optimize(self, data_size: int | None = None):
             self._wait_for_index_ready(client, expected_total=data_size)
             self._maybe_log_bench_status(client, "optimize_end", force=True)
 
+    def prepare_filter(self, filters: Filter):
+        if filters.type == FilterOp.NonFilter:
+            self._filter_query = None
+        elif filters.type == FilterOp.NumGE:
+            self._filter_query = {
+                "numeric_range": {
+                    "field": filters.int_field,
+                    "min": filters.int_value,
+                    "inclusive_min": True,
+                }
+            }
+        elif filters.type == FilterOp.StrEqual:
+            self._filter_query = {"term": {filters.label_field: filters.label_value}}
+        else:
+            msg = f"Unsupported Antfly filter: {filters}"
+            raise ValueError(msg)
+
     def insert_embeddings(
         self,
         embeddings: list[list[float]],
         metadata: list[int],
+        labels_data: list[str] | None = None,
         **kwargs: Any,
     ) -> tuple[int, Exception]:
         total = len(embeddings)
+        if self.with_scalar_labels and labels_data is None:
+            return 0, ValueError("Antfly label-filter load requires labels_data")
         try:
-            use_cosine = self._uses_cosine_distance()
             for start in range(0, total, BATCH_CHUNK_SIZE):
                 end = min(start + BATCH_CHUNK_SIZE, total)
                 inserts = {}
                 for i in range(start, end):
                     key = f"key:{metadata[i]}"
-                    embedding = embeddings[i]
-                    if use_cosine:
-                        embedding = self._normalize_vector(embedding)
-                    serialized_embedding = self._serialize_insert_vector(embedding)
+                    serialized_embedding = self._serialize_insert_vector(embeddings[i])
                     inserts[key] = {
                         "id": metadata[i],
                         "metadata": metadata[i],
                         SOURCE_FIELD: str(metadata[i]),
                         "_embeddings": {"vec": serialized_embedding},
                     }
-                payload = {"inserts": inserts, "sync_level": "write"}
+                    if self.with_scalar_labels:
+                        inserts[key]["labels"] = labels_data[i]
+                payload = {"inserts": inserts, "sync_level": self._write_sync_level}
                 r = self.client.post(f"/tables/{self.collection_name}/batch", json=payload)
                 r.raise_for_status()
                 self._maybe_log_bench_status(self.client, "insert")
@@ -562,12 +589,16 @@ def search_embedding(
         self,
         query: list[float],
         k: int = 100,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
         filters: dict | None = None,
         timeout: int | None = None,
         **kwargs: Any,
     ) -> list[int]:
-        if self._uses_cosine_distance():
-            query = self._normalize_vector(query)
+        if payload_profile != PayloadProfile.IDS_ONLY:
+            msg = f"Antfly VDBBench adapter only supports payload_profile={PayloadProfile.IDS_ONLY.value}"
+            raise NotImplementedError(msg)
+        if self._use_direct_store_search and self._filter_query is not None:
+            raise ValueError("Antfly filtered ANN requires the public metadata query API")
 
         if self._use_direct_store_search:
             if self._direct_shard_id is None:
diff --git a/vectordb_bench/backend/clients/antfly/cli.py b/vectordb_bench/backend/clients/antfly/cli.py
index 2c8566521..25029c30a 100644
--- a/vectordb_bench/backend/clients/antfly/cli.py
+++ b/vectordb_bench/backend/clients/antfly/cli.py
@@ -7,9 +7,12 @@
     CommonTypedDict,
     cli,
     click_parameter_decorators_from_typed_dict,
+    get_custom_case_config,
     run,
 )
+from ...cases import CaseType
 from .. import DB
+from ..api import MetricType
 
 
 class AntflyTypedDict(TypedDict):
@@ -56,6 +59,15 @@ class AntflyTypedDict(TypedDict):
             show_default=True,
         ),
     ]
+    metric_type: Annotated[
+        str | None,
+        click.option(
+            "--metric-type",
+            type=click.Choice([metric.value for metric in MetricType]),
+            help="Distance metric override. Defaults to the selected VDBBench case dataset metric.",
+            default=None,
+        ),
+    ]
 
 
 class AntflyAKNNTypedDict(CommonTypedDict, AntflyTypedDict): ...
@@ -66,6 +78,12 @@ class AntflyAKNNTypedDict(CommonTypedDict, AntflyTypedDict): ...
 def AntflyAKNN(**parameters: Unpack[AntflyAKNNTypedDict]):
     from .config import AntflyConfig, AntflyIndexConfig
 
+    metric_type = (
+        MetricType(parameters["metric_type"])
+        if parameters["metric_type"]
+        else CaseType[parameters["case_type"]].case_cls(get_custom_case_config(parameters)).dataset.data.metric_type
+    )
+
     run(
         db=DB.Antfly,
         db_config=AntflyConfig(
@@ -83,6 +101,7 @@ def AntflyAKNN(**parameters: Unpack[AntflyAKNNTypedDict]):
         db_case_config=AntflyIndexConfig(
             num_shards=parameters["num_shards"],
             search_effort=parameters["search_effort"],
+            metric_type=metric_type,
         ),
         **parameters,
     )
diff --git a/vectordb_bench/backend/clients/api.py b/vectordb_bench/backend/clients/api.py
index 5de201664..bac90b7be 100644
--- a/vectordb_bench/backend/clients/api.py
+++ b/vectordb_bench/backend/clients/api.py
@@ -1,11 +1,12 @@
 from abc import ABC, abstractmethod
 from contextlib import contextmanager
 from enum import StrEnum
-from typing import Any
+from typing import ClassVar
 
-from pydantic import BaseModel, ConfigDict, model_validator
+from pydantic import BaseModel, model_validator
 
 from vectordb_bench.backend.filter import Filter, FilterOp
+from vectordb_bench.backend.payload import PayloadProfile
 
 
 class MetricType(StrEnum):
@@ -13,6 +14,7 @@ class MetricType(StrEnum):
     COSINE = "COSINE"
     IP = "IP"
     DP = "DP"
+    BM25 = "BM25"
     HAMMING = "HAMMING"
     JACCARD = "JACCARD"
 
@@ -32,6 +34,7 @@ class IndexType(StrEnum):
     IVF_RABITQ = "IVF_RABITQ"
     Flat = "FLAT"
     AUTOINDEX = "AUTOINDEX"
+    FTS = "FTS"
     ES_HNSW = "hnsw"
     ES_HNSW_INT8 = "int8_hnsw"
     ES_HNSW_INT4 = "int4_hnsw"
@@ -43,9 +46,16 @@ class IndexType(StrEnum):
     GPU_IVF_PQ = "GPU_IVF_PQ"
     GPU_CAGRA = "GPU_CAGRA"
     SCANN = "scann"
+    VCHORDRQ = "vchordrq"
+    VCHORDG = "vchordg"
     SCANN_MILVUS = "SCANN_MILVUS"
+    SVS_VAMANA = "SVS_VAMANA"
+    SVS_VAMANA_LVQ = "SVS_VAMANA_LVQ"
+    SVS_VAMANA_LEANVEC = "SVS_VAMANA_LEANVEC"
     Hologres_HGraph = "HGraph"
     Hologres_Graph = "Graph"
+    IVF_HNSW_SQ = "IVF_HNSW_SQ"
+    IVF_HNSW_PQ = "IVF_HNSW_PQ"
     NONE = "NONE"
 
 
@@ -58,6 +68,29 @@ class SQType(StrEnum):
     FP32 = "FP32"
 
 
+class NonRetryableInsertError(RuntimeError):
+    non_retryable = True
+
+
+class PartialInsertError(NonRetryableInsertError):
+    def __init__(
+        self,
+        message: str,
+        *,
+        inserted_count: int,
+        successful_tenants: dict[str, int] | None = None,
+        failed_tenant: str | None = None,
+        failed_tenant_count: int | None = None,
+        cause: Exception | None = None,
+    ):
+        super().__init__(message)
+        self.inserted_count = inserted_count
+        self.successful_tenants = successful_tenants or {}
+        self.failed_tenant = failed_tenant
+        self.failed_tenant_count = failed_tenant_count
+        self.__cause__ = cause
+
+
 class DBConfig(ABC, BaseModel):
     """DBConfig contains the connection info of vector database
 
@@ -69,12 +102,13 @@ class DBConfig(ABC, BaseModel):
             ZillizCloudConfig.db_label = 1cu-perf
     """
 
-    model_config = ConfigDict(validate_default=True)
-
     db_label: str = ""
     version: str = ""
     note: str = ""
 
+    # Field names subclasses allow to be empty (optional creds, alt-route fields).
+    _extra_empty_skip: ClassVar[frozenset[str]] = frozenset()
+
     @staticmethod
     def common_short_configs() -> list[str]:
         """
@@ -95,15 +129,14 @@ def to_dict(self) -> dict:
 
     @model_validator(mode="before")
     @classmethod
-    def not_empty_field(cls, data: Any) -> Any:
-        if isinstance(data, dict):
-            skip = set(cls.common_short_configs()) | set(cls.common_long_configs())
-            for name, v in data.items():
-                if name in skip:
-                    continue
-                if isinstance(v, str) and len(v) == 0:
-                    msg = f"Empty string for field '{name}'!"
-                    raise ValueError(msg)
+    def not_empty_field(cls, data: any) -> any:
+        if not isinstance(data, dict):
+            return data
+        skip = set(cls.common_short_configs()) | set(cls.common_long_configs()) | cls._extra_empty_skip
+        empty = [k for k, v in data.items() if k not in skip and isinstance(v, str) and not v]
+        if empty:
+            msg = f"Empty field(s): {', '.join(empty)}"
+            raise ValueError(msg)
         return data
 
 
@@ -149,6 +182,11 @@ class VectorDB(ABC):
     supported_filter_types: list[FilterOp] = [FilterOp.NonFilter]
     name: str = ""
 
+    # Whether the client can share a single connection across threads.
+    # If False, concurrent runners will deep-copy the instance and call
+    # init() per thread instead of sharing the parent connection.
+    thread_safe: bool = True
+
     @classmethod
     def filter_supported(cls, filters: Filter) -> bool:
         """Ensure that the filters are supported before testing filtering cases."""
@@ -206,12 +244,91 @@ def need_normalize_cosine(self) -> bool:
         """Wheather this database need to normalize dataset to support COSINE"""
         return False
 
+    def supports_payload_profile(self, payload_profile: PayloadProfile) -> bool:
+        return payload_profile == PayloadProfile.IDS_ONLY
+
+    def has_text_field(self) -> bool:
+        return False
+
+    def supports_document_payload_profile(self, payload_profile: PayloadProfile) -> bool:
+        if payload_profile == PayloadProfile.IDS_ONLY:
+            return True
+        if payload_profile == PayloadProfile.TEXT:
+            return self.has_text_field()
+        return False
+
+    def poll_insert_readiness(self, expected_count: int) -> dict:
+        return {"fully_searchable": True, "fully_indexed": True, "additional_parameters": {}}
+
+    def set_multitenant_context(self, tenant_labels: list[str]) -> None:
+        self.multitenant_tenant_labels = tenant_labels
+
+    def supports_multitenant(self) -> bool:
+        return False
+
+    def validate_multitenant_schema(self) -> None:
+        return None
+
+    @classmethod
+    def supports_full_text_search(cls) -> bool:
+        """Return whether this client implements the full-text search API.
+
+        Backends that return True must implement insert_documents and
+        search_documents for raw text documents.
+        """
+        return False
+
+    def insert_documents(
+        self,
+        texts: list[str],
+        doc_ids: list[str],
+        **kwargs,
+    ) -> tuple[int, Exception | None]:
+        """Insert raw text documents for full-text search cases.
+
+        Args:
+            texts(list[str]): raw text documents to index.
+            doc_ids(list[str]): stable document IDs aligned with texts.
+            **kwargs(Any): backend or runner specific insert parameters.
+
+        Returns:
+            tuple[int, Exception | None]: inserted document count and an optional
+            error. Implementations should return the count of successfully
+            inserted documents even when reporting a partial failure.
+        """
+        msg = f"{self.name or self.__class__.__name__} does not support full-text document insert"
+        raise NotImplementedError(msg)
+
+    def search_documents(
+        self,
+        query: str,
+        k: int = 100,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+        **kwargs,
+    ) -> list[str]:
+        """Search full-text documents and return ranked document IDs.
+
+        Args:
+            query(str): raw query text.
+            k(int): number of nearest documents to return. Defaults to 100.
+            payload_profile(PayloadProfile): response payload shape requested by
+                the benchmark. The API still returns document IDs only; use
+                supports_document_payload_profile to reject unsupported profiles.
+            **kwargs(Any): backend or runner specific search parameters.
+
+        Returns:
+            list[str]: ranked document IDs for the query.
+        """
+        msg = f"{self.name or self.__class__.__name__} does not support full-text document search"
+        raise NotImplementedError(msg)
+
     @abstractmethod
     def insert_embeddings(
         self,
         embeddings: list[list[float]],
         metadata: list[int],
         labels_data: list[str] | None = None,
+        tenant_labels_data: list[str] | None = None,
         **kwargs,
     ) -> tuple[int, Exception]:
         """Insert the embeddings to the vector database. The default number of embeddings for
@@ -232,6 +349,8 @@ def search_embedding(
         self,
         query: list[float],
         k: int = 100,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+        tenant: str | None = None,
     ) -> list[int]:
         """Get k most similar embeddings to query vector.
 
diff --git a/vectordb_bench/backend/clients/aws_opensearch/aws_opensearch.py b/vectordb_bench/backend/clients/aws_opensearch/aws_opensearch.py
index 8d77d263b..eb0195b88 100644
--- a/vectordb_bench/backend/clients/aws_opensearch/aws_opensearch.py
+++ b/vectordb_bench/backend/clients/aws_opensearch/aws_opensearch.py
@@ -5,6 +5,7 @@
 
 from opensearchpy import OpenSearch
 
+from vectordb_bench import config
 from vectordb_bench.backend.filter import Filter, FilterOp
 
 from ..api import VectorDB
@@ -48,6 +49,7 @@ def __init__(
 
         log.info(f"AWS_OpenSearch client config: {self.db_config}")
         log.info(f"AWS_OpenSearch db case config : {self.case_config}")
+        self._is_serverless = ".aoss." in self.db_config.get("hosts", [{}])[0].get("host", "")
         client = OpenSearch(**self.db_config)
         if drop_old:
             log.info(f"AWS_OpenSearch client drop old index: {self.index_name}")
@@ -80,6 +82,9 @@ def _log_index_creation_info(self) -> None:
         log.info(f"All case_config parameters: {self.case_config.__dict__}")
 
     def _configure_cluster_settings(self, client: OpenSearch) -> None:
+        if self._is_serverless:
+            log.info("Skipping cluster settings for OpenSearch Serverless")
+            return
         cluster_settings_body = {
             "persistent": {
                 "knn.algo_param.index_thread_qty": self.case_config.index_thread_qty,
@@ -89,17 +94,19 @@ def _configure_cluster_settings(self, client: OpenSearch) -> None:
         client.cluster.put_settings(body=cluster_settings_body)
 
     def _build_index_settings(self) -> dict:
-        return {
+        settings = {
             "index": {
                 "knn": True,
                 "number_of_shards": self.case_config.number_of_shards,
                 "number_of_replicas": self.case_config.number_of_replicas,
-                "translog.flush_threshold_size": self.case_config.flush_threshold_size,
-                "knn.advanced.approximate_threshold": "-1",
-                "knn.algo_param.ef_search": self.case_config.ef_search,
             },
-            "refresh_interval": self.case_config.refresh_interval,
         }
+        if not self._is_serverless:
+            settings["index"]["translog.flush_threshold_size"] = self.case_config.flush_threshold_size
+            settings["index"]["knn.advanced.approximate_threshold"] = "-1"
+            settings["index"]["knn.algo_param.ef_search"] = self.case_config.ef_search
+            settings["refresh_interval"] = self.case_config.refresh_interval
+        return settings
 
     def _build_vector_field_config(self) -> dict:
         method_config = self.case_config.index_param()
@@ -108,6 +115,21 @@ def _build_vector_field_config(self) -> dict:
         if self.case_config.engine == AWSOS_Engine.s3vector:
             method_config = {"engine": "s3vector"}
 
+        # OpenSearch Serverless does not support 'engine' or 'encoder' in method config
+        if self._is_serverless and "engine" in method_config:
+            space_type = method_config.pop("space_type", self.case_config.parse_metric())
+            method_config.pop("engine", None)
+            if "parameters" in method_config:
+                method_config["parameters"].pop("encoder", None)
+            vector_field_config = {
+                "type": "knn_vector",
+                "dimension": self.dim,
+                "space_type": space_type,
+                "method": method_config,
+            }
+            log.info(f"Serverless vector field config: {vector_field_config}")
+            return vector_field_config
+
         if self.case_config.on_disk:
             space_type = self.case_config.parse_metric()
             vector_field_config = {
@@ -157,6 +179,12 @@ def _build_mappings(self, vector_field_config: dict) -> dict:
                 },
             }
             log.info("Using standard mappings with _source configuration for non-s3vector engines")
+
+        # Serverless stores the benchmark id in a dedicated numeric field (custom _id
+        # is not supported). Map it as a long so NumGE range filters work.
+        if self._is_serverless:
+            mappings["properties"]["id"] = {"type": "long"}
+
         return mappings
 
     def _create_opensearch_index(self, client: OpenSearch, settings: dict, mappings: dict) -> None:
@@ -210,6 +238,12 @@ def insert_embeddings(
         num_clients = self.case_config.number_of_indexing_clients or 1
         log.info(f"Number of indexing clients from case_config: {num_clients}")
 
+        # OpenSearch Serverless requires the single-client path because it does not
+        # support custom _id and needs the benchmark id stored in _source.
+        if self._is_serverless:
+            log.info("Using single client for data insertion (OpenSearch Serverless)")
+            return self._insert_with_single_client(embeddings, metadata, labels_data)
+
         if num_clients <= 1:
             log.info("Using single client for data insertion")
             return self._insert_with_single_client(embeddings, metadata, labels_data)
@@ -222,25 +256,49 @@ def _insert_with_single_client(
         metadata: list[int],
         labels_data: list[str] | None = None,
     ) -> tuple[int, Exception]:
-        insert_data = []
-        for i in range(len(embeddings)):
-            index_data = {"index": {"_index": self.index_name, self.id_col_name: metadata[i]}}
-            if self.with_scalar_labels and self.case_config.use_routing and labels_data is not None:
-                index_data["routing"] = labels_data[i]
-            insert_data.append(index_data)
-
-            other_data = {self.vector_col_name: embeddings[i]}
-            if self.with_scalar_labels and labels_data is not None:
-                other_data[self.label_col_name] = labels_data[i]
-            insert_data.append(other_data)
+        embeddings_list = list(embeddings)
+        batch_size = config.NUM_PER_BATCH if self._is_serverless else len(embeddings_list)
+        if self._is_serverless and batch_size <= 0:
+            raise ValueError("NUM_PER_BATCH must be greater than 0 for OpenSearch Serverless")
+        total_inserted = 0
 
-        try:
-            self.client.bulk(body=insert_data)
-            return len(embeddings), None
-        except Exception as e:
-            log.warning(f"Failed to insert data: {self.index_name} error: {e!s}")
-            time.sleep(10)
-            return self._insert_with_single_client(embeddings, metadata, labels_data)
+        for i in range(0, len(embeddings_list), batch_size):
+            batch_embeddings = embeddings_list[i : i + batch_size]
+            batch_metadata = metadata[i : i + batch_size]
+            batch_labels = labels_data[i : i + batch_size] if labels_data else None
+
+            insert_data = []
+            for j in range(len(batch_embeddings)):
+                if self._is_serverless:
+                    index_data = {"index": {"_index": self.index_name}}
+                else:
+                    index_data = {"index": {"_index": self.index_name, self.id_col_name: batch_metadata[j]}}
+
+                if self.with_scalar_labels and self.case_config.use_routing and batch_labels is not None:
+                    index_data["routing"] = batch_labels[j]
+                insert_data.append(index_data)
+
+                other_data = {self.vector_col_name: batch_embeddings[j]}
+                if self._is_serverless:
+                    other_data["id"] = batch_metadata[j]
+                if self.with_scalar_labels and batch_labels is not None:
+                    other_data[self.label_col_name] = batch_labels[j]
+                insert_data.append(other_data)
+
+            try:
+                self.client.bulk(body=insert_data)
+                total_inserted += len(batch_embeddings)
+            except Exception as e:
+                log.warning(f"Failed to insert batch: {self.index_name} error: {e!s}")
+                time.sleep(10)
+                try:
+                    self.client.bulk(body=insert_data)
+                    total_inserted += len(batch_embeddings)
+                except Exception as retry_e:
+                    log.warning(f"Retry failed for batch: {retry_e!s}")
+                    return total_inserted, retry_e
+
+        return total_inserted, None
 
     def _insert_with_multiple_clients(
         self,
@@ -402,24 +460,37 @@ def search_embedding(
         }
 
         try:
-            resp = self.client.search(
-                index=self.index_name,
-                body=body,
-                size=k,
-                _source=False,
-                docvalue_fields=[self.id_col_name],
-                stored_fields="_none_",
-                preference="_only_local" if self.case_config.number_of_shards == 1 else None,
-                routing=self.routing_key,
-            )
-            log.debug(f"Search took: {resp['took']}")
-            log.debug(f"Search shards: {resp['_shards']}")
-            log.debug(f"Search hits total: {resp['hits']['total']}")
-            try:
-                return [int(h["fields"][self.id_col_name][0]) for h in resp["hits"]["hits"]]
-            except Exception:
-                # empty results
-                return []
+            if self._is_serverless:
+                resp = self.client.search(
+                    index=self.index_name,
+                    body=body,
+                    size=k,
+                    _source=["id"],
+                    preference="_only_local" if self.case_config.number_of_shards == 1 else None,
+                    routing=self.routing_key,
+                )
+                try:
+                    return [int(h["_source"]["id"]) for h in resp["hits"]["hits"]]
+                except Exception:
+                    return []
+            else:
+                resp = self.client.search(
+                    index=self.index_name,
+                    body=body,
+                    size=k,
+                    _source=False,
+                    docvalue_fields=[self.id_col_name],
+                    stored_fields="_none_",
+                    preference="_only_local" if self.case_config.number_of_shards == 1 else None,
+                    routing=self.routing_key,
+                )
+                log.debug(f"Search took: {resp['took']}")
+                log.debug(f"Search shards: {resp['_shards']}")
+                log.debug(f"Search hits total: {resp['hits']['total']}")
+                try:
+                    return [int(h["fields"][self.id_col_name][0]) for h in resp["hits"]["hits"]]
+                except Exception:
+                    return []
         except Exception as e:
             log.warning(f"Failed to search: {self.index_name} error: {e!s}")
             raise e from None
@@ -429,7 +500,10 @@ def prepare_filter(self, filters: Filter):
         if filters.type == FilterOp.NonFilter:
             self.filter = None
         elif filters.type == FilterOp.NumGE:
-            self.filter = {"range": {self.id_col_name: {"gt": filters.int_value}}}
+            # Serverless stores the benchmark id in the "id" field of _source since it
+            # does not support custom _id. Filter on that stored field instead of _id.
+            filter_field = "id" if self._is_serverless else self.id_col_name
+            self.filter = {"range": {filter_field: {"gt": filters.int_value}}}
         elif filters.type == FilterOp.StrEqual:
             self.filter = {"term": {self.label_col_name: filters.label_value}}
             if self.case_config.use_routing:
@@ -468,6 +542,10 @@ def _update_ef_search(self):
             log.warning(f"Failed to update ef_search parameter: {e}")
 
     def _update_replicas(self):
+        if self._is_serverless:
+            log.info("Skipping replica updates for OpenSearch Serverless")
+            return
+
         index_settings = self.client.indices.get_settings(index=self.index_name)
         current_number_of_replicas = int(index_settings[self.index_name]["settings"]["index"]["number_of_replicas"])
         log.info(
@@ -490,6 +568,11 @@ def _wait_till_green(self):
         log.info(f"Index {self.index_name} is green..")
 
     def _refresh_index(self):
+        if self._is_serverless:
+            log.info("Skipping manual refresh for OpenSearch Serverless, waiting for auto-refresh...")
+            time.sleep(10)
+            return
+
         log.debug(f"Starting refresh for index {self.index_name}")
         while True:
             try:
@@ -505,6 +588,10 @@ def _refresh_index(self):
         log.debug(f"Completed refresh for index {self.index_name}")
 
     def _do_force_merge(self):
+        if self._is_serverless:
+            log.info("Skipping force merge for OpenSearch Serverless")
+            return
+
         log.info(f"Updating the Index thread qty to {self.case_config.index_thread_qty_during_force_merge}.")
 
         cluster_settings_body = {
@@ -530,6 +617,10 @@ def _do_force_merge(self):
         log.info(f"Completed force merge for index {self.index_name}")
 
     def _load_graphs_to_memory(self, client: OpenSearch):
+        if self._is_serverless:
+            log.info("Skipping warmup API for OpenSearch Serverless")
+            return
+
         if self.case_config.engine != AWSOS_Engine.lucene:
             log.info("Calling warmup API to load graphs into memory")
             warmup_endpoint = f"/_plugins/_knn/warmup/{self.index_name}"
diff --git a/vectordb_bench/backend/clients/aws_opensearch/cli.py b/vectordb_bench/backend/clients/aws_opensearch/cli.py
index 5bc80a687..59bb68a23 100644
--- a/vectordb_bench/backend/clients/aws_opensearch/cli.py
+++ b/vectordb_bench/backend/clients/aws_opensearch/cli.py
@@ -25,8 +25,14 @@ def optional_secret_str(value: str | None) -> SecretStr | None:
 class AWSOpenSearchTypedDict(TypedDict):
     host: Annotated[str, click.option("--host", type=str, help="Db host", required=True)]
     port: Annotated[int, click.option("--port", type=int, default=80, help="Db Port")]
-    user: Annotated[str | None, click.option("--user", type=str, help="Db User")]
-    password: Annotated[str | None, click.option("--password", type=str, help="Db password")]
+    user: Annotated[str | None, click.option("--user", type=str, help="Db User (not needed for Serverless)")]
+    password: Annotated[
+        str | None, click.option("--password", type=str, help="Db password (not needed for Serverless)")
+    ]
+    is_serverless: Annotated[bool, click.option("--serverless", is_flag=True, help="Use OpenSearch Serverless")]
+    aws_region: Annotated[
+        str, click.option("--aws-region", type=str, default="us-east-1", help="AWS region for Serverless")
+    ]
     number_of_shards: Annotated[
         int,
         click.option("--number-of-shards", type=int, help="Number of primary shards for the index", default=1),
@@ -172,6 +178,12 @@ class AWSOpenSearchHNSWTypedDict(CommonTypedDict, AWSOpenSearchTypedDict, HNSWFl
 def AWSOpenSearch(**parameters: Unpack[AWSOpenSearchHNSWTypedDict]):
     from .config import AWSOpenSearchConfig, AWSOpenSearchIndexConfig
 
+    is_serverless = parameters.get("serverless", False)
+    log.info(f"Is Serverless: {is_serverless}")
+
+    if not is_serverless and not parameters.get("user"):
+        log.warning("Standard OpenSearch mode requires user and password.")
+
     # Set default values for HNSW parameters if not provided and not using s3vector
     engine = AWSOS_Engine(parameters["engine"])
     ef_construction = parameters.get("ef_construction")
@@ -192,8 +204,10 @@ def AWSOpenSearch(**parameters: Unpack[AWSOpenSearchHNSWTypedDict]):
         db_config=AWSOpenSearchConfig(
             host=parameters["host"],
             port=parameters["port"],
-            user=parameters["user"],
-            password=optional_secret_str(parameters["password"]),
+            user=parameters.get("user"),
+            password=optional_secret_str(parameters.get("password")),
+            is_serverless=is_serverless,
+            aws_region=parameters.get("aws_region", "us-east-1"),
         ),
         db_case_config=AWSOpenSearchIndexConfig(
             number_of_shards=parameters["number_of_shards"],
diff --git a/vectordb_bench/backend/clients/aws_opensearch/config.py b/vectordb_bench/backend/clients/aws_opensearch/config.py
index f6c81923d..134da869b 100644
--- a/vectordb_bench/backend/clients/aws_opensearch/config.py
+++ b/vectordb_bench/backend/clients/aws_opensearch/config.py
@@ -1,8 +1,8 @@
 import logging
 from enum import Enum
-from typing import Any
+from typing import ClassVar
 
-from pydantic import BaseModel, SecretStr, model_validator
+from pydantic import BaseModel, SecretStr
 
 from ..api import DBCaseConfig, DBConfig, MetricType
 
@@ -10,12 +10,66 @@
 
 
 class AWSOpenSearchConfig(DBConfig, BaseModel):
+    _extra_empty_skip: ClassVar[frozenset[str]] = frozenset({"user", "password", "host"})
+
     host: str = ""
     port: int = 80
     user: str | None = None
     password: SecretStr | None = None
+    is_serverless: bool = False
+    aws_region: str = "us-east-1"
 
     def to_dict(self) -> dict:
+        if self.is_serverless:
+            return self._serverless_config()
+        return self._standard_config()
+
+    def _serverless_config(self) -> dict:
+        """Configuration for OpenSearch Serverless using AWS SigV4 authentication."""
+        log.info(f"Configuring OpenSearch Serverless - Host: {self.host}, Region: {self.aws_region}")
+
+        try:
+            import boto3
+        except ImportError as e:
+            raise ImportError("boto3 is required for OpenSearch Serverless. Install with: pip install boto3") from e
+
+        try:
+            from opensearchpy import RequestsHttpConnection
+            from requests_aws4auth import AWS4Auth
+        except ImportError as e:
+            raise ImportError(
+                "requests-aws4auth is required for OpenSearch Serverless. "
+                "Install with: pip install requests-aws4auth"
+            ) from e
+
+        session = boto3.Session()
+        credentials = session.get_credentials()
+        if not credentials:
+            raise ValueError("AWS credentials not found. Please configure AWS credentials.")
+
+        credentials = credentials.get_frozen_credentials()
+        auth = AWS4Auth(
+            credentials.access_key,
+            credentials.secret_key,
+            self.aws_region,
+            "aoss",
+            session_token=credentials.token,
+        )
+
+        return {
+            "hosts": [{"host": self.host, "port": 443}],
+            "http_auth": auth,
+            "use_ssl": True,
+            "verify_certs": True,
+            "connection_class": RequestsHttpConnection,
+            "timeout": 600,
+            "max_retries": 3,
+            "retry_on_timeout": True,
+            "http_compress": False,
+        }
+
+    def _standard_config(self) -> dict:
+        """Configuration for standard OpenSearch with basic auth."""
         use_ssl = self.port == 443
         http_auth = (
             (self.user, self.password.get_secret_value())
@@ -33,19 +87,6 @@ def to_dict(self) -> dict:
             "timeout": 600,
         }
 
-    @model_validator(mode="before")
-    @classmethod
-    def not_empty_field(cls, data: Any) -> Any:
-        if isinstance(data, dict):
-            skip = set(cls.common_short_configs()) | set(cls.common_long_configs()) | {"user", "password", "host"}
-            for name, v in data.items():
-                if name in skip:
-                    continue
-                if isinstance(v, str) and len(v) == 0:
-                    msg = f"Empty string for field '{name}'!"
-                    raise ValueError(msg)
-        return data
-
 
 class AWSOS_Engine(Enum):
     faiss = "faiss"
diff --git a/vectordb_bench/backend/clients/chroma/chroma.py b/vectordb_bench/backend/clients/chroma/chroma.py
index 6942f4fc9..909c47966 100644
--- a/vectordb_bench/backend/clients/chroma/chroma.py
+++ b/vectordb_bench/backend/clients/chroma/chroma.py
@@ -3,6 +3,8 @@
 
 import chromadb
 
+from vectordb_bench.backend.filter import Filter, FilterOp
+
 from ..api import VectorDB
 from .config import ChromaIndexConfig
 
@@ -17,6 +19,11 @@ class ChromaClient(VectorDB):
     To change to running in process, modify the HttpClient() in __init__() and init().
     """
 
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+    ]
+
     def __init__(
         self,
         dim: int,
@@ -35,13 +42,14 @@ def __init__(
 
         if drop_old:
             try:
-                client.reset()
-            except Exception:
-                drop_old = False
                 log.info(f"Chroma client drop_old collection: {self.collection_name}")
+                client.delete_collection(self.collection_name)
+            except Exception as e:
+                log.info(f"Chroma client collection was not dropped: {self.collection_name}, error: {e!s}")
 
         self.client = None
         self.collection = None
+        self._where_filter: dict | None = None
 
     @contextmanager
     def init(self):
@@ -85,10 +93,17 @@ def search_embedding(
         self, query: list[float], k: int = 100, filters: dict | None = None, timeout: int | None = None
     ) -> list[int]:
         assert self.client is not None, "Please call self.init() before"
-        if filters:
-            results = self.collection.query(
-                query_embeddings=[query], n_results=k, where={"id": {"$gt": filters.get("id")}}
-            )
+        if self._where_filter is not None:
+            results = self.collection.query(query_embeddings=[query], n_results=k, where=self._where_filter)
         else:
             results = self.collection.query(query_embeddings=[query], n_results=k)
         return [int(idx) for idx in results["ids"][0]]
+
+    def prepare_filter(self, filters: Filter):
+        if filters.type == FilterOp.NonFilter:
+            self._where_filter = None
+        elif filters.type == FilterOp.NumGE:
+            self._where_filter = {"index": {"$gte": filters.int_value}}
+        else:
+            msg = f"Unsupported Chroma filter: {filters}"
+            raise ValueError(msg)
diff --git a/vectordb_bench/backend/clients/chroma/cli.py b/vectordb_bench/backend/clients/chroma/cli.py
index 64a2f972f..3445660a8 100644
--- a/vectordb_bench/backend/clients/chroma/cli.py
+++ b/vectordb_bench/backend/clients/chroma/cli.py
@@ -50,6 +50,7 @@ def Chroma(**parameters: Unpack[ChromaTypeDict]):
     run(
         db=DBTYPE,
         db_config=ChromaConfig(
+            db_label=parameters["db_label"],
             user=parameters["user"],
             password=SecretStr(parameters["password"]) if parameters["password"] else None,
             host=SecretStr(parameters["host"]),
diff --git a/vectordb_bench/backend/clients/chroma/config.py b/vectordb_bench/backend/clients/chroma/config.py
index 8c1cac286..a1d9903d1 100644
--- a/vectordb_bench/backend/clients/chroma/config.py
+++ b/vectordb_bench/backend/clients/chroma/config.py
@@ -26,7 +26,7 @@ def to_dict(self) -> dict:
 
 
 class ChromaIndexConfig(ChromaConfig, DBCaseConfig):
-    metric_type: MetricType = MetricType.COSINE
+    metric_type: MetricType = "cosine"
     m: int = 16
     ef_construct: int = 100
     ef_search: int | None = 100
diff --git a/vectordb_bench/backend/clients/doris/config.py b/vectordb_bench/backend/clients/doris/config.py
index 9caf72eb3..7c79ba728 100644
--- a/vectordb_bench/backend/clients/doris/config.py
+++ b/vectordb_bench/backend/clients/doris/config.py
@@ -1,5 +1,4 @@
 import logging
-from typing import Any
 
 from pydantic import BaseModel, SecretStr, model_validator
 
@@ -20,8 +19,8 @@ class DorisConfig(DBConfig):
 
     @model_validator(mode="before")
     @classmethod
-    def not_empty_field(cls, data: Any) -> Any:
-        return data  # allow all fields including empty strings
+    def not_empty_field(cls, data: any) -> any:
+        return data
 
     def to_dict(self) -> dict:
         pwd_str = self.password.get_secret_value()
diff --git a/vectordb_bench/backend/clients/doris/doris.py b/vectordb_bench/backend/clients/doris/doris.py
index 82b3a12da..01984d665 100644
--- a/vectordb_bench/backend/clients/doris/doris.py
+++ b/vectordb_bench/backend/clients/doris/doris.py
@@ -13,6 +13,8 @@
 
 
 class Doris(VectorDB):
+    thread_safe: bool = False
+
     def __init__(
         self,
         dim: int,
diff --git a/vectordb_bench/backend/clients/elastic_cloud/cli.py b/vectordb_bench/backend/clients/elastic_cloud/cli.py
index 55e521d8f..f277a6ff2 100644
--- a/vectordb_bench/backend/clients/elastic_cloud/cli.py
+++ b/vectordb_bench/backend/clients/elastic_cloud/cli.py
@@ -17,11 +17,60 @@
 class ElasticCloudTypedDict(TypedDict):
     cloud_id: Annotated[
         str,
-        click.option("--cloud-id", type=str, help="Elastic Cloud ID", required=True),
+        click.option(
+            "--cloud-id",
+            type=str,
+            help="Elastic Cloud ID. Omit when connecting to a self-hosted ES via --host.",
+            required=False,
+            default="",
+        ),
+    ]
+    scheme: Annotated[
+        str,
+        click.option(
+            "--scheme",
+            type=click.Choice(["http", "https"], case_sensitive=False),
+            help="Scheme for host-based connection.",
+            required=False,
+            default="https",
+            show_default=True,
+        ),
+    ]
+    host: Annotated[
+        str,
+        click.option(
+            "--host",
+            type=str,
+            help="Elasticsearch host (for self-hosted ES; alternative to --cloud-id).",
+            required=False,
+            default="",
+        ),
+    ]
+    port: Annotated[
+        int,
+        click.option(
+            "--port",
+            type=int,
+            help="Elasticsearch port (for host-based connection).",
+            required=False,
+            default=9200,
+            show_default=True,
+        ),
+    ]
+    user: Annotated[
+        str,
+        click.option(
+            "--user",
+            type=str,
+            help="Elasticsearch user.",
+            required=False,
+            default="elastic",
+            show_default=True,
+        ),
     ]
     password: Annotated[
         str,
-        click.option("--password", type=str, help="Elastic Cloud password", required=True),
+        click.option("--password", type=str, help="Elasticsearch password", required=True),
     ]
     number_of_shards: Annotated[
         int,
@@ -170,7 +219,11 @@ def ElasticCloudHNSW(**parameters: Unpack[ElasticCloudHNSWTypedDict]):
         db=DBTYPE,
         db_config=ElasticCloudConfig(
             db_label=parameters["db_label"],
-            cloud_id=SecretStr(parameters["cloud_id"]),
+            cloud_id=SecretStr(parameters["cloud_id"]) if parameters["cloud_id"] else None,
+            scheme=parameters["scheme"],
+            host=parameters["host"],
+            port=parameters["port"],
+            user=parameters["user"],
             password=SecretStr(parameters["password"]),
         ),
         db_case_config=ElasticCloudIndexConfig(
@@ -202,7 +255,11 @@ def ElasticCloudHNSWInt8(**parameters: Unpack[ElasticCloudHNSWTypedDict]):
         db=DBTYPE,
         db_config=ElasticCloudConfig(
             db_label=parameters["db_label"],
-            cloud_id=SecretStr(parameters["cloud_id"]),
+            cloud_id=SecretStr(parameters["cloud_id"]) if parameters["cloud_id"] else None,
+            scheme=parameters["scheme"],
+            host=parameters["host"],
+            port=parameters["port"],
+            user=parameters["user"],
             password=SecretStr(parameters["password"]),
         ),
         db_case_config=ElasticCloudIndexConfig(
@@ -234,7 +291,11 @@ def ElasticCloudHNSWInt4(**parameters: Unpack[ElasticCloudHNSWTypedDict]):
         db=DBTYPE,
         db_config=ElasticCloudConfig(
             db_label=parameters["db_label"],
-            cloud_id=SecretStr(parameters["cloud_id"]),
+            cloud_id=SecretStr(parameters["cloud_id"]) if parameters["cloud_id"] else None,
+            scheme=parameters["scheme"],
+            host=parameters["host"],
+            port=parameters["port"],
+            user=parameters["user"],
             password=SecretStr(parameters["password"]),
         ),
         db_case_config=ElasticCloudIndexConfig(
@@ -266,7 +327,11 @@ def ElasticCloudHNSWBBQ(**parameters: Unpack[ElasticCloudHNSWTypedDict]):
         db=DBTYPE,
         db_config=ElasticCloudConfig(
             db_label=parameters["db_label"],
-            cloud_id=SecretStr(parameters["cloud_id"]),
+            cloud_id=SecretStr(parameters["cloud_id"]) if parameters["cloud_id"] else None,
+            scheme=parameters["scheme"],
+            host=parameters["host"],
+            port=parameters["port"],
+            user=parameters["user"],
             password=SecretStr(parameters["password"]),
         ),
         db_case_config=ElasticCloudIndexConfig(
diff --git a/vectordb_bench/backend/clients/elastic_cloud/config.py b/vectordb_bench/backend/clients/elastic_cloud/config.py
index 7ee54b49c..9f9961efc 100644
--- a/vectordb_bench/backend/clients/elastic_cloud/config.py
+++ b/vectordb_bench/backend/clients/elastic_cloud/config.py
@@ -1,19 +1,69 @@
 from enum import StrEnum
+from typing import ClassVar
 
-from pydantic import BaseModel, SecretStr
+from pydantic import BaseModel, SecretStr, model_validator
 
 from ..api import DBCaseConfig, DBConfig, IndexType, MetricType
+from ..elasticsearch_compatible import build_bm25_similarity_settings, build_fts_index_param
 
 
 class ElasticCloudConfig(DBConfig, BaseModel):
-    cloud_id: SecretStr
-    password: SecretStr
+    _extra_empty_skip: ClassVar[frozenset[str]] = frozenset(
+        {"cloud_id", "scheme", "host", "user", "user_name", "password"}
+    )
+
+    cloud_id: SecretStr | None = None
+    scheme: str | None = None
+    host: SecretStr | None = None
+    port: int = 9200
+    user: str | None = None
+    user_name: str | None = None
+    password: SecretStr | None = None
+    use_ssl: bool = False
+    verify_certs: bool = True
+
+    @model_validator(mode="after")
+    def _check_connection_target(self) -> "ElasticCloudConfig":
+        has_cloud_id = bool(self.cloud_id and self.cloud_id.get_secret_value())
+        if not has_cloud_id and not self.host:
+            msg = "Either cloud_id or host must be set"
+            raise ValueError(msg)
+        return self
+
+    def _auth_user(self) -> str:
+        return self.user_name or self.user or "elastic"
 
     def to_dict(self) -> dict:
-        return {
-            "cloud_id": self.cloud_id.get_secret_value(),
-            "basic_auth": ("elastic", self.password.get_secret_value()),
+        if self.cloud_id and self.cloud_id.get_secret_value():
+            if not self.password:
+                msg = "password is required when cloud_id is set"
+                raise ValueError(msg)
+            return {
+                "cloud_id": self.cloud_id.get_secret_value(),
+                "basic_auth": (self._auth_user(), self.password.get_secret_value()),
+            }
+
+        if not self.host:
+            msg = "Either cloud_id or host must be set"
+            raise ValueError(msg)
+
+        host = self.host.get_secret_value()
+        if host.startswith(("http://", "https://")):
+            url = host
+        else:
+            scheme = self.scheme or ("https" if self.use_ssl else "http")
+            url = f"{scheme}://{host}:{self.port}"
+
+        config = {
+            "hosts": [url],
+            "verify_certs": self.verify_certs,
         }
+        if self.password:
+            config["basic_auth"] = (self._auth_user(), self.password.get_secret_value())
+        elif self.user_name or (self.user and self.user != "elastic"):
+            msg = "password is required when user_name is set"
+            raise ValueError(msg)
+        return config
 
 
 class ESElementType(StrEnum):
@@ -85,3 +135,22 @@ def search_param(self) -> dict:
         return {
             "num_candidates": self.num_candidates,
         }
+
+
+class ElasticCloudFtsConfig(BaseModel, DBCaseConfig):
+    number_of_shards: int = 1
+    number_of_replicas: int = 0
+    refresh_interval: str = "30s"
+    use_force_merge: bool = True
+    metric_type: MetricType = MetricType.BM25
+    bm25_k1: float | None = None
+    bm25_b: float | None = None
+
+    def index_param(self) -> dict:
+        return build_fts_index_param(self.bm25_k1, self.bm25_b)
+
+    def search_param(self) -> dict:
+        return {}
+
+    def similarity_settings(self) -> dict:
+        return build_bm25_similarity_settings(self.bm25_k1, self.bm25_b)
diff --git a/vectordb_bench/backend/clients/elastic_cloud/elastic_cloud.py b/vectordb_bench/backend/clients/elastic_cloud/elastic_cloud.py
index ae3350ddf..92951a0dc 100644
--- a/vectordb_bench/backend/clients/elastic_cloud/elastic_cloud.py
+++ b/vectordb_bench/backend/clients/elastic_cloud/elastic_cloud.py
@@ -6,9 +6,10 @@
 from elasticsearch.helpers import bulk
 
 from vectordb_bench.backend.filter import Filter, FilterOp
+from vectordb_bench.backend.payload import PayloadProfile
 
 from ..api import VectorDB
-from .config import ElasticCloudIndexConfig
+from .config import ElasticCloudFtsConfig, ElasticCloudIndexConfig
 
 for logger in ("elasticsearch", "elastic_transport"):
     logging.getLogger(logger).setLevel(logging.WARNING)
@@ -30,7 +31,7 @@ def __init__(
         self,
         dim: int,
         db_config: dict,
-        db_case_config: ElasticCloudIndexConfig,
+        db_case_config: ElasticCloudIndexConfig | ElasticCloudFtsConfig,
         indice: str = "vdb_bench_indice",  # must be lowercase
         id_col_name: str = "id",
         label_col_name: str = "label",
@@ -47,6 +48,11 @@ def __init__(
         self.label_col_name = label_col_name
         self.vector_col_name = vector_col_name
         self.with_scalar_labels = with_scalar_labels
+        self._is_fts = isinstance(db_case_config, ElasticCloudFtsConfig)
+        self.text_col_name = "text"
+        self.filter_id_col_name = "filter_id"
+        if self._is_fts:
+            self.id_col_name = "doc_id"
 
         from elasticsearch import Elasticsearch
 
@@ -59,6 +65,13 @@ def __init__(
                 client.indices.delete(index=self.indice)
             self._create_indice(client)
 
+    @classmethod
+    def supports_full_text_search(cls) -> bool:
+        return True
+
+    def has_text_field(self) -> bool:
+        return bool(getattr(self, "_is_fts", False) and getattr(self, "text_col_name", None))
+
     @contextmanager
     def init(self) -> None:
         """connect to elasticsearch"""
@@ -71,6 +84,18 @@ def init(self) -> None:
         del self.client
 
     def _create_indice(self, client: any) -> None:
+        if self._is_fts:
+            mappings = self.case_config.index_param()
+            index_settings = {
+                "number_of_shards": self.case_config.number_of_shards,
+                "number_of_replicas": self.case_config.number_of_replicas,
+                "refresh_interval": self.case_config.refresh_interval,
+            }
+            index_settings.update(self.case_config.similarity_settings())
+            settings = {"index": index_settings}
+            client.indices.create(index=self.indice, mappings=mappings, settings=settings)
+            return
+
         mappings = {
             "_source": {"excludes": [self.vector_col_name]},
             "properties": {
@@ -149,13 +174,64 @@ def insert_embeddings(
             log.warning(f"Failed to insert data: {self.indice} error: {e!s}")
             return (0, e)
 
+    def insert_documents(
+        self,
+        texts: Iterable[str],
+        doc_ids: list[str],
+        **kwargs,
+    ) -> tuple[int, Exception | None]:
+        if not getattr(self, "_is_fts", False):
+            msg = "ElasticCloud full-text insert requires ElasticCloudFtsConfig"
+            raise RuntimeError(msg)
+        assert self.client is not None, "should self.init() first"
+        docs = list(texts)
+        if len(docs) != len(doc_ids):
+            msg = f"Mismatch between texts ({len(docs)}) and doc_ids ({len(doc_ids)}) lengths"
+            raise ValueError(msg)
+        filter_ids = kwargs.get("filter_ids")
+        if filter_ids is not None and len(filter_ids) != len(docs):
+            msg = f"Mismatch between texts ({len(docs)}) and filter_ids ({len(filter_ids)}) lengths"
+            raise ValueError(msg)
+
+        actions = []
+        for i, doc in enumerate(docs):
+            source = {
+                self.id_col_name: str(doc_ids[i]),
+                self.text_col_name: doc,
+            }
+            if filter_ids is not None:
+                source[self.filter_id_col_name] = int(filter_ids[i])
+            actions.append(
+                {
+                    "_index": self.indice,
+                    "_id": str(doc_ids[i]),
+                    "_source": source,
+                }
+            )
+        try:
+            result = bulk(self.client, actions)
+            return result[0], None
+        except Exception as e:
+            log.warning(f"Failed to insert FTS docs: {self.indice} error: {e!s}")
+            return 0, e
+
     def prepare_filter(self, filters: Filter):
         self.routing_key = None
+        is_fts = getattr(self, "_is_fts", False)
         if filters.type == FilterOp.NonFilter:
-            self.filter = []
+            self.filter = None if is_fts else []
         elif filters.type == FilterOp.NumGE:
-            self.filter = {"range": {self.id_col_name: {"gt": filters.int_value}}}
+            if is_fts:
+                if getattr(filters, "int_field", None) != self.filter_id_col_name:
+                    msg = f"ElasticCloud FTS filters only support int_field='{self.filter_id_col_name}'"
+                    raise ValueError(msg)
+                self.filter = {"range": {self.filter_id_col_name: {"gte": filters.int_value}}}
+            else:
+                self.filter = {"range": {self.id_col_name: {"gte": filters.int_value}}}
         elif filters.type == FilterOp.StrEqual:
+            if is_fts:
+                msg = f"Not support Filter for ElasticCloud FTS - {filters}"
+                raise ValueError(msg)
             self.filter = {"term": {self.label_col_name: filters.label_value}}
             if self.case_config.use_routing:
                 self.routing_key = filters.label_value
@@ -203,6 +279,50 @@ def search_embedding(
         )
         return [h["fields"][self.id_col_name][0] for h in res["hits"]["hits"]]
 
+    def search_documents(
+        self,
+        query: str,
+        k: int = 100,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+        **kwargs,
+    ) -> list[str]:
+        if not getattr(self, "_is_fts", False):
+            msg = "ElasticCloud full-text search requires ElasticCloudFtsConfig"
+            raise RuntimeError(msg)
+        if not self.supports_document_payload_profile(payload_profile):
+            msg = f"ElasticCloud does not support document payload_profile={payload_profile.value}"
+            raise NotImplementedError(msg)
+        assert self.client is not None, "should self.init() first"
+        source = [self.text_col_name] if payload_profile == PayloadProfile.TEXT else False
+        filter_path = ["hits.hits._id", f"hits.hits.fields.{self.id_col_name}"]
+        if payload_profile == PayloadProfile.TEXT:
+            filter_path.append(f"hits.hits._source.{self.text_col_name}")
+        query_clause = {"match": {self.text_col_name: query}}
+        if getattr(self, "filter", None):
+            query_clause = {"bool": {"must": query_clause, "filter": self.filter}}
+        search_kwargs = {
+            "index": self.indice,
+            "query": query_clause,
+            "size": k,
+            "_source": source,
+            "docvalue_fields": [self.id_col_name],
+            "filter_path": filter_path,
+        }
+        if payload_profile != PayloadProfile.TEXT:
+            search_kwargs["stored_fields"] = "_none_"
+        res = self.client.search(**search_kwargs)
+        doc_ids = []
+        for hit in res.get("hits", {}).get("hits", []):
+            if hit.get("_id") is not None:
+                doc_ids.append(str(hit["_id"]))
+                continue
+
+            fields = hit.get("fields", {})
+            values = fields.get(self.id_col_name, [])
+            if values:
+                doc_ids.append(str(values[0]))
+        return doc_ids
+
     def optimize(self, data_size: int | None = None):
         """optimize will be called between insertion and search in performance cases."""
         assert self.client is not None, "should self.init() first"
diff --git a/vectordb_bench/backend/clients/elasticsearch_compatible.py b/vectordb_bench/backend/clients/elasticsearch_compatible.py
new file mode 100644
index 000000000..6e040b3c6
--- /dev/null
+++ b/vectordb_bench/backend/clients/elasticsearch_compatible.py
@@ -0,0 +1,23 @@
+def build_fts_index_param(bm25_k1: float | None, bm25_b: float | None) -> dict:
+    text_mapping = {"type": "text"}
+    if bm25_k1 is not None or bm25_b is not None:
+        text_mapping["similarity"] = "vdbbench_bm25"
+    return {
+        "properties": {
+            "doc_id": {"type": "keyword"},
+            "filter_id": {"type": "long"},
+            "text": text_mapping,
+        },
+    }
+
+
+def build_bm25_similarity_settings(bm25_k1: float | None, bm25_b: float | None) -> dict:
+    if bm25_k1 is None and bm25_b is None:
+        return {}
+
+    bm25_settings = {"type": "BM25"}
+    if bm25_k1 is not None:
+        bm25_settings["k1"] = bm25_k1
+    if bm25_b is not None:
+        bm25_settings["b"] = bm25_b
+    return {"similarity": {"vdbbench_bm25": bm25_settings}}
diff --git a/vectordb_bench/backend/clients/endee/cli.py b/vectordb_bench/backend/clients/endee/cli.py
index fb6e3c37c..5c9f0c9d3 100644
--- a/vectordb_bench/backend/clients/endee/cli.py
+++ b/vectordb_bench/backend/clients/endee/cli.py
@@ -10,6 +10,7 @@
     click_parameter_decorators_from_typed_dict,
     get_custom_case_config,
     parse_task_stages,
+    resolve_db_note,
 )
 from vectordb_bench.models import (
     CaseConfig,
@@ -94,6 +95,7 @@ def Endee(**parameters):
 
     # Filter out None values before creating config
     params_for_nd = {k: v for k, v in parameters.items() if v is not None}
+    params_for_nd["note"] = resolve_db_note(parameters["note"], parameters["note_file"])
     db_config = EndeeConfig(**params_for_nd)
 
     custom_case_config = get_custom_case_config(parameters)
diff --git a/vectordb_bench/backend/clients/hologres/hologres.py b/vectordb_bench/backend/clients/hologres/hologres.py
index 9fecc5c01..c396e23b0 100644
--- a/vectordb_bench/backend/clients/hologres/hologres.py
+++ b/vectordb_bench/backend/clients/hologres/hologres.py
@@ -2,6 +2,8 @@
 
 import json
 import logging
+import struct
+import threading
 from collections.abc import Generator
 from contextlib import contextmanager
 from io import StringIO
@@ -9,6 +11,8 @@
 
 import psycopg
 from psycopg import Connection, Cursor, sql
+from psycopg.adapt import Dumper
+from psycopg.pq import Format
 
 from ..api import VectorDB
 from .config import HologresConfig, HologresIndexConfig
@@ -16,11 +20,39 @@
 log = logging.getLogger(__name__)
 
 
+class HoloFloat4Array:
+    """Lightweight wrapper for float arrays - 1 object per query instead of 768 Float4 wrappers."""
+
+    __slots__ = ("data",)
+
+    def __init__(self, data: list[float]):
+        self.data = data
+
+
+class HoloFloat4ArrayDumper(Dumper):
+    """Custom Dumper that serializes float arrays to PostgreSQL binary float4[] format."""
+
+    format = Format.BINARY
+    oid = 1021  # PostgreSQL OID for float4[] (_float4)
+
+    def dump(self, obj: HoloFloat4Array) -> bytes:
+        # PostgreSQL binary array format:
+        # Header: ndim(4) + has_null(4) + OID elemtype(4) + dim_size(4) + lower_bound(4) = 20 bytes
+        # For each element: itemlen(4) + float32(4) = 8 bytes per element
+        header = struct.pack(">iiIii", 1, 0, 700, len(obj.data), 1)
+        return header + b"".join(struct.pack(">if", 4, x) for x in obj.data)
+
+
+# Register HoloFloat4ArrayDumper globally so it's available for all connections and cursors.
+# This must happen at module load time, before any connections/cursors are created,
+# because psycopg cursors snapshot the adapter map at creation time.
+psycopg.adapters.register_dumper(HoloFloat4Array, HoloFloat4ArrayDumper)
+
+
 class Hologres(VectorDB):
     """Use psycopg instructions"""
 
-    conn: psycopg.Connection[Any] | None = None
-    cursor: psycopg.Cursor[Any] | None = None
+    thread_safe: bool = True  # each thread gets its own connection via threading.local()
 
     _tg_name: str = "vdb_bench_tg_1"
 
@@ -42,13 +74,20 @@ def __init__(
         self._primary_field = "id"
         self._vector_field = "embedding"
 
-        # construct basic units
-        self.conn, self.cursor = self._create_connection(**self.db_config)
+        # Thread-local storage for per-thread connections (thread-safe concurrent inserts)
+        self._local = threading.local()
+        self._all_conns: list[psycopg.Connection[Any]] = []
+        self._conns_lock = threading.Lock()
+
+        # Temporary connection for setup, closed at end of __init__
+        conn, cursor = self._create_connection(**self.db_config)
+        self._local.conn = conn
+        self._local.cursor = cursor
 
         # create vector extension
         if self.case_config.is_proxima():
-            self.cursor.execute("CREATE EXTENSION proxima;")
-            self.conn.commit()
+            cursor.execute("CREATE EXTENSION proxima;")
+            conn.commit()
 
         log.info(f"{self.name} config values: {self.db_config}\n{self.case_config}")
         if not any(
@@ -70,10 +109,24 @@ def __init__(
             if self.case_config.create_index_before_load:
                 self._create_index()
 
-        self.cursor.close()
-        self.conn.close()
-        self.cursor = None
-        self.conn = None
+        cursor.close()
+        conn.close()
+        self._local.cursor = None
+        self._local.conn = None
+
+    def __getstate__(self):
+        # Exclude unpicklable threading objects; recreated in __setstate__
+        state = self.__dict__.copy()
+        state.pop("_local", None)
+        state.pop("_conns_lock", None)
+        state.pop("_all_conns", None)
+        return state
+
+    def __setstate__(self, state: dict) -> None:
+        self.__dict__.update(state)
+        self._local = threading.local()
+        self._conns_lock = threading.Lock()
+        self._all_conns = []
 
     @staticmethod
     def _create_connection(**kwargs) -> tuple[Connection, Cursor]:
@@ -86,51 +139,103 @@ def _create_connection(**kwargs) -> tuple[Connection, Cursor]:
 
         return conn, cursor
 
+    def _get_conn(self) -> Connection:
+        """Return this thread's connection, creating one if needed."""
+        conn = getattr(self._local, "conn", None)
+        if conn is None or conn.closed:
+            conn, cursor = self._create_connection(**self.db_config)
+            self._local.conn = conn
+            self._local.cursor = cursor
+            self._set_search_guc_on(conn, cursor)
+            with self._conns_lock:
+                self._all_conns.append(conn)
+        return conn
+
+    def _get_cursor(self) -> Cursor:
+        """Return this thread's cursor, creating connection if needed."""
+        cursor = getattr(self._local, "cursor", None)
+        if cursor is None or cursor.closed:
+            self._get_conn()  # creates both conn and cursor
+            cursor = self._local.cursor
+        return cursor
+
+    def _set_search_guc_on(self, conn: Connection, cursor: Cursor) -> None:
+        """Set session-level search GUC on the given connection."""
+        sql_guc = sql.SQL(f"SET hg_vector_ef_search = {self.case_config.ef_search};")
+        log.info(f"{self.name} client set search guc: {sql_guc.as_string()}")
+        cursor.execute(sql_guc)
+        conn.commit()
+
+    def _set_search_guc(self) -> None:
+        """Set session-level search GUC on this thread's connection."""
+        self._set_search_guc_on(self._get_conn(), self._get_cursor())
+
     @contextmanager
     def init(self) -> Generator[None, None, None]:
-        """
-        Examples:
-            >>> with self.init():
-            >>>     self.insert_embeddings()
-            >>>     self.search_embedding()
-        """
+        """Open connection for calling thread, prepare for operations.
 
-        self.conn, self.cursor = self._create_connection(**self.db_config)
+        Worker threads lazily create their own connections via _get_conn().
+        On exit, closes all thread-local connections.
+        """
+        conn, cursor = self._create_connection(**self.db_config)
+        self._local.conn = conn
+        self._local.cursor = cursor
+        self._set_search_guc_on(conn, cursor)
+        with self._conns_lock:
+            self._all_conns.append(conn)
+
+        self._search_query_no_filter = sql.SQL("""
+            SELECT id
+            FROM {table_name}
+            ORDER BY {distance_function}(embedding, %b)
+            {order_direction}
+            LIMIT %s;
+            """).format(
+            table_name=sql.Identifier(self.table_name),
+            distance_function=sql.SQL(self.case_config.distance_function()),
+            order_direction=sql.SQL(self.case_config.order_direction()),
+        )
 
-        self._set_search_guc()
+        self._search_query_with_filter = sql.SQL("""
+            SELECT id
+            FROM {table_name}
+            WHERE id >= %s
+            ORDER BY {distance_function}(embedding, %b)
+            {order_direction}
+            LIMIT %s;
+            """).format(
+            table_name=sql.Identifier(self.table_name),
+            distance_function=sql.SQL(self.case_config.distance_function()),
+            order_direction=sql.SQL(self.case_config.order_direction()),
+        )
 
         try:
             yield
         finally:
-            self.cursor.close()
-            self.conn.close()
-            self.cursor = None
-            self.conn = None
-
-    def _set_search_guc(self):
-        assert self.conn is not None, "Connection is not initialized"
-        assert self.cursor is not None, "Cursor is not initialized"
-
-        sql_guc = sql.SQL(f"SET hg_vector_ef_search = {self.case_config.ef_search};")
-        log.info(f"{self.name} client set search guc: {sql_guc.as_string()}")
-        self.cursor.execute(sql_guc)
-        self.conn.commit()
+            with self._conns_lock:
+                conns_to_close = list(self._all_conns)
+                self._all_conns.clear()
+            for c in conns_to_close:
+                if not c.closed:
+                    c.close()
+            self._local.conn = None
+            self._local.cursor = None
 
     def _drop_table(self):
-        assert self.conn is not None, "Connection is not initialized"
-        assert self.cursor is not None, "Cursor is not initialized"
+        conn = self._get_conn()
+        cursor = self._get_cursor()
 
         log.info(f"{self.name} client drop table : {self.table_name}")
-        self.cursor.execute(
+        cursor.execute(
             sql.SQL("DROP TABLE IF EXISTS {table_name};").format(
                 table_name=sql.Identifier(self.table_name),
             ),
         )
-        self.conn.commit()
+        conn.commit()
 
         try:
             log.info(f"{self.name} client purge table recycle bin: {self.table_name}")
-            self.cursor.execute(
+            cursor.execute(
                 sql.SQL("purge TABLE {table_name};").format(
                     table_name=sql.Identifier(self.table_name),
                 ),
@@ -138,23 +243,23 @@ def _drop_table(self):
         except Exception as e:
             log.info(f"{self.name} client purge table {self.table_name} recycle bin failed, error: {e}, ignore.")
         finally:
-            self.conn.commit()
+            conn.commit()
 
         try:
             log.info(f"{self.name} client drop table group : {self._tg_name}")
-            self.cursor.execute(sql.SQL(f"CALL HG_DROP_TABLE_GROUP('{self._tg_name}');"))
+            cursor.execute(sql.SQL(f"CALL HG_DROP_TABLE_GROUP('{self._tg_name}');"))
         except Exception as e:
             log.info(f"{self.name} client drop table group : {self._tg_name} failed, error: {e}, ignore.")
         finally:
-            self.conn.commit()
+            conn.commit()
 
         try:
             log.info(f"{self.name} client free cache")
-            self.cursor.execute("select hg_admin_command('freecache');")
+            cursor.execute("select hg_admin_command('freecache');")
         except Exception as e:
             log.info(f"{self.name} client free cache failed, error: {e}, ignore.")
         finally:
-            self.conn.commit()
+            conn.commit()
 
     def optimize(self, data_size: int | None = None):
         if self.case_config.create_index_after_load:
@@ -163,12 +268,13 @@ def optimize(self, data_size: int | None = None):
         self._analyze()
 
     def _vacuum(self):
+        conn = self._get_conn()
         log.info(f"{self.name} client vacuum table : {self.table_name}")
         try:
             # VACUUM cannot run inside a transaction block
             # it's better to new a connection
-            self.conn.autocommit = True
-            with self.conn.cursor() as cursor:
+            conn.autocommit = True
+            with conn.cursor() as cursor:
                 cursor.execute(
                     sql.SQL("""
                         VACUUM {table_name};
@@ -181,16 +287,18 @@ def _vacuum(self):
             log.warning(f"Failed to vacuum table: {self.table_name} error: {e}")
             raise e from None
         finally:
-            self.conn.autocommit = True
+            conn.autocommit = True
 
     def _analyze(self):
+        cursor = self._get_cursor()
         log.info(f"{self.name} client analyze table : {self.table_name}")
-        self.cursor.execute(sql.SQL(f"ANALYZE {self.table_name};"))
+        cursor.execute(sql.SQL(f"ANALYZE {self.table_name};"))
         log.info(f"{self.name} client analyze table : {self.table_name} done")
 
     def _full_compact(self):
+        cursor = self._get_cursor()
         log.info(f"{self.name} client full compact table : {self.table_name}")
-        self.cursor.execute(
+        cursor.execute(
             sql.SQL("""
                 SELECT hologres.hg_full_compact_table(
                     '{table_name}',
@@ -204,8 +312,8 @@ def _full_compact(self):
         log.info(f"{self.name} client full compact table : {self.table_name} done")
 
     def _create_index(self):
-        assert self.conn is not None, "Connection is not initialized"
-        assert self.cursor is not None, "Cursor is not initialized"
+        conn = self._get_conn()
+        cursor = self._get_cursor()
 
         sql_index = sql.SQL("""
             CALL set_table_property ('{table_name}', 'vectors', '{{
@@ -224,15 +332,15 @@ def _create_index(self):
 
         log.info(f"{self.name} client create index on table : {self.table_name}, with sql: {sql_index.as_string()}")
         try:
-            self.cursor.execute(sql_index)
-            self.conn.commit()
+            cursor.execute(sql_index)
+            conn.commit()
         except Exception as e:
             log.warning(f"Failed to create index on table: {self.table_name} error: {e}")
             raise e from None
 
     def _set_replica_count(self, replica_count: int = 2):
-        assert self.conn is not None, "Connection is not initialized"
-        assert self.cursor is not None, "Cursor is not initialized"
+        conn = self._get_conn()
+        cursor = self._get_cursor()
 
         try:
             # non-warehouse mode by default
@@ -243,13 +351,13 @@ def _set_replica_count(self, replica_count: int = 2):
             # check warehouse mode
             sql_check = sql.SQL("select count(*) from hologres.hg_warehouses;")
             log.info(f"check warehouse mode with sql: {sql_check}")
-            self.cursor.execute(sql_check)
-            result_check = self.cursor.fetchone()[0]
+            cursor.execute(sql_check)
+            result_check = cursor.fetchone()[0]
             if result_check > 0:
                 # get warehouse name
                 sql_get_warehouse_name = sql.SQL("select current_warehouse();")
                 log.info(f"get warehouse name with sql: {sql_get_warehouse_name}")
-                self.cursor.execute(sql_get_warehouse_name)
+                cursor.execute(sql_get_warehouse_name)
                 sql_tg_replica = sql.SQL("""
                     CALL hg_table_group_set_warehouse_replica_count (
                         '{dbname}.{tg_name}',
@@ -258,29 +366,29 @@ def _set_replica_count(self, replica_count: int = 2):
                     );
                     """).format(
                     tg_name=sql.SQL(self._tg_name),
-                    warehouse_name=sql.SQL(self.cursor.fetchone()[0]),
+                    warehouse_name=sql.SQL(cursor.fetchone()[0]),
                     dbname=sql.SQL(self.db_config["dbname"]),
                     replica_count=replica_count,
                 )
             log.info(f"{self.name} client set table group replica: {self._tg_name}, with sql: {sql_tg_replica}")
-            self.cursor.execute(sql_tg_replica)
+            cursor.execute(sql_tg_replica)
         except Exception as e:
             log.warning(f"Failed to set replica count, error: {e}, ignore")
         finally:
-            self.conn.commit()
+            conn.commit()
 
     def _create_table(self, dim: int):
-        assert self.conn is not None, "Connection is not initialized"
-        assert self.cursor is not None, "Cursor is not initialized"
+        conn = self._get_conn()
+        cursor = self._get_cursor()
 
         sql_tg = sql.SQL(f"CALL HG_CREATE_TABLE_GROUP ('{self._tg_name}', 1);")
         log.info(f"{self.name} client create table group : {self._tg_name}, with sql: {sql_tg}")
         try:
-            self.cursor.execute(sql_tg)
+            cursor.execute(sql_tg)
         except Exception as e:
             log.warning(f"Failed to create table group : {self._tg_name} error: {e}, ignore")
         finally:
-            self.conn.commit()
+            conn.commit()
 
         self._set_replica_count(replica_count=2)
 
@@ -297,8 +405,8 @@ def _create_table(self, dim: int):
         )
         log.info(f"{self.name} client create table : {self.table_name}, with sql: {sql_table.as_string()}")
         try:
-            self.cursor.execute(sql_table)
-            self.conn.commit()
+            cursor.execute(sql_table)
+            conn.commit()
         except Exception as e:
             log.warning(f"Failed to create table : {self.table_name} error: {e}")
             raise e from None
@@ -309,8 +417,8 @@ def insert_embeddings(
         metadata: list[int],
         **kwargs: Any,
     ) -> tuple[int, Exception | None]:
-        assert self.conn is not None, "Connection is not initialized"
-        assert self.cursor is not None, "Cursor is not initialized"
+        conn = self._get_conn()
+        cursor = self._get_cursor()
 
         try:
             buffer = StringIO()
@@ -318,45 +426,17 @@ def insert_embeddings(
                 buffer.write("%d\t%s\n" % (metadata[i], "{" + ",".join("%f" % x for x in embeddings[i]) + "}"))
             buffer.seek(0)
 
-            with self.cursor.copy(
+            with cursor.copy(
                 sql.SQL("COPY {table_name} FROM STDIN").format(table_name=sql.Identifier(self.table_name))
             ) as copy:
                 copy.write(buffer.getvalue())
-            self.conn.commit()
+            conn.commit()
 
             return len(metadata), None
         except Exception as e:
             log.warning(f"Failed to insert data into table ({self.table_name}), error: {e}")
             return 0, e
 
-    def _compose_query_and_params(self, vec: list[float], topk: int, ge_id: int | None = None):
-        params = []
-
-        where_clause = sql.SQL("")
-        if ge_id is not None:
-            where_clause = sql.SQL(" WHERE id >= %s ")
-            params.append(ge_id)
-
-        vec_float4 = [psycopg._wrappers.Float4(i) for i in vec]
-        params.append(vec_float4)
-        params.append(topk)
-
-        query = sql.SQL("""
-            SELECT id
-            FROM {table_name}
-            {where_clause}
-            ORDER BY {distance_function}(embedding, %b)
-            {order_direction}
-            LIMIT %s;
-            """).format(
-            table_name=sql.Identifier(self.table_name),
-            distance_function=sql.SQL(self.case_config.distance_function()),
-            where_clause=where_clause,
-            order_direction=sql.SQL(self.case_config.order_direction()),
-        )
-
-        return query, params
-
     def search_embedding(
         self,
         query: list[float],
@@ -364,10 +444,16 @@ def search_embedding(
         filters: dict | None = None,
         timeout: int | None = None,
     ) -> list[int]:
-        assert self.conn is not None, "Connection is not initialized"
-        assert self.cursor is not None, "Cursor is not initialized"
+        cursor = self._get_cursor()
 
         ge = filters.get("id") if filters else None
-        q, params = self._compose_query_and_params(query, k, ge)
-        result = self.cursor.execute(q, params, prepare=True, binary=True)
+        q = HoloFloat4Array(query)
+
+        if ge is not None:
+            params = (ge, q, k)
+            result = cursor.execute(self._search_query_with_filter, params, prepare=True, binary=True)
+        else:
+            params = (q, k)
+            result = cursor.execute(self._search_query_no_filter, params, prepare=True, binary=True)
+
         return [int(i[0]) for i in result.fetchall()]
diff --git a/vectordb_bench/backend/clients/lancedb/cli.py b/vectordb_bench/backend/clients/lancedb/cli.py
index 219ae114e..494c28a2b 100644
--- a/vectordb_bench/backend/clients/lancedb/cli.py
+++ b/vectordb_bench/backend/clients/lancedb/cli.py
@@ -1,3 +1,4 @@
+import os
 from typing import Annotated, Unpack
 
 import click
@@ -22,21 +23,134 @@ class LanceDBTypedDict(CommonTypedDict):
         str | None,
         click.option("--token", type=str, help="Authentication token", required=False),
     ]
+    cos_secret_id: Annotated[
+        str | None,
+        click.option(
+            "--cos-secret-id",
+            type=str,
+            help="Tencent COS secret ID (or set COS_SECRET_ID env var)",
+            required=False,
+        ),
+    ]
+    cos_secret_key: Annotated[
+        str | None,
+        click.option(
+            "--cos-secret-key",
+            type=str,
+            help="Tencent COS secret key (or set COS_SECRET_KEY env var)",
+            required=False,
+        ),
+    ]
+    cos_endpoint: Annotated[
+        str | None,
+        click.option(
+            "--cos-endpoint",
+            type=str,
+            help="Tencent COS endpoint (or set COS_ENDPOINT env var)",
+            required=False,
+        ),
+    ]
+    cos_region: Annotated[
+        str | None,
+        click.option(
+            "--cos-region",
+            type=str,
+            help="Tencent COS region (or set TENCENTCLOUD_REGION env var)",
+            required=False,
+        ),
+    ]
+
+
+def _build_storage_options(**parameters) -> dict[str, str] | None:
+    """Build storage_options based on URI scheme.
+
+    Supports:
+    - cos:// / s3://  → Tencent COS credentials
+    - goosefs://      → GooseFS authentication options
+    """
+    uri = parameters.get("uri", "")
+
+    # --- GooseFS storage options ---
+    if uri.startswith("goosefs://"):
+        return _build_goosefs_storage_options(**parameters)
+
+    # --- COS / S3 storage options ---
+    if uri.startswith(("cos://", "s3://")):
+        return _build_cos_storage_options(**parameters)
+
+    return None
+
+
+def _build_cos_storage_options(**parameters) -> dict[str, str] | None:
+    """Build storage_options for COS/S3 if credentials are provided."""
+    secret_id = parameters.get("cos_secret_id") or os.environ.get("COS_SECRET_ID")
+    secret_key = parameters.get("cos_secret_key") or os.environ.get("COS_SECRET_KEY")
+    endpoint = parameters.get("cos_endpoint") or os.environ.get("COS_ENDPOINT")
+    region = parameters.get("cos_region") or os.environ.get("TENCENTCLOUD_REGION")
+
+    if not (secret_id and secret_key):
+        return None
+
+    storage_options = {
+        "aws_access_key_id": secret_id,
+        "aws_secret_access_key": secret_key,
+    }
+    if endpoint:
+        storage_options["endpoint"] = endpoint
+    if region:
+        storage_options["region"] = region
+
+    return storage_options
+
+
+def _build_goosefs_storage_options(**_parameters: str) -> dict[str, str] | None:
+    """Build storage_options for GooseFS.
+
+    Recognized environment variables:
+    - GOOSEFS_AUTH_TYPE       → goosefs_auth_type (simple / nosasl)
+    - GOOSEFS_AUTH_USERNAME   → goosefs_auth_username
+    - GOOSEFS_WRITE_TYPE      → goosefs_write_type (CACHE_THROUGH etc.)
+    - GOOSEFS_BLOCK_SIZE      → goosefs_block_size
+    - GOOSEFS_CHUNK_SIZE      → goosefs_chunk_size
+    """
+    storage_options: dict[str, str] = {}
+
+    _goosefs_env_keys = {
+        "GOOSEFS_AUTH_TYPE": "goosefs_auth_type",
+        "GOOSEFS_AUTH_USERNAME": "goosefs_auth_username",
+        "GOOSEFS_WRITE_TYPE": "goosefs_write_type",
+        "GOOSEFS_BLOCK_SIZE": "goosefs_block_size",
+        "GOOSEFS_CHUNK_SIZE": "goosefs_chunk_size",
+    }
+
+    for env_key, opt_key in _goosefs_env_keys.items():
+        value = os.environ.get(env_key)
+        if value:
+            storage_options[opt_key] = value
+
+    return storage_options if storage_options else None
+
+
+def _build_db_config(**parameters):
+    from .config import LanceDBConfig
+
+    return LanceDBConfig(
+        db_label=parameters["db_label"],
+        uri=parameters["uri"],
+        token=SecretStr(parameters["token"]) if parameters.get("token") else None,
+        storage_options=_build_storage_options(**parameters),
+    )
 
 
 @cli.command()
 @click_parameter_decorators_from_typed_dict(LanceDBTypedDict)
 def LanceDB(**parameters: Unpack[LanceDBTypedDict]):
-    from .config import LanceDBConfig, _lancedb_case_config
+    from .config import LanceDBNoIndexConfig
 
     run(
         db=DB.LanceDB,
-        db_config=LanceDBConfig(
-            db_label=parameters["db_label"],
-            uri=parameters["uri"],
-            token=SecretStr(parameters["token"]) if parameters.get("token") else None,
-        ),
-        db_case_config=_lancedb_case_config.get("NONE")(),
+        db_config=_build_db_config(**parameters),
+        db_case_config=LanceDBNoIndexConfig(),
         **parameters,
     )
 
@@ -44,16 +158,12 @@ def LanceDB(**parameters: Unpack[LanceDBTypedDict]):
 @cli.command()
 @click_parameter_decorators_from_typed_dict(LanceDBTypedDict)
 def LanceDBAutoIndex(**parameters: Unpack[LanceDBTypedDict]):
-    from .config import LanceDBConfig, _lancedb_case_config
+    from .config import LanceDBAutoIndexConfig
 
     run(
         db=DB.LanceDB,
-        db_config=LanceDBConfig(
-            db_label=parameters["db_label"],
-            uri=parameters["uri"],
-            token=SecretStr(parameters["token"]) if parameters.get("token") else None,
-        ),
-        db_case_config=_lancedb_case_config.get(IndexType.AUTOINDEX)(),
+        db_config=_build_db_config(**parameters),
+        db_case_config=LanceDBAutoIndexConfig(),
         **parameters,
     )
 
@@ -65,7 +175,8 @@ class LanceDBIVFPQTypedDict(CommonTypedDict, LanceDBTypedDict):
             "--num-partitions",
             type=int,
             default=0,
-            help="Number of partitions for IVFPQ index, unset = use LanceDB default",
+            help="Number of partitions for IVF_PQ index, 0 = use LanceDB default",
+            show_default=True,
         ),
     ]
     num_sub_vectors: Annotated[
@@ -74,7 +185,8 @@ class LanceDBIVFPQTypedDict(CommonTypedDict, LanceDBTypedDict):
             "--num-sub-vectors",
             type=int,
             default=0,
-            help="Number of sub-vectors for IVFPQ index, unset = use LanceDB default",
+            help="Number of sub-vectors for IVF_PQ index, 0 = use LanceDB default",
+            show_default=True,
         ),
     ]
     nbits: Annotated[
@@ -83,13 +195,28 @@ class LanceDBIVFPQTypedDict(CommonTypedDict, LanceDBTypedDict):
             "--nbits",
             type=int,
             default=8,
-            help="Number of bits for IVFPQ index (must be 4 or 8), unset = use LanceDB default",
+            help="Number of bits for quantization (4 or 8)",
+            show_default=True,
         ),
     ]
     nprobes: Annotated[
         int,
         click.option(
-            "--nprobes", type=int, default=0, help="Number of probes for IVFPQ search, unset = use LanceDB default"
+            "--nprobes",
+            type=int,
+            default=0,
+            help="Number of probes for IVF search, 0 = use LanceDB default",
+            show_default=True,
+        ),
+    ]
+    refine_factor: Annotated[
+        int,
+        click.option(
+            "--refine-factor",
+            type=int,
+            default=0,
+            help="Refine factor for better recall, 0 = disabled",
+            show_default=True,
         ),
     ]
 
@@ -97,50 +224,171 @@ class LanceDBIVFPQTypedDict(CommonTypedDict, LanceDBTypedDict):
 @cli.command()
 @click_parameter_decorators_from_typed_dict(LanceDBIVFPQTypedDict)
 def LanceDBIVFPQ(**parameters: Unpack[LanceDBIVFPQTypedDict]):
-    from .config import LanceDBConfig, LanceDBIndexConfig
+    from .config import LanceDBIndexConfig
 
     run(
         db=DB.LanceDB,
-        db_config=LanceDBConfig(
-            db_label=parameters["db_label"],
-            uri=parameters["uri"],
-            token=SecretStr(parameters["token"]) if parameters.get("token") else None,
-        ),
+        db_config=_build_db_config(**parameters),
         db_case_config=LanceDBIndexConfig(
             index=IndexType.IVFPQ,
             num_partitions=parameters["num_partitions"],
             num_sub_vectors=parameters["num_sub_vectors"],
             nbits=parameters["nbits"],
             nprobes=parameters["nprobes"],
+            refine_factor=parameters["refine_factor"],
         ),
         **parameters,
     )
 
 
-class LanceDBHNSWTypedDict(CommonTypedDict, LanceDBTypedDict):
-    m: Annotated[int, click.option("--m", type=int, default=0, help="HNSW parameter m")]
+class LanceDBIVFHNSWSQTypedDict(CommonTypedDict, LanceDBTypedDict):
+    num_partitions: Annotated[
+        int,
+        click.option(
+            "--num-partitions",
+            type=int,
+            default=0,
+            help="Number of IVF partitions, 0 = use LanceDB default",
+            show_default=True,
+        ),
+    ]
+    m: Annotated[
+        int,
+        click.option("--m", type=int, default=0, help="HNSW parameter m, 0 = use LanceDB default", show_default=True),
+    ]
     ef_construction: Annotated[
-        int, click.option("--ef-construction", type=int, default=0, help="HNSW parameter ef_construction")
+        int,
+        click.option(
+            "--ef-construction",
+            type=int,
+            default=0,
+            help="HNSW ef_construction, 0 = use LanceDB default",
+            show_default=True,
+        ),
+    ]
+    ef: Annotated[
+        int,
+        click.option("--ef", type=int, default=0, help="HNSW search ef, 0 = use LanceDB default", show_default=True),
+    ]
+    nprobes: Annotated[
+        int,
+        click.option(
+            "--nprobes",
+            type=int,
+            default=0,
+            help="Number of probes for IVF search, 0 = use LanceDB default",
+            show_default=True,
+        ),
+    ]
+    refine_factor: Annotated[
+        int,
+        click.option(
+            "--refine-factor",
+            type=int,
+            default=0,
+            help="Refine factor for better recall, 0 = disabled",
+            show_default=True,
+        ),
     ]
-    ef: Annotated[int, click.option("--ef", type=int, default=0, help="HNSW search parameter ef")]
 
 
 @cli.command()
-@click_parameter_decorators_from_typed_dict(LanceDBHNSWTypedDict)
-def LanceDBHNSW(**parameters: Unpack[LanceDBHNSWTypedDict]):
-    from .config import LanceDBConfig, LanceDBHNSWIndexConfig
+@click_parameter_decorators_from_typed_dict(LanceDBIVFHNSWSQTypedDict)
+def LanceDBIVFHNSWSQ(**parameters: Unpack[LanceDBIVFHNSWSQTypedDict]):
+    from .config import LanceDBIVFHNSWSQConfig
 
     run(
         db=DB.LanceDB,
-        db_config=LanceDBConfig(
-            db_label=parameters["db_label"],
-            uri=parameters["uri"],
-            token=SecretStr(parameters["token"]) if parameters.get("token") else None,
+        db_config=_build_db_config(**parameters),
+        db_case_config=LanceDBIVFHNSWSQConfig(
+            num_partitions=parameters["num_partitions"],
+            m=parameters["m"],
+            ef_construction=parameters["ef_construction"],
+            ef=parameters["ef"],
+            nprobes=parameters["nprobes"],
+            refine_factor=parameters["refine_factor"],
+        ),
+        **parameters,
+    )
+
+
+class LanceDBIVFHNSWPQTypedDict(CommonTypedDict, LanceDBTypedDict):
+    num_partitions: Annotated[
+        int,
+        click.option(
+            "--num-partitions",
+            type=int,
+            default=0,
+            help="Number of IVF partitions, 0 = use LanceDB default",
+            show_default=True,
+        ),
+    ]
+    num_sub_vectors: Annotated[
+        int,
+        click.option(
+            "--num-sub-vectors",
+            type=int,
+            default=0,
+            help="Number of PQ sub-vectors, 0 = use LanceDB default",
+            show_default=True,
         ),
-        db_case_config=LanceDBHNSWIndexConfig(
+    ]
+    m: Annotated[
+        int,
+        click.option("--m", type=int, default=0, help="HNSW parameter m, 0 = use LanceDB default", show_default=True),
+    ]
+    ef_construction: Annotated[
+        int,
+        click.option(
+            "--ef-construction",
+            type=int,
+            default=0,
+            help="HNSW ef_construction, 0 = use LanceDB default",
+            show_default=True,
+        ),
+    ]
+    ef: Annotated[
+        int,
+        click.option("--ef", type=int, default=0, help="HNSW search ef, 0 = use LanceDB default", show_default=True),
+    ]
+    nprobes: Annotated[
+        int,
+        click.option(
+            "--nprobes",
+            type=int,
+            default=0,
+            help="Number of probes for IVF search, 0 = use LanceDB default",
+            show_default=True,
+        ),
+    ]
+    refine_factor: Annotated[
+        int,
+        click.option(
+            "--refine-factor",
+            type=int,
+            default=0,
+            help="Refine factor for better recall, 0 = disabled",
+            show_default=True,
+        ),
+    ]
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(LanceDBIVFHNSWPQTypedDict)
+def LanceDBIVFHNSWPQ(**parameters: Unpack[LanceDBIVFHNSWPQTypedDict]):
+    from .config import LanceDBIVFHNSWPQConfig
+
+    run(
+        db=DB.LanceDB,
+        db_config=_build_db_config(**parameters),
+        db_case_config=LanceDBIVFHNSWPQConfig(
+            num_partitions=parameters["num_partitions"],
+            num_sub_vectors=parameters["num_sub_vectors"],
             m=parameters["m"],
             ef_construction=parameters["ef_construction"],
             ef=parameters["ef"],
+            nprobes=parameters["nprobes"],
+            refine_factor=parameters["refine_factor"],
         ),
         **parameters,
     )
diff --git a/vectordb_bench/backend/clients/lancedb/config.py b/vectordb_bench/backend/clients/lancedb/config.py
index b0621c22e..8a98c36f3 100644
--- a/vectordb_bench/backend/clients/lancedb/config.py
+++ b/vectordb_bench/backend/clients/lancedb/config.py
@@ -6,18 +6,22 @@
 class LanceDBConfig(DBConfig):
     """LanceDB connection configuration."""
 
-    db_label: str
-    uri: str
+    db_label: str = ""
+    uri: str = "/tmp/lancedb"
     token: SecretStr | None = None
+    storage_options: dict[str, str] | None = None
 
     def to_dict(self) -> dict:
         return {
             "uri": self.uri,
             "token": self.token.get_secret_value() if self.token else None,
+            "storage_options": self.storage_options,
         }
 
 
 class LanceDBIndexConfig(BaseModel, DBCaseConfig):
+    """Default IVF_PQ index configuration."""
+
     index: IndexType = IndexType.IVFPQ
     metric_type: MetricType = MetricType.L2
     num_partitions: int = 0
@@ -26,91 +30,166 @@ class LanceDBIndexConfig(BaseModel, DBCaseConfig):
     sample_rate: int = 256
     max_iterations: int = 50
     nprobes: int = 0
+    refine_factor: int = 0
+
+    def parse_metric(self) -> str:
+        if self.metric_type in (MetricType.L2, MetricType.COSINE):
+            return self.metric_type.value.lower()
+        if self.metric_type in (MetricType.IP, MetricType.DP):
+            return "dot"
+        msg = f"Metric type {self.metric_type} is not supported for LanceDB!"
+        raise ValueError(msg)
 
     def index_param(self) -> dict:
-        if self.index not in [
-            IndexType.IVFPQ,
-            IndexType.HNSW,
-            IndexType.AUTOINDEX,
-            IndexType.NONE,
-        ]:
-            msg = f"Index type {self.index} is not supported for LanceDB!"
-            raise ValueError(msg)
-
-        # See https://lancedb.github.io/lancedb/python/python/#lancedb.table.Table.create_index
         params = {
             "metric": self.parse_metric(),
             "num_bits": self.nbits,
             "sample_rate": self.sample_rate,
             "max_iterations": self.max_iterations,
         }
-
         if self.num_partitions > 0:
             params["num_partitions"] = self.num_partitions
         if self.num_sub_vectors > 0:
             params["num_sub_vectors"] = self.num_sub_vectors
-
         return params
 
     def search_param(self) -> dict:
         params = {}
         if self.nprobes > 0:
             params["nprobes"] = self.nprobes
-
+        if self.refine_factor > 0:
+            params["refine_factor"] = self.refine_factor
         return params
 
-    def parse_metric(self) -> str:
-        if self.metric_type in [MetricType.L2, MetricType.COSINE]:
-            return self.metric_type.value.lower()
-        if self.metric_type in [MetricType.IP, MetricType.DP]:
-            return "dot"
-        msg = f"Metric type {self.metric_type} is not supported for LanceDB!"
-        raise ValueError(msg)
-
 
 class LanceDBNoIndexConfig(LanceDBIndexConfig):
+    """No index — brute-force scan."""
+
     index: IndexType = IndexType.NONE
 
     def index_param(self) -> dict:
         return {}
 
+    def search_param(self) -> dict:
+        params = {}
+        if self.refine_factor > 0:
+            params["refine_factor"] = self.refine_factor
+        return params
+
 
 class LanceDBAutoIndexConfig(LanceDBIndexConfig):
+    """AutoIndex — let LanceDB decide."""
+
     index: IndexType = IndexType.AUTOINDEX
 
     def index_param(self) -> dict:
-        return {}
+        return {"metric": self.parse_metric()}
 
+    def search_param(self) -> dict:
+        params = {}
+        if self.nprobes > 0:
+            params["nprobes"] = self.nprobes
+        if self.refine_factor > 0:
+            params["refine_factor"] = self.refine_factor
+        return params
+
+
+class LanceDBIVFHNSWSQConfig(BaseModel, DBCaseConfig):
+    """IVF_HNSW_SQ index — IVF partitioning + HNSW graph + scalar quantization."""
 
-class LanceDBHNSWIndexConfig(LanceDBIndexConfig):
-    index: IndexType = IndexType.HNSW
+    index: IndexType = IndexType.IVF_HNSW_SQ
+    metric_type: MetricType = MetricType.L2
+    num_partitions: int = 0
     m: int = 0
     ef_construction: int = 0
     ef: int = 0
+    nprobes: int = 0
+    refine_factor: int = 0
 
-    def index_param(self) -> dict:
-        params = LanceDBIndexConfig.index_param(self)
+    def parse_metric(self) -> str:
+        if self.metric_type in (MetricType.L2, MetricType.COSINE):
+            return self.metric_type.value.lower()
+        if self.metric_type in (MetricType.IP, MetricType.DP):
+            return "dot"
+        msg = f"Metric type {self.metric_type} is not supported for LanceDB!"
+        raise ValueError(msg)
 
-        # See https://lancedb.github.io/lancedb/python/python/#lancedb.index.HnswSq
-        params["index_type"] = "IVF_HNSW_SQ"
+    def index_param(self) -> dict:
+        params = {
+            "metric": self.parse_metric(),
+            "index_type": "IVF_HNSW_SQ",
+        }
+        if self.num_partitions > 0:
+            params["num_partitions"] = self.num_partitions
         if self.m > 0:
             params["m"] = self.m
         if self.ef_construction > 0:
             params["ef_construction"] = self.ef_construction
-
         return params
 
     def search_param(self) -> dict:
         params = {}
-        if self.ef != 0:
-            params = {"ef": self.ef}
+        if self.ef > 0:
+            params["ef"] = self.ef
+        if self.nprobes > 0:
+            params["nprobes"] = self.nprobes
+        if self.refine_factor > 0:
+            params["refine_factor"] = self.refine_factor
+        return params
+
+
+class LanceDBIVFHNSWPQConfig(BaseModel, DBCaseConfig):
+    """IVF_HNSW_PQ index — IVF partitioning + HNSW graph + product quantization."""
 
+    index: IndexType = IndexType.IVF_HNSW_PQ
+    metric_type: MetricType = MetricType.L2
+    num_partitions: int = 0
+    num_sub_vectors: int = 0
+    m: int = 0
+    ef_construction: int = 0
+    ef: int = 0
+    nprobes: int = 0
+    refine_factor: int = 0
+
+    def parse_metric(self) -> str:
+        if self.metric_type in (MetricType.L2, MetricType.COSINE):
+            return self.metric_type.value.lower()
+        if self.metric_type in (MetricType.IP, MetricType.DP):
+            return "dot"
+        msg = f"Metric type {self.metric_type} is not supported for LanceDB!"
+        raise ValueError(msg)
+
+    def index_param(self) -> dict:
+        params = {
+            "metric": self.parse_metric(),
+            "index_type": "IVF_HNSW_PQ",
+        }
+        if self.num_partitions > 0:
+            params["num_partitions"] = self.num_partitions
+        if self.num_sub_vectors > 0:
+            params["num_sub_vectors"] = self.num_sub_vectors
+        if self.m > 0:
+            params["m"] = self.m
+        if self.ef_construction > 0:
+            params["ef_construction"] = self.ef_construction
+        return params
+
+    def search_param(self) -> dict:
+        params = {}
+        if self.ef > 0:
+            params["ef"] = self.ef
+        if self.nprobes > 0:
+            params["nprobes"] = self.nprobes
+        if self.refine_factor > 0:
+            params["refine_factor"] = self.refine_factor
         return params
 
 
 _lancedb_case_config = {
     IndexType.IVFPQ: LanceDBIndexConfig,
     IndexType.AUTOINDEX: LanceDBAutoIndexConfig,
-    IndexType.HNSW: LanceDBHNSWIndexConfig,
+    IndexType.IVF_HNSW_SQ: LanceDBIVFHNSWSQConfig,
+    IndexType.IVF_HNSW_PQ: LanceDBIVFHNSWPQConfig,
+    IndexType.HNSW: LanceDBIVFHNSWSQConfig,  # backward compat: HNSW maps to IVF_HNSW_SQ
     IndexType.NONE: LanceDBNoIndexConfig,
 }
diff --git a/vectordb_bench/backend/clients/lancedb/lancedb.py b/vectordb_bench/backend/clients/lancedb/lancedb.py
index 65330e2cb..2febc2f3c 100644
--- a/vectordb_bench/backend/clients/lancedb/lancedb.py
+++ b/vectordb_bench/backend/clients/lancedb/lancedb.py
@@ -1,29 +1,44 @@
+"""Wrapper around the LanceDB vector database over VectorDB"""
+
 import logging
+import os
 from contextlib import contextmanager
 
 import lancedb
 import pyarrow as pa
-from lancedb.pydantic import LanceModel
+
+from vectordb_bench.backend.filter import Filter, FilterOp
 
 from ..api import IndexType, VectorDB
-from .config import LanceDBConfig, LanceDBIndexConfig
+from .config import LanceDBIndexConfig
 
 log = logging.getLogger(__name__)
 
-
-class VectorModel(LanceModel):
-    id: int
-    vector: list[float]
+# Rows per ``table.add`` call. Each call produces a new Lance data file
+# (fragment), so enlarging this value directly controls the on-disk fragment
+# size. Override via the ``LANCEDB_BATCH_SIZE`` environment variable.
+#
+# Rough sizing for float32 vectors: bytes_per_fragment ≈ rows * dim * 4.
+# Example: dim=768, rows=170000 -> ~498 MB per fragment.
+LANCEDB_BATCH_SIZE = int(os.environ.get("LANCEDB_BATCH_SIZE", "5000"))
 
 
 class LanceDB(VectorDB):
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+        FilterOp.StrEqual,
+    ]
+    thread_safe: bool = False
+
     def __init__(
         self,
         dim: int,
-        db_config: LanceDBConfig,
+        db_config: dict,
         db_case_config: LanceDBIndexConfig,
         collection_name: str = "vector_bench_test",
         drop_old: bool = False,
+        with_scalar_labels: bool = False,
         **kwargs,
     ):
         self.name = "LanceDB"
@@ -32,79 +47,206 @@ def __init__(
         self.table_name = collection_name
         self.dim = dim
         self.uri = db_config["uri"]
-        # avoid the search_param being called every time during the search process
-        self.search_config = db_case_config.search_param()
+        self.storage_options = db_config.get("storage_options") or None
+        self.with_scalar_labels = with_scalar_labels
+        self.where_clause = None
+
+        self._id_field = "id"
+        self._vector_field = "vector"
+        self._label_field = "label"
 
-        log.info(f"Search config: {self.search_config}")
+        # cache search params to avoid repeated calls
+        self.search_config = db_case_config.search_param()
+        log.info(f"LanceDB search config: {self.search_config}")
 
-        db = lancedb.connect(self.uri)
+        connect_kwargs = {}
+        if self.storage_options:
+            connect_kwargs["storage_options"] = self.storage_options
+        db = lancedb.connect(self.uri, **connect_kwargs)
 
         if drop_old:
             try:
                 db.drop_table(self.table_name)
+                log.info(f"LanceDB dropped old table: {self.table_name}")
             except Exception as e:
                 log.warning(f"Failed to drop table {self.table_name}: {e}")
-
-        try:
-            db.open_table(self.table_name)
-        except Exception:
-            schema = pa.schema(
-                [pa.field("id", pa.int64()), pa.field("vector", pa.list_(pa.float32(), list_size=self.dim))]
-            )
+            # Always create a fresh table with the correct schema after drop.
+            # On remote storage (e.g. GooseFS) drop_table may not fully purge
+            # metadata immediately, causing open_table to succeed with a stale
+            # schema that is missing expected fields like 'id'.
+            schema = self._build_schema()
             db.create_table(self.table_name, schema=schema, mode="overwrite")
+            log.info(f"LanceDB created table: {self.table_name} (schema: {schema})")
+        else:
+            try:
+                db.open_table(self.table_name)
+            except Exception:
+                schema = self._build_schema()
+                db.create_table(self.table_name, schema=schema, mode="overwrite")
+                log.info(f"LanceDB created table: {self.table_name} (schema: {schema})")
+
+    def _build_schema(self) -> pa.Schema:
+        fields = [
+            pa.field(self._id_field, pa.int64()),
+            pa.field(self._vector_field, pa.list_(pa.float32(), list_size=self.dim)),
+        ]
+        if self.with_scalar_labels:
+            fields.append(pa.field(self._label_field, pa.utf8()))
+        return pa.schema(fields)
 
     @contextmanager
     def init(self):
-        self.db = lancedb.connect(self.uri)
+        connect_kwargs = {}
+        if self.storage_options:
+            connect_kwargs["storage_options"] = self.storage_options
+        self.db = lancedb.connect(self.uri, **connect_kwargs)
         self.table = self.db.open_table(self.table_name)
         yield
         self.db = None
         self.table = None
 
+    def __deepcopy__(self, memo: dict) -> "LanceDB":
+        """Custom deepcopy: skip live connection/table handles.
+
+        The LanceDB ``Connection`` / ``Table`` objects wrap Rust bindings that
+        are not picklable. ``ConcurrentInsertRunner`` deep-copies the client
+        per thread for non-thread-safe DBs; the caller will then invoke
+        ``init()`` on the copy, which re-opens a fresh connection.
+        """
+        cls = self.__class__
+        new_obj = cls.__new__(cls)
+        memo[id(self)] = new_obj
+        from copy import deepcopy as _dc
+
+        for k, v in self.__dict__.items():
+            if k in ("db", "table"):
+                new_obj.__dict__[k] = None
+            else:
+                new_obj.__dict__[k] = _dc(v, memo)
+        return new_obj
+
     def insert_embeddings(
         self,
         embeddings: list[list[float]],
         metadata: list[int],
+        labels_data: list[str] | None = None,
         **kwargs,
     ) -> tuple[int, Exception | None]:
+        assert self.table is not None, "Please call self.init() before"
         try:
-            data = [{"id": meta, "vector": emb} for meta, emb in zip(metadata, embeddings, strict=False)]
-            self.table.add(data)
+            log.info(
+                f"LanceDB insert_embeddings called with {len(embeddings)} rows, "
+                f"LANCEDB_BATCH_SIZE={LANCEDB_BATCH_SIZE} -> "
+                f"{(len(embeddings) + LANCEDB_BATCH_SIZE - 1) // LANCEDB_BATCH_SIZE} fragment(s)"
+            )
+            for offset in range(0, len(embeddings), LANCEDB_BATCH_SIZE):
+                batch_emb = embeddings[offset : offset + LANCEDB_BATCH_SIZE]
+                batch_ids = metadata[offset : offset + LANCEDB_BATCH_SIZE]
+
+                id_arr = pa.array(batch_ids, type=pa.int64())
+                vec_arr = pa.FixedSizeListArray.from_arrays(
+                    pa.array([v for emb in batch_emb for v in emb], type=pa.float32()),
+                    list_size=self.dim,
+                )
+
+                if self.with_scalar_labels and labels_data is not None:
+                    batch_labels = labels_data[offset : offset + LANCEDB_BATCH_SIZE]
+                    label_arr = pa.array(batch_labels, type=pa.utf8())
+                    batch_table = pa.table(
+                        {
+                            self._id_field: id_arr,
+                            self._vector_field: vec_arr,
+                            self._label_field: label_arr,
+                        }
+                    )
+                else:
+                    batch_table = pa.table(
+                        {
+                            self._id_field: id_arr,
+                            self._vector_field: vec_arr,
+                        }
+                    )
+                self.table.add(batch_table)
+
             return len(metadata), None
         except Exception as e:
             log.warning(f"Failed to insert data into LanceDB table ({self.table_name}), error: {e}")
             return 0, e
 
+    def prepare_filter(self, filters: Filter):
+        if filters.type == FilterOp.NonFilter:
+            self.where_clause = None
+        elif filters.type == FilterOp.NumGE:
+            self.where_clause = f"{self._id_field} >= {filters.int_value}"
+        elif filters.type == FilterOp.StrEqual:
+            self.where_clause = f"{self._label_field} = '{filters.label_value}'"
+        else:
+            msg = f"Unsupported filter for LanceDB: {filters}"
+            raise ValueError(msg)
+
     def search_embedding(
         self,
         query: list[float],
         k: int = 100,
-        filters: dict | None = None,
+        **kwargs,
     ) -> list[int]:
-        if filters:
-            results = self.table.search(query).select(["id"]).where(f"id >= {filters['id']}", prefilter=True).limit(k)
-            if self.case_config.index == IndexType.IVFPQ and "nprobes" in self.search_config:
-                results = results.nprobes(self.search_config["nprobes"]).to_list()
-            elif self.case_config.index == IndexType.HNSW and "ef" in self.search_config:
-                results = results.ef(self.search_config["ef"]).to_list()
-            else:
-                results = results.to_list()
-        else:
-            results = self.table.search(query).select(["id"]).limit(k)
-            if self.case_config.index == IndexType.IVFPQ and "nprobes" in self.search_config:
-                results = results.nprobes(self.search_config["nprobes"]).to_list()
-            elif self.case_config.index == IndexType.HNSW and "ef" in self.search_config:
-                results = results.ef(self.search_config["ef"]).to_list()
-            else:
-                results = results.to_list()
-
-        return [int(result["id"]) for result in results]
+        assert self.table is not None, "Please call self.init() before"
+
+        # Include ``_distance`` in the projection to opt in to LanceDB's
+        # upcoming default behaviour. Without it, lance logs a per-query
+        # deprecation warning ("This search specified output columns but did
+        # not include `_distance`"). We only consume ``id`` downstream, so the
+        # extra column adds negligible overhead.
+        #
+        # Always pin the query metric to the case-configured metric. Otherwise
+        # the no-index path (brute-force scan) silently falls back to LanceDB's
+        # default (L2), which corrupts recall on cosine/IP datasets whose
+        # ground-truth is computed with a different metric. For indexed paths
+        # this is a no-op when the index metric already matches.
+        q = (
+            self.table.search(query)
+            .metric(self.case_config.parse_metric())
+            .select([self._id_field, "_distance"])
+            .limit(k)
+        )
+
+        # apply filter
+        if self.where_clause:
+            q = q.where(self.where_clause, prefilter=True)
+
+        # apply search parameters based on config
+        search_cfg = self.search_config
+        if "nprobes" in search_cfg:
+            q = q.nprobes(search_cfg["nprobes"])
+        if "ef" in search_cfg:
+            q = q.ef(search_cfg["ef"])
+        if "refine_factor" in search_cfg:
+            q = q.refine_factor(search_cfg["refine_factor"])
+
+        results = q.to_list()
+        return [int(r[self._id_field]) for r in results]
 
     def optimize(self, data_size: int | None = None):
-        if self.table and hasattr(self, "case_config") and self.case_config.index != IndexType.NONE:
-            log.info(f"Creating index for LanceDB table ({self.table_name})")
-            log.info(f"Index parameters: {self.case_config.index_param()}")
-            self.table.create_index(**self.case_config.index_param())
-            # Better recall with IVF_PQ (though still bad) but breaks HNSW: https://github.com/lancedb/lancedb/issues/2369
-            if self.case_config.index in (IndexType.IVFPQ, IndexType.AUTOINDEX):
+        assert self.table is not None, "Please call self.init() before"
+
+        # Build index if configured
+        if self.case_config.index != IndexType.NONE:
+            index_params = self.case_config.index_param()
+            log.info(f"LanceDB creating index on table ({self.table_name}), params: {index_params}")
+            self.table.create_index(**index_params)
+
+        # Compact fragments and clean up old versions for better performance.
+        # Prefer the unified ``table.optimize()`` API (lancedb >= 0.10), which
+        # internally handles both compaction and version cleanup without
+        # requiring the optional ``pylance`` package. Fall back to the legacy
+        # split APIs only if ``optimize`` is unavailable.
+        try:
+            if hasattr(self.table, "optimize"):
                 self.table.optimize()
+                log.info(f"LanceDB optimize completed for table ({self.table_name})")
+            else:
+                self.table.compact_files()
+                self.table.cleanup_old_versions()
+                log.info(f"LanceDB compact_files + cleanup_old_versions completed for table ({self.table_name})")
+        except Exception as e:
+            log.warning(f"LanceDB optimize failed (non-fatal): {e}")
diff --git a/vectordb_bench/backend/clients/lindorm/cli.py b/vectordb_bench/backend/clients/lindorm/cli.py
index df466e517..cd4f69ae6 100644
--- a/vectordb_bench/backend/clients/lindorm/cli.py
+++ b/vectordb_bench/backend/clients/lindorm/cli.py
@@ -34,7 +34,10 @@ class LindormTypedDict(CommonTypedDict):
     ]
 
 
-class LindormHNSWTypedDict(CommonTypedDict, LindormTypedDict, HNSWFlavor3): ...
+class LindormHNSWTypedDict(CommonTypedDict, LindormTypedDict, HNSWFlavor3):
+    reorder_factor: Annotated[
+        int, click.option("--reorder-factor", type=int, help="reorder factor", required=False, default=2)
+    ]
 
 
 @cli.command()
@@ -56,6 +59,7 @@ def LindormHNSW(**parameters: Unpack[LindormHNSWTypedDict]):
             efConstruction=parameters["ef_construction"],
             efSearch=parameters["ef_search"],
             filter_type=parameters["filter_type"],
+            reorder_factor=parameters["reorder_factor"],
             number_of_regions=parameters["number_of_regions"],
         ),
         **parameters,
diff --git a/vectordb_bench/backend/clients/lindorm/config.py b/vectordb_bench/backend/clients/lindorm/config.py
index 0e0d4aae6..f8976103d 100644
--- a/vectordb_bench/backend/clients/lindorm/config.py
+++ b/vectordb_bench/backend/clients/lindorm/config.py
@@ -48,6 +48,7 @@ class HNSWConfig(LindormIndexConfig, DBCaseConfig):
     efSearch: int | None = None
     filter_type: str | None = "efficient_filter"
     k_expand_scope: int | None = 1000
+    reorder_factor: int | None = 2
 
     def index_param(self, dim: int | None = None) -> dict:
         return {
@@ -61,7 +62,7 @@ def index_param(self, dim: int | None = None) -> dict:
         }
 
     def search_param(self, do_filter: bool = False) -> dict:
-        search_ext_param = {"lvector": {"ef_search": str(self.efSearch)}}
+        search_ext_param = {"lvector": {"ef_search": str(self.efSearch), "reorder_factor": str(self.reorder_factor)}}
         if do_filter:
             search_ext_param["lvector"]["filter_type"] = self.filter_type
             if self.filter_type == "efficient_filter":
@@ -80,7 +81,7 @@ class IVFPQConfig(LindormIndexConfig, DBCaseConfig):
     centroids_hnsw_efSearch: int | None = None
     filter_type: str | None = "efficient_filter"
 
-    reorder_factor: int | None = 10
+    reorder_factor: int | None = 2
     client_refactor: bool = False
     k_expand_scope: int | None = 1000
 
@@ -125,7 +126,7 @@ class IVFBQConfig(LindormIndexConfig, DBCaseConfig):
     centroids_hnsw_efSearch: int | None = None
     filter_type: str | None = "efficient_filter"
 
-    reorder_factor: int | None = 10
+    reorder_factor: int | None = 2
     client_refactor: bool = False
     k_expand_scope: int | None = 1000
 
diff --git a/vectordb_bench/backend/clients/mariadb/mariadb.py b/vectordb_bench/backend/clients/mariadb/mariadb.py
index db3863c85..e6053a0d8 100644
--- a/vectordb_bench/backend/clients/mariadb/mariadb.py
+++ b/vectordb_bench/backend/clients/mariadb/mariadb.py
@@ -108,13 +108,13 @@ def init(self):
                 self.cursor.execute(f"SET mhnsw_ef_search = {search_param['ef_search']}")
             self.cursor.execute("COMMIT")
 
-        self.insert_sql = f"INSERT INTO {self.db_name}.{self.table_name} (id, v) VALUES (%s, %s)"  # noqa: S608
+        self.insert_sql = f"INSERT INTO {self.db_name}.{self.table_name} (id, v) VALUES (%s, %s)"
         self.select_sql = (
-            f"SELECT id FROM {self.db_name}.{self.table_name}"  # noqa: S608
+            f"SELECT id FROM {self.db_name}.{self.table_name}"
             f"ORDER by vec_distance_{search_param['metric_type']}(v, %s) LIMIT %d"
         )
         self.select_sql_with_filter = (
-            f"SELECT id FROM {self.db_name}.{self.table_name} WHERE id >= %d "  # noqa: S608
+            f"SELECT id FROM {self.db_name}.{self.table_name} WHERE id >= %d "
             f"ORDER by vec_distance_{search_param['metric_type']}(v, %s) LIMIT %d"
         )
 
diff --git a/vectordb_bench/backend/clients/milvus/cli.py b/vectordb_bench/backend/clients/milvus/cli.py
index 2f2a286be..738248ac3 100644
--- a/vectordb_bench/backend/clients/milvus/cli.py
+++ b/vectordb_bench/backend/clients/milvus/cli.py
@@ -1,7 +1,7 @@
 from typing import Annotated, TypedDict, Unpack
 
 import click
-from pydantic import SecretStr
+from pydantic import BaseModel, SecretStr
 
 from vectordb_bench.backend.clients import DB
 from vectordb_bench.cli.cli import (
@@ -16,6 +16,31 @@
 DBTYPE = DB.Milvus
 
 
+def _use_partition_key(parameters: dict) -> bool:
+    explicit = parameters.get("use_partition_key")
+    if explicit is not None:
+        return explicit
+    return parameters.get("case_type") == "CloudMultiTenantSearchCase"
+
+
+def _with_partition_key(db_case_config: BaseModel, parameters: dict) -> BaseModel:
+    return db_case_config.model_copy(update={"use_partition_key": _use_partition_key(parameters)})
+
+
+def _build_milvus_config(parameters: dict) -> BaseModel:
+    from .config import MilvusConfig
+
+    return MilvusConfig(
+        db_label=parameters["db_label"],
+        uri=SecretStr(parameters["uri"]),
+        user=parameters["user_name"],
+        password=SecretStr(parameters["password"]) if parameters["password"] else None,
+        num_shards=int(parameters["num_shards"]),
+        replica_number=int(parameters["replica_number"]),
+        collection_name=parameters["collection_name"],
+    )
+
+
 class MilvusTypedDict(TypedDict):
     uri: Annotated[
         str,
@@ -51,6 +76,28 @@ class MilvusTypedDict(TypedDict):
             show_default=True,
         ),
     ]
+    collection_name: Annotated[
+        str,
+        click.option(
+            "--collection-name",
+            type=str,
+            help="Collection name for Milvus",
+            required=False,
+            default="VDBBench",
+            show_default=True,
+        ),
+    ]
+    use_partition_key: Annotated[
+        bool | None,
+        click.option(
+            "--use-partition-key/--no-use-partition-key",
+            default=None,
+            help=(
+                "Use the Milvus partition key on the label field. "
+                "Defaults to enabled for CloudMultiTenantSearchCase and disabled otherwise."
+            ),
+        ),
+    ]
 
 
 class MilvusAutoIndexTypedDict(CommonTypedDict, MilvusTypedDict): ...
@@ -59,19 +106,12 @@ class MilvusAutoIndexTypedDict(CommonTypedDict, MilvusTypedDict): ...
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusAutoIndexTypedDict)
 def MilvusAutoIndex(**parameters: Unpack[MilvusAutoIndexTypedDict]):
-    from .config import AutoIndexConfig, MilvusConfig
+    from .config import AutoIndexConfig
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=AutoIndexConfig(),
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(AutoIndexConfig(), parameters),
         **parameters,
     )
 
@@ -79,19 +119,12 @@ def MilvusAutoIndex(**parameters: Unpack[MilvusAutoIndexTypedDict]):
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusAutoIndexTypedDict)
 def MilvusFlat(**parameters: Unpack[MilvusAutoIndexTypedDict]):
-    from .config import FLATConfig, MilvusConfig
+    from .config import FLATConfig
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=FLATConfig(),
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(FLATConfig(), parameters),
         **parameters,
     )
 
@@ -102,22 +135,18 @@ class MilvusHNSWTypedDict(CommonTypedDict, MilvusTypedDict, HNSWFlavor3): ...
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusHNSWTypedDict)
 def MilvusHNSW(**parameters: Unpack[MilvusHNSWTypedDict]):
-    from .config import HNSWConfig, MilvusConfig
+    from .config import HNSWConfig
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=HNSWConfig(
-            M=parameters["m"],
-            efConstruction=parameters["ef_construction"],
-            ef=parameters["ef_search"],
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            HNSWConfig(
+                M=parameters["m"],
+                efConstruction=parameters["ef_construction"],
+                ef=parameters["ef_search"],
+            ),
+            parameters,
         ),
         **parameters,
     )
@@ -167,26 +196,22 @@ class MilvusHNSWPQTypedDict(CommonTypedDict, MilvusTypedDict, MilvusHNSWTypedDic
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusHNSWPQTypedDict)
 def MilvusHNSWPQ(**parameters: Unpack[MilvusHNSWPQTypedDict]):
-    from .config import HNSWPQConfig, MilvusConfig
+    from .config import HNSWPQConfig
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=HNSWPQConfig(
-            M=parameters["m"],
-            efConstruction=parameters["ef_construction"],
-            ef=parameters["ef_search"],
-            nbits=parameters["nbits"],
-            refine=parameters["refine"],
-            refine_type=parameters["refine_type"],
-            refine_k=parameters["refine_k"],
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            HNSWPQConfig(
+                M=parameters["m"],
+                efConstruction=parameters["ef_construction"],
+                ef=parameters["ef_search"],
+                nbits=parameters["nbits"],
+                refine=parameters["refine"],
+                refine_type=parameters["refine_type"],
+                refine_k=parameters["refine_k"],
+            ),
+            parameters,
         ),
         **parameters,
     )
@@ -211,27 +236,23 @@ class MilvusHNSWPRQTypedDict(
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusHNSWPRQTypedDict)
 def MilvusHNSWPRQ(**parameters: Unpack[MilvusHNSWPRQTypedDict]):
-    from .config import HNSWPRQConfig, MilvusConfig
+    from .config import HNSWPRQConfig
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=HNSWPRQConfig(
-            M=parameters["m"],
-            efConstruction=parameters["ef_construction"],
-            ef=parameters["ef_search"],
-            nbits=parameters["nbits"],
-            refine=parameters["refine"],
-            refine_type=parameters["refine_type"],
-            refine_k=parameters["refine_k"],
-            nrq=parameters["nrq"],
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            HNSWPRQConfig(
+                M=parameters["m"],
+                efConstruction=parameters["ef_construction"],
+                ef=parameters["ef_search"],
+                nbits=parameters["nbits"],
+                refine=parameters["refine"],
+                refine_type=parameters["refine_type"],
+                refine_k=parameters["refine_k"],
+                nrq=parameters["nrq"],
+            ),
+            parameters,
         ),
         **parameters,
     )
@@ -252,26 +273,22 @@ class MilvusHNSWSQTypedDict(CommonTypedDict, MilvusTypedDict, MilvusHNSWTypedDic
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusHNSWSQTypedDict)
 def MilvusHNSWSQ(**parameters: Unpack[MilvusHNSWSQTypedDict]):
-    from .config import HNSWSQConfig, MilvusConfig
+    from .config import HNSWSQConfig
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=HNSWSQConfig(
-            M=parameters["m"],
-            efConstruction=parameters["ef_construction"],
-            ef=parameters["ef_search"],
-            sq_type=parameters["sq_type"],
-            refine=parameters["refine"],
-            refine_type=parameters["refine_type"],
-            refine_k=parameters["refine_k"],
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            HNSWSQConfig(
+                M=parameters["m"],
+                efConstruction=parameters["ef_construction"],
+                ef=parameters["ef_search"],
+                sq_type=parameters["sq_type"],
+                refine=parameters["refine"],
+                refine_type=parameters["refine_type"],
+                refine_k=parameters["refine_k"],
+            ),
+            parameters,
         ),
         **parameters,
     )
@@ -283,21 +300,17 @@ class MilvusIVFFlatTypedDict(CommonTypedDict, MilvusTypedDict, IVFFlatTypedDictN
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusIVFFlatTypedDict)
 def MilvusIVFFlat(**parameters: Unpack[MilvusIVFFlatTypedDict]):
-    from .config import IVFFlatConfig, MilvusConfig
+    from .config import IVFFlatConfig
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=IVFFlatConfig(
-            nlist=parameters["nlist"],
-            nprobe=parameters["nprobe"],
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            IVFFlatConfig(
+                nlist=parameters["nlist"],
+                nprobe=parameters["nprobe"],
+            ),
+            parameters,
         ),
         **parameters,
     )
@@ -306,21 +319,17 @@ def MilvusIVFFlat(**parameters: Unpack[MilvusIVFFlatTypedDict]):
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusIVFFlatTypedDict)
 def MilvusIVFSQ8(**parameters: Unpack[MilvusIVFFlatTypedDict]):
-    from .config import IVFSQ8Config, MilvusConfig
+    from .config import IVFSQ8Config
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=IVFSQ8Config(
-            nlist=parameters["nlist"],
-            nprobe=parameters["nprobe"],
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            IVFSQ8Config(
+                nlist=parameters["nlist"],
+                nprobe=parameters["nprobe"],
+            ),
+            parameters,
         ),
         **parameters,
     )
@@ -368,25 +377,21 @@ class MilvusIVFRABITQTypedDict(CommonTypedDict, MilvusTypedDict, MilvusIVFFlatTy
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusIVFRABITQTypedDict)
 def MilvusIVFRabitQ(**parameters: Unpack[MilvusIVFRABITQTypedDict]):
-    from .config import IVFRABITQConfig, MilvusConfig
+    from .config import IVFRABITQConfig
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=IVFRABITQConfig(
-            nlist=parameters["nlist"],
-            nprobe=parameters["nprobe"],
-            rbq_bits_query=parameters["rbq_bits_query"],
-            refine=parameters["refine"],
-            refine_type=parameters["refine_type"],
-            refine_k=parameters["refine_k"],
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            IVFRABITQConfig(
+                nlist=parameters["nlist"],
+                nprobe=parameters["nprobe"],
+                rbq_bits_query=parameters["rbq_bits_query"],
+                refine=parameters["refine"],
+                refine_type=parameters["refine_type"],
+                refine_k=parameters["refine_k"],
+            ),
+            parameters,
         ),
         **parameters,
     )
@@ -399,20 +404,16 @@ class MilvusDISKANNTypedDict(CommonTypedDict, MilvusTypedDict):
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusDISKANNTypedDict)
 def MilvusDISKANN(**parameters: Unpack[MilvusDISKANNTypedDict]):
-    from .config import DISKANNConfig, MilvusConfig
+    from .config import DISKANNConfig
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=DISKANNConfig(
-            search_list=parameters["search_list"],
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            DISKANNConfig(
+                search_list=parameters["search_list"],
+            ),
+            parameters,
         ),
         **parameters,
     )
@@ -429,23 +430,19 @@ class MilvusGPUIVFTypedDict(CommonTypedDict, MilvusTypedDict, MilvusIVFFlatTyped
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusGPUIVFTypedDict)
 def MilvusGPUIVFFlat(**parameters: Unpack[MilvusGPUIVFTypedDict]):
-    from .config import GPUIVFFlatConfig, MilvusConfig
+    from .config import GPUIVFFlatConfig
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=GPUIVFFlatConfig(
-            nlist=parameters["nlist"],
-            nprobe=parameters["nprobe"],
-            cache_dataset_on_device=parameters["cache_dataset_on_device"],
-            refine_ratio=parameters.get("refine_ratio"),
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            GPUIVFFlatConfig(
+                nlist=parameters["nlist"],
+                nprobe=parameters["nprobe"],
+                cache_dataset_on_device=parameters["cache_dataset_on_device"],
+                refine_ratio=parameters.get("refine_ratio"),
+            ),
+            parameters,
         ),
         **parameters,
     )
@@ -465,21 +462,168 @@ class MilvusGPUBruteForceTypedDict(CommonTypedDict, MilvusTypedDict):
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusGPUBruteForceTypedDict)
 def MilvusGPUBruteForce(**parameters: Unpack[MilvusGPUBruteForceTypedDict]):
-    from .config import GPUBruteForceConfig, MilvusConfig
+    from .config import GPUBruteForceConfig
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=GPUBruteForceConfig(
-            metric_type=parameters["metric_type"],
-            limit=parameters["limit"],  # top-k for search
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            GPUBruteForceConfig(
+                metric_type=parameters["metric_type"],
+                limit=parameters["limit"],  # top-k for search
+            ),
+            parameters,
+        ),
+        **parameters,
+    )
+
+
+class MilvusSVSVamanaTypedDict(CommonTypedDict, MilvusTypedDict):
+    svs_graph_max_degree: Annotated[
+        int,
+        click.option(
+            "--svs-graph-max-degree",
+            type=int,
+            help="Maximum degree of the Vamana graph (4-256).",
+            required=True,
+        ),
+    ]
+    svs_construction_window_size: Annotated[
+        int,
+        click.option(
+            "--svs-construction-window-size",
+            type=int,
+            help="Window size for graph construction.",
+            required=False,
+            default=40,
+            show_default=True,
+        ),
+    ]
+    svs_alpha: Annotated[
+        float | None,
+        click.option(
+            "--svs-alpha",
+            type=float,
+            help="Pruning parameter (default: 1.2 for L2, 0.95 for IP/COSINE).",
+            required=False,
+            default=None,
+        ),
+    ]
+    svs_storage_kind: Annotated[
+        str,
+        click.option(
+            "--svs-storage-kind",
+            type=click.Choice(
+                ["fp32", "fp16", "sqi8", "lvq4x0", "lvq4x4", "lvq4x8", "leanvec4x4", "leanvec4x8", "leanvec8x8"],
+                case_sensitive=False,
+            ),
+            help="Data storage format.",
+            required=False,
+            default="fp32",
+            show_default=True,
+        ),
+    ]
+    svs_search_window_size: Annotated[
+        int | None,
+        click.option(
+            "--svs-search-window-size",
+            type=int,
+            help="Window size for search (1-10000).",
+            required=False,
+            default=None,
+        ),
+    ]
+    svs_search_buffer_capacity: Annotated[
+        int | None,
+        click.option(
+            "--svs-search-buffer-capacity",
+            type=int,
+            help="Buffer capacity for search priority queue (1-10000).",
+            required=False,
+            default=None,
+        ),
+    ]
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(MilvusSVSVamanaTypedDict)
+def MilvusSVSVamana(**parameters: Unpack[MilvusSVSVamanaTypedDict]):
+    from .config import SVSVamanaConfig
+
+    run(
+        db=DBTYPE,
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            SVSVamanaConfig(
+                svs_graph_max_degree=parameters["svs_graph_max_degree"],
+                svs_construction_window_size=parameters["svs_construction_window_size"],
+                svs_alpha=parameters["svs_alpha"],
+                svs_storage_kind=parameters["svs_storage_kind"],
+                svs_search_window_size=parameters["svs_search_window_size"],
+                svs_search_buffer_capacity=parameters["svs_search_buffer_capacity"],
+            ),
+            parameters,
+        ),
+        **parameters,
+    )
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(MilvusSVSVamanaTypedDict)
+def MilvusSVSVamanaLVQ(**parameters: Unpack[MilvusSVSVamanaTypedDict]):
+    from .config import SVSVamanaLVQConfig
+
+    run(
+        db=DBTYPE,
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            SVSVamanaLVQConfig(
+                svs_graph_max_degree=parameters["svs_graph_max_degree"],
+                svs_construction_window_size=parameters["svs_construction_window_size"],
+                svs_alpha=parameters["svs_alpha"],
+                svs_storage_kind=parameters["svs_storage_kind"],
+                svs_search_window_size=parameters["svs_search_window_size"],
+                svs_search_buffer_capacity=parameters["svs_search_buffer_capacity"],
+            ),
+            parameters,
+        ),
+        **parameters,
+    )
+
+
+class MilvusSVSVamanaLeanVecTypedDict(MilvusSVSVamanaTypedDict):
+    svs_leanvec_dim: Annotated[
+        int,
+        click.option(
+            "--svs-leanvec-dim",
+            type=int,
+            help="Dimensionality for LeanVec compression (0 = d/2).",
+            required=False,
+            default=0,
+            show_default=True,
+        ),
+    ]
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(MilvusSVSVamanaLeanVecTypedDict)
+def MilvusSVSVamanaLeanVec(**parameters: Unpack[MilvusSVSVamanaLeanVecTypedDict]):
+    from .config import SVSVamanaLeanVecConfig
+
+    run(
+        db=DBTYPE,
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            SVSVamanaLeanVecConfig(
+                svs_graph_max_degree=parameters["svs_graph_max_degree"],
+                svs_construction_window_size=parameters["svs_construction_window_size"],
+                svs_alpha=parameters["svs_alpha"],
+                svs_storage_kind=parameters["svs_storage_kind"],
+                svs_search_window_size=parameters["svs_search_window_size"],
+                svs_search_buffer_capacity=parameters["svs_search_buffer_capacity"],
+                svs_leanvec_dim=parameters["svs_leanvec_dim"],
+            ),
+            parameters,
         ),
         **parameters,
     )
@@ -498,25 +642,21 @@ class MilvusGPUIVFPQTypedDict(
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusGPUIVFPQTypedDict)
 def MilvusGPUIVFPQ(**parameters: Unpack[MilvusGPUIVFPQTypedDict]):
-    from .config import GPUIVFPQConfig, MilvusConfig
+    from .config import GPUIVFPQConfig
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=GPUIVFPQConfig(
-            nlist=parameters["nlist"],
-            nprobe=parameters["nprobe"],
-            m=parameters["m"],
-            nbits=parameters["nbits"],
-            cache_dataset_on_device=parameters["cache_dataset_on_device"],
-            refine_ratio=parameters["refine_ratio"],
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            GPUIVFPQConfig(
+                nlist=parameters["nlist"],
+                nprobe=parameters["nprobe"],
+                m=parameters["m"],
+                nbits=parameters["nbits"],
+                cache_dataset_on_device=parameters["cache_dataset_on_device"],
+                refine_ratio=parameters["refine_ratio"],
+            ),
+            parameters,
         ),
         **parameters,
     )
@@ -539,29 +679,63 @@ class MilvusGPUCAGRATypedDict(CommonTypedDict, MilvusTypedDict, MilvusGPUIVFType
 @cli.command()
 @click_parameter_decorators_from_typed_dict(MilvusGPUCAGRATypedDict)
 def MilvusGPUCAGRA(**parameters: Unpack[MilvusGPUCAGRATypedDict]):
-    from .config import GPUCAGRAConfig, MilvusConfig
+    from .config import GPUCAGRAConfig
+
+    run(
+        db=DBTYPE,
+        db_config=_build_milvus_config(parameters),
+        db_case_config=_with_partition_key(
+            GPUCAGRAConfig(
+                intermediate_graph_degree=parameters["intermediate_graph_degree"],
+                graph_degree=parameters["graph_degree"],
+                itopk_size=parameters["itopk_size"],
+                team_size=parameters["team_size"],
+                search_width=parameters["search_width"],
+                min_iterations=parameters["min_iterations"],
+                max_iterations=parameters["max_iterations"],
+                build_algo=parameters["build_algo"],
+                cache_dataset_on_device=parameters["cache_dataset_on_device"],
+                refine_ratio=parameters["refine_ratio"],
+            ),
+            parameters,
+        ),
+        **parameters,
+    )
+
+
+class MilvusFTSTypedDict(CommonTypedDict, MilvusTypedDict):
+    """TypedDict for Milvus FTS command parameters."""
+
+    drop_ratio_search: Annotated[
+        float | None,
+        click.option(
+            "--drop-ratio-search",
+            type=float,
+            help="Drop ratio for search (optional, for performance tuning)",
+            required=False,
+            default=None,
+        ),
+    ]
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(MilvusFTSTypedDict)
+def MilvusFTS(**parameters: Unpack[MilvusFTSTypedDict]):
+    """Run FTS (Full-Text Search) benchmark on Milvus using BM25.
+
+    This command uses the MS MARCO dev/small dataset for FTS testing.
+    """
+    from .config import MilvusFtsConfig
+
+    # Set default case_type to large dataset if not specified
+    if parameters.get("case_type") == "Performance1536D50K":  # Default from CommonTypedDict
+        parameters["case_type"] = "FTSBm25Performance"
 
     run(
         db=DBTYPE,
-        db_config=MilvusConfig(
-            db_label=parameters["db_label"],
-            uri=SecretStr(parameters["uri"]),
-            user=parameters["user_name"],
-            password=SecretStr(parameters["password"]) if parameters["password"] else None,
-            num_shards=int(parameters["num_shards"]),
-            replica_number=int(parameters["replica_number"]),
-        ),
-        db_case_config=GPUCAGRAConfig(
-            intermediate_graph_degree=parameters["intermediate_graph_degree"],
-            graph_degree=parameters["graph_degree"],
-            itopk_size=parameters["itopk_size"],
-            team_size=parameters["team_size"],
-            search_width=parameters["search_width"],
-            min_iterations=parameters["min_iterations"],
-            max_iterations=parameters["max_iterations"],
-            build_algo=parameters["build_algo"],
-            cache_dataset_on_device=parameters["cache_dataset_on_device"],
-            refine_ratio=parameters["refine_ratio"],
+        db_config=_build_milvus_config(parameters),
+        db_case_config=MilvusFtsConfig(
+            drop_ratio_search=parameters.get("drop_ratio_search"),
         ),
         **parameters,
     )
diff --git a/vectordb_bench/backend/clients/milvus/config.py b/vectordb_bench/backend/clients/milvus/config.py
index b04901a95..99d5ac8cf 100644
--- a/vectordb_bench/backend/clients/milvus/config.py
+++ b/vectordb_bench/backend/clients/milvus/config.py
@@ -1,16 +1,19 @@
-from typing import Any
+from typing import ClassVar
 
-from pydantic import BaseModel, SecretStr, model_validator
+from pydantic import BaseModel, SecretStr
 
 from ..api import DBCaseConfig, DBConfig, IndexType, MetricType, SQType
 
 
 class MilvusConfig(DBConfig):
+    _extra_empty_skip: ClassVar[frozenset[str]] = frozenset({"user", "password"})
+
     uri: SecretStr = "http://localhost:19530"
     user: str | None = None
     password: SecretStr | None = None
     num_shards: int = 1
     replica_number: int = 1
+    collection_name: str = "VDBBench"
 
     def to_dict(self) -> dict:
         return {
@@ -19,21 +22,9 @@ def to_dict(self) -> dict:
             "password": self.password.get_secret_value() if self.password else None,
             "num_shards": self.num_shards,
             "replica_number": self.replica_number,
+            "collection_name": self.collection_name,
         }
 
-    @model_validator(mode="before")
-    @classmethod
-    def not_empty_field(cls, data: Any) -> Any:
-        if isinstance(data, dict):
-            skip = set(cls.common_short_configs()) | set(cls.common_long_configs()) | {"user", "password"}
-            for name, v in data.items():
-                if name in skip:
-                    continue
-                if isinstance(v, str) and len(v) == 0:
-                    msg = f"Empty string for field '{name}'!"
-                    raise ValueError(msg)
-        return data
-
 
 class MilvusIndexConfig(BaseModel):
     """Base config for milvus"""
@@ -444,8 +435,163 @@ def search_param(self) -> dict:
         }
 
 
+class SVSVamanaConfig(MilvusIndexConfig, DBCaseConfig):
+    svs_graph_max_degree: int
+    svs_construction_window_size: int = 40
+    svs_alpha: float | None = None
+    svs_storage_kind: str = "fp32"
+    svs_search_window_size: int | None = None
+    svs_search_buffer_capacity: int | None = None
+    index: IndexType = IndexType.SVS_VAMANA
+
+    def index_param(self) -> dict:
+        params = {
+            "svs_graph_max_degree": self.svs_graph_max_degree,
+            "svs_construction_window_size": self.svs_construction_window_size,
+            "svs_storage_kind": self.svs_storage_kind,
+        }
+        if self.svs_alpha is not None:
+            params["svs_alpha"] = self.svs_alpha
+        return {
+            "metric_type": self.parse_metric(),
+            "index_type": self.index.value,
+            "params": params,
+        }
+
+    def search_param(self) -> dict:
+        return {
+            "metric_type": self.parse_metric(),
+            "params": {
+                "svs_search_window_size": self.svs_search_window_size,
+                "svs_search_buffer_capacity": self.svs_search_buffer_capacity,
+            },
+        }
+
+
+class SVSVamanaLVQConfig(SVSVamanaConfig):
+    svs_storage_kind: str = "lvq4x4"
+    index: IndexType = IndexType.SVS_VAMANA_LVQ
+
+
+class SVSVamanaLeanVecConfig(SVSVamanaConfig):
+    svs_storage_kind: str = "leanvec4x4"
+    svs_leanvec_dim: int = 0
+    index: IndexType = IndexType.SVS_VAMANA_LEANVEC
+
+    def index_param(self) -> dict:
+        params = {
+            "svs_graph_max_degree": self.svs_graph_max_degree,
+            "svs_construction_window_size": self.svs_construction_window_size,
+            "svs_storage_kind": self.svs_storage_kind,
+            "svs_leanvec_dim": self.svs_leanvec_dim,
+        }
+        if self.svs_alpha is not None:
+            params["svs_alpha"] = self.svs_alpha
+        return {
+            "metric_type": self.parse_metric(),
+            "index_type": self.index.value,
+            "params": params,
+        }
+
+
+class MilvusFtsConfig(BaseModel, DBCaseConfig):
+    """
+    1. inverted_index_algo: Index algorithm selection
+       - "DAAT_MAXSCORE" (default): Suitable for high k values or queries with many terms, balanced performance
+       - "DAAT_WAND": Suitable for small k values or short queries, faster
+       - "TAAT_NAIVE": Dynamically adapts to collection changes (e.g., avgdl), but slower
+    2. bm25_k1: BM25 term frequency saturation control [1.2, 2.0], default None
+       - None: Do not override Milvus product default
+       - Higher values: Increase importance of term frequency in document ranking
+       - Recommended range: 1.2-1.8, adjust based on query characteristics
+    3. bm25_b: BM25 document length normalization control [0.0, 1.0], default None
+       - None: Do not override Milvus product default
+       - 1.0: No length normalization, longer documents have advantage
+       - 0.0: Full normalization, shorter documents have advantage
+       - 0.75: Balanced length normalization, commonly used default
+    4. analyzer_tokenizer: Tokenizer type, default "standard"
+       - "standard": Standard tokenizer, suitable for English text
+       - "whitespace": Split by whitespace characters
+       - "keyword": No tokenization, preserve original text
+    5. analyzer_enable_lowercase: Enable lowercase conversion, default True
+       - True: Convert all text to lowercase, improve matching rate
+       - False: Preserve original case
+    6. analyzer_max_token_length: Maximum length of individual tokens, default 40
+       - Limit length of overly long words
+       - Set to None to disable this limit
+    7. analyzer_stop_words: Stop words list, default None
+       - Comma-separated stop words, e.g., "of,to,the,and,or"
+       - These words will be filtered out and not participate in indexing/search
+    8. drop_ratio_search: Ratio of minimum values to ignore during search [0.0, 1.0], default None
+       - 0.0: Keep all values, highest recall
+       - 0.1-0.3: Improve search speed by 10-20%, slight impact on recall
+    """
+
+    index_type: str = "SPARSE_INVERTED_INDEX"
+    metric_type: MetricType = MetricType.BM25
+    inverted_index_algo: str = "DAAT_MAXSCORE"  # DAAT_MAXSCORE | DAAT_WAND | TAAT_NAIVE
+    bm25_k1: float | None = None
+    bm25_b: float | None = None
+    analyzer_tokenizer: str = "standard"
+    analyzer_enable_lowercase: bool = True
+    analyzer_max_token_length: int | None = None
+    analyzer_stop_words: str | None = None
+    drop_ratio_search: float | None = None
+
+    def analyzer_param(self) -> dict:
+        analyzer_params = {}
+        if self.analyzer_tokenizer:
+            analyzer_params["tokenizer"] = self.analyzer_tokenizer
+
+        filters = []
+        if self.analyzer_enable_lowercase:
+            filters.append("lowercase")
+
+        if self.analyzer_max_token_length:
+            filters.append({"type": "length", "max": self.analyzer_max_token_length})
+
+        if self.analyzer_stop_words:
+            stop_words = [word.strip() for word in self.analyzer_stop_words.split(",") if word.strip()]
+            if stop_words:
+                filters.append({"type": "stop", "stop_words": stop_words})
+
+        if filters:
+            analyzer_params["filter"] = filters
+
+        return analyzer_params or {"tokenizer": "standard"}
+
+    def sparse_index_param(self) -> dict:
+        params = {
+            "inverted_index_algo": self.inverted_index_algo,
+        }
+        if self.bm25_k1 is not None:
+            params["bm25_k1"] = self.bm25_k1
+        if self.bm25_b is not None:
+            params["bm25_b"] = self.bm25_b
+
+        return {
+            "index_type": self.index_type,
+            "metric_type": self.metric_type.value,
+            "params": params,
+        }
+
+    def index_param(self) -> dict:
+        return {**self.sparse_index_param(), "analyzer_params": self.analyzer_param()}
+
+    def search_param(self) -> dict:
+
+        params: dict = {}
+        if self.drop_ratio_search is not None:
+            params["drop_ratio_search"] = self.drop_ratio_search
+        return {
+            "metric_type": self.metric_type.value,
+            "params": params,
+        }
+
+
 _milvus_case_config = {
     IndexType.AUTOINDEX: AutoIndexConfig,
+    IndexType.FTS: MilvusFtsConfig,
     IndexType.HNSW: HNSWConfig,
     IndexType.HNSW_SQ: HNSWSQConfig,
     IndexType.HNSW_PQ: HNSWPQConfig,
@@ -461,4 +607,7 @@ def search_param(self) -> dict:
     IndexType.GPU_CAGRA: GPUCAGRAConfig,
     IndexType.GPU_BRUTE_FORCE: GPUBruteForceConfig,
     IndexType.SCANN_MILVUS: SCANNConfig,
+    IndexType.SVS_VAMANA: SVSVamanaConfig,
+    IndexType.SVS_VAMANA_LVQ: SVSVamanaLVQConfig,
+    IndexType.SVS_VAMANA_LEANVEC: SVSVamanaLeanVecConfig,
 }
diff --git a/vectordb_bench/backend/clients/milvus/milvus.py b/vectordb_bench/backend/clients/milvus/milvus.py
index 26257c7c4..73c3c242e 100644
--- a/vectordb_bench/backend/clients/milvus/milvus.py
+++ b/vectordb_bench/backend/clients/milvus/milvus.py
@@ -4,17 +4,23 @@
 import time
 from collections.abc import Iterable
 from contextlib import contextmanager
+from typing import Any
 
-from pymilvus import Collection, CollectionSchema, DataType, FieldSchema, MilvusException, utility
+from pymilvus import DataType, Function, FunctionType, MilvusClient, MilvusException
 
 from vectordb_bench.backend.filter import Filter, FilterOp
+from vectordb_bench.backend.payload import PayloadProfile
 
 from ..api import VectorDB
-from .config import MilvusIndexConfig
+from .config import MilvusFtsConfig, MilvusIndexConfig
 
 log = logging.getLogger(__name__)
 
 MILVUS_LOAD_REQS_SIZE = 1.5 * 1024 * 1024
+MILVUS_FTS_BATCH_SIZE = 1000
+MILVUS_FORCE_MERGE_TARGET_SIZE_MB = ((1 << 63) - 1) // (1024**2)
+MILVUS_FORCE_MERGE_MAX_ATTEMPTS = 10
+MILVUS_FORCE_MERGE_RETRY_INTERVAL_SECONDS = 30
 
 
 class Milvus(VectorDB):
@@ -24,15 +30,23 @@ class Milvus(VectorDB):
         FilterOp.StrEqual,
     ]
 
-    def __init__(
+    @classmethod
+    def supports_full_text_search(cls) -> bool:
+        return True
+
+    def has_text_field(self) -> bool:
+        return bool(getattr(self, "_is_fts", False) and getattr(self, "_text_field", None))
+
+    def __init__(  # noqa: PLR0915
         self,
         dim: int,
         db_config: dict,
-        db_case_config: MilvusIndexConfig,
+        db_case_config: MilvusIndexConfig | MilvusFtsConfig,
         collection_name: str = "VDBBench",
         drop_old: bool = False,
         name: str = "Milvus",
         with_scalar_labels: bool = False,
+        fts_batch_size: int | None = None,
         **kwargs,
     ):
         """Initialize wrapper around the milvus vector database."""
@@ -40,87 +54,217 @@ def __init__(
         self.db_config = db_config
         self.case_config = db_case_config
         self.collection_name = collection_name
-        self.batch_size = int(MILVUS_LOAD_REQS_SIZE / (dim * 4))
         self.with_scalar_labels = with_scalar_labels
 
-        self._primary_field = "pk"
-        self._scalar_id_field = "id"
         self._scalar_label_field = "label"
-        self._vector_field = "vector"
-        self._vector_index_name = "vector_idx"
-        self._scalar_id_index_name = "id_sort_idx"
+        self._scalar_payload_label_field = self._scalar_label_field
+        self._multitenant_partition_key_field = self._scalar_label_field
         self._scalar_labels_index_name = "labels_idx"
+        self._is_fts = isinstance(self.case_config, MilvusFtsConfig)
+
+        if self._is_fts:
+            self.batch_size = fts_batch_size or MILVUS_FTS_BATCH_SIZE
+            self._primary_field = "doc_id"
+            self._text_field = "text"
+            self._filter_id_field = "filter_id"
+            self._sparse_field = "sparse_vector"
+            self._sparse_index_name = "sparse_vector_idx"
+            self._doc_id_sort_index_name = "doc_id_sort_idx"
+            self._filter_id_sort_index_name = "filter_id_sort_idx"
+            self._main_index_name = self._sparse_index_name
+            self._sort_index_name = self._doc_id_sort_index_name
+            self._sort_index_field = self._primary_field
+        else:
+            self.batch_size = int(MILVUS_LOAD_REQS_SIZE / (dim * 4))
+            self._primary_field = "pk"
+            self._scalar_id_field = "id"
+            self._vector_field = "vector"
+            self._vector_index_name = "vector_idx"
+            self._scalar_id_index_name = "id_sort_idx"
+            self._main_index_name = self._vector_index_name
+            self._sort_index_name = self._scalar_id_index_name
+            self._sort_index_field = self._scalar_id_field
+
+        self.multitenant_tenant_labels: list[str] = kwargs.get("multitenant_tenant_labels", [])
+        if self.multitenant_tenant_labels:
+            self._multitenant_partition_key_field = "labels"
+            if self.with_scalar_labels:
+                self._scalar_payload_label_field = "scalar_label"
 
-        from pymilvus import connections
-
-        connections.connect(
+        client = MilvusClient(
             uri=self.db_config.get("uri"),
             user=self.db_config.get("user"),
             password=self.db_config.get("password"),
+            token=self.db_config.get("token", ""),
             timeout=30,
         )
-        if drop_old and utility.has_collection(self.collection_name):
+
+        if drop_old and client.has_collection(self.collection_name):
             log.info(f"{self.name} client drop_old collection: {self.collection_name}")
-            utility.drop_collection(self.collection_name)
-
-        if not utility.has_collection(self.collection_name):
-            fields = [
-                FieldSchema(self._primary_field, DataType.INT64, is_primary=True),
-                FieldSchema(self._scalar_id_field, DataType.INT64),
-                FieldSchema(self._vector_field, DataType.FLOAT_VECTOR, dim=dim),
-            ]
-            if self.with_scalar_labels:
-                is_partition_key = db_case_config.use_partition_key
-                log.info(f"with_scalar_labels, add a new varchar field, as partition_key: {is_partition_key}")
-                fields.append(
-                    FieldSchema(
-                        self._scalar_label_field,
+            client.drop_collection(self.collection_name)
+
+        if not client.has_collection(self.collection_name):
+            schema = MilvusClient.create_schema()
+            if self._is_fts:
+                analyzer_params = (
+                    self.case_config.analyzer_param()
+                    if hasattr(self.case_config, "analyzer_param")
+                    else self.case_config.index_param().get("analyzer_params", {"type": "english"})
+                )
+                schema.add_field(self._primary_field, DataType.VARCHAR, max_length=512, is_primary=True)
+                schema.add_field(self._filter_id_field, DataType.INT64)
+                schema.add_field(
+                    self._text_field,
+                    DataType.VARCHAR,
+                    max_length=65535,
+                    enable_analyzer=True,
+                    enable_match=True,
+                    analyzer_params=analyzer_params,
+                )
+                schema.add_field(self._sparse_field, DataType.SPARSE_FLOAT_VECTOR)
+                if self.with_scalar_labels:
+                    schema.add_field(self._scalar_label_field, DataType.VARCHAR, max_length=256)
+                schema.add_function(
+                    Function(
+                        name="text_bm25_emb",
+                        function_type=FunctionType.BM25,
+                        input_field_names=[self._text_field],
+                        output_field_names=[self._sparse_field],
+                        params={},
+                    )
+                )
+            else:
+                schema.add_field(self._primary_field, DataType.INT64, is_primary=True)
+                schema.add_field(self._scalar_id_field, DataType.INT64)
+                schema.add_field(self._vector_field, DataType.FLOAT_VECTOR, dim=dim)
+
+                if self.multitenant_tenant_labels:
+                    schema.add_field(
+                        self._multitenant_partition_key_field,
                         DataType.VARCHAR,
                         max_length=256,
-                        is_partition_key=is_partition_key,
+                        is_partition_key=True,
                     )
-                )
+
+                if self.with_scalar_labels:
+                    is_partition_key = db_case_config.use_partition_key
+                    log.info(f"with_scalar_labels, add a new varchar field, as partition_key: {is_partition_key}")
+                    if not self.multitenant_tenant_labels or (
+                        self._scalar_payload_label_field != self._multitenant_partition_key_field
+                    ):
+                        schema.add_field(
+                            self._scalar_payload_label_field,
+                            DataType.VARCHAR,
+                            max_length=256,
+                            is_partition_key=is_partition_key and not self.multitenant_tenant_labels,
+                        )
 
             log.info(f"{self.name} create collection: {self.collection_name}")
 
-            # Create the collection
-            col = Collection(
-                name=self.collection_name,
-                schema=CollectionSchema(fields),
-                consistency_level="Session",
+            index_params = self._build_index_params()
+            client.create_collection(
+                collection_name=self.collection_name,
+                schema=schema,
                 num_shards=self.db_config.get("num_shards", 1),
+                consistency_level="Session",
+            )
+            client.create_index(self.collection_name, index_params)
+            client.load_collection(
+                self.collection_name,
+                replica_number=self.db_config.get("replica_number", 1),
             )
 
-            self.create_index()
-            col.load(replica_number=self.db_config.get("replica_number", 1))
-
-        connections.disconnect("default")
+        client.close()
 
-    def create_index(self):
-        col = Collection(self.collection_name)
-        # vector index
-        col.create_index(
-            self._vector_field,
-            self.case_config.index_param(),
-            index_name=self._vector_index_name,
-        )
-        # scalar index for range-expr (int-filter)
-        col.create_index(
-            self._scalar_id_field,
-            index_params={
-                "index_type": "STL_SORT",
-            },
-            index_name=self._scalar_id_index_name,
+    def _build_index_params(self):
+        index_params = MilvusClient.prepare_index_params()
+        if self._is_fts:
+            sparse_idx = (
+                self.case_config.sparse_index_param()
+                if hasattr(self.case_config, "sparse_index_param")
+                else self.case_config.index_param()
+            )
+            index_params.add_index(
+                field_name=self._sparse_field,
+                index_name=self._main_index_name,
+                index_type=sparse_idx.get("index_type", ""),
+                metric_type=sparse_idx.get("metric_type", ""),
+                params=sparse_idx.get("params", {}),
+            )
+        else:
+            vec_idx = self.case_config.index_param()
+            index_params.add_index(
+                field_name=self._vector_field,
+                index_name=self._vector_index_name,
+                index_type=vec_idx.get("index_type", ""),
+                metric_type=vec_idx.get("metric_type", ""),
+                params=vec_idx.get("params", {}),
+            )
+        index_params.add_index(
+            field_name=self._sort_index_field,
+            index_name=self._sort_index_name,
+            index_type="STL_SORT",
         )
-        # scalar index for varchar (label-filter)
+        if self._is_fts:
+            index_params.add_index(
+                field_name=self._filter_id_field,
+                index_name=self._filter_id_sort_index_name,
+                index_type="STL_SORT",
+            )
         if self.with_scalar_labels:
-            col.create_index(
-                self._scalar_label_field,
-                index_params={
-                    "index_type": "BITMAP",
-                },
+            index_params.add_index(
+                field_name=self._scalar_payload_label_field,
                 index_name=self._scalar_labels_index_name,
+                index_type="BITMAP",
             )
+        return index_params
+
+    def supports_multitenant(self) -> bool:
+        return True
+
+    def validate_multitenant_schema(self) -> None:
+        client = MilvusClient(
+            uri=self.db_config.get("uri"),
+            user=self.db_config.get("user"),
+            password=self.db_config.get("password"),
+            token=self.db_config.get("token", ""),
+            timeout=30,
+        )
+        try:
+            desc = client.describe_collection(self.collection_name)
+            fields = desc.get("fields", []) if isinstance(desc, dict) else []
+            fields_by_name = {self._field_property(field, "name"): field for field in fields}
+            partition_key_field = self._find_multitenant_partition_key_field(fields_by_name)
+            if partition_key_field is None:
+                label_field = fields_by_name.get(self._scalar_label_field)
+                if label_field is None:
+                    msg = f"{self.name} multitenant collection {self.collection_name} is missing tenant label field"
+                    raise ValueError(msg)
+                msg = f"{self.name} multitenant collection {self.collection_name} label field is not a partition key"
+                raise ValueError(msg)
+            self._multitenant_partition_key_field = partition_key_field
+            if "scalar_label" in fields_by_name:
+                self._scalar_payload_label_field = "scalar_label"
+        finally:
+            client.close()
+
+    def _find_multitenant_partition_key_field(self, fields_by_name: dict[str, dict | object]) -> str | None:
+        for field_name in [self._scalar_label_field, "labels"]:
+            field = fields_by_name.get(field_name)
+            if field is not None and self._field_property(field, "is_partition_key", False):
+                return field_name
+        return None
+
+    @staticmethod
+    def _field_property(field: dict | object, name: str, default: Any = None):
+        if isinstance(field, dict):
+            if name in field:
+                return field[name]
+            params = field.get("params")
+            if isinstance(params, dict) and name in params:
+                return params[name]
+            return default
+        return getattr(field, name, default)
 
     @contextmanager
     def init(self):
@@ -130,68 +274,121 @@ def init(self):
             >>>     self.insert_embeddings()
             >>>     self.search_embedding()
         """
-        from pymilvus import connections
+        self.client: MilvusClient | None = None
+        self.client = MilvusClient(
+            uri=self.db_config.get("uri"),
+            user=self.db_config.get("user"),
+            password=self.db_config.get("password"),
+            token=self.db_config.get("token", ""),
+            timeout=60,
+        )
+        yield
+        self.client.close()
+        self.client = None
+
+    def _wait_for_segments_sorted(self):
+        while True:
+            segments = self.client.list_persistent_segments(self.collection_name)
+            unsorted = [s for s in segments if not s.is_sorted]
+            if not unsorted:
+                log.info(f"{self.name} all persistent segments are sorted.")
+                break
+            log.debug(f"{self.name} waiting for {len(unsorted)} segments to be sorted...")
+            time.sleep(5)
+
+    def _wait_for_index(self):
+        while True:
+            info = self.client.describe_index(self.collection_name, self._main_index_name)
+            if info.get("pending_index_rows", -1) == 0:
+                break
+            time.sleep(5)
+
+    def _wait_for_compaction_ready(self):
+        self._wait_for_segments_sorted()
+        self._wait_for_index()
+
+    def _wait_for_compaction(self, compaction_id: int):
+        while True:
+            state = self.client.get_compaction_state(compaction_id)
+            if state == "Completed":
+                break
+            time.sleep(0.5)
+
+    def _force_merge_source_segment_ids(self) -> set[int]:
+        # isCompacting is not exposed by MilvusClient, so verify plan coverage
+        # against every persistent data segment that force merge should consume.
+        segments = self.client.list_persistent_segments(self.collection_name)
+        return {
+            segment.segment_id
+            for segment in segments
+            if segment.state_name == "Flushed" and segment.level_name not in {"L0", "L2"}
+        }
+
+    def _force_merge(self, max_attempts: int = MILVUS_FORCE_MERGE_MAX_ATTEMPTS):
+        if max_attempts <= 0:
+            message = "force merge max_attempts must be greater than zero"
+            raise ValueError(message)
+
+        for attempt in range(1, max_attempts + 1):
+            self._wait_for_compaction_ready()
+            expected_source_ids = self._force_merge_source_segment_ids()
+            compaction_id = self.client.compact(self.collection_name, target_size=MILVUS_FORCE_MERGE_TARGET_SIZE_MB)
+            if compaction_id <= 0:
+                failure_detail = f"generated no plan for snapshot {sorted(expected_source_ids)}"
+            else:
+                self._wait_for_compaction(compaction_id)
+                plans = self.client.get_compaction_plans(compaction_id)
+                actual_source_ids = {source_id for plan in plans.plans for source_id in plan.sources}
+                attempt_missing_source_ids = expected_source_ids - actual_source_ids
+                if not attempt_missing_source_ids:
+                    return
 
-        self.col: Collection | None = None
+                failure_detail = f"missing segments: {sorted(attempt_missing_source_ids)}"
 
-        connections.connect(**self.db_config, timeout=60)
-        # Grab the existing colection with connections
-        self.col = Collection(self.collection_name)
+            if attempt == max_attempts:
+                message = f"{self.name} force merge failed after {max_attempts} attempts; {failure_detail}"
+                raise RuntimeError(message)
 
-        yield
-        connections.disconnect("default")
+            log.info(f"{self.name} force merge attempt {attempt}/{max_attempts} {failure_detail}; retrying...")
+            time.sleep(MILVUS_FORCE_MERGE_RETRY_INTERVAL_SECONDS)
 
     def _optimize(self):
         log.info(f"{self.name} optimizing before search")
-        self._post_insert()
-        try:
-            self.col.load(refresh=True)
-        except Exception as e:
-            log.warning(f"{self.name} optimize error: {e}")
-            raise e from None
-
-    def _post_insert(self):
         try:
-            self.col.flush()
-            # wait for index done and load refresh
-            self.create_index()
-
-            utility.wait_for_index_building_complete(self.collection_name, index_name=self._vector_index_name)
-
-            def wait_index():
-                while True:
-                    progress = utility.index_building_progress(self.collection_name, index_name=self._vector_index_name)
-                    if progress.get("pending_index_rows", -1) == 0:
-                        break
-                    time.sleep(5)
-
-            wait_index()
+            self.client.flush(self.collection_name)
 
-            # Skip compaction if use GPU indexType
-            if self.case_config.is_gpu_index:
-                log.debug("skip compaction for gpu index type.")
+            if getattr(self.case_config, "is_gpu_index", False):
+                log.debug("skip force merge compaction for gpu index type.")
             else:
                 try:
-                    self.col.compact()
-                    self.col.wait_for_compaction_completed()
-                    log.info("compaction completed. waiting for the rest of index building.")
+                    self._wait_for_compaction_ready()
+                    compaction_id = self.client.compact(self.collection_name)
+                    if compaction_id > 0:
+                        self._wait_for_compaction(compaction_id)
+                    self._force_merge()
+                    log.info(f"{self.name} force merge compaction completed.")
                 except Exception as e:
-                    log.warning(f"{self.name} compact error: {e}")
-                    if hasattr(e, "code") and e.code().name == "PERMISSION_DENIED":
-                        log.warning("Skip compact due to permission denied.")
+                    log.warning(f"{self.name} compact or list segments error: {e}")
+                    if getattr(getattr(e, "code", None), "name", None) == "PERMISSION_DENIED":
+                        log.warning("Skip compact due to list segments or compact permission denied.")
                     else:
                         raise e from None
-                wait_index()
+
+            # wait for index no matter what
+            self._wait_for_index()
+            self.client.refresh_load(self.collection_name)
         except Exception as e:
             log.warning(f"{self.name} optimize error: {e}")
             raise e from None
 
     def optimize(self, data_size: int | None = None):
-        assert self.col, "Please call self.init() before"
+        assert self.client, "Please call self.init() before"
         self._optimize()
 
     def need_normalize_cosine(self) -> bool:
         """Wheather this database need to normalize dataset to support COSINE"""
+        if self._is_fts:
+            return False
         if self.case_config.is_gpu_index:
             log.info("current gpu_index only supports IP / L2, cosine dataset need normalize.")
             return True
@@ -203,58 +400,201 @@ def insert_embeddings(
         embeddings: Iterable[list[float]],
         metadata: list[int],
         labels_data: list[str] | None = None,
+        tenant_labels_data: list[str] | None = None,
         **kwargs,
     ) -> tuple[int, Exception]:
         """Insert embeddings into Milvus. should call self.init() first"""
-        # use the first insert_embeddings to init collection
-        assert self.col is not None
+        assert self.client is not None
         assert len(embeddings) == len(metadata)
         insert_count = 0
         try:
             for batch_start_offset in range(0, len(embeddings), self.batch_size):
                 batch_end_offset = min(batch_start_offset + self.batch_size, len(embeddings))
-                insert_data = [
-                    metadata[batch_start_offset:batch_end_offset],
-                    metadata[batch_start_offset:batch_end_offset],
-                    embeddings[batch_start_offset:batch_end_offset],
-                ]
-                if self.with_scalar_labels:
-                    insert_data.append(labels_data[batch_start_offset:batch_end_offset])
-                res = self.col.insert(insert_data)
-                insert_count += len(res.primary_keys)
+                batch_data = []
+                for i in range(batch_start_offset, batch_end_offset):
+                    row = {
+                        self._primary_field: metadata[i],
+                        self._scalar_id_field: metadata[i],
+                        self._vector_field: embeddings[i],
+                    }
+                    if tenant_labels_data is not None:
+                        row[self._multitenant_partition_key_field] = tenant_labels_data[i]
+                    if self.with_scalar_labels:
+                        row[self._scalar_payload_label_field] = labels_data[i]
+                    batch_data.append(row)
+                res = self.client.insert(self.collection_name, batch_data)
+                insert_count += res["insert_count"]
         except MilvusException as e:
             log.info(f"Failed to insert data: {e}")
             return insert_count, e
         return insert_count, None
 
+    def insert_documents(
+        self,
+        texts: Iterable[str],
+        doc_ids: list[str],
+        **kwargs,
+    ) -> tuple[int, Exception | None]:
+        """Insert documents into a Milvus BM25 full-text collection."""
+        if not self._is_fts:
+            msg = "insert_documents is only valid in FTS mode"
+            raise RuntimeError(msg)
+        assert self.client is not None
+
+        docs = list(texts)
+        if len(docs) != len(doc_ids):
+            msg = f"Mismatch between texts ({len(docs)}) and doc_ids ({len(doc_ids)}) lengths"
+            raise ValueError(msg)
+
+        batch_size = kwargs.get("batch_size", self.batch_size)
+        labels_data = kwargs.get("labels_data")
+        filter_ids = kwargs.get("filter_ids")
+        if filter_ids is not None and len(filter_ids) != len(docs):
+            msg = f"Mismatch between texts ({len(docs)}) and filter_ids ({len(filter_ids)}) lengths"
+            raise ValueError(msg)
+
+        insert_count = 0
+        try:
+            for batch_start_offset in range(0, len(docs), batch_size):
+                batch_end_offset = min(batch_start_offset + batch_size, len(docs))
+                rows = []
+                for i in range(batch_start_offset, batch_end_offset):
+                    row = {
+                        self._primary_field: str(doc_ids[i]),
+                        self._text_field: docs[i],
+                    }
+                    if filter_ids is not None:
+                        row[self._filter_id_field] = int(filter_ids[i])
+                    if self.with_scalar_labels:
+                        row[self._scalar_label_field] = labels_data[i] if labels_data is not None else ""
+                    rows.append(row)
+
+                res = self.client.insert(self.collection_name, rows)
+                insert_count += res["insert_count"]
+                if batch_start_offset // batch_size % 10 == 0:
+                    log.debug(
+                        f"{self.name} batch insert progress: {batch_end_offset}/{len(docs)} "
+                        f"({batch_end_offset / len(docs) * 100:.1f}%)"
+                    )
+        except MilvusException as e:
+            log.info(f"{self.name} insert error: {e}")
+            return insert_count, e
+        return insert_count, None
+
     def prepare_filter(self, filters: Filter):
+        if self._is_fts:
+            if filters.type == FilterOp.NonFilter:
+                self.expr = ""
+                return
+            if filters.type == FilterOp.NumGE:
+                if getattr(filters, "int_field", None) != self._filter_id_field:
+                    msg = f"Milvus FTS filters only support int_field='{self._filter_id_field}'"
+                    raise ValueError(msg)
+                self.expr = f"{self._filter_id_field} >= {filters.int_value}"
+                return
+            msg = f"Not support Filter for Milvus FTS - {filters}"
+            raise ValueError(msg)
         if filters.type == FilterOp.NonFilter:
             self.expr = ""
         elif filters.type == FilterOp.NumGE:
             self.expr = f"{self._scalar_id_field} >= {filters.int_value}"
         elif filters.type == FilterOp.StrEqual:
-            self.expr = f"{self._scalar_label_field} == '{filters.label_value}'"
+            self.expr = f"{self._scalar_payload_label_field} == '{filters.label_value}'"
         else:
             msg = f"Not support Filter for Milvus - {filters}"
             raise ValueError(msg)
 
+    def supports_payload_profile(self, payload_profile: PayloadProfile) -> bool:
+        return payload_profile in {
+            PayloadProfile.IDS_ONLY,
+            PayloadProfile.VECTOR,
+            PayloadProfile.SCALAR_LABEL,
+        }
+
+    def poll_insert_readiness(self, expected_count: int) -> dict:
+        assert self.client is not None
+        self.client.flush(self.collection_name)
+        stats = self.client.get_collection_stats(self.collection_name)
+        count = int(stats.get("row_count", stats.get("num_entities", 0)))
+        progress = self.client.describe_index(self.collection_name, self._main_index_name)
+        return {
+            "fully_searchable": count >= expected_count,
+            "fully_indexed": progress.get("pending_index_rows", -1) == 0,
+            "additional_parameters": {},
+        }
+
     def search_embedding(
         self,
         query: list[float],
         k: int = 100,
         timeout: int | None = None,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+        tenant: str | None = None,
     ) -> list[int]:
         """Perform a search on a query embedding and return results."""
-        assert self.col is not None
-
-        # Perform the search.
-        res = self.col.search(
-            data=[query],
-            anns_field=self._vector_field,
-            param=self.case_config.search_param(),
+        assert self.client is not None
+
+        output_fields = None
+        if payload_profile == PayloadProfile.VECTOR:
+            output_fields = [self._vector_field]
+        elif payload_profile == PayloadProfile.SCALAR_LABEL:
+            output_fields = [getattr(self, "_scalar_payload_label_field", self._scalar_label_field)]
+
+        expr = self.expr
+        if tenant is not None:
+            tenant_field = getattr(self, "_multitenant_partition_key_field", self._scalar_label_field)
+            tenant_expr = f"{tenant_field} == '{tenant}'"
+            expr = tenant_expr if not expr else f"({expr}) and ({tenant_expr})"
+
+        search_kwargs = {
+            "collection_name": self.collection_name,
+            "data": [query],
+            "anns_field": self._vector_field,
+            "search_params": self.case_config.search_param(),
+            "limit": k,
+            "filter": expr,
+            "output_fields": output_fields,
+        }
+        res = self.client.search(**search_kwargs)
+
+        return [result[self._primary_field] for result in res[0]]
+
+    def search_documents(
+        self,
+        query: str,
+        k: int = 100,
+        timeout: int | None = None,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+    ) -> list[str]:
+        """Search a Milvus BM25 full-text collection and return document IDs."""
+        if not self._is_fts:
+            msg = "search_documents only valid in FTS mode"
+            raise RuntimeError(msg)
+        if not self.supports_document_payload_profile(payload_profile):
+            msg = f"{getattr(self, 'name', 'Milvus')} does not support document payload_profile={payload_profile.value}"
+            raise NotImplementedError(msg)
+        assert self.client is not None
+
+        output_fields = [self._primary_field]
+        if payload_profile == PayloadProfile.TEXT:
+            output_fields.append(self._text_field)
+
+        res = self.client.search(
+            collection_name=self.collection_name,
+            data=[str(query)],
+            anns_field=self._sparse_field,
+            search_params=self.case_config.search_param(),
             limit=k,
-            expr=self.expr,
+            filter=getattr(self, "expr", ""),
+            output_fields=output_fields,
         )
 
-        # Organize results.
-        return [result.id for result in res[0]]
+        hits = res[0] if res else []
+        doc_ids = []
+        for hit in hits:
+            entity = hit.get("entity", hit) if isinstance(hit, dict) else hit
+            if isinstance(entity, dict):
+                doc_ids.append(str(entity.get(self._primary_field)))
+            else:
+                doc_ids.append(str(getattr(entity, self._primary_field)))
+        return doc_ids
diff --git a/vectordb_bench/backend/clients/oceanbase/cli.py b/vectordb_bench/backend/clients/oceanbase/cli.py
index 61583cc82..81ccacc79 100644
--- a/vectordb_bench/backend/clients/oceanbase/cli.py
+++ b/vectordb_bench/backend/clients/oceanbase/cli.py
@@ -31,9 +31,40 @@ class OceanBaseTypedDict(CommonTypedDict):
     ]
     database: Annotated[str, click.option("--database", type=str, help="DataBase name", required=True)]
     port: Annotated[int, click.option("--port", type=int, help="OceanBase port", required=True)]
+    create_index_parallel: Annotated[
+        int,
+        click.option(
+            "--create-index-parallel",
+            type=int,
+            default=16,
+            show_default=True,
+            help="PARALLEL hint degree for CREATE VECTOR INDEX",
+        ),
+    ]
+    partitions: Annotated[
+        int,
+        click.option(
+            "--partitions",
+            type=int,
+            default=0,
+            show_default=True,
+            help="Number of KEY partitions for the table. 0 or 1 means no partitioning.",
+        ),
+    ]
 
 
-class OceanBaseHNSWTypedDict(CommonTypedDict, OceanBaseTypedDict, HNSWFlavor4): ...
+class OceanBaseHNSWTypedDict(CommonTypedDict, OceanBaseTypedDict, HNSWFlavor4):
+    extra_info_max_size: Annotated[
+        int | None,
+        click.option(
+            "--extra-info-max-size",
+            type=int,
+            default=32,
+            show_default=True,
+            help="extra_info_max_size for HNSW index. Set to 0 to omit.",
+            required=False,
+        ),
+    ]
 
 
 @cli.command()
@@ -55,6 +86,9 @@ def OceanBaseHNSW(**parameters: Unpack[OceanBaseHNSWTypedDict]):
             m=parameters["m"],
             efConstruction=parameters["ef_construction"],
             ef_search=parameters["ef_search"],
+            extra_info_max_size=parameters["extra_info_max_size"] or None,
+            create_index_parallel=parameters["create_index_parallel"],
+            partitions=parameters["partitions"],
             index=parameters["index_type"],
         ),
         **parameters,
@@ -94,6 +128,8 @@ def OceanBaseIVF(**parameters: Unpack[OceanBaseIVFTypedDict]):
             nlist=parameters["nlist"],
             sample_per_nlist=parameters["sample_per_nlist"],
             nbits=parameters["nbits"],
+            create_index_parallel=parameters["create_index_parallel"],
+            partitions=parameters["partitions"],
             index=input_index_type,
             ivf_nprobes=parameters["ivf_nprobes"],
         ),
diff --git a/vectordb_bench/backend/clients/oceanbase/config.py b/vectordb_bench/backend/clients/oceanbase/config.py
index 1f37cfc75..384a7c263 100644
--- a/vectordb_bench/backend/clients/oceanbase/config.py
+++ b/vectordb_bench/backend/clients/oceanbase/config.py
@@ -36,20 +36,18 @@ class OceanBaseIndexConfig(BaseModel):
     index: IndexType
     metric_type: MetricType | None = None
     lib: str = "vsag"
+    create_index_parallel: int = 16
+    partitions: int = 0
 
     def parse_metric(self) -> str:
-        if self.metric_type == MetricType.L2 or (
-            self.index == IndexType.HNSW_BQ and self.metric_type == MetricType.COSINE
-        ):
+        if self.metric_type == MetricType.L2:
             return "l2"
         if self.metric_type == MetricType.IP:
             return "inner_product"
         return "cosine"
 
     def parse_metric_func_str(self) -> str:
-        if self.metric_type == MetricType.L2 or (
-            self.index == IndexType.HNSW_BQ and self.metric_type == MetricType.COSINE
-        ):
+        if self.metric_type == MetricType.L2:
             return "l2_distance"
         if self.metric_type == MetricType.IP:
             return "negative_inner_product"
@@ -60,6 +58,7 @@ class OceanBaseHNSWConfig(OceanBaseIndexConfig, DBCaseConfig):
     m: int
     efConstruction: int
     ef_search: int | None = None
+    extra_info_max_size: int | None = 32
     index: IndexType
 
     def index_param(self) -> dict:
diff --git a/vectordb_bench/backend/clients/oceanbase/oceanbase.py b/vectordb_bench/backend/clients/oceanbase/oceanbase.py
index 93c42aac1..a34161037 100644
--- a/vectordb_bench/backend/clients/oceanbase/oceanbase.py
+++ b/vectordb_bench/backend/clients/oceanbase/oceanbase.py
@@ -23,6 +23,8 @@ class OceanBase(VectorDB):
         FilterOp.NumGE,
         FilterOp.StrEqual,
     ]
+    # mysql-connector cursor cannot be shared across threads
+    thread_safe: bool = False
 
     def __init__(
         self,
@@ -109,27 +111,28 @@ def _create_table(self):
         if not self._cursor:
             raise ValueError("Cursor is not initialized")
 
-        log.info(f"Creating table {self.table_name}")
-        create_table_query = f"""
-        CREATE TABLE {self.table_name} (
-            id INT PRIMARY KEY,
-            embedding VECTOR({self.dim})
-        );
-        """
+        partitions = getattr(self.db_case_config, "partitions", 0)
+        log.info(f"Creating table {self.table_name} (partitions={partitions})")
+
+        create_table_query = f"CREATE TABLE {self.table_name} (id INT PRIMARY KEY, embedding VECTOR({self.dim}))"
+        if partitions > 1:
+            create_table_query += f" PARTITION BY KEY(id) PARTITIONS {partitions}"
+        create_table_query += ";"
         self._cursor.execute(create_table_query)
 
     def optimize(self, data_size: int):
         index_params = self.db_case_config.index_param()
         index_args = ", ".join(f"{k}={v}" for k, v in index_params["params"].items())
         index_query = (
-            f"CREATE /*+ PARALLEL(18) */ VECTOR INDEX idx1 "
+            f"CREATE /*+ PARALLEL({self.db_case_config.create_index_parallel}) */ VECTOR INDEX idx1 "
             f"ON {self.table_name}(embedding) "
             f"WITH (distance={self.db_case_config.parse_metric()}, "
             f"type={index_params['index_type']}, lib={index_params['lib']}, {index_args}"
         )
 
-        if self.db_case_config.index in {IndexType.HNSW, IndexType.HNSW_SQ, IndexType.HNSW_BQ}:
-            index_query += ", extra_info_max_size=32"
+        extra_info = getattr(self.db_case_config, "extra_info_max_size", None)
+        if extra_info is not None:
+            index_query += f", extra_info_max_size={extra_info}"
 
         index_query += ")"
 
@@ -153,10 +156,6 @@ def optimize(self, data_size: int):
             raise
 
     def need_normalize_cosine(self) -> bool:
-        if self.db_case_config.index == IndexType.HNSW_BQ:
-            log.info("current HNSW_BQ only supports L2, cosine dataset need normalize.")
-            return True
-
         return False
 
     def _wait_for_major_compaction(self):
@@ -185,9 +184,7 @@ def insert_embeddings(
                 batch_end = min(batch_start + self.load_batch_size, len(embeddings))
                 batch = [(metadata[i], embeddings[i]) for i in range(batch_start, batch_end)]
                 values = ", ".join(f"({item_id}, '[{','.join(map(str, embedding))}]')" for item_id, embedding in batch)
-                self._cursor.execute(
-                    f"INSERT /*+ ENABLE_PARALLEL_DML PARALLEL(32) */ INTO {self.table_name} VALUES {values}"  # noqa: S608
-                )
+                self._cursor.execute(f"INSERT INTO {self.table_name} VALUES {values}")
                 insert_count += len(batch)
         except mysql.Error:
             log.exception("Failed to insert embeddings")
@@ -217,7 +214,7 @@ def search_embedding(
         packed = struct.pack(f"<{len(query)}f", *query)
         hex_vec = packed.hex()
         query_str = (
-            f"SELECT id FROM {self.table_name} "  # noqa: S608
+            f"SELECT id FROM {self.table_name} "
             f"{self.expr} ORDER BY "
             f"{self.db_case_config.parse_metric_func_str()}(embedding, X'{hex_vec}') "
             f"APPROXIMATE LIMIT {k}"
diff --git a/vectordb_bench/backend/clients/oss_opensearch/cli.py b/vectordb_bench/backend/clients/oss_opensearch/cli.py
index 0c4b694ea..03da18fdb 100644
--- a/vectordb_bench/backend/clients/oss_opensearch/cli.py
+++ b/vectordb_bench/backend/clients/oss_opensearch/cli.py
@@ -11,6 +11,7 @@
     click_parameter_decorators_from_typed_dict,
     run,
 )
+from ...cases import CaseType
 from .. import DB
 from .config import OSSOpenSearchQuantization, OSSOS_Engine
 
@@ -18,6 +19,7 @@
 
 
 class OSSOpenSearchTypedDict(TypedDict):
+    index_name: Annotated[str, click.option("--index-name", type=str, help="Db index name", default="vdb_bench_index")]
     host: Annotated[str, click.option("--host", type=str, help="Db host", required=True)]
     port: Annotated[int, click.option("--port", type=int, default=80, help="Db Port")]
     user: Annotated[str, click.option("--user", type=str, help="Db User")]
@@ -29,7 +31,7 @@ class OSSOpenSearchTypedDict(TypedDict):
     number_of_replicas: Annotated[
         int,
         click.option(
-            "--number-of-replicas", type=int, help="Number of replica copies for each primary shard", default=1
+            "--number-of-replicas", type=int, help="Number of replica copies for each primary shard", default=0
         ),
     ]
     index_thread_qty: Annotated[
@@ -147,17 +149,17 @@ class OSSOpenSearchHNSWTypedDict(CommonTypedDict, OSSOpenSearchTypedDict, HNSWFl
 @cli.command()
 @click_parameter_decorators_from_typed_dict(OSSOpenSearchHNSWTypedDict)
 def OSSOpenSearch(**parameters: Unpack[OSSOpenSearchHNSWTypedDict]):
-    from .config import OSSOpenSearchConfig, OSSOpenSearchIndexConfig
+    from .config import OSSOpenSearchConfig, OSSOpenSearchFtsConfig, OSSOpenSearchIndexConfig
 
-    run(
-        db=DB.OSSOpenSearch,
-        db_config=OSSOpenSearchConfig(
-            host=parameters["host"],
-            port=parameters["port"],
-            user=parameters["user"],
-            password=SecretStr(parameters["password"]),
-        ),
-        db_case_config=OSSOpenSearchIndexConfig(
+    if parameters["case_type"] == CaseType.FTSBm25Performance.name:
+        db_case_config = OSSOpenSearchFtsConfig(
+            number_of_shards=parameters["number_of_shards"],
+            number_of_replicas=parameters["number_of_replicas"],
+            refresh_interval=parameters["refresh_interval"],
+            force_merge_enabled=parameters["force_merge_enabled"],
+        )
+    else:
+        db_case_config = OSSOpenSearchIndexConfig(
             number_of_shards=parameters["number_of_shards"],
             number_of_replicas=parameters["number_of_replicas"],
             index_thread_qty=parameters["index_thread_qty"],
@@ -174,6 +176,17 @@ def OSSOpenSearch(**parameters: Unpack[OSSOpenSearchHNSWTypedDict]):
             quantization_type=OSSOpenSearchQuantization(parameters["quantization_type"]),
             confidence_interval=parameters["confidence_interval"],
             clip=parameters["clip"],
+        )
+
+    run(
+        db=DB.OSSOpenSearch,
+        db_config=OSSOpenSearchConfig(
+            index_name=parameters["index_name"],
+            host=parameters["host"],
+            port=parameters["port"],
+            user=parameters["user"],
+            password=SecretStr(parameters["password"]),
         ),
+        db_case_config=db_case_config,
         **parameters,
     )
diff --git a/vectordb_bench/backend/clients/oss_opensearch/config.py b/vectordb_bench/backend/clients/oss_opensearch/config.py
index d153eb429..05f8f584a 100644
--- a/vectordb_bench/backend/clients/oss_opensearch/config.py
+++ b/vectordb_bench/backend/clients/oss_opensearch/config.py
@@ -1,15 +1,19 @@
 import logging
 from enum import Enum
-from typing import Any
+from typing import ClassVar
 
 from pydantic import BaseModel, SecretStr, field_validator, model_validator
 
 from ..api import DBCaseConfig, DBConfig, MetricType
+from ..elasticsearch_compatible import build_bm25_similarity_settings, build_fts_index_param
 
 log = logging.getLogger(__name__)
 
 
 class OSSOpenSearchConfig(DBConfig, BaseModel):
+    _extra_empty_skip: ClassVar[frozenset[str]] = frozenset({"user", "password", "host"})
+
+    index_name: str = "vdb_bench_index"
     host: str = ""
     port: int = 80
     user: str | None = None
@@ -23,6 +27,7 @@ def to_dict(self) -> dict:
             else ()
         )
         return {
+            "index_name": self.index_name,
             "hosts": [{"host": self.host, "port": self.port}],
             "http_auth": http_auth,
             "use_ssl": use_ssl,
@@ -33,19 +38,6 @@ def to_dict(self) -> dict:
             "timeout": 600,
         }
 
-    @model_validator(mode="before")
-    @classmethod
-    def not_empty_field(cls, data: Any) -> Any:
-        if isinstance(data, dict):
-            skip = set(cls.common_short_configs()) | set(cls.common_long_configs()) | {"user", "password", "host"}
-            for name, v in data.items():
-                if name in skip:
-                    continue
-                if isinstance(v, str) and len(v) == 0:
-                    msg = f"Empty string for field '{name}'!"
-                    raise ValueError(msg)
-        return data
-
 
 class OSSOS_Engine(Enum):
     faiss = "faiss"
@@ -115,7 +107,7 @@ class OSSOpenSearchIndexConfig(BaseModel, DBCaseConfig):
 
     @field_validator("quantization_type", mode="before")
     @classmethod
-    def validate_quantization_type(cls, value: Any) -> OSSOpenSearchQuantization:
+    def validate_quantization_type(cls, value: any):
         """Convert string values to enum"""
         if not value:
             return OSSOpenSearchQuantization.NONE
@@ -133,9 +125,11 @@ def validate_quantization_type(cls, value: Any) -> OSSOpenSearchQuantization:
 
     @model_validator(mode="before")
     @classmethod
-    def validate_engine_name(cls, data: Any) -> Any:
-        """Map engine_name string from UI to engine enum"""
-        if isinstance(data, dict) and data.get("engine_name"):
+    def validate_engine_name(cls, data: any) -> any:
+        if not isinstance(data, dict):
+            return data
+        # Map engine_name to engine enum
+        if data.get("engine_name"):
             engine_name = data["engine_name"].lower()
             if engine_name == "faiss":
                 data["engine"] = OSSOS_Engine.faiss
@@ -256,3 +250,22 @@ def index_param(self) -> dict:
 
     def search_param(self) -> dict:
         return {"ef_search": self.efSearch}
+
+
+class OSSOpenSearchFtsConfig(BaseModel, DBCaseConfig):
+    number_of_shards: int = 1
+    number_of_replicas: int = 0
+    refresh_interval: str = "30s"
+    force_merge_enabled: bool = True
+    metric_type: MetricType = MetricType.BM25
+    bm25_k1: float | None = None
+    bm25_b: float | None = None
+
+    def index_param(self) -> dict:
+        return build_fts_index_param(self.bm25_k1, self.bm25_b)
+
+    def search_param(self) -> dict:
+        return {}
+
+    def similarity_settings(self) -> dict:
+        return build_bm25_similarity_settings(self.bm25_k1, self.bm25_b)
diff --git a/vectordb_bench/backend/clients/oss_opensearch/oss_opensearch.py b/vectordb_bench/backend/clients/oss_opensearch/oss_opensearch.py
index f71850a17..d26514497 100644
--- a/vectordb_bench/backend/clients/oss_opensearch/oss_opensearch.py
+++ b/vectordb_bench/backend/clients/oss_opensearch/oss_opensearch.py
@@ -9,15 +9,16 @@
 from packaging.version import parse as parse_version
 
 from vectordb_bench.backend.filter import Filter, FilterOp
+from vectordb_bench.backend.payload import PayloadProfile
 
 from ..api import VectorDB
-from .config import OSSOpenSearchIndexConfig, OSSOS_Engine
+from .config import OSSOpenSearchFtsConfig, OSSOpenSearchIndexConfig, OSSOS_Engine
 
 log = logging.getLogger(__name__)
 
 WAITING_FOR_REFRESH_SEC: Final[int] = 30
 WAITING_FOR_FORCE_MERGE_SEC: Final[int] = 30
-SECONDS_WAITING_FOR_REPLICAS_TO_BE_ENABLED_SEC: Final[int] = 30
+REPLICA_HEALTH_TIMEOUT: Final[str] = "30m"
 
 # Central registry for version-dependent OpenSearch index settings.
 # Add new rules here to automatically support future versions.
@@ -194,7 +195,7 @@ def __init__(
         self,
         dim: int,
         db_config: dict[str, Any],
-        db_case_config: OSSOpenSearchIndexConfig,
+        db_case_config: OSSOpenSearchIndexConfig | OSSOpenSearchFtsConfig,
         index_name: str = "vdb_bench_index",  # must be lowercase
         id_col_name: str = "_id",
         label_col_name: str = "label",
@@ -205,13 +206,19 @@ def __init__(
     ) -> None:
         """Initialize the OpenSearch client."""
         self.dim = dim
-        self.db_config = db_config
+        self.db_config = dict(db_config)
+        configured_index_name = self.db_config.pop("index_name", None)
         self.case_config = db_case_config
-        self.index_name = index_name
+        self.index_name = configured_index_name or index_name
         self.id_col_name = id_col_name
         self.label_col_name = label_col_name
         self.vector_col_name = vector_col_name
         self.with_scalar_labels = with_scalar_labels
+        self._is_fts = isinstance(db_case_config, OSSOpenSearchFtsConfig)
+        self.text_col_name = "text"
+        self.filter_id_col_name = "filter_id"
+        if self._is_fts:
+            self.id_col_name = "doc_id"
 
         # Initialize client state
         self.client: OpenSearch | None = None
@@ -236,13 +243,21 @@ def _handle_index_initialization(self, client: OpenSearch, drop_old: bool) -> No
             if not is_existed:
                 self._create_index(client)
                 log.info(f"OSS_OpenSearch client create index: {self.index_name}")
-            self._update_ef_search_before_search(client)
-            self._load_graphs_to_memory(client)
+            if not self._is_fts:
+                self._update_ef_search_before_search(client)
+                self._load_graphs_to_memory(client)
 
     def need_normalize_cosine(self) -> bool:
         """Whether this database needs to normalize dataset to support COSINE metric."""
         return True
 
+    @classmethod
+    def supports_full_text_search(cls) -> bool:
+        return True
+
+    def has_text_field(self) -> bool:
+        return bool(getattr(self, "_is_fts", False) and getattr(self, "text_col_name", None))
+
     def _get_cluster_version(self, client: OpenSearch) -> Version:
         """
         Return the OpenSearch cluster version as a comparable Version object.
@@ -307,7 +322,31 @@ def _get_bulk_manager(self, client: OpenSearch) -> BulkInsertManager:
         """Get bulk insert manager for the given client."""
         return BulkInsertManager(client, self.index_name, self.case_config)
 
+    def _create_fts_index(self, client: OpenSearch) -> None:
+        mappings = self.case_config.index_param()
+        index_settings = {
+            "number_of_shards": self.case_config.number_of_shards,
+            "number_of_replicas": self.case_config.number_of_replicas,
+            "refresh_interval": self.case_config.refresh_interval,
+        }
+        index_settings.update(self.case_config.similarity_settings())
+        settings = {"index": index_settings}
+        try:
+            log.info(f"Creating FTS index with settings: {settings}")
+            log.info(f"Creating FTS index with mappings: {mappings}")
+            client.indices.create(
+                index=self.index_name,
+                body={"settings": settings, "mappings": mappings},
+            )
+        except Exception as e:
+            log.warning(f"Failed to create FTS index: {self.index_name} error: {e!s}")
+            raise e from None
+
     def _create_index(self, client: OpenSearch) -> None:
+        if self._is_fts:
+            self._create_fts_index(client)
+            return
+
         cluster_version = self._get_cluster_version(client)
 
         if self.case_config.on_disk and cluster_version < Version("2.17"):
@@ -420,6 +459,94 @@ def insert_embeddings(
         log.info(f"Using {num_clients} parallel clients for data insertion")
         return self._insert_with_multiple_clients(embeddings, metadata, num_clients, labels_data)
 
+    def insert_documents(
+        self,
+        texts: Iterable[str],
+        doc_ids: list[str],
+        **kwargs: Any,
+    ) -> tuple[int, Exception | None]:
+        if not getattr(self, "_is_fts", False):
+            msg = "OSSOpenSearch full-text insert requires OSSOpenSearchFtsConfig"
+            raise RuntimeError(msg)
+        assert self.client is not None, "should self.init() first"
+        docs = list(texts)
+        if len(docs) != len(doc_ids):
+            msg = f"Mismatch between texts ({len(docs)}) and doc_ids ({len(doc_ids)}) lengths"
+            raise ValueError(msg)
+        filter_ids = kwargs.get("filter_ids")
+        if filter_ids is not None and len(filter_ids) != len(docs):
+            msg = f"Mismatch between texts ({len(docs)}) and filter_ids ({len(filter_ids)}) lengths"
+            raise ValueError(msg)
+
+        insert_data: list[dict[str, Any]] = []
+        for i, doc in enumerate(docs):
+            doc_id = str(doc_ids[i])
+            source = {self.id_col_name: doc_id, self.text_col_name: doc}
+            if filter_ids is not None:
+                source[self.filter_id_col_name] = int(filter_ids[i])
+            insert_data.append({"index": {"_index": self.index_name, "_id": doc_id}})
+            insert_data.append(source)
+
+        try:
+            response = self.client.bulk(body=insert_data)
+        except Exception as e:
+            log.warning(f"Failed to insert FTS docs: {self.index_name} error: {e!s}")
+            return 0, e
+        insert_count, error = self._parse_fts_bulk_response(response, len(docs))
+        if error is not None:
+            log.warning("FTS bulk insert failed: %s", error)
+        return insert_count, error
+
+    @staticmethod
+    def _parse_fts_bulk_response(response: dict[str, Any], expected_count: int) -> tuple[int, Exception | None]:
+        if not response.get("errors"):
+            return expected_count, None
+
+        items = response.get("items")
+        if not isinstance(items, list):
+            error = RuntimeError("OpenSearch FTS bulk response reported errors without an items list")
+            return 0, error
+
+        success_count = 0
+        failure_samples = []
+        for position, item in enumerate(items):
+            if not isinstance(item, dict) or len(item) != 1:
+                failure_samples.append(f"item[{position}]=malformed")
+                continue
+
+            operation, result = next(iter(item.items()))
+            if not isinstance(result, dict):
+                failure_samples.append(f"item[{position}] {operation}=malformed")
+                continue
+
+            status = result.get("status")
+            if isinstance(status, int) and 200 <= status < 300 and "error" not in result:
+                success_count += 1
+                continue
+
+            error_detail = result.get("error")
+            if isinstance(error_detail, dict):
+                error_type = error_detail.get("type", "unknown")
+                reason = error_detail.get("reason", "unknown")
+                error_summary = f"{error_type}: {reason}"
+            else:
+                error_summary = str(error_detail or "unknown")
+            failure_samples.append(
+                f"item[{position}] {operation} id={result.get('_id', 'unknown')} "
+                f"status={status or 'unknown'} error={error_summary}"
+            )
+
+        success_count = min(success_count, expected_count)
+        failed_count = max(expected_count - success_count, 1)
+        sample_summary = "; ".join(failure_samples[:3]) or "no failed item details"
+        if len(items) != expected_count:
+            sample_summary = f"response_items={len(items)}, expected_items={expected_count}; {sample_summary}"
+        error = RuntimeError(
+            f"OpenSearch FTS bulk insert failed for {failed_count}/{expected_count} documents; "
+            f"successful={success_count}; {sample_summary}"
+        )
+        return success_count, error
+
     def _insert_with_single_client(
         self,
         embeddings: Iterable[list[float]],
@@ -569,14 +696,67 @@ def search_embedding(
             log.warning(f"Failed to search: {self.index_name} error: {e!s}")
             raise e from None
 
+    def search_documents(
+        self,
+        query: str,
+        k: int = 100,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+        **kwargs: Any,
+    ) -> list[str]:
+        if not getattr(self, "_is_fts", False):
+            msg = "OSSOpenSearch full-text search requires OSSOpenSearchFtsConfig"
+            raise RuntimeError(msg)
+        if not self.supports_document_payload_profile(payload_profile):
+            msg = f"OSSOpenSearch does not support document payload_profile={payload_profile.value}"
+            raise NotImplementedError(msg)
+        assert self.client is not None, "should self.init() first"
+
+        source = [self.text_col_name] if payload_profile == PayloadProfile.TEXT else False
+        filter_path = ["hits.hits._id", f"hits.hits.fields.{self.id_col_name}"]
+        if payload_profile == PayloadProfile.TEXT:
+            filter_path.append(f"hits.hits._source.{self.text_col_name}")
+        query_clause: dict[str, Any] = {"match": {self.text_col_name: query}}
+        if self.filter:
+            query_clause = {"bool": {"must": query_clause, "filter": self.filter}}
+        search_kwargs: dict[str, Any] = {
+            "index": self.index_name,
+            "body": {"query": query_clause},
+            "size": k,
+            "_source": source,
+            "docvalue_fields": [self.id_col_name],
+            "filter_path": filter_path,
+        }
+        if payload_profile != PayloadProfile.TEXT:
+            search_kwargs["stored_fields"] = "_none_"
+        response = self.client.search(**search_kwargs)
+
+        doc_ids = []
+        for hit in response.get("hits", {}).get("hits", []):
+            if hit.get("_id") is not None:
+                doc_ids.append(str(hit["_id"]))
+                continue
+            values = hit.get("fields", {}).get(self.id_col_name, [])
+            if values:
+                doc_ids.append(str(values[0]))
+        return doc_ids
+
     def prepare_filter(self, filters: Filter) -> None:
         """Prepare filter conditions for search operations."""
         self.routing_key = None
         if filters.type == FilterOp.NonFilter:
             self.filter = None
         elif filters.type == FilterOp.NumGE:
-            self.filter = {"range": {self.id_col_name: {"gt": filters.int_value}}}
+            if self._is_fts:
+                if getattr(filters, "int_field", None) != self.filter_id_col_name:
+                    msg = f"OSSOpenSearch FTS filters only support int_field='{self.filter_id_col_name}'"
+                    raise ValueError(msg)
+                self.filter = {"range": {self.filter_id_col_name: {"gte": filters.int_value}}}
+            else:
+                self.filter = {"range": {self.id_col_name: {"gt": filters.int_value}}}
         elif filters.type == FilterOp.StrEqual:
+            if self._is_fts:
+                msg = f"Not support Filter for OSSOpenSearch FTS - {filters}"
+                raise ValueError(msg)
             self.filter = {"term": {self.label_col_name: filters.label_value}}
             if self.case_config.use_routing:
                 self.routing_key = filters.label_value
@@ -587,6 +767,14 @@ def prepare_filter(self, filters: Filter) -> None:
 
     def optimize(self, data_size: int | None = None) -> None:
         """Optimize the index for better search performance."""
+        if self._is_fts:
+            self._refresh_index()
+            if self.case_config.force_merge_enabled:
+                self._do_fts_force_merge()
+                self._refresh_index()
+            self._update_replicas()
+            self._refresh_index()
+            return
         self._update_ef_search()
         # Call refresh first to ensure that all segments are created
         self._refresh_index()
@@ -621,13 +809,20 @@ def _update_replicas(self):
 
     def _wait_till_green(self):
         log.info("Wait for index to become green..")
-        while True:
-            res = self.client.cat.indices(index=self.index_name, h="health", format="json")
-            health = res[0]["health"]
-            if health == "green":
-                break
-            log.info(f"The index {self.index_name} has health : {health} and is not green. Retrying")
-            time.sleep(SECONDS_WAITING_FOR_REPLICAS_TO_BE_ENABLED_SEC)
+        response = self.client.cluster.health(
+            index=self.index_name,
+            wait_for_status="green",
+            timeout=REPLICA_HEALTH_TIMEOUT,
+        )
+        health = response.get("status", "unknown")
+        if response.get("timed_out") or health != "green":
+            msg = (
+                f"Index {self.index_name} did not reach green health within {REPLICA_HEALTH_TIMEOUT}: "
+                f"status={health}, configured_replicas={self.case_config.number_of_replicas}, "
+                f"unassigned_shards={response.get('unassigned_shards', 'unknown')}, "
+                f"number_of_nodes={response.get('number_of_nodes', 'unknown')}"
+            )
+            raise OpenSearchError(msg)
         log.info(f"Index {self.index_name} is green..")
 
     def _refresh_index(self):
@@ -645,6 +840,17 @@ def _refresh_index(self):
                 continue
         log.debug(f"Completed refresh for index {self.index_name}")
 
+    def _do_fts_force_merge(self):
+        log.info(f"Starting FTS force merge for index {self.index_name}")
+        force_merge_endpoint = f"/{self.index_name}/_forcemerge?max_num_segments=1&wait_for_completion=false"
+        force_merge_task_id = self.client.transport.perform_request("POST", force_merge_endpoint)["task"]
+        while True:
+            time.sleep(WAITING_FOR_FORCE_MERGE_SEC)
+            task_status = self.client.tasks.get(task_id=force_merge_task_id)
+            if task_status["completed"]:
+                break
+        log.info(f"Completed FTS force merge for index {self.index_name}")
+
     def _do_force_merge(self):
         log.info(f"Updating the Index thread qty to {self.case_config.index_thread_qty_during_force_merge}.")
 
diff --git a/vectordb_bench/backend/clients/pgdiskann/cli.py b/vectordb_bench/backend/clients/pgdiskann/cli.py
index f1e44e2f2..4f372c779 100644
--- a/vectordb_bench/backend/clients/pgdiskann/cli.py
+++ b/vectordb_bench/backend/clients/pgdiskann/cli.py
@@ -80,7 +80,7 @@ class PgDiskAnnTypedDict(CommonTypedDict):
         click.option(
             "--reranking-metric",
             type=click.Choice(
-                [metric.value for metric in MetricType if metric.value not in ["HAMMING", "JACCARD", "DP"]],
+                [metric.value for metric in MetricType if metric.value not in ["HAMMING", "JACCARD", "DP", "BM25"]],
             ),
             help="Distance metric for reranking",
             default="COSINE",
diff --git a/vectordb_bench/backend/clients/pgdiskann/pgdiskann.py b/vectordb_bench/backend/clients/pgdiskann/pgdiskann.py
index 5f069ace5..46e8fabd4 100644
--- a/vectordb_bench/backend/clients/pgdiskann/pgdiskann.py
+++ b/vectordb_bench/backend/clients/pgdiskann/pgdiskann.py
@@ -10,6 +10,8 @@
 from pgvector.psycopg import register_vector
 from psycopg import Connection, Cursor, sql
 
+from vectordb_bench.backend.filter import Filter, FilterOp
+
 from ..api import VectorDB
 from .config import PgDiskANNConfigDict, PgDiskANNIndexConfig
 
@@ -19,11 +21,16 @@
 class PgDiskANN(VectorDB):
     """Use psycopg instructions"""
 
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+        FilterOp.StrEqual,
+    ]
+
     conn: psycopg.Connection[Any] | None = None
-    coursor: psycopg.Cursor[Any] | None = None
+    cursor: psycopg.Cursor[Any] | None = None
 
-    _filtered_search: sql.Composed
-    _unfiltered_search: sql.Composed
+    _search: sql.Composed
 
     def __init__(
         self,
@@ -32,6 +39,7 @@ def __init__(
         db_case_config: PgDiskANNIndexConfig,
         collection_name: str = "pg_diskann_collection",
         drop_old: bool = False,
+        with_scalar_labels: bool = False,
         **kwargs,
     ):
         self.name = "PgDiskANN"
@@ -39,6 +47,9 @@ def __init__(
         self.case_config = db_case_config
         self.table_name = collection_name
         self.dim = dim
+        self.with_scalar_labels = with_scalar_labels
+        self._scalar_label_field = "label"
+        self.where_clause = ""
 
         self._index_name = "pgdiskann_index"
         self._primary_field = "id"
@@ -86,83 +97,58 @@ def _create_connection(**kwargs) -> tuple[Connection, Cursor]:
 
         return conn, cursor
 
-    @contextmanager
-    def init(self) -> Generator[None, None, None]:
-        self.conn, self.cursor = self._create_connection(**self.db_config)
-
-        session_options: dict[str, Any] = self.case_config.session_param()
-
-        if len(session_options) > 0:
-            for setting_name, setting_val in session_options.items():
-                command = sql.SQL("SET {setting_name} = {setting_val};").format(
-                    setting_name=sql.Identifier(setting_name), setting_val=sql.Literal(setting_val)
-                )
-                log.debug(command.as_string(self.cursor))
-                self.cursor.execute(command)
-            self.conn.commit()
-
+    def _generate_search_query(self) -> sql.Composed:
+        """Generate search query with where_clause placeholder"""
         search_params = self.case_config.search_param()
 
         if search_params.get("reranking"):
-            # Reranking-enabled queries
-            self._filtered_search = sql.SQL("""
+            search_query = sql.SQL("""
                 SELECT i.id
                 FROM (
                     SELECT id, embedding
                     FROM public.{table_name}
-                    WHERE id >= %s
+                    {where_clause}
                     ORDER BY embedding {metric_fun_op} %s::vector
                     LIMIT {quantized_fetch_limit}::int
                 ) i
                 ORDER BY i.embedding {reranking_metric_fun_op} %s::vector
                 LIMIT %s::int
-            """).format(
+                """).format(
                 table_name=sql.Identifier(self.table_name),
+                where_clause=sql.SQL(self.where_clause),
                 metric_fun_op=sql.SQL(search_params["metric_fun_op"]),
                 reranking_metric_fun_op=sql.SQL(search_params["reranking_metric_fun_op"]),
                 quantized_fetch_limit=sql.Literal(search_params["quantized_fetch_limit"]),
             )
-
-            self._unfiltered_search = sql.SQL("""
-                SELECT i.id
-                FROM (
-                    SELECT id, embedding
-                    FROM public.{table_name}
-                    ORDER BY embedding {metric_fun_op} %s::vector
-                    LIMIT {quantized_fetch_limit}::int
-                ) i
-                ORDER BY i.embedding {reranking_metric_fun_op} %s::vector
-                LIMIT %s::int
-            """).format(
-                table_name=sql.Identifier(self.table_name),
-                metric_fun_op=sql.SQL(search_params["metric_fun_op"]),
-                reranking_metric_fun_op=sql.SQL(search_params["reranking_metric_fun_op"]),
-                quantized_fetch_limit=sql.Literal(search_params["quantized_fetch_limit"]),
-            )
-
         else:
-            self._filtered_search = sql.Composed(
+            search_query = sql.Composed(
                 [
-                    sql.SQL(
-                        "SELECT id FROM public.{table_name} WHERE id >= %s ORDER BY embedding ",
-                    ).format(table_name=sql.Identifier(self.table_name)),
-                    sql.SQL(search_params["metric_fun_op"]),
-                    sql.SQL(" %s::vector LIMIT %s::int"),
-                ]
-            )
-
-            self._unfiltered_search = sql.Composed(
-                [
-                    sql.SQL("SELECT id FROM public.{table_name} ORDER BY embedding ").format(
-                        table_name=sql.Identifier(self.table_name)
+                    sql.SQL("SELECT id FROM public.{table_name} {where_clause} ORDER BY embedding ").format(
+                        table_name=sql.Identifier(self.table_name),
+                        where_clause=sql.SQL(self.where_clause),
                     ),
                     sql.SQL(search_params["metric_fun_op"]),
                     sql.SQL(" %s::vector LIMIT %s::int"),
                 ]
             )
 
-        log.debug(f"Unfiltered search query={self._unfiltered_search.as_string(self.conn)}")
-        log.debug(f"Filtered search query={self._filtered_search.as_string(self.conn)}")
+        return search_query
+
+    @contextmanager
+    def init(self) -> Generator[None, None, None]:
+        self.conn, self.cursor = self._create_connection(**self.db_config)
+
+        session_options: dict[str, Any] = self.case_config.session_param()
+
+        if len(session_options) > 0:
+            for setting_name, setting_val in session_options.items():
+                command = sql.SQL("SET {setting_name} = {setting_val};").format(
+                    setting_name=sql.Identifier(setting_name),
+                    setting_val=sql.Literal(setting_val),
+                )
+                log.debug(command.as_string(self.cursor))
+                self.cursor.execute(command)
+            self.conn.commit()
 
         try:
             yield
@@ -281,12 +267,10 @@ def _create_index(self):
 
         with_clause = sql.SQL("WITH ({});").format(sql.SQL(", ").join(options)) if any(options) else sql.Composed(())
 
-        index_create_sql = sql.SQL(
-            """
+        index_create_sql = sql.SQL("""
             CREATE INDEX IF NOT EXISTS {index_name} ON public.{table_name}
             USING {index_type} (embedding {embedding_metric})
-            """,
-        ).format(
+            """).format(
             index_name=sql.Identifier(self._index_name),
             table_name=sql.Identifier(self.table_name),
             index_type=sql.Identifier(index_param["index_type"].lower()),
@@ -304,11 +288,36 @@ def _create_table(self, dim: int):
         try:
             log.info(f"{self.name} client create table : {self.table_name}")
 
+            if self.with_scalar_labels:
+                self.cursor.execute(
+                    sql.SQL("""
+                        CREATE TABLE IF NOT EXISTS public.{table_name}
+                        ({primary_field} BIGINT PRIMARY KEY, embedding vector({dim}), {label_field} VARCHAR(64));
+                        """).format(
+                        table_name=sql.Identifier(self.table_name),
+                        dim=dim,
+                        primary_field=sql.Identifier(self._primary_field),
+                        label_field=sql.Identifier(self._scalar_label_field),
+                    ),
+                )
+            else:
+                self.cursor.execute(
+                    sql.SQL("""
+                        CREATE TABLE IF NOT EXISTS public.{table_name}
+                        ({primary_field} BIGINT PRIMARY KEY, embedding vector({dim}));
+                        """).format(
+                        table_name=sql.Identifier(self.table_name),
+                        dim=dim,
+                        primary_field=sql.Identifier(self._primary_field),
+                    ),
+                )
+
             self.cursor.execute(
-                sql.SQL(
-                    "CREATE TABLE IF NOT EXISTS public.{table_name} (id BIGINT PRIMARY KEY, embedding vector({dim}));",
-                ).format(table_name=sql.Identifier(self.table_name), dim=dim),
+                sql.SQL("ALTER TABLE public.{table_name} ALTER COLUMN embedding SET STORAGE PLAIN;").format(
+                    table_name=sql.Identifier(self.table_name)
+                ),
             )
+
             self.conn.commit()
         except Exception as e:
             log.warning(f"Failed to create pgdiskann table: {self.table_name} error: {e}")
@@ -318,11 +327,15 @@ def insert_embeddings(
         self,
         embeddings: list[list[float]],
         metadata: list[int],
+        labels_data: list[str] | None = None,
         **kwargs: Any,
     ) -> tuple[int, Exception | None]:
         assert self.conn is not None, "Connection is not initialized"
         assert self.cursor is not None, "Cursor is not initialized"
 
+        if self.with_scalar_labels:
+            assert labels_data is not None, "labels_data should be provided if with_scalar_labels is set to True"
+
         try:
             metadata_arr = np.array(metadata)
             embeddings_arr = np.array(embeddings)
@@ -332,9 +345,14 @@ def insert_embeddings(
                     table_name=sql.Identifier(self.table_name),
                 ),
             ) as copy:
-                copy.set_types(["bigint", "vector"])
-                for i, row in enumerate(metadata_arr):
-                    copy.write_row((row, embeddings_arr[i]))
+                if self.with_scalar_labels:
+                    copy.set_types(["bigint", "vector", "varchar"])
+                    for i, row in enumerate(metadata_arr):
+                        copy.write_row((row, embeddings_arr[i], labels_data[i]))
+                else:
+                    copy.set_types(["bigint", "vector"])
+                    for i, row in enumerate(metadata_arr):
+                        copy.write_row((row, embeddings_arr[i]))
             self.conn.commit()
 
             if kwargs.get("last_batch"):
@@ -345,49 +363,39 @@ def insert_embeddings(
             log.warning(f"Failed to insert data into table ({self.table_name}), error: {e}")
             return 0, e
 
+    def prepare_filter(self, filters: Filter):
+        """Prepare filter - builds where_clause"""
+        if filters.type == FilterOp.NonFilter:
+            self.where_clause = ""
+        elif filters.type == FilterOp.NumGE:
+            self.where_clause = f"WHERE {self._primary_field} >= {filters.int_value}"
+        elif filters.type == FilterOp.StrEqual:
+            self.where_clause = f"WHERE {self._scalar_label_field} = '{filters.label_value}'"
+        else:
+            msg = f"Not support Filter for PgDiskANN - {filters}"
+            raise ValueError(msg)
+
+        self._search = self._generate_search_query()
+        log.debug(f"Search query={self._search.as_string(self.conn)}")
+
     def search_embedding(
         self,
         query: list[float],
         k: int = 100,
-        filters: dict | None = None,
         timeout: int | None = None,
+        **kwargs: Any,
     ) -> list[int]:
         assert self.conn is not None, "Connection is not initialized"
         assert self.cursor is not None, "Cursor is not initialized"
 
         search_params = self.case_config.search_param()
-        is_reranking = search_params.get("reranking", False)
-
         q = np.asarray(query)
-        if filters:
-            gt = filters.get("id")
-            if is_reranking:
-                result = self.cursor.execute(
-                    self._filtered_search,
-                    (gt, q, q, k),
-                    prepare=True,
-                    binary=True,
-                )
-            else:
-                result = self.cursor.execute(
-                    self._filtered_search,
-                    (gt, q, k),
-                    prepare=True,
-                    binary=True,
-                )
-        elif is_reranking:
-            result = self.cursor.execute(
-                self._unfiltered_search,
-                (q, q, k),
-                prepare=True,
-                binary=True,
-            )
-        else:
-            result = self.cursor.execute(
-                self._unfiltered_search,
-                (q, k),
-                prepare=True,
-                binary=True,
-            )
+
+        result = self.cursor.execute(
+            self._search,
+            (q, q, k) if search_params.get("reranking", False) else (q, k),
+            prepare=True,
+            binary=True,
+        )
 
         return [int(i[0]) for i in result.fetchall()]
diff --git a/vectordb_bench/backend/clients/pgvector/cli.py b/vectordb_bench/backend/clients/pgvector/cli.py
index 2d56c4815..7da41d5c6 100644
--- a/vectordb_bench/backend/clients/pgvector/cli.py
+++ b/vectordb_bench/backend/clients/pgvector/cli.py
@@ -109,7 +109,7 @@ class PgVectorTypedDict(CommonTypedDict):
         click.option(
             "--reranking-metric",
             type=click.Choice(
-                [metric.value for metric in MetricType if metric.value not in ["HAMMING", "JACCARD"]],
+                [metric.value for metric in MetricType if metric.value not in ["HAMMING", "JACCARD", "BM25"]],
             ),
             help="Distance metric for reranking",
             default="COSINE",
diff --git a/vectordb_bench/backend/clients/pgvector/pgvector.py b/vectordb_bench/backend/clients/pgvector/pgvector.py
index 42fa7533d..41060af27 100644
--- a/vectordb_bench/backend/clients/pgvector/pgvector.py
+++ b/vectordb_bench/backend/clients/pgvector/pgvector.py
@@ -21,6 +21,7 @@
 class PgVector(VectorDB):
     """Use psycopg instructions"""
 
+    thread_safe: bool = False
     supported_filter_types: list[FilterOp] = [
         FilterOp.NonFilter,
         FilterOp.NumGE,
@@ -334,6 +335,13 @@ def _create_index(self):
 
         index_param = self.case_config.index_param()
         self._set_parallel_index_build_param()
+        # pgvector registers access methods in lowercase ("hnsw", "ivfflat") but
+        # IndexType enum values are uppercase; also IVFFlat maps to "ivfflat" (no underscore).
+        index_type_lower = index_param["index_type"].lower()
+        if index_type_lower == "ivf_flat":
+            index_type_lower = "ivfflat"
+        log.info(f"index_type (original={index_param['index_type']}, normalized={index_type_lower})")
+
         options = []
         for option in index_param["index_creation_with_options"]:
             if option["val"] is not None:
@@ -359,8 +367,8 @@ def _create_index(self):
                     if index_param["quantization_type"] == "bit"
                     else sql.Identifier("embedding")
                 ),
-                index_type=sql.Identifier(index_param["index_type"]),
-                # This assumes that the quantization_type value matches the quantization function name
+                index_type=sql.Identifier(index_type_lower),
+                # quantization_type value matches the quantization function name
                 quantization_type=sql.SQL(index_param["quantization_type"]),
                 dim=self.dim,
                 embedding_metric=sql.Identifier(index_param["metric"]),
@@ -374,7 +382,7 @@ def _create_index(self):
             ).format(
                 index_name=sql.Identifier(self._index_name),
                 table_name=sql.Identifier(self.table_name),
-                index_type=sql.Identifier(index_param["index_type"]),
+                index_type=sql.Identifier(index_type_lower),
                 embedding_metric=sql.Identifier(index_param["metric"]),
             )
 
diff --git a/vectordb_bench/backend/clients/pinecone/cli.py b/vectordb_bench/backend/clients/pinecone/cli.py
index 03e1b5f1a..0529fc691 100644
--- a/vectordb_bench/backend/clients/pinecone/cli.py
+++ b/vectordb_bench/backend/clients/pinecone/cli.py
@@ -26,10 +26,6 @@ class PineconeTypedDict(TypedDict):
         str,
         click.option("--version", type=str, help="Database version", default="", show_default=True),
     ]
-    note: Annotated[
-        str,
-        click.option("--note", type=str, help="Additional notes", default="", show_default=True),
-    ]
 
 
 class PineconeIndexTypedDict(CommonTypedDict, PineconeTypedDict): ...
@@ -45,7 +41,6 @@ def Pinecone(**parameters: Unpack[PineconeIndexTypedDict]):
         db_config=PineconeConfig(
             db_label=parameters["db_label"],
             version=parameters["version"],
-            note=parameters["note"],
             api_key=SecretStr(parameters["api_key"]),
             index_name=parameters["index_name"],
         ),
diff --git a/vectordb_bench/backend/clients/pinecone/config.py b/vectordb_bench/backend/clients/pinecone/config.py
index fe1a039ed..c42c876ff 100644
--- a/vectordb_bench/backend/clients/pinecone/config.py
+++ b/vectordb_bench/backend/clients/pinecone/config.py
@@ -6,9 +6,11 @@
 class PineconeConfig(DBConfig):
     api_key: SecretStr
     index_name: str
+    multitenant_namespace_prefix: str = "vdbbench_mt_"
 
     def to_dict(self) -> dict:
         return {
             "api_key": self.api_key.get_secret_value(),
             "index_name": self.index_name,
+            "multitenant_namespace_prefix": self.multitenant_namespace_prefix,
         }
diff --git a/vectordb_bench/backend/clients/pinecone/pinecone.py b/vectordb_bench/backend/clients/pinecone/pinecone.py
index 9c2b38888..f123ae231 100644
--- a/vectordb_bench/backend/clients/pinecone/pinecone.py
+++ b/vectordb_bench/backend/clients/pinecone/pinecone.py
@@ -1,18 +1,27 @@
 """Wrapper around the Pinecone vector database over VectorDB"""
 
 import logging
+import os
+import threading
+import time
 from contextlib import contextmanager
+from typing import Any
 
 import pinecone
 
 from vectordb_bench.backend.filter import Filter, FilterOp
+from vectordb_bench.backend.payload import PayloadProfile
 
-from ..api import DBCaseConfig, VectorDB
+from ..api import DBCaseConfig, PartialInsertError, VectorDB
 
 log = logging.getLogger(__name__)
 
 PINECONE_MAX_NUM_PER_BATCH = 1000
 PINECONE_MAX_SIZE_PER_BATCH = 2 * 1024 * 1024  # 2MB
+PINECONE_QUERY_MAX_RETRIES_ENV = "PINECONE_QUERY_MAX_RETRIES"
+PINECONE_QUERY_RETRY_SLEEP_ENV = "PINECONE_QUERY_RETRY_SLEEP_SECONDS"
+PINECONE_QUERY_DEFAULT_MAX_RETRIES = 10
+PINECONE_QUERY_DEFAULT_RETRY_SLEEP_SECONDS = 0.5
 
 
 class Pinecone(VectorDB):
@@ -34,15 +43,28 @@ def __init__(
         """Initialize wrapper around the milvus vector database."""
         self.index_name = db_config.get("index_name", "")
         self.api_key = db_config.get("api_key", "")
+        self.multitenant_namespace_prefix = db_config.get("multitenant_namespace_prefix", "vdbbench_mt_")
+        self.multitenant_tenant_labels: list[str] = kwargs.get("multitenant_tenant_labels", [])
+        self._multitenant_insert_counts: dict[str, int] = {}
+        self._multitenant_insert_counts_lock = threading.Lock()
         self.batch_size = int(
             min(PINECONE_MAX_SIZE_PER_BATCH / (dim * 5), PINECONE_MAX_NUM_PER_BATCH),
         )
+        self._last_write_lsn: int | None = None
+        self._last_write_lsn_lock = threading.Lock()
+        self._readiness_probe_vector = [0.0] * dim
 
         pc = pinecone.Pinecone(api_key=self.api_key)
         index = pc.Index(self.index_name)
 
         self.with_scalar_labels = with_scalar_labels
-        if drop_old:
+        self.expr = None
+        if drop_old and self.multitenant_tenant_labels:
+            for tenant in self.multitenant_tenant_labels:
+                namespace = self._namespace_for_tenant(tenant)
+                log.info(f"Pinecone index delete multitenant namespace: {namespace}")
+                index.delete(delete_all=True, namespace=namespace)
+        elif drop_old:
             index_stats = index.describe_index_stats()
             index_dim = index_stats["dimension"]
             if index_dim != dim:
@@ -61,18 +83,154 @@ def init(self):
         self.index = pc.Index(self.index_name)
         yield
 
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state.pop("_last_write_lsn_lock", None)
+        state.pop("_multitenant_insert_counts_lock", None)
+        return state
+
     def optimize(self, data_size: int | None = None):
         pass
 
+    def supports_payload_profile(self, payload_profile: PayloadProfile) -> bool:
+        return payload_profile in {
+            PayloadProfile.IDS_ONLY,
+            PayloadProfile.SCALAR_LABEL,
+            PayloadProfile.VECTOR,
+        }
+
+    def supports_multitenant(self) -> bool:
+        return True
+
+    def set_multitenant_context(self, tenant_labels: list[str]) -> None:
+        self.multitenant_tenant_labels = tenant_labels
+
+    def _namespace_for_tenant(self, tenant: str | None) -> str | None:
+        if tenant is None:
+            return None
+        return f"{self.multitenant_namespace_prefix}{tenant}"
+
+    def poll_insert_readiness(self, expected_count: int) -> dict:
+        stats = self.index.describe_index_stats()
+        if getattr(self, "multitenant_tenant_labels", []):
+            namespaces = stats.get("namespaces", {})
+            expected_by_tenant = self._expected_multitenant_counts(expected_count)
+            count_ready = True
+            for tenant, expected_tenant_count in expected_by_tenant.items():
+                namespace = self._namespace_for_tenant(tenant)
+                namespace_stats = namespaces.get(namespace, {})
+                namespace_count = namespace_stats.get("vector_count", 0)
+                count_ready = count_ready and namespace_count >= expected_tenant_count
+            fresh = self._multitenant_lsn_ready(expected_by_tenant)
+            return {
+                "fully_searchable": count_ready and fresh,
+                "fully_indexed": count_ready and fresh,
+                "additional_parameters": {},
+            }
+
+        count = stats.get("total_vector_count", 0)
+        count_ready = count >= expected_count
+        last_write_lsn = getattr(self, "_last_write_lsn", None)
+        if last_write_lsn is None:
+            return {
+                "fully_searchable": count_ready,
+                "fully_indexed": count_ready,
+                "additional_parameters": {},
+            }
+        query_res = self.index.query(vector=self._readiness_probe_vector, top_k=1)
+        indexed_lsn = self._extract_lsn(query_res, "x-pinecone-max-indexed-lsn")
+        if indexed_lsn is None:
+            return {
+                "fully_searchable": count_ready,
+                "fully_indexed": count_ready,
+                "additional_parameters": {},
+            }
+        fresh = indexed_lsn >= last_write_lsn
+        return {
+            "fully_searchable": count_ready and fresh,
+            "fully_indexed": count_ready and fresh,
+            "additional_parameters": {},
+        }
+
+    def _expected_multitenant_counts(self, expected_count: int) -> dict[str, int]:
+        insert_counts = self._multitenant_insert_count_snapshot()
+        if insert_counts:
+            return insert_counts
+        tenant_labels = self.multitenant_tenant_labels
+        tenant_count = len(tenant_labels)
+        base_count = expected_count // tenant_count if tenant_count else 0
+        remainder = expected_count % tenant_count if tenant_count else 0
+        return {tenant: base_count + (1 if idx < remainder else 0) for idx, tenant in enumerate(tenant_labels)}
+
+    def _multitenant_insert_count_snapshot(self) -> dict[str, int]:
+        if not hasattr(self, "_multitenant_insert_counts_lock"):
+            self._multitenant_insert_counts_lock = threading.Lock()
+        with self._multitenant_insert_counts_lock:
+            return dict(getattr(self, "_multitenant_insert_counts", {}))
+
+    def _record_multitenant_insert_count(self, tenant: str, count: int) -> None:
+        if not hasattr(self, "_multitenant_insert_counts_lock"):
+            self._multitenant_insert_counts_lock = threading.Lock()
+        with self._multitenant_insert_counts_lock:
+            # Pinecone readiness is count-based per tenant namespace. Unlike
+            # providers that only track touched tenants, this counter must keep
+            # every successful write from concurrent insert workers.
+            self._multitenant_insert_counts[tenant] = self._multitenant_insert_counts.get(tenant, 0) + count
+
+    def _multitenant_lsn_ready(self, expected_by_tenant: dict[str, int]) -> bool:
+        last_write_lsn = getattr(self, "_last_write_lsn", None)
+        if last_write_lsn is None:
+            return True
+        for tenant, expected_tenant_count in expected_by_tenant.items():
+            if expected_tenant_count <= 0:
+                continue
+            query_res = self.index.query(
+                vector=self._readiness_probe_vector,
+                top_k=1,
+                namespace=self._namespace_for_tenant(tenant),
+            )
+            indexed_lsn = self._extract_lsn(query_res, "x-pinecone-max-indexed-lsn")
+            if indexed_lsn is not None and indexed_lsn < last_write_lsn:
+                return False
+        return True
+
+    @staticmethod
+    def _extract_lsn(response: Any, header_name: str) -> int | None:
+        response_info = getattr(response, "_response_info", None)
+        if not response_info:
+            return None
+        raw_headers = response_info.get("raw_headers", {})
+        value = raw_headers.get(header_name.lower()) or raw_headers.get(header_name)
+        if value is None:
+            return None
+        try:
+            return int(value)
+        except (TypeError, ValueError):
+            return None
+
+    def _record_write_lsn(self, write_lsn: int) -> None:
+        if not hasattr(self, "_last_write_lsn_lock"):
+            self._last_write_lsn_lock = threading.Lock()
+        with self._last_write_lsn_lock:
+            self._last_write_lsn = max(getattr(self, "_last_write_lsn", 0) or 0, write_lsn)
+
+    @staticmethod
+    def _matches(response: Any) -> list:
+        if isinstance(response, dict):
+            return response["matches"]
+        return response.matches
+
     def insert_embeddings(
         self,
         embeddings: list[list[float]],
         metadata: list[int],
         labels_data: list[str] | None = None,
+        tenant_labels_data: list[str] | None = None,
         **kwargs,
     ) -> tuple[int, Exception]:
         assert len(embeddings) == len(metadata)
         insert_count = 0
+        successful_tenants: dict[str, int] = {}
         try:
             for batch_start_offset in range(0, len(embeddings), self.batch_size):
                 batch_end_offset = min(batch_start_offset + self.batch_size, len(embeddings))
@@ -87,7 +245,46 @@ def insert_embeddings(
                         metadata_dict,
                     )
                     insert_datas.append(insert_data)
-                self.index.upsert(insert_datas)
+                if tenant_labels_data is None:
+                    upsert_res = self.index.upsert(insert_datas)
+                    write_lsn = self._extract_lsn(upsert_res, "x-pinecone-request-lsn")
+                    if write_lsn is not None:
+                        self._record_write_lsn(write_lsn)
+                else:
+                    batch_tenant_labels = tenant_labels_data[batch_start_offset:batch_end_offset]
+                    for tenant in sorted(set(batch_tenant_labels)):
+                        tenant_insert_datas = [
+                            insert_data
+                            for insert_data, tenant_label in zip(insert_datas, batch_tenant_labels, strict=True)
+                            if tenant_label == tenant
+                        ]
+                        try:
+                            upsert_res = self.index.upsert(
+                                tenant_insert_datas,
+                                namespace=self._namespace_for_tenant(tenant),
+                            )
+                        except Exception as e:
+                            msg = (
+                                "Pinecone multitenant insert failed for "
+                                f"tenant={tenant} after writing {insert_count} rows; "
+                                f"successful_tenants={successful_tenants}; "
+                                f"failed_tenant_count={len(tenant_insert_datas)}"
+                            )
+                            return insert_count, PartialInsertError(
+                                msg,
+                                inserted_count=insert_count,
+                                successful_tenants=successful_tenants,
+                                failed_tenant=tenant,
+                                failed_tenant_count=len(tenant_insert_datas),
+                                cause=e,
+                            )
+                        write_lsn = self._extract_lsn(upsert_res, "x-pinecone-request-lsn")
+                        if write_lsn is not None:
+                            self._record_write_lsn(write_lsn)
+                        insert_count += len(tenant_insert_datas)
+                        successful_tenants[tenant] = successful_tenants.get(tenant, 0) + len(tenant_insert_datas)
+                        self._record_multitenant_insert_count(tenant, len(tenant_insert_datas))
+                    continue
                 insert_count += batch_end_offset - batch_start_offset
         except Exception as e:
             return insert_count, e
@@ -98,15 +295,38 @@ def search_embedding(
         query: list[float],
         k: int = 100,
         timeout: int | None = None,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+        tenant: str | None = None,
     ) -> list[int]:
         pinecone_filters = self.expr
-        res = self.index.query(
-            top_k=k,
-            vector=query,
-            filter=pinecone_filters,
-        )["matches"]
+        include_metadata = payload_profile == PayloadProfile.SCALAR_LABEL
+        include_values = payload_profile == PayloadProfile.VECTOR
+        max_retries = int(os.getenv(PINECONE_QUERY_MAX_RETRIES_ENV, PINECONE_QUERY_DEFAULT_MAX_RETRIES))
+        retry_sleep = float(
+            os.getenv(PINECONE_QUERY_RETRY_SLEEP_ENV, PINECONE_QUERY_DEFAULT_RETRY_SLEEP_SECONDS),
+        )
+        for retry_idx in range(max_retries + 1):
+            try:
+                query_res = self.index.query(
+                    top_k=k,
+                    vector=query,
+                    filter=pinecone_filters,
+                    include_metadata=include_metadata,
+                    include_values=include_values,
+                    namespace=self._namespace_for_tenant(tenant),
+                )
+                res = self._matches(query_res)
+                break
+            except Exception as exc:
+                if not self._is_rate_limited(exc) or retry_idx >= max_retries:
+                    raise
+                time.sleep(retry_sleep)
         return [int(one_res["id"]) for one_res in res]
 
+    @staticmethod
+    def _is_rate_limited(exc: Exception) -> bool:
+        return getattr(exc, "status", None) == 429
+
     def prepare_filter(self, filters: Filter):
         if filters.type == FilterOp.NonFilter:
             self.expr = None
diff --git a/vectordb_bench/backend/clients/pinot/__init__.py b/vectordb_bench/backend/clients/pinot/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/vectordb_bench/backend/clients/pinot/cli.py b/vectordb_bench/backend/clients/pinot/cli.py
new file mode 100644
index 000000000..e51160b05
--- /dev/null
+++ b/vectordb_bench/backend/clients/pinot/cli.py
@@ -0,0 +1,202 @@
+from typing import Annotated, TypedDict, Unpack
+
+import click
+from pydantic import SecretStr
+
+from ....cli.cli import (
+    CommonTypedDict,
+    HNSWFlavor2,
+    click_parameter_decorators_from_typed_dict,
+    run,
+)
+from .. import DB
+
+
+class PinotTypedDict(TypedDict):
+    controller_host: Annotated[
+        str,
+        click.option("--controller-host", type=str, default="localhost", help="Pinot Controller host"),
+    ]
+    controller_port: Annotated[
+        int,
+        click.option("--controller-port", type=int, default=9000, help="Pinot Controller port"),
+    ]
+    broker_host: Annotated[
+        str,
+        click.option("--broker-host", type=str, default="localhost", help="Pinot Broker host"),
+    ]
+    broker_port: Annotated[
+        int,
+        click.option("--broker-port", type=int, default=8099, help="Pinot Broker port"),
+    ]
+    username: Annotated[
+        str,
+        click.option("--username", type=str, default=None, help="Pinot username (optional)"),
+    ]
+    password: Annotated[
+        str,
+        click.option("--password", type=str, default=None, help="Pinot password (optional)"),
+    ]
+    ingest_batch_size: Annotated[
+        int,
+        click.option(
+            "--ingest-batch-size",
+            type=int,
+            default=100_000,
+            show_default=True,
+            help=(
+                "Rows buffered before flushing one Pinot segment (one ingestFromFile call). "
+                "Larger values mean fewer segments and better IVF training / query performance. "
+                "Reduce if memory is constrained (100K x 768-dim float32 ~= 300 MB)."
+            ),
+        ),
+    ]
+
+
+def _pinot_db_config(parameters: dict):
+    from .config import PinotConfig
+
+    return PinotConfig(
+        db_label=parameters["db_label"],
+        controller_host=parameters["controller_host"],
+        controller_port=parameters["controller_port"],
+        broker_host=parameters["broker_host"],
+        broker_port=parameters["broker_port"],
+        username=parameters.get("username"),
+        password=SecretStr(parameters["password"]) if parameters.get("password") else None,
+        ingest_batch_size=parameters["ingest_batch_size"],
+    )
+
+
+@click.group()
+def Pinot():
+    """Apache Pinot vector search benchmarks."""
+
+
+# ---------------------------------------------------------------------------
+# HNSW
+# ---------------------------------------------------------------------------
+
+
+class PinotHNSWTypedDict(CommonTypedDict, PinotTypedDict, HNSWFlavor2): ...
+
+
+@Pinot.command("hnsw")
+@click_parameter_decorators_from_typed_dict(PinotHNSWTypedDict)
+def pinot_hnsw(**parameters: Unpack[PinotHNSWTypedDict]):
+    from .config import PinotHNSWConfig
+
+    run(
+        db=DB.Pinot,
+        db_config=_pinot_db_config(parameters),
+        db_case_config=PinotHNSWConfig(
+            m=parameters["m"],
+            ef_construction=parameters["ef_construction"],
+            ef=parameters["ef_runtime"],
+        ),
+        **parameters,
+    )
+
+
+# ---------------------------------------------------------------------------
+# IVF_FLAT
+# ---------------------------------------------------------------------------
+
+
+class PinotIVFFlatTypedDict(CommonTypedDict, PinotTypedDict):
+    nlist: Annotated[
+        int,
+        click.option("--nlist", type=int, default=128, help="Number of Voronoi cells (IVF nlist)"),
+    ]
+    quantizer: Annotated[
+        str,
+        click.option(
+            "--quantizer",
+            type=click.Choice(["FLAT", "SQ8", "SQ4"]),
+            default="FLAT",
+            help="Quantizer type for IVF_FLAT",
+        ),
+    ]
+    nprobe: Annotated[
+        int,
+        click.option("--nprobe", type=int, default=8, help="Number of cells to probe at query time"),
+    ]
+    train_sample_size: Annotated[
+        int,
+        click.option(
+            "--train-sample-size",
+            type=int,
+            default=None,
+            help="Training sample size (defaults to max(nlist*50, 1000) if not set)",
+        ),
+    ]
+
+
+@Pinot.command("ivf-flat")
+@click_parameter_decorators_from_typed_dict(PinotIVFFlatTypedDict)
+def pinot_ivf_flat(**parameters: Unpack[PinotIVFFlatTypedDict]):
+    from .config import PinotIVFFlatConfig
+
+    run(
+        db=DB.Pinot,
+        db_config=_pinot_db_config(parameters),
+        db_case_config=PinotIVFFlatConfig(
+            nlist=parameters["nlist"],
+            quantizer=parameters["quantizer"],
+            nprobe=parameters["nprobe"],
+            train_sample_size=parameters.get("train_sample_size"),
+        ),
+        **parameters,
+    )
+
+
+# ---------------------------------------------------------------------------
+# IVF_PQ
+# ---------------------------------------------------------------------------
+
+
+class PinotIVFPQTypedDict(CommonTypedDict, PinotTypedDict):
+    nlist: Annotated[
+        int,
+        click.option("--nlist", type=int, default=128, help="Number of Voronoi cells (IVF nlist)"),
+    ]
+    pq_m: Annotated[
+        int,
+        click.option("--pq-m", type=int, default=8, help="Number of PQ sub-quantizers (must divide dimension)"),
+    ]
+    pq_nbits: Annotated[
+        int,
+        click.option(
+            "--pq-nbits",
+            type=click.Choice(["4", "6", "8"]),
+            default="8",
+            help="Bits per PQ code (4, 6, or 8)",
+        ),
+    ]
+    train_sample_size: Annotated[
+        int,
+        click.option("--train-sample-size", type=int, default=6400, help="Training sample size (must be >= nlist)"),
+    ]
+    nprobe: Annotated[
+        int,
+        click.option("--nprobe", type=int, default=8, help="Number of cells to probe at query time"),
+    ]
+
+
+@Pinot.command("ivf-pq")
+@click_parameter_decorators_from_typed_dict(PinotIVFPQTypedDict)
+def pinot_ivf_pq(**parameters: Unpack[PinotIVFPQTypedDict]):
+    from .config import PinotIVFPQConfig
+
+    run(
+        db=DB.Pinot,
+        db_config=_pinot_db_config(parameters),
+        db_case_config=PinotIVFPQConfig(
+            nlist=parameters["nlist"],
+            pq_m=parameters["pq_m"],
+            pq_nbits=int(parameters["pq_nbits"]),
+            train_sample_size=parameters["train_sample_size"],
+            nprobe=parameters["nprobe"],
+        ),
+        **parameters,
+    )
diff --git a/vectordb_bench/backend/clients/pinot/config.py b/vectordb_bench/backend/clients/pinot/config.py
new file mode 100644
index 000000000..e29db0bec
--- /dev/null
+++ b/vectordb_bench/backend/clients/pinot/config.py
@@ -0,0 +1,94 @@
+from pydantic import BaseModel, SecretStr
+
+from ..api import DBCaseConfig, DBConfig, MetricType
+
+
+class PinotConfig(DBConfig):
+    controller_host: str = "localhost"
+    controller_port: int = 9000
+    broker_host: str = "localhost"
+    broker_port: int = 8099
+    username: str | None = None
+    password: SecretStr | None = None
+    # Rows buffered before flushing one Pinot segment (one ingestFromFile call).
+    # Larger values → fewer segments → better IVF training & query perf.
+    # 100_000 rows x 768-dim float32 ~= 300 MB in-memory.
+    ingest_batch_size: int = 100_000
+
+    def to_dict(self) -> dict:
+        return {
+            "controller_host": self.controller_host,
+            "controller_port": self.controller_port,
+            "broker_host": self.broker_host,
+            "broker_port": self.broker_port,
+            "username": self.username,
+            "password": self.password.get_secret_value() if self.password else None,
+            "ingest_batch_size": self.ingest_batch_size,
+        }
+
+
+class PinotHNSWConfig(BaseModel, DBCaseConfig):
+    """HNSW vector index config for Apache Pinot (Lucene-based)."""
+
+    metric_type: MetricType | None = None
+    m: int = 16  # maxCon: max connections per node
+    ef_construction: int = 100  # beamWidth: construction beam width
+    ef: int | None = None  # ef_search: HNSW candidate list size at query time (default=k)
+
+    def index_param(self) -> dict:
+        return {
+            "vectorIndexType": "HNSW",
+            "maxCon": str(self.m),
+            "beamWidth": str(self.ef_construction),
+        }
+
+    def search_param(self) -> dict:
+        # ef controls the HNSW candidate list during search via vectorSimilarity(col, q, ef).
+        # Larger ef → better recall, slightly higher latency. Defaults to k if not set.
+        return {"ef": self.ef} if self.ef is not None else {}
+
+
+class PinotIVFFlatConfig(BaseModel, DBCaseConfig):
+    """IVF_FLAT vector index config for Apache Pinot."""
+
+    metric_type: MetricType | None = None
+    nlist: int = 128  # number of Voronoi cells (centroids)
+    quantizer: str = "FLAT"  # FLAT, SQ8, or SQ4
+    train_sample_size: int | None = None  # defaults to max(nlist*50, 1000) if None
+    nprobe: int = 8  # number of cells to probe at query time
+
+    def index_param(self) -> dict:
+        params: dict = {
+            "vectorIndexType": "IVF_FLAT",
+            "nlist": str(self.nlist),
+            "quantizer": self.quantizer,
+        }
+        if self.train_sample_size is not None:
+            params["trainSampleSize"] = str(self.train_sample_size)
+        return params
+
+    def search_param(self) -> dict:
+        return {"nprobe": self.nprobe}
+
+
+class PinotIVFPQConfig(BaseModel, DBCaseConfig):
+    """IVF_PQ vector index config for Apache Pinot (residual product quantization)."""
+
+    metric_type: MetricType | None = None
+    nlist: int = 128  # number of Voronoi cells (centroids)
+    pq_m: int = 8  # number of sub-quantizers (must divide vectorDimension)
+    pq_nbits: int = 8  # bits per sub-quantizer code: 4, 6, or 8
+    train_sample_size: int = 6400  # training sample size (must be >= nlist)
+    nprobe: int = 8  # number of cells to probe at query time
+
+    def index_param(self) -> dict:
+        return {
+            "vectorIndexType": "IVF_PQ",
+            "nlist": str(self.nlist),
+            "pqM": str(self.pq_m),
+            "pqNbits": str(self.pq_nbits),
+            "trainSampleSize": str(self.train_sample_size),
+        }
+
+    def search_param(self) -> dict:
+        return {"nprobe": self.nprobe}
diff --git a/vectordb_bench/backend/clients/pinot/pinot.py b/vectordb_bench/backend/clients/pinot/pinot.py
new file mode 100644
index 000000000..bdae58d64
--- /dev/null
+++ b/vectordb_bench/backend/clients/pinot/pinot.py
@@ -0,0 +1,449 @@
+"""Wrapper around Apache Pinot vector database over VectorDB"""
+
+import json
+import logging
+import tempfile
+import threading
+import time
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Any
+
+import requests
+
+from ...filter import Filter, FilterOp
+from ..api import DBCaseConfig, MetricType, VectorDB
+
+log = logging.getLogger(__name__)
+
+# Rows accumulated before flushing a segment to Pinot.
+# Large enough to avoid thousands of tiny segments (which breaks IVF training
+# and hurts query performance), yet small enough to keep memory use bounded.
+# For 768-dim float32 vectors: 100K rows ≈ 300 MB in-memory.
+DEFAULT_INGEST_BATCH_SIZE = 100_000
+
+
+class Pinot(VectorDB):
+    """Apache Pinot vector database client for VectorDBBench."""
+
+    name = "Pinot"
+    # thread_safe=True: each flush uses a fresh requests.Session (not a shared one),
+    # and each worker thread has its own row buffer via threading.local().
+    # This lets the framework spawn multiple load workers that flush segments in parallel.
+    thread_safe: bool = True
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+        FilterOp.StrEqual,
+    ]
+
+    def __init__(
+        self,
+        dim: int,
+        db_config: dict,
+        db_case_config: DBCaseConfig,
+        collection_name: str = "VectorBenchCollection",
+        drop_old: bool = False,
+        with_scalar_labels: bool = False,
+        **kwargs,
+    ):
+        self.dim = dim
+        self.case_config = db_case_config
+        self.table_name = collection_name
+        self._primary_field = "id"
+        self._vector_field = "embedding"
+        self._label_field = "labels"
+        self.with_scalar_labels = with_scalar_labels
+        self._filter_where: str = ""  # set by prepare_filter(); applied in search_embedding()
+
+        controller_host = db_config["controller_host"]
+        controller_port = db_config["controller_port"]
+        broker_host = db_config["broker_host"]
+        broker_port = db_config["broker_port"]
+        self._controller_url = f"http://{controller_host}:{controller_port}"
+        self._broker_url = f"http://{broker_host}:{broker_port}"
+
+        self._auth = None
+        if db_config.get("username") and db_config.get("password"):
+            self._auth = (db_config["username"], db_config["password"])
+
+        # Per-thread row buffers — each worker thread accumulates rows independently
+        # and flushes its own segment when the threshold is reached.
+        # _registered_buffers tracks all thread-local objects so init() teardown
+        # can flush any remaining rows from every worker thread.
+        self._thread_local = threading.local()
+        self._registered_buffers: list = []
+        self._buffers_lock = threading.Lock()
+        self._ingest_batch_size: int = db_config.get("ingest_batch_size", DEFAULT_INGEST_BATCH_SIZE)
+
+        self.session = None
+
+        with requests.Session() as setup_session:
+            if self._auth:
+                setup_session.auth = self._auth
+
+            if drop_old:
+                self._delete_table(setup_session)
+                self._delete_schema(setup_session)
+
+            if not self._schema_exists(setup_session):
+                self._create_schema(setup_session)
+
+            if not self._table_exists(setup_session):
+                self._create_table(setup_session)
+
+    def _schema_exists(self, session: requests.Session) -> bool:
+        resp = session.get(f"{self._controller_url}/schemas/{self.table_name}")
+        return resp.status_code == 200
+
+    def _table_exists(self, session: requests.Session) -> bool:
+        resp = session.get(f"{self._controller_url}/tables/{self.table_name}")
+        if resp.status_code != 200:
+            return False
+        data = resp.json()
+        return bool(data.get("OFFLINE") or data.get("tables"))
+
+    def _delete_table(self, session: requests.Session):
+        resp = session.delete(f"{self._controller_url}/tables/{self.table_name}?type=offline")
+        if resp.status_code not in (200, 404):
+            log.warning(f"Failed to delete Pinot table {self.table_name}: {resp.text}")
+        else:
+            log.info(f"Deleted Pinot table: {self.table_name}")
+        # Wait for Pinot to finish cleaning up the external view
+        for _ in range(30):
+            check = session.get(f"{self._controller_url}/tables/{self.table_name}/externalview")
+            if check.status_code == 404 or not check.json():
+                break
+            log.info(f"Waiting for Pinot external view cleanup for {self.table_name}...")
+            time.sleep(2)
+        else:
+            log.warning(f"External view for {self.table_name} did not clear within 60s")
+
+    def _delete_schema(self, session: requests.Session):
+        resp = session.delete(f"{self._controller_url}/schemas/{self.table_name}")
+        if resp.status_code not in (200, 404):
+            log.warning(f"Failed to delete Pinot schema {self.table_name}: {resp.text}")
+        else:
+            log.info(f"Deleted Pinot schema: {self.table_name}")
+
+    def _create_schema(self, session: requests.Session):
+        dimension_fields = [
+            {"name": self._primary_field, "dataType": "INT"},
+            {"name": self._vector_field, "dataType": "FLOAT", "singleValueField": False},
+        ]
+        if self.with_scalar_labels:
+            dimension_fields.append({"name": self._label_field, "dataType": "STRING"})
+
+        schema = {
+            "schemaName": self.table_name,
+            "dimensionFieldSpecs": dimension_fields,
+        }
+        resp = session.post(
+            f"{self._controller_url}/schemas",
+            json=schema,
+            headers={"Content-Type": "application/json"},
+        )
+        if not resp.ok:
+            log.error(f"Failed to create Pinot schema: {resp.text}")
+            resp.raise_for_status()
+        log.info(f"Created Pinot schema: {self.table_name}")
+
+    def _create_table(self, session: requests.Session):
+        metric_str = self._get_index_metric_str()
+        index_params = self.case_config.index_param()
+
+        # Pull vectorIndexType out of index_params; remaining entries are type-specific properties.
+        vector_index_type = index_params.pop("vectorIndexType", "HNSW")
+        properties: dict = {
+            "vectorIndexType": vector_index_type,
+            "vectorDimension": str(self.dim),
+            "vectorDistanceFunction": metric_str,
+            "version": "1",
+        }
+        properties.update(index_params)
+
+        table_config = {
+            "tableName": self.table_name,
+            "tableType": "OFFLINE",
+            "segmentsConfig": {
+                "replication": "1",
+                "schemaName": self.table_name,
+            },
+            "tenants": {},
+            "tableIndexConfig": {
+                "loadMode": "MMAP",
+                # Inverted index on id for fast equality lookups; range index for >=/<= filters.
+                "invertedIndexColumns": [self._primary_field],
+                "rangeIndexColumns": [self._primary_field],
+            },
+            "fieldConfigList": [
+                {
+                    "encodingType": "RAW",
+                    "indexType": "VECTOR",
+                    "name": self._vector_field,
+                    "properties": properties,
+                }
+            ],
+            "ingestionConfig": {
+                "batchIngestionConfig": {
+                    "segmentIngestionType": "APPEND",
+                    "segmentIngestionFrequency": "DAILY",
+                }
+            },
+            "metadata": {},
+        }
+        resp = session.post(
+            f"{self._controller_url}/tables",
+            json=table_config,
+            headers={"Content-Type": "application/json"},
+        )
+        if not resp.ok:
+            log.error(f"Failed to create Pinot table: {resp.text}")
+            resp.raise_for_status()
+        log.info(f"Created Pinot table: {self.table_name}")
+
+    def _get_index_metric_str(self) -> str:
+        if self.case_config.metric_type == MetricType.COSINE:
+            return "COSINE"
+        if self.case_config.metric_type == MetricType.IP:
+            return "INNER_PRODUCT"
+        return "L2"
+
+    def _get_query_distance_fn(self) -> tuple[str, str]:
+        """Returns (sql_function_name, sort_order) for vector search."""
+        if self.case_config.metric_type == MetricType.COSINE:
+            return "cosineDistance", "ASC"
+        if self.case_config.metric_type == MetricType.IP:
+            return "innerProduct", "DESC"
+        return "l2Distance", "ASC"
+
+    def prepare_filter(self, filters: Filter):
+        """Pre-compute the SQL WHERE fragment for the given filter condition."""
+        if filters.type == FilterOp.NonFilter:
+            self._filter_where = ""
+        elif filters.type == FilterOp.NumGE:
+            self._filter_where = f"{filters.int_field} >= {filters.int_value}"
+        elif filters.type == FilterOp.StrEqual:
+            self._filter_where = f"{self._label_field} = '{filters.label_value}'"
+
+    def __getstate__(self):
+        # threading.local and Lock cannot be pickled; the framework pickles the DB
+        # instance to send it to the load subprocess, so we must exclude them here
+        # and recreate them in __setstate__ after unpickling.
+        state = self.__dict__.copy()
+        state.pop("_thread_local", None)
+        state.pop("_buffers_lock", None)
+        state.pop("_registered_buffers", None)
+        return state
+
+    def __setstate__(self, state: dict) -> None:
+        self.__dict__.update(state)
+        self._thread_local = threading.local()
+        self._buffers_lock = threading.Lock()
+        self._registered_buffers = []
+
+    def _get_thread_buffer(self) -> list:
+        """Return this thread's row buffer, creating and registering it on first access.
+
+        The list itself (not the threading.local container) is registered so that the
+        teardown in init() can access buffered rows from the main thread, which has its
+        own (empty) thread-local slot and cannot see other threads' data through the
+        threading.local object.
+        """
+        if not hasattr(self._thread_local, "pending_rows"):
+            self._thread_local.pending_rows = []
+            with self._buffers_lock:
+                # Register the list itself, not self._thread_local, so teardown can
+                # read the contents from any thread (main thread included).
+                self._registered_buffers.append(self._thread_local.pending_rows)
+        return self._thread_local.pending_rows
+
+    @contextmanager
+    def init(self):
+        self.session = requests.Session()
+        if self._auth:
+            self.session.auth = self._auth
+        # Reset buffer registry so teardown only flushes buffers from this init() scope.
+        self._registered_buffers = []
+        try:
+            yield
+        finally:
+            # Flush any rows that were buffered but not yet sent to Pinot.
+            # Each worker thread may have its own partially-filled buffer.
+            # This must happen here — not in optimize() — because optimize() runs
+            # in a separate subprocess where the buffers are always empty.
+            for pending in self._registered_buffers:
+                if pending:
+                    log.info(f"Pinot init teardown: flushing {len(pending)} remaining buffered rows")
+                    _, err = self._flush_rows(pending)
+                    if err:
+                        log.warning(f"Pinot init teardown: flush error: {err}")
+            self.session.close()
+            self.session = None
+
+    def _flush_rows(self, rows: list) -> tuple[int, Exception | None]:
+        """Flush the given row list to Pinot as one segment using a fresh HTTP session.
+
+        Using a fresh session (not self.session) makes this method safe to call
+        from multiple threads concurrently.  On success the list is cleared in-place.
+        Returns (rows_flushed, error). On error the list is left intact so the caller
+        can decide whether to retry.
+        """
+        if not rows:
+            return 0, None
+
+        n = len(rows)
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False, prefix="pinot_ingest_") as f:
+            for row in rows:
+                f.write(json.dumps(row) + "\n")
+            tmp_path = f.name
+
+        try:
+            batch_config = json.dumps({"inputFormat": "json"})
+            params = {
+                "tableNameWithType": f"{self.table_name}_OFFLINE",
+                "batchConfigMapStr": batch_config,
+            }
+            last_err = None
+            with requests.Session() as session:
+                if self._auth:
+                    session.auth = self._auth
+                for attempt in range(3):
+                    try:
+                        with Path(tmp_path).open("rb") as f:
+                            resp = session.post(
+                                f"{self._controller_url}/ingestFromFile",
+                                params=params,
+                                files={"file": (Path(tmp_path).name, f, "application/json")},
+                                timeout=1800,  # HNSW index building for 100K x 768D can take 10+ min
+                            )
+                        if resp.ok:
+                            rows.clear()
+                            log.debug(f"Pinot: flushed segment with {n} rows")
+                            return n, None
+                        last_err = Exception(f"HTTP {resp.status_code}: {resp.text[:200]}")
+                        log.warning(f"Pinot flush attempt {attempt + 1} failed: {last_err}")
+                        time.sleep(1 + attempt)
+                    except Exception as e:
+                        last_err = e
+                        log.warning(f"Pinot flush attempt {attempt + 1} error: {e}")
+                        time.sleep(1 + attempt)
+            return 0, last_err
+        finally:
+            Path(tmp_path).unlink()
+
+    def insert_embeddings(
+        self,
+        embeddings: list[list[float]],
+        metadata: list[int],
+        labels_data: list[str] | None = None,
+        **kwargs: Any,
+    ) -> tuple[int, Exception]:
+        # Each thread has its own buffer; no locking needed here.
+        pending = self._get_thread_buffer()
+
+        for i, (emb, meta) in enumerate(zip(embeddings, metadata, strict=False)):
+            row = {self._primary_field: meta, self._vector_field: list(emb)}
+            if self.with_scalar_labels and labels_data is not None:
+                row[self._label_field] = labels_data[i]
+            pending.append(row)
+
+        if len(pending) >= self._ingest_batch_size:
+            _flushed, err = self._flush_rows(pending)
+            if err:
+                log.warning(f"Failed to flush Pinot buffer: {err}")
+                return 0, err
+
+        return len(embeddings), None
+
+    def search_embedding(
+        self,
+        query: list[float],
+        k: int = 100,
+        filters: dict | None = None,
+        timeout: int | None = None,
+    ) -> list[int]:
+        assert self.session is not None, "Session not initialized"
+
+        query_arr = ",".join(str(v) for v in query)
+        dist_fn, order = self._get_query_distance_fn()
+
+        search_params = self.case_config.search_param()
+        nprobe = search_params.get("nprobe")
+
+        filter_clause = self._filter_where
+
+        if nprobe is not None:
+            # IVF-based index: set probe count via session option, use ORDER BY for top-k
+            where = f"WHERE {filter_clause} " if filter_clause else ""
+            sql = (
+                f"set vectorNprobe={nprobe}; "
+                f"SELECT {self._primary_field} "
+                f"FROM {self.table_name} "
+                f"{where}"
+                f"ORDER BY {dist_fn}({self._vector_field}, ARRAY[{query_arr}]) {order} "
+                f"LIMIT {k}"
+            )
+        else:
+            # HNSW index: WHERE vectorSimilarity(..., ef) triggers Lucene HNSW graph search
+            # with a candidate list of size ef (defaults to k).  Larger ef → better recall.
+            # ORDER BY dist re-ranks the ANN candidates for correct final recall.
+            ef = search_params.get("ef") or k
+            extra = f" AND {filter_clause}" if filter_clause else ""
+            sql = (
+                f"SELECT {self._primary_field} "
+                f"FROM {self.table_name} "
+                f"WHERE vectorSimilarity({self._vector_field}, ARRAY[{query_arr}], {ef}){extra} "
+                f"ORDER BY {dist_fn}({self._vector_field}, ARRAY[{query_arr}]) {order} "
+                f"LIMIT {k}"
+            )
+
+        resp = self.session.post(
+            f"{self._broker_url}/query/sql",
+            json={"sql": sql},
+            headers={"Content-Type": "application/json"},
+            timeout=timeout,
+        )
+        resp.raise_for_status()
+        result = resp.json()
+
+        rows = result.get("resultTable", {}).get("rows", [])
+        return [row[0] for row in rows]
+
+    def optimize(self, data_size: int | None = None):
+        """Wait for all ingested data to be queryable in Pinot.
+
+        Remaining buffered rows are flushed on init() teardown (in the insert
+        subprocess), so by the time optimize() runs they are already in Pinot.
+        """
+        if self.session is None:
+            return
+
+        if data_size is None:
+            time.sleep(5)
+            return
+
+        max_wait = 600
+        check_interval = 10
+        start = time.time()
+
+        while time.time() - start < max_wait:
+            try:
+                resp = self.session.post(
+                    f"{self._broker_url}/query/sql",
+                    json={"sql": f"SELECT COUNT(*) FROM {self.table_name}"},
+                    headers={"Content-Type": "application/json"},
+                )
+                if resp.status_code == 200:
+                    rows = resp.json().get("resultTable", {}).get("rows", [])
+                    current_count = rows[0][0] if rows else 0
+                    if current_count >= data_size:
+                        log.info(f"Pinot: all {data_size} rows are queryable")
+                        return
+                    log.info(f"Pinot: {current_count}/{data_size} rows queryable, waiting...")
+            except Exception as e:
+                log.warning(f"Pinot optimize check error: {e}")
+
+            time.sleep(check_interval)
+
+        log.warning(f"Pinot optimize timed out after {max_wait}s")
diff --git a/vectordb_bench/backend/clients/polardb/__init__.py b/vectordb_bench/backend/clients/polardb/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/vectordb_bench/backend/clients/polardb/cli.py b/vectordb_bench/backend/clients/polardb/cli.py
new file mode 100644
index 000000000..6f7a8f79c
--- /dev/null
+++ b/vectordb_bench/backend/clients/polardb/cli.py
@@ -0,0 +1,248 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Annotated, Unpack
+
+if TYPE_CHECKING:
+    from .config import PolarDBConfig
+
+import click
+from pydantic import SecretStr
+
+from vectordb_bench.backend.clients import DB
+
+from ....cli.cli import (
+    CommonTypedDict,
+    cli,
+    click_parameter_decorators_from_typed_dict,
+    run,
+)
+
+
+class PolarDBTypedDict(CommonTypedDict):
+    user_name: Annotated[
+        str,
+        click.option(
+            "--username",
+            type=str,
+            help="Username",
+            required=True,
+        ),
+    ]
+    password: Annotated[
+        str,
+        click.option(
+            "--password",
+            type=str,
+            help="Password",
+            default="",
+        ),
+    ]
+
+    host: Annotated[
+        str,
+        click.option(
+            "--host",
+            type=str,
+            help="Db host",
+            default="127.0.0.1",
+        ),
+    ]
+
+    port: Annotated[
+        int,
+        click.option(
+            "--port",
+            type=int,
+            default=3306,
+            help="Db Port",
+        ),
+    ]
+
+    database: Annotated[
+        str,
+        click.option(
+            "--database",
+            type=str,
+            help="Database name",
+            default="vectordbbench",
+        ),
+    ]
+
+    unix_socket: Annotated[
+        str,
+        click.option(
+            "--unix-socket",
+            type=str,
+            help="Unix socket path (overrides host/port if set)",
+            default="",
+        ),
+    ]
+
+
+class PolarDBHNSWTypedDict(PolarDBTypedDict):
+    m: Annotated[
+        int,
+        click.option(
+            "--m",
+            type=int,
+            help="M parameter (max_degree) in HNSW",
+            default=16,
+        ),
+    ]
+
+    ef_construction: Annotated[
+        int,
+        click.option(
+            "--ef-construction",
+            type=int,
+            help="ef_construction parameter in HNSW",
+            default=200,
+        ),
+    ]
+
+    ef_search: Annotated[
+        int,
+        click.option(
+            "--ef-search",
+            type=int,
+            help="polar_vector_index_hnsw_ef_search session variable",
+            default=64,
+        ),
+    ]
+
+    insert_workers: Annotated[
+        int,
+        click.option(
+            "--insert-workers",
+            type=int,
+            help="Number of concurrent threads for data insertion",
+            default=10,
+        ),
+    ]
+
+    post_load_index: Annotated[
+        bool,
+        click.option(
+            "--post-load-index/--inline-index",
+            type=bool,
+            help="Create vector index via ALTER TABLE after data load; "
+            "otherwise create index inline during table creation",
+            default=False,
+        ),
+    ]
+
+
+class PolarDBHNSWPQTypedDict(PolarDBHNSWTypedDict):
+    pq_m: Annotated[
+        int,
+        click.option(
+            "--pq-m",
+            type=int,
+            help="PQ subquantizer count (must divide dimension)",
+            default=1,
+        ),
+    ]
+
+    pq_nbits: Annotated[
+        int,
+        click.option(
+            "--pq-nbits",
+            type=int,
+            help="PQ bits per subquantizer (max 24)",
+            default=8,
+        ),
+    ]
+
+
+class PolarDBHNSWSQTypedDict(PolarDBHNSWTypedDict):
+    sq_type: Annotated[
+        str,
+        click.option(
+            "--sq-type",
+            type=str,
+            help="SQ quantizer type (8bit, 4bit, fp16, bf16, 6bit, etc.)",
+            default="8bit",
+        ),
+    ]
+
+
+def _build_db_config(parameters: dict) -> PolarDBConfig:
+    from .config import PolarDBConfig
+
+    pwd = parameters["password"]
+    sock = parameters["unix_socket"]
+    return PolarDBConfig(
+        db_label=parameters["db_label"],
+        user_name=parameters["username"],
+        password=SecretStr(pwd) if pwd else None,
+        host=parameters["host"],
+        port=parameters["port"],
+        database=parameters["database"],
+        unix_socket=sock if sock else None,
+    )
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(PolarDBHNSWTypedDict)
+def PolarDBHNSWFlat(
+    **parameters: Unpack[PolarDBHNSWTypedDict],
+):
+    from .config import PolarDBHNSWFlatConfig
+
+    run(
+        db=DB.PolarDB,
+        db_config=_build_db_config(parameters),
+        db_case_config=PolarDBHNSWFlatConfig(
+            M=parameters["m"],
+            ef_construction=parameters["ef_construction"],
+            ef_search=parameters["ef_search"],
+            insert_workers=parameters["insert_workers"],
+            post_load_index=parameters["post_load_index"],
+        ),
+        **parameters,
+    )
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(PolarDBHNSWPQTypedDict)
+def PolarDBHNSWPQ(
+    **parameters: Unpack[PolarDBHNSWPQTypedDict],
+):
+    from .config import PolarDBHNSWPQConfig
+
+    run(
+        db=DB.PolarDB,
+        db_config=_build_db_config(parameters),
+        db_case_config=PolarDBHNSWPQConfig(
+            M=parameters["m"],
+            ef_construction=parameters["ef_construction"],
+            ef_search=parameters["ef_search"],
+            insert_workers=parameters["insert_workers"],
+            post_load_index=parameters["post_load_index"],
+            pq_m=parameters["pq_m"],
+            pq_nbits=parameters["pq_nbits"],
+        ),
+        **parameters,
+    )
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(PolarDBHNSWSQTypedDict)
+def PolarDBHNSWSQ(
+    **parameters: Unpack[PolarDBHNSWSQTypedDict],
+):
+    from .config import PolarDBHNSWSQConfig
+
+    run(
+        db=DB.PolarDB,
+        db_config=_build_db_config(parameters),
+        db_case_config=PolarDBHNSWSQConfig(
+            M=parameters["m"],
+            ef_construction=parameters["ef_construction"],
+            ef_search=parameters["ef_search"],
+            insert_workers=parameters["insert_workers"],
+            post_load_index=parameters["post_load_index"],
+            sq_type=parameters["sq_type"],
+        ),
+        **parameters,
+    )
diff --git a/vectordb_bench/backend/clients/polardb/config.py b/vectordb_bench/backend/clients/polardb/config.py
new file mode 100644
index 000000000..5121c1e19
--- /dev/null
+++ b/vectordb_bench/backend/clients/polardb/config.py
@@ -0,0 +1,148 @@
+from typing import TypedDict
+
+from pydantic import BaseModel, SecretStr
+
+from ..api import DBCaseConfig, DBConfig, IndexType, MetricType
+
+
+class PolarDBConfigDict(TypedDict):
+    user: str
+    password: str
+    host: str
+    port: int
+    database: str
+    unix_socket: str | None = None
+
+
+class PolarDBConfig(DBConfig):
+    user_name: str = "root"
+    password: SecretStr | None = None
+    host: str = "127.0.0.1"
+    port: int = 3306
+    database: str = "vectordbbench"
+    unix_socket: str | None = None
+
+    @staticmethod
+    def common_long_configs() -> list[str]:
+        return ["note", "unix_socket"]
+
+    def to_dict(self) -> PolarDBConfigDict:
+        pwd_str = self.password.get_secret_value() if self.password else ""
+        return {
+            "host": self.host,
+            "port": self.port,
+            "user": self.user_name,
+            "password": pwd_str,
+            "database": self.database,
+            "unix_socket": self.unix_socket or None,
+        }
+
+
+class PolarDBIndexConfig(BaseModel):
+    """Base config for PolarDB vector index"""
+
+    metric_type: MetricType | None = None
+
+    def parse_metric(self) -> str:
+        if self.metric_type == MetricType.L2:
+            return "EUCLIDEAN"
+        if self.metric_type == MetricType.COSINE:
+            return "COSINE"
+        if self.metric_type == MetricType.IP:
+            return "INNER_PRODUCT"
+        msg = f"Metric type {self.metric_type} is not supported!"
+        raise ValueError(msg)
+
+    def parse_metric_for_distance(self) -> str:
+        """Return the metric name used in DISTANCE() function"""
+        if self.metric_type == MetricType.L2:
+            return "EUCLIDEAN"
+        if self.metric_type == MetricType.COSINE:
+            return "COSINE"
+        if self.metric_type == MetricType.IP:
+            return "DOT"
+        msg = f"Metric type {self.metric_type} is not supported!"
+        raise ValueError(msg)
+
+
+class PolarDBHNSWBaseConfig(PolarDBIndexConfig, DBCaseConfig):
+    """Shared HNSW config fields for all PolarDB HNSW variants."""
+
+    M: int = 16
+    ef_construction: int = 200
+    ef_search: int = 64
+    insert_workers: int = 10
+    post_load_index: bool = False  # If True, create index after data load via ALTER TABLE
+    index: IndexType = IndexType.HNSW
+
+    def search_param(self) -> dict:
+        return {
+            "metric_type": self.parse_metric_for_distance(),
+            "ef_search": self.ef_search,
+        }
+
+
+class PolarDBHNSWFlatConfig(PolarDBHNSWBaseConfig):
+    def index_param(self) -> dict:
+        return {
+            "metric_type": self.parse_metric(),
+            "metric_type_distance": self.parse_metric_for_distance(),
+            "index_type": "FAISS_HNSW_FLAT",
+            "M": self.M,
+            "ef_construction": self.ef_construction,
+            "vector_index_comment": (
+                f"imci_vector_index=FAISS_HNSW_FLAT("
+                f"metric={self.parse_metric()},"
+                f"max_degree={self.M},"
+                f"ef_construction={self.ef_construction})"
+            ),
+        }
+
+
+class PolarDBHNSWPQConfig(PolarDBHNSWBaseConfig):
+    pq_m: int = 1
+    pq_nbits: int = 8
+
+    def index_param(self) -> dict:
+        return {
+            "metric_type": self.parse_metric(),
+            "metric_type_distance": self.parse_metric_for_distance(),
+            "index_type": "FAISS_HNSW_PQ",
+            "M": self.M,
+            "ef_construction": self.ef_construction,
+            "vector_index_comment": (
+                f"imci_vector_index=FAISS_HNSW_PQ("
+                f"metric={self.parse_metric()},"
+                f"max_degree={self.M},"
+                f"ef_construction={self.ef_construction},"
+                f"pq_m={self.pq_m},"
+                f"pq_nbits={self.pq_nbits})"
+            ),
+        }
+
+
+class PolarDBHNSWSQConfig(PolarDBHNSWBaseConfig):
+    sq_type: str = "8bit"
+
+    def index_param(self) -> dict:
+        return {
+            "metric_type": self.parse_metric(),
+            "metric_type_distance": self.parse_metric_for_distance(),
+            "index_type": "FAISS_HNSW_SQ",
+            "M": self.M,
+            "ef_construction": self.ef_construction,
+            "vector_index_comment": (
+                f"imci_vector_index=FAISS_HNSW_SQ("
+                f"metric={self.parse_metric()},"
+                f"max_degree={self.M},"
+                f"ef_construction={self.ef_construction},"
+                f"sq_type={self.sq_type})"
+            ),
+        }
+
+
+_polardb_case_config = {
+    IndexType.HNSW: PolarDBHNSWFlatConfig,
+    IndexType.HNSW_PQ: PolarDBHNSWPQConfig,
+    IndexType.HNSW_SQ: PolarDBHNSWSQConfig,
+}
diff --git a/vectordb_bench/backend/clients/polardb/polardb.py b/vectordb_bench/backend/clients/polardb/polardb.py
new file mode 100644
index 000000000..d53638b0e
--- /dev/null
+++ b/vectordb_bench/backend/clients/polardb/polardb.py
@@ -0,0 +1,286 @@
+import concurrent.futures
+import logging
+import time
+from contextlib import contextmanager
+
+import numpy as np
+import pymysql
+
+from ..api import VectorDB
+from .config import PolarDBConfigDict, PolarDBIndexConfig
+
+log = logging.getLogger(__name__)
+
+
+class PolarDB(VectorDB):
+    def __init__(
+        self,
+        dim: int,
+        db_config: PolarDBConfigDict,
+        db_case_config: PolarDBIndexConfig,
+        collection_name: str = "vec_collection",
+        drop_old: bool = False,
+        **kwargs,
+    ):
+        self.name = "PolarDB"
+        self.db_config = db_config
+        self.case_config = db_case_config
+        self.table_name = collection_name
+        self.dim = dim
+
+        conn, cursor = self._create_connection()
+
+        if drop_old:
+            log.info(f"PolarDB dropping old table: {self.table_name}")
+            self._create_db_table(cursor, dim)
+
+        cursor.close()
+        conn.close()
+
+    def _create_connection(self):
+        connect_kwargs = {
+            "user": self.db_config["user"],
+            "password": self.db_config["password"],
+            "autocommit": True,
+        }
+        if self.db_config.get("unix_socket"):
+            connect_kwargs["unix_socket"] = self.db_config["unix_socket"]
+        else:
+            connect_kwargs["host"] = self.db_config["host"]
+            connect_kwargs["port"] = self.db_config["port"]
+
+        conn = pymysql.connect(**connect_kwargs)
+        cursor = conn.cursor()
+        # Disable query cache to ensure accurate benchmarking
+        cursor.execute("SET query_cache_type = OFF")
+        return conn, cursor
+
+    def _create_db_table(self, cursor: pymysql.cursors.Cursor, dim: int) -> None:
+        index_param = self.case_config.index_param()
+        vector_index_comment = index_param["vector_index_comment"]
+        post_load_index = getattr(self.case_config, "post_load_index", False)
+
+        try:
+            log.info(f"PolarDB creating database: {self.db_config['database']}")
+            cursor.execute(f"CREATE DATABASE IF NOT EXISTS {self.db_config['database']}")
+            cursor.execute(f"USE {self.db_config['database']}")
+            cursor.execute(f"DROP TABLE IF EXISTS {self.table_name}")
+
+            if post_load_index:
+                # Post-load mode: create table without vector index, will add via ALTER TABLE later
+                create_sql = (
+                    f"CREATE TABLE {self.table_name} ("
+                    f"id INT PRIMARY KEY, "
+                    f"v VECTOR({dim}) NOT NULL"
+                    f") ENGINE=InnoDB COMMENT 'COLUMNAR=1'"
+                )
+                log.info(f"PolarDB creating table (post-load index mode): {create_sql}")
+            else:
+                # Inline mode: create table with vector index comment
+                create_sql = (
+                    f"CREATE TABLE {self.table_name} ("
+                    f"id INT PRIMARY KEY, "
+                    f'v VECTOR({dim}) NOT NULL COMMENT "{vector_index_comment}"'
+                    f") ENGINE=InnoDB COMMENT 'COLUMNAR=1'"
+                )
+                log.info(f"PolarDB creating table: {create_sql}")
+            cursor.execute(create_sql)
+        except Exception as e:
+            log.warning(f"Failed to create table: {self.table_name} error: {e}")
+            raise
+
+    @contextmanager
+    def init(self):
+        self.conn, self.cursor = self._create_connection()
+
+        search_param = self.case_config.search_param()
+
+        # Force PolarDB vector search to use the IMCI engine.
+        self.cursor.execute("SET use_imci_engine = FORCED")
+        self.cursor.execute("SET imci_enable_vector_search = ON")
+        self.cursor.execute("SET imci_max_dop = 1")
+        self.cursor.execute("SET cost_threshold_for_imci = 0")
+
+        # Set ef_search
+        if search_param.get("ef_search") is not None:
+            self.cursor.execute(f"SET polar_vector_index_hnsw_ef_search = {search_param['ef_search']}")
+
+        metric_type = search_param["metric_type"]
+        db_name = self.db_config["database"]
+        hint = "/*+ SET_VAR(imci_enable_fast_vector_search=on) */"
+
+        self.insert_sql = f"INSERT INTO {db_name}.{self.table_name} (id, v) VALUES (%s, _binary %s)"
+        self.select_sql = (
+            f"SELECT {hint} id FROM {db_name}.{self.table_name} "
+            f"ORDER BY DISTANCE(v, _binary %s, '{metric_type}') "
+            f"LIMIT %s"
+        )
+        self.select_sql_with_filter = (
+            f"SELECT id FROM {db_name}.{self.table_name} "
+            f"WHERE id >= %s "
+            f"ORDER BY DISTANCE(v, _binary %s, '{metric_type}') "
+            f"LIMIT %s"
+        )
+
+        try:
+            yield
+        finally:
+            self.cursor.close()
+            self.conn.close()
+            self.cursor = None
+            self.conn = None
+
+    def ready_to_load(self) -> bool:
+        pass
+
+    def optimize(self, data_size: int | None = None) -> None:
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        db_name = self.db_config["database"]
+        index_param = self.case_config.index_param()
+        post_load_index = getattr(self.case_config, "post_load_index", False)
+
+        # Ensure vector index builds even for small datasets
+        try:
+            self.cursor.execute("SET GLOBAL imci_vector_index_dump_rows_threshold = 1")
+        except Exception as e:
+            log.warning(f"Cannot SET GLOBAL imci_vector_index_dump_rows_threshold (need SUPER): {e}")
+
+        start_time = time.time()
+
+        if post_load_index:
+            # Post-load mode: issue ALTER TABLE to add vector index after data load
+            vector_index_comment = index_param["vector_index_comment"]
+            alter_sql = (
+                f"ALTER TABLE {db_name}.{self.table_name} "
+                f'MODIFY COLUMN v VECTOR({self.dim}) NOT NULL COMMENT "{vector_index_comment}"'
+            )
+            log.info(f"PolarDB creating vector index via ALTER TABLE: {alter_sql}")
+            self.cursor.execute(alter_sql)
+            log.info("ALTER TABLE completed.")
+
+        analyze_sql = f"/* FORCE_IMCI_NODES */ ANALYZE TABLE {db_name}.{self.table_name}"
+        log.info(f"PolarDB analyzing table: {analyze_sql}")
+        analyze_start = time.time()
+        self.cursor.execute(analyze_sql)
+        analyze_elapsed = time.time() - analyze_start
+
+        log.info(f"ANALYZE TABLE completed in {analyze_elapsed:.1f}s, waiting for vector index to be built...")
+
+        last_vectors = 0
+        while True:
+            self.cursor.execute(
+                "SELECT VECTORS FROM information_schema.imci_vector_index_stats "
+                "WHERE SCHEMA_NAME=%s AND TABLE_NAME=%s",
+                (db_name, self.table_name),
+            )
+            result = self.cursor.fetchone()
+
+            if result is None:
+                log.info("Vector index stats not yet available, waiting...")
+                time.sleep(2)
+                continue
+
+            vectors = int(result[0])
+
+            if vectors != last_vectors:
+                elapsed = max(0.0, time.time() - start_time - analyze_elapsed)
+                log.info(
+                    f"Vector index building: {vectors} vectors indexed "
+                    f"(target: {data_size}), elapsed: {elapsed:.1f}s"
+                )
+                last_vectors = vectors
+
+            if data_size is not None and vectors >= data_size:
+                break
+
+            # Also check imci_index_stats for vector_rows as secondary indicator
+            self.cursor.execute(
+                "SELECT VECTOR_ROWS FROM information_schema.imci_index_stats WHERE SCHEMA_NAME=%s AND TABLE_NAME=%s",
+                (db_name, self.table_name),
+            )
+            idx_result = self.cursor.fetchone()
+            if idx_result and data_size is not None and int(idx_result[0]) >= data_size:
+                break
+
+            time.sleep(2)
+
+        total_time = max(0.0, time.time() - start_time - analyze_elapsed)
+        log.info(f"PolarDB vector index build completed in {total_time:.1f}s")
+
+    @staticmethod
+    def vector_to_hex(v: list[float]) -> bytes:
+        return np.array(v, "float32").tobytes()
+
+    def _insert_batch(self, embeddings: list[list[float]], metadata: list[int], offset: int, size: int) -> None:
+        """Insert a batch of embeddings using a dedicated connection."""
+        conn, cursor = self._create_connection()
+        try:
+            db_name = self.db_config["database"]
+            insert_sql = f"INSERT INTO {db_name}.{self.table_name} (id, v) VALUES (%s, _binary %s)"
+            batch_data = []
+            for i in range(offset, offset + size):
+                batch_data.append((int(metadata[i]), self.vector_to_hex(embeddings[i])))
+
+            cursor.executemany(insert_sql, batch_data)
+        finally:
+            cursor.close()
+            conn.close()
+
+    def insert_embeddings(
+        self,
+        embeddings: list[list[float]],
+        metadata: list[int],
+        **kwargs,
+    ) -> tuple[int, Exception]:
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        try:
+            workers = self.case_config.insert_workers
+            total = len(embeddings)
+            batch_size = max(1, total // workers)
+
+            with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as executor:
+                futures = []
+                for i in range(0, total, batch_size):
+                    offset = i
+                    size = min(batch_size, total - i)
+                    future = executor.submit(self._insert_batch, embeddings, metadata, offset, size)
+                    futures.append(future)
+
+                done, pending = concurrent.futures.wait(futures, return_when=concurrent.futures.FIRST_EXCEPTION)
+                for future in done:
+                    future.result()
+                for future in pending:
+                    future.cancel()
+
+            return len(metadata), None
+        except Exception as e:
+            log.warning(f"Failed to insert data into table ({self.table_name}), error: {e}")
+            return 0, e
+
+    def search_embedding(
+        self,
+        query: list[float],
+        k: int = 100,
+        filters: dict | None = None,
+        timeout: int | None = None,
+        **kwargs,
+    ) -> list[int]:
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        try:
+            if filters:
+                self.cursor.execute(
+                    self.select_sql_with_filter,
+                    (filters.get("id"), self.vector_to_hex(query), k),
+                )
+            else:
+                self.cursor.execute(self.select_sql, (self.vector_to_hex(query), k))
+            return [row[0] for row in self.cursor.fetchall()]
+        except Exception:
+            log.exception("Failed to execute search query")
+            raise
diff --git a/vectordb_bench/backend/clients/qdrant_cloud/config.py b/vectordb_bench/backend/clients/qdrant_cloud/config.py
index 45373754f..c4466dc17 100644
--- a/vectordb_bench/backend/clients/qdrant_cloud/config.py
+++ b/vectordb_bench/backend/clients/qdrant_cloud/config.py
@@ -1,6 +1,6 @@
-from typing import Any, TypeVar
+from typing import ClassVar, TypeVar
 
-from pydantic import BaseModel, SecretStr, model_validator
+from pydantic import BaseModel, SecretStr
 
 from ..api import DBCaseConfig, DBConfig, MetricType
 
@@ -10,6 +10,8 @@
 
 # Allowing `api_key` to be left empty, to ensure compatibility with the open-source Qdrant.
 class QdrantConfig(DBConfig):
+    _extra_empty_skip: ClassVar[frozenset[str]] = frozenset({"api_key"})
+
     url: SecretStr
     api_key: SecretStr | None = None
 
@@ -25,19 +27,6 @@ def to_dict(self) -> dict:
             "url": self.url.get_secret_value(),
         }
 
-    @model_validator(mode="before")
-    @classmethod
-    def not_empty_field(cls, data: Any) -> Any:
-        if isinstance(data, dict):
-            skip = set(cls.common_short_configs()) | set(cls.common_long_configs()) | {"api_key"}
-            for name, v in data.items():
-                if name in skip:
-                    continue
-                if isinstance(v, str) and len(v) == 0:
-                    msg = f"Empty string for field '{name}'!"
-                    raise ValueError(msg)
-        return data
-
 
 class QdrantIndexConfig(BaseModel, DBCaseConfig):
     metric_type: MetricType | None = None
diff --git a/vectordb_bench/backend/clients/qdrant_local/cli.py b/vectordb_bench/backend/clients/qdrant_local/cli.py
index 7995b99b3..ee3309dc2 100644
--- a/vectordb_bench/backend/clients/qdrant_local/cli.py
+++ b/vectordb_bench/backend/clients/qdrant_local/cli.py
@@ -49,7 +49,10 @@ def QdrantLocal(**parameters: Unpack[QdrantLocalTypedDict]):
 
     run(
         db=DBTYPE,
-        db_config=QdrantLocalConfig(url=SecretStr(parameters["url"])),
+        db_config=QdrantLocalConfig(
+            db_label=parameters["db_label"],
+            url=SecretStr(parameters["url"]),
+        ),
         db_case_config=QdrantLocalIndexConfig(
             on_disk=parameters["on_disk"],
             m=parameters["m"],
diff --git a/vectordb_bench/backend/clients/qdrant_local/qdrant_local.py b/vectordb_bench/backend/clients/qdrant_local/qdrant_local.py
index 15c790c61..e8e253ac2 100644
--- a/vectordb_bench/backend/clients/qdrant_local/qdrant_local.py
+++ b/vectordb_bench/backend/clients/qdrant_local/qdrant_local.py
@@ -19,6 +19,9 @@
     VectorParams,
 )
 
+from vectordb_bench.backend.filter import Filter as BenchFilter
+from vectordb_bench.backend.filter import FilterOp
+
 from ..api import VectorDB
 from .config import QdrantLocalIndexConfig
 
@@ -40,6 +43,11 @@ def qdrant_collection_exists(client: QdrantClient, collection_name: str) -> bool
 
 
 class QdrantLocal(VectorDB):
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+    ]
+
     def __init__(
         self,
         dim: int,
@@ -60,6 +68,7 @@ def __init__(
 
         self._primary_field = "pk"
         self._vector_field = "vector"
+        self._query_filter: Filter | None = None
 
         client = QdrantClient(**self.db_config)
 
@@ -209,25 +218,29 @@ def search_embedding(
         """
         assert self.client is not None
 
-        f = None
-        if filters:
-            f = Filter(
-                must=[
-                    FieldCondition(
-                        key=self._primary_field,
-                        range=Range(
-                            gt=filters.get("id"),
-                        ),
-                    ),
-                ],
-            )
         res = self.client.query_points(
             collection_name=self.collection_name,
             query=query,
             limit=k,
-            query_filter=f,
+            query_filter=self._query_filter,
             search_params=SearchParams(**self.search_parameter),
             timeout=timeout,
         ).points
 
         return [result.id for result in res]
+
+    def prepare_filter(self, filters: BenchFilter):
+        if filters.type == FilterOp.NonFilter:
+            self._query_filter = None
+        elif filters.type == FilterOp.NumGE:
+            self._query_filter = Filter(
+                must=[
+                    FieldCondition(
+                        key=self._primary_field,
+                        range=Range(gte=filters.int_value),
+                    )
+                ]
+            )
+        else:
+            msg = f"Unsupported QdrantLocal filter: {filters}"
+            raise ValueError(msg)
diff --git a/vectordb_bench/backend/clients/seekdb/cli.py b/vectordb_bench/backend/clients/seekdb/cli.py
new file mode 100644
index 000000000..9c135220f
--- /dev/null
+++ b/vectordb_bench/backend/clients/seekdb/cli.py
@@ -0,0 +1,60 @@
+import os
+from typing import Annotated, Unpack
+
+import click
+from pydantic import SecretStr
+
+from vectordb_bench.backend.clients import DB
+from vectordb_bench.cli.cli import (
+    CommonTypedDict,
+    HNSWFlavor3,
+    cli,
+    click_parameter_decorators_from_typed_dict,
+    run,
+)
+
+
+class SeekDBTypedDict(CommonTypedDict):
+    host: Annotated[str, click.option("--host", type=str, help="SeekDB host", required=True)]
+    user: Annotated[str, click.option("--user", type=str, help="SeekDB username", required=True)]
+    password: Annotated[
+        str,
+        click.option(
+            "--password",
+            type=str,
+            help="SeekDB password",
+            default=lambda: os.environ.get("SEEKDB_PASSWORD", ""),
+        ),
+    ]
+    database: Annotated[str, click.option("--database", type=str, help="Database name", required=True)]
+    port: Annotated[int, click.option("--port", type=int, help="SeekDB port", default=3306, show_default=True)]
+
+
+class SeekDBHNSWTypedDict(SeekDBTypedDict, HNSWFlavor3): ...
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(SeekDBHNSWTypedDict)
+def SeekDBHNSW(**parameters: Unpack[SeekDBHNSWTypedDict]):
+    """Run VectorDBBench against SeekDB with an HNSW index."""
+    from ..api import IndexType
+    from .config import SeekDBConfig, SeekDBHNSWConfig
+
+    run(
+        DB.SeekDB,
+        SeekDBConfig(
+            db_label=parameters["db_label"],
+            user=SecretStr(parameters["user"]),
+            password=SecretStr(parameters["password"]),
+            host=parameters["host"],
+            port=parameters["port"],
+            database=parameters["database"],
+        ),
+        SeekDBHNSWConfig(
+            m=parameters["m"],
+            ef_construction=parameters["ef_construction"],
+            ef_search=parameters["ef_search"],
+            index=IndexType.HNSW,
+        ),
+        **parameters,
+    )
diff --git a/vectordb_bench/backend/clients/seekdb/config.py b/vectordb_bench/backend/clients/seekdb/config.py
new file mode 100644
index 000000000..01a43ce4b
--- /dev/null
+++ b/vectordb_bench/backend/clients/seekdb/config.py
@@ -0,0 +1,77 @@
+from typing import TypedDict
+
+from pydantic import BaseModel, SecretStr
+
+from ..api import DBCaseConfig, DBConfig, IndexType, MetricType
+
+
+class SeekDBConfigDict(TypedDict):
+    user: str
+    host: str
+    port: int
+    password: str
+    database: str
+
+
+class SeekDBConfig(DBConfig):
+    user: SecretStr = SecretStr("root")
+    password: SecretStr
+    host: str
+    port: int = 3306
+    database: str
+
+    def to_dict(self) -> SeekDBConfigDict:
+        return {
+            "user": self.user.get_secret_value(),
+            "host": self.host,
+            "port": self.port,
+            "password": self.password.get_secret_value(),
+            "database": self.database,
+        }
+
+
+class SeekDBIndexConfig(BaseModel):
+    index: IndexType
+    metric_type: MetricType | None = None
+
+    def parse_metric(self) -> str:
+        if self.metric_type == MetricType.L2:
+            return "l2"
+        if self.metric_type == MetricType.IP:
+            return "inner_product"
+        return "cosine"
+
+    def parse_metric_func_str(self) -> str:
+        if self.metric_type == MetricType.L2:
+            return "l2_distance"
+        if self.metric_type == MetricType.IP:
+            return "negative_inner_product"
+        return "cosine_distance"
+
+
+class SeekDBHNSWConfig(SeekDBIndexConfig, DBCaseConfig):
+    m: int
+    ef_construction: int
+    ef_search: int
+    index: IndexType = IndexType.HNSW
+
+    def index_param(self) -> dict:
+        return {
+            "metric_type": self.parse_metric(),
+            "index_type": self.index.value,
+            "params": {
+                "m": self.m,
+                "ef_construction": self.ef_construction,
+            },
+        }
+
+    def search_param(self) -> dict:
+        return {
+            "metric_type": self.parse_metric_func_str(),
+            "params": {"ef_search": self.ef_search},
+        }
+
+
+_seekdb_case_config = {
+    IndexType.HNSW: SeekDBHNSWConfig,
+}
diff --git a/vectordb_bench/backend/clients/seekdb/seekdb.py b/vectordb_bench/backend/clients/seekdb/seekdb.py
new file mode 100644
index 000000000..f4551a577
--- /dev/null
+++ b/vectordb_bench/backend/clients/seekdb/seekdb.py
@@ -0,0 +1,282 @@
+import logging
+import re
+import struct
+from collections.abc import Generator
+from contextlib import contextmanager
+from typing import Any
+
+import mysql.connector as mysql
+
+from vectordb_bench.backend.filter import Filter, FilterOp
+
+from ..api import IndexType, VectorDB
+from .config import SeekDBConfigDict, SeekDBHNSWConfig
+
+log = logging.getLogger(__name__)
+
+SEEKDB_DEFAULT_LOAD_BATCH_SIZE = 256
+
+# Minimum SeekDB version for dbms_index_manager.refresh() after bulk load (see VERSION()).
+_SEEKDB_REFRESH_MIN_VERSION = (1, 3, 0)
+_SEEKDB_VERSION_IN_VERSION_STRING = re.compile(r"seekdb-v(\d+(?:\.\d+)*)", re.IGNORECASE)
+
+
+def _seekdb_version_tuple(version_row: str | None) -> tuple[int, ...] | None:
+    if not version_row:
+        return None
+    m = _SEEKDB_VERSION_IN_VERSION_STRING.search(version_row.strip())
+    if not m:
+        return None
+    return tuple(int(p) for p in m.group(1).split(".") if p.isdigit())
+
+
+def _version_tuple_ge(parsed: tuple[int, ...], minimum: tuple[int, ...]) -> bool:
+    n = max(len(parsed), len(minimum))
+    for i in range(n):
+        p = parsed[i] if i < len(parsed) else 0
+        m = minimum[i] if i < len(minimum) else 0
+        if p != m:
+            return p > m
+    return True
+
+
+class SeekDB(VectorDB):
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+    ]
+    # mysql.connector is not thread-safe; ConcurrentInsertRunner uses max_workers=1 when False.
+    # Streaming fixed-rate inserts use rate_runner with per-thread deepcopy+init() instead.
+    thread_safe: bool = False
+
+    def __init__(
+        self,
+        dim: int,
+        db_config: SeekDBConfigDict,
+        db_case_config: SeekDBHNSWConfig,
+        collection_name: str = "items",
+        drop_old: bool = False,
+        **kwargs,
+    ):
+        self.name = "SeekDB"
+        self.dim = dim
+        self.db_config = db_config
+        self.db_case_config = db_case_config
+        self.table_name = collection_name
+        self.load_batch_size = SEEKDB_DEFAULT_LOAD_BATCH_SIZE
+        self._index_name = "vidx"
+        self._primary_field = "id"
+        self._vector_field = "embedding"
+        self.expr = ""
+
+        log.info(
+            f"{self.name} initialized with config:\nDatabase: {self.db_config}\nCase Config: {self.db_case_config}"
+        )
+
+        self._conn = None
+        self._cursor = None
+
+        try:
+            self._connect()
+            self._apply_system_settings()
+            if drop_old:
+                self._drop_table()
+                self._create_table()
+                self._create_index()
+        finally:
+            self._disconnect()
+
+    def _connect(self):
+        try:
+            self._conn = mysql.connect(
+                host=self.db_config["host"],
+                user=self.db_config["user"],
+                port=self.db_config["port"],
+                password=self.db_config["password"],
+                database=self.db_config["database"],
+            )
+            self._cursor = self._conn.cursor()
+        except mysql.Error:
+            log.exception("Failed to connect to SeekDB")
+            raise
+
+    def _disconnect(self):
+        if self._cursor:
+            self._cursor.close()
+            self._cursor = None
+        if self._conn:
+            self._conn.close()
+            self._conn = None
+
+    def _apply_system_settings(self):
+        if not self._cursor:
+            raise ValueError("Cursor is not initialized")
+
+        self._cursor.execute('ALTER SYSTEM SET memory_limit = "0M"')
+        self._cursor.execute("ALTER SYSTEM SET cpu_count = 0")
+
+    def _init_session_settings(self):
+        if not self._cursor:
+            raise ValueError("Cursor is not initialized")
+
+        self._cursor.execute("SET autocommit=1")
+        if self.db_case_config.index == IndexType.HNSW:
+            ef_search = self.db_case_config.search_param()["params"]["ef_search"]
+            # SeekDB uses OceanBase-style session vars (not plain hnsw_ef_search).
+            self._cursor.execute(f"SET ob_hnsw_ef_search={ef_search}")
+
+    @contextmanager
+    def init(self) -> Generator[None, None, None]:
+        try:
+            self._connect()
+            self._init_session_settings()
+            yield
+        finally:
+            self._disconnect()
+
+    def _drop_table(self):
+        if not self._cursor:
+            raise ValueError("Cursor is not initialized")
+        log.info(f"Dropping table {self.table_name}")
+        self._cursor.execute(f"DROP TABLE IF EXISTS {self.table_name}")
+
+    def _create_table(self):
+        """Create a heap table with a vector column.
+
+        ORGANIZATION HEAP specifies a heap-organized table (no clustered primary
+        key order), which is required by SeekDB for vector workloads.
+        """
+        if not self._cursor:
+            raise ValueError("Cursor is not initialized")
+
+        log.info(f"Creating heap table {self.table_name}")
+        create_table_query = f"""
+        CREATE TABLE {self.table_name} (
+            id INT,
+            embedding VECTOR({self.dim})
+        ) ORGANIZATION HEAP;
+        """
+        self._cursor.execute(create_table_query)
+
+    def _create_index(self):
+        """Create the HNSW vector index immediately after table creation.
+
+        Following Milvus's approach: the index is built upfront so that
+        streaming inserts are indexed incrementally and searches can run
+        concurrently with writes (StreamingPerformanceCase).
+        """
+        if not self._cursor:
+            raise ValueError("Cursor is not initialized")
+
+        index_params = self.db_case_config.index_param()
+        params = index_params["params"]
+        index_args = ", ".join(f"{k}={v}" for k, v in params.items())
+
+        index_query = (
+            f"CREATE VECTOR INDEX {self._index_name} "
+            f"ON {self.table_name}({self._vector_field}) "
+            f"WITH (distance={index_params['metric_type']}, "
+            f"type={index_params['index_type']}, {index_args})"
+        )
+
+        log.info("Creating HNSW index: %s", index_query)
+        try:
+            self._cursor.execute(index_query)
+            log.info("HNSW index created successfully")
+        except mysql.Error:
+            log.exception("Failed to create HNSW index")
+            raise
+
+    def optimize(self, data_size: int | None = None):
+        """Post-load hook: refresh index metadata on SeekDB >= 1.3.0 when available.
+
+        Older releases rely on incremental HNSW indexing only. From 1.3.0 onward,
+        ``CALL dbms_index_manager.refresh()`` aligns on-disk index state after bulk
+        load (VERSION() strings look like ``... seekdb-v1.3.0.0``).
+        """
+        if not self._cursor:
+            raise ValueError("Cursor is not initialized")
+
+        self._cursor.execute("SELECT VERSION()")
+        row = self._cursor.fetchone()
+        version_str = row[0] if row else None
+        parsed = _seekdb_version_tuple(version_str)
+
+        if parsed is None or not _version_tuple_ge(parsed, _SEEKDB_REFRESH_MIN_VERSION):
+            log.info(
+                "%s optimize: skip dbms_index_manager.refresh (version=%r parsed=%s; need >= %s)",
+                self.name,
+                version_str,
+                parsed,
+                ".".join(map(str, _SEEKDB_REFRESH_MIN_VERSION)),
+            )
+            return
+
+        log.info(
+            "%s optimize: SeekDB %s >= %s, calling dbms_index_manager.refresh()",
+            self.name,
+            ".".join(map(str, parsed)),
+            ".".join(map(str, _SEEKDB_REFRESH_MIN_VERSION)),
+        )
+        try:
+            self._cursor.execute("CALL dbms_index_manager.refresh()")
+        except mysql.Error:
+            log.exception("dbms_index_manager.refresh() failed")
+            raise
+
+    def insert_embeddings(
+        self,
+        embeddings: list[list[float]],
+        metadata: list[int],
+        **kwargs: Any,
+    ) -> tuple[int, Exception | None]:
+        if not self._cursor:
+            raise ValueError("Cursor is not initialized")
+
+        insert_count = 0
+        try:
+            for batch_start in range(0, len(embeddings), self.load_batch_size):
+                batch_end = min(batch_start + self.load_batch_size, len(embeddings))
+                batch = [(metadata[i], embeddings[i]) for i in range(batch_start, batch_end)]
+                values = ", ".join(f"({item_id}, '[{','.join(map(str, embedding))}]')" for item_id, embedding in batch)
+                self._cursor.execute(f"INSERT INTO {self.table_name} VALUES {values}")
+                insert_count += len(batch)
+        except mysql.Error:
+            log.exception("Failed to insert embeddings")
+            raise
+
+        return insert_count, None
+
+    def prepare_filter(self, filters: Filter):
+        if filters.type == FilterOp.NonFilter:
+            self.expr = ""
+        elif filters.type == FilterOp.NumGE:
+            self.expr = f"WHERE id >= {filters.int_value}"
+        else:
+            msg = f"Unsupported filter for SeekDB: {filters}"
+            raise ValueError(msg)
+
+    def search_embedding(
+        self,
+        query: list[float],
+        k: int = 100,
+    ) -> list[int]:
+        if not self._cursor:
+            raise ValueError("Cursor is not initialized")
+
+        packed = struct.pack(f"<{len(query)}f", *query)
+        hex_vec = packed.hex()
+
+        query_str = (
+            f"SELECT id FROM {self.table_name} "
+            f"{self.expr} ORDER BY "
+            f"{self.db_case_config.parse_metric_func_str()}({self._vector_field}, X'{hex_vec}') "
+            f"APPROXIMATE LIMIT {k}"
+        )
+
+        try:
+            self._cursor.execute(query_str)
+            return [row[0] for row in self._cursor.fetchall()]
+        except mysql.Error:
+            log.exception("Failed to execute search query")
+            raise
diff --git a/vectordb_bench/backend/clients/tidb/config.py b/vectordb_bench/backend/clients/tidb/config.py
index 72abb9111..5e2f032d4 100644
--- a/vectordb_bench/backend/clients/tidb/config.py
+++ b/vectordb_bench/backend/clients/tidb/config.py
@@ -1,6 +1,6 @@
-from typing import Any, TypedDict
+from typing import ClassVar, TypedDict
 
-from pydantic import BaseModel, SecretStr, model_validator
+from pydantic import BaseModel, SecretStr
 
 from ..api import DBCaseConfig, DBConfig, MetricType
 
@@ -16,6 +16,8 @@ class TiDBConfigDict(TypedDict):
 
 
 class TiDBConfig(DBConfig):
+    _extra_empty_skip: ClassVar[frozenset[str]] = frozenset({"password"})
+
     user_name: str = "root"
     password: SecretStr
     host: str = "127.0.0.1"
@@ -35,19 +37,6 @@ def to_dict(self) -> TiDBConfigDict:
             "ssl_verify_identity": self.ssl,
         }
 
-    @model_validator(mode="before")
-    @classmethod
-    def not_empty_field(cls, data: Any) -> Any:
-        if isinstance(data, dict):
-            skip = {"password", "db_label"}
-            for name, v in data.items():
-                if name in skip:
-                    continue
-                if isinstance(v, str) and len(v) == 0:
-                    msg = f"Empty string for field '{name}'!"
-                    raise ValueError(msg)
-        return data
-
 
 class TiDBIndexConfig(BaseModel, DBCaseConfig):
     metric_type: MetricType | None = None
diff --git a/vectordb_bench/backend/clients/tidb/tidb.py b/vectordb_bench/backend/clients/tidb/tidb.py
index a5c99bbe4..fba60d41c 100644
--- a/vectordb_bench/backend/clients/tidb/tidb.py
+++ b/vectordb_bench/backend/clients/tidb/tidb.py
@@ -119,7 +119,7 @@ def _optimize_check_tiflash_replica_progress(self):
                 cursor.execute(f"""
                     SELECT PROGRESS FROM information_schema.tiflash_replica
                     WHERE TABLE_SCHEMA = "{database}" AND TABLE_NAME = "{self.table_name}"
-                    """)  # noqa: S608
+                    """)
                 result = cursor.fetchone()
                 return result[0]
         except Exception as e:
@@ -131,7 +131,7 @@ def _optimize_wait_tiflash_catch_up(self):
             with self._get_connection() as (conn, cursor):
                 cursor.execute('SET @@TIDB_ISOLATION_READ_ENGINES="tidb,tiflash"')
                 conn.commit()
-                cursor.execute(f"SELECT COUNT(*) FROM {self.table_name}")  # noqa: S608
+                cursor.execute(f"SELECT COUNT(*) FROM {self.table_name}")
                 result = cursor.fetchone()
                 return result[0]
         except Exception as e:
@@ -155,7 +155,7 @@ def _optimize_get_tiflash_index_pending_rows(self):
                     SELECT SUM(ROWS_STABLE_NOT_INDEXED)
                     FROM information_schema.tiflash_indexes
                     WHERE TIDB_DATABASE = "{database}" AND TIDB_TABLE = "{self.table_name}"
-                    """)  # noqa: S608
+                    """)
                 result = cursor.fetchone()
                 return result[0]
         except Exception as e:
@@ -172,7 +172,7 @@ def _insert_embeddings_serial(
         try:
             with self._get_connection() as (conn, cursor):
                 buf = io.StringIO()
-                buf.write(f"INSERT INTO {self.table_name} (id, embedding) VALUES ")  # noqa: S608
+                buf.write(f"INSERT INTO {self.table_name} (id, embedding) VALUES ")
                 for i in range(offset, offset + size):
                     if i > offset:
                         buf.write(",")
@@ -220,6 +220,6 @@ def search_embedding(
         self.cursor.execute(f"""
             SELECT id FROM {self.table_name}
             ORDER BY {self.search_fn}(embedding, "{query!s}") LIMIT {k};
-            """)  # noqa: S608
+            """)
         result = self.cursor.fetchall()
         return [int(i[0]) for i in result]
diff --git a/vectordb_bench/backend/clients/turbopuffer/cli.py b/vectordb_bench/backend/clients/turbopuffer/cli.py
index 6fd91f2a8..def442d69 100644
--- a/vectordb_bench/backend/clients/turbopuffer/cli.py
+++ b/vectordb_bench/backend/clients/turbopuffer/cli.py
@@ -10,33 +10,134 @@
     run,
 )
 from .. import DB
+from ..api import MetricType
+from .config import TurboPufferMultitenantWarmupPolicy
+
+DEFAULT_PIN_TIMEOUT = 45 * 60
+
+ApiKeyOption = Annotated[
+    str,
+    click.option("--api-key", type=str, help="TurboPuffer API key", required=True),
+]
+RegionOption = Annotated[
+    str,
+    click.option(
+        "--region",
+        type=str,
+        help="TurboPuffer region (e.g. aws-us-east-1, gcp-us-central1)",
+        required=True,
+    ),
+]
+ApiBaseUrlOption = Annotated[
+    str,
+    click.option(
+        "--api-base-url",
+        type=str,
+        help="Override the region-based API URL",
+        required=False,
+        default="",
+        show_default=False,
+    ),
+]
+NamespaceOption = Annotated[
+    str,
+    click.option(
+        "--namespace",
+        type=str,
+        help="TurboPuffer namespace",
+        required=False,
+        default="vdbbench_test",
+        show_default=True,
+    ),
+]
+PinTimeoutOption = Annotated[
+    int,
+    click.option(
+        "--pin-timeout",
+        type=click.IntRange(min=1),
+        default=DEFAULT_PIN_TIMEOUT,
+        show_default=True,
+        help="Seconds to wait for TurboPuffer namespace pinning or unpinning to complete",
+    ),
+]
 
 
 class TurboPufferTypedDict(TypedDict):
-    api_key: Annotated[
+    api_key: ApiKeyOption
+    region: RegionOption
+    api_base_url: ApiBaseUrlOption
+    namespace: NamespaceOption
+    multitenant_namespace_prefix: Annotated[
         str,
-        click.option("--api-key", type=str, help="TurboPuffer API key", required=True),
+        click.option(
+            "--multitenant-namespace-prefix",
+            type=str,
+            help="Namespace prefix for CloudMultiTenantSearchCase tenant namespaces",
+            required=False,
+            default="vdbbench_mt_",
+            show_default=True,
+        ),
     ]
-    api_base_url: Annotated[
+    scalar_payload_label_field: Annotated[
         str,
         click.option(
-            "--api-base-url",
+            "--scalar-payload-label-field",
             type=str,
-            help="TurboPuffer API base URL",
+            help="TurboPuffer attribute used for scalar_label payload and label filtering",
             required=False,
-            default="https://api.turbopuffer.com",
+            default="label",
             show_default=True,
         ),
     ]
-    namespace: Annotated[
+    metric_type: Annotated[
         str,
         click.option(
-            "--namespace",
-            type=str,
-            help="TurboPuffer namespace",
+            "--metric-type",
+            type=click.Choice([MetricType.COSINE.value, MetricType.L2.value]),
+            help="TurboPuffer distance metric type",
             required=False,
-            default="vdbbench_test",
+            default=MetricType.COSINE.value,
+            show_default=True,
+        ),
+    ]
+    disable_backpressure: Annotated[
+        bool,
+        click.option(
+            "--disable-backpressure/--enable-backpressure",
+            type=bool,
+            default=False,
+            show_default=True,
+            help="Disable Turbopuffer write backpressure",
+        ),
+    ]
+    pin_namespace: Annotated[
+        bool,
+        click.option(
+            "--pin-namespace/--no-pin-namespace",
+            default=False,
+            show_default=True,
+            help="Pin TurboPuffer namespace(s) before benchmark workers run",
+        ),
+    ]
+    pin_replicas: Annotated[
+        int,
+        click.option(
+            "--pin-replicas",
+            type=click.IntRange(min=1),
+            default=1,
+            show_default=True,
+            help="Number of TurboPuffer pinning replicas to request",
+        ),
+    ]
+    pin_timeout: PinTimeoutOption
+    multitenant_warmup_policy: Annotated[
+        str,
+        click.option(
+            "--multitenant-warmup-policy",
+            type=click.Choice([policy.value for policy in TurboPufferMultitenantWarmupPolicy]),
+            default=TurboPufferMultitenantWarmupPolicy.NONE.value,
             show_default=True,
+            help="TurboPuffer cache warmup policy for CloudMultiTenantSearchCase tenant namespaces",
         ),
     ]
 
@@ -44,19 +145,111 @@ class TurboPufferTypedDict(TypedDict):
 class TurboPufferIndexTypedDict(CommonTypedDict, TurboPufferTypedDict): ...
 
 
+class TurboPufferUnpinTypedDict(TypedDict):
+    """Options for explicit TurboPuffer namespace pinning cleanup.
+
+    Namespace pinning is persistent service state: enabling it before a benchmark
+    reserves replicas for the namespace until it is cleared.  Unpinning is kept
+    as a separate command so failed or interrupted benchmark runs can be cleaned
+    up later, and so a billing-affecting teardown action is not hidden behind
+    ordinary benchmark flags.
+    """
+
+    api_key: ApiKeyOption
+    region: RegionOption
+    api_base_url: ApiBaseUrlOption
+    namespace: NamespaceOption
+    pin_timeout: PinTimeoutOption
+
+
+def target_namespaces_for_pinning(parameters: TurboPufferIndexTypedDict) -> list[str]:
+    if parameters.get("case_type") != "CloudMultiTenantSearchCase":
+        return [parameters["namespace"]]
+
+    namespace_prefix = parameters["multitenant_namespace_prefix"]
+    tenant_prefix = parameters["tenant_prefix"]
+    tenant_id_width = parameters["tenant_id_width"]
+    return [
+        f"{namespace_prefix}{tenant_prefix}{tenant_id:0{tenant_id_width}d}"
+        for tenant_id in range(parameters["tenant_count"])
+    ]
+
+
+def pin_namespaces_once(parameters: TurboPufferIndexTypedDict) -> None:
+    from .turbopuffer import namespace_metadata_request, wait_for_namespace_pinning
+
+    for namespace in target_namespaces_for_pinning(parameters):
+        namespace_metadata_request(
+            parameters["api_key"],
+            parameters["region"],
+            namespace,
+            "PATCH",
+            {"pinning": {"replicas": parameters["pin_replicas"]}},
+            parameters["api_base_url"] or None,
+        )
+        wait_for_namespace_pinning(
+            parameters["api_key"],
+            parameters["region"],
+            namespace,
+            parameters["pin_replicas"],
+            parameters["api_base_url"] or None,
+            parameters["pin_timeout"],
+        )
+
+
 @cli.command()
 @click_parameter_decorators_from_typed_dict(TurboPufferIndexTypedDict)
 def TurboPuffer(**parameters: Unpack[TurboPufferIndexTypedDict]):
     from .config import TurboPufferConfig, TurboPufferIndexConfig
 
+    pin_target_namespace_count = len(target_namespaces_for_pinning(parameters)) if parameters["pin_namespace"] else 0
+    if parameters["pin_namespace"] and not parameters["dry_run"]:
+        pin_namespaces_once(parameters)
+
     run(
         db=DB.TurboPuffer,
         db_config=TurboPufferConfig(
             db_label=parameters["db_label"],
             api_key=SecretStr(parameters["api_key"]),
-            api_base_url=parameters["api_base_url"],
+            region=parameters["region"],
+            api_base_url=parameters["api_base_url"] or None,
             namespace=parameters["namespace"],
+            multitenant_namespace_prefix=parameters["multitenant_namespace_prefix"],
+            scalar_payload_label_field=parameters["scalar_payload_label_field"],
+            pin_namespace=False,
+            pin_namespace_requested=parameters["pin_namespace"],
+            pin_replicas=parameters["pin_replicas"],
+            pin_timeout=parameters["pin_timeout"],
+            pin_target_namespace_count=pin_target_namespace_count,
+        ),
+        db_case_config=TurboPufferIndexConfig(
+            metric_type=MetricType(parameters["metric_type"]),
+            disable_backpressure=parameters["disable_backpressure"],
+            multitenant_warmup_policy=TurboPufferMultitenantWarmupPolicy(parameters["multitenant_warmup_policy"]),
         ),
-        db_case_config=TurboPufferIndexConfig(),
         **parameters,
     )
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(TurboPufferUnpinTypedDict)
+def TurboPufferUnpin(**parameters: Unpack[TurboPufferUnpinTypedDict]):
+    from .turbopuffer import namespace_metadata_request, wait_for_namespace_pinning
+
+    namespace_metadata_request(
+        parameters["api_key"],
+        parameters["region"],
+        parameters["namespace"],
+        "PATCH",
+        {"pinning": None},
+        parameters["api_base_url"] or None,
+    )
+    meta = wait_for_namespace_pinning(
+        parameters["api_key"],
+        parameters["region"],
+        parameters["namespace"],
+        None,
+        parameters["api_base_url"] or None,
+        parameters["pin_timeout"],
+    )
+    click.echo(f"TurboPuffer namespace unpinned: {parameters['namespace']} pinning={meta.get('pinning')}")
diff --git a/vectordb_bench/backend/clients/turbopuffer/config.py b/vectordb_bench/backend/clients/turbopuffer/config.py
index c552299e3..cdb7e8328 100644
--- a/vectordb_bench/backend/clients/turbopuffer/config.py
+++ b/vectordb_bench/backend/clients/turbopuffer/config.py
@@ -1,18 +1,41 @@
+from enum import StrEnum
+
 from pydantic import BaseModel, SecretStr
 
 from ..api import DBCaseConfig, DBConfig, MetricType
 
 
+class TurboPufferMultitenantWarmupPolicy(StrEnum):
+    NONE = "none"
+    ALL = "all"
+
+
 class TurboPufferConfig(DBConfig):
     api_key: SecretStr
-    api_base_url: str
+    region: str
+    api_base_url: str | None = None
     namespace: str = "vdbbench_test"
+    multitenant_namespace_prefix: str = "vdbbench_mt_"
+    scalar_payload_label_field: str = "label"
+    pin_namespace: bool = False
+    pin_namespace_requested: bool = False
+    pin_replicas: int = 1
+    pin_timeout: int = 45 * 60
+    pin_target_namespace_count: int = 0
 
     def to_dict(self) -> dict:
         return {
             "api_key": self.api_key.get_secret_value(),
+            "region": self.region,
             "api_base_url": self.api_base_url,
             "namespace": self.namespace,
+            "multitenant_namespace_prefix": self.multitenant_namespace_prefix,
+            "scalar_payload_label_field": self.scalar_payload_label_field,
+            "pin_namespace": self.pin_namespace,
+            "pin_namespace_requested": self.pin_namespace_requested,
+            "pin_replicas": self.pin_replicas,
+            "pin_timeout": self.pin_timeout,
+            "pin_target_namespace_count": self.pin_target_namespace_count,
         }
 
 
@@ -20,6 +43,8 @@ class TurboPufferIndexConfig(BaseModel, DBCaseConfig):
     metric_type: MetricType | None = None
     use_multi_ns_for_filter: bool = False
     time_wait_warmup: int = 60 * 1  # 1min
+    disable_backpressure: bool = False
+    multitenant_warmup_policy: TurboPufferMultitenantWarmupPolicy = TurboPufferMultitenantWarmupPolicy.NONE
 
     def parse_metric(self) -> str:
         if self.metric_type == MetricType.COSINE:
@@ -35,3 +60,15 @@ def index_param(self) -> dict:
 
     def search_param(self) -> dict:
         return {}
+
+
+class TurboPufferFtsConfig(BaseModel, DBCaseConfig):
+    metric_type: MetricType = MetricType.BM25
+    time_wait_warmup: int = 60
+    disable_backpressure: bool = False
+
+    def index_param(self) -> dict:
+        return {}
+
+    def search_param(self) -> dict:
+        return {}
diff --git a/vectordb_bench/backend/clients/turbopuffer/turbopuffer.py b/vectordb_bench/backend/clients/turbopuffer/turbopuffer.py
index 241551792..918112ede 100644
--- a/vectordb_bench/backend/clients/turbopuffer/turbopuffer.py
+++ b/vectordb_bench/backend/clients/turbopuffer/turbopuffer.py
@@ -1,17 +1,85 @@
-"""Wrapper around the Pinecone vector database over VectorDB"""
+"""Wrapper around the TurboPuffer vector database over VectorDB"""
 
 import logging
+import os
 import time
 from contextlib import contextmanager
+from json import dumps, loads
+from typing import Any
+from urllib.error import HTTPError
+from urllib.parse import quote
+from urllib.request import Request, urlopen
 
 import turbopuffer as tpuf
 
-from vectordb_bench.backend.clients.turbopuffer.config import TurboPufferIndexConfig
+from vectordb_bench.backend.clients.turbopuffer.config import (
+    TurboPufferFtsConfig,
+    TurboPufferIndexConfig,
+    TurboPufferMultitenantWarmupPolicy,
+)
 from vectordb_bench.backend.filter import Filter, FilterOp
+from vectordb_bench.backend.payload import PayloadProfile
 
-from ..api import VectorDB
+from ..api import PartialInsertError, VectorDB
 
 log = logging.getLogger(__name__)
+TURBOPUFFER_SEARCHABLE_UNINDEXED_BYTES = 2 * 1024 * 1024 * 1024
+PINNING_POLL_INTERVAL = 10
+PINNING_TIMEOUT = 45 * 60
+
+
+def namespace_metadata_request(
+    api_key: str,
+    region: str,
+    namespace: str,
+    method: str,
+    payload: dict[str, Any] | None = None,
+    api_base_url: str | None = None,
+) -> dict:
+    base_url = api_base_url or f"https://{region}.turbopuffer.com"
+    url = f"{base_url.rstrip('/')}/v1/namespaces/{quote(namespace, safe='')}/metadata"
+    req = Request(  # noqa: S310
+        url,
+        data=dumps(payload).encode() if payload is not None else None,
+        method=method,
+        headers={
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+        },
+    )
+    try:
+        with urlopen(req, timeout=60) as resp:  # noqa: S310
+            return loads(resp.read().decode() or "{}")
+    except HTTPError as e:
+        detail = e.read().decode(errors="replace")
+        msg = f"Failed to update TurboPuffer namespace metadata: {e.code} {detail}"
+        raise RuntimeError(msg) from e
+
+
+def wait_for_namespace_pinning(
+    api_key: str,
+    region: str,
+    namespace: str,
+    replicas: int | None,
+    api_base_url: str | None = None,
+    timeout: int = PINNING_TIMEOUT,
+) -> dict:
+    deadline = time.monotonic() + timeout
+    while True:
+        meta = namespace_metadata_request(api_key, region, namespace, "GET", api_base_url=api_base_url)
+        pinning = meta.get("pinning")
+        if replicas is None:
+            if pinning is None:
+                return meta
+        else:
+            status = pinning.get("status", {}) if isinstance(pinning, dict) else {}
+            if pinning and pinning.get("replicas") == replicas and status.get("ready_replicas") == replicas:
+                return meta
+        if time.monotonic() >= deadline:
+            msg = f"Timed out waiting for TurboPuffer pinning state on namespace {namespace}"
+            raise TimeoutError(msg)
+        log.info("Waiting for TurboPuffer pinning state on %s: %s", namespace, pinning)
+        time.sleep(PINNING_POLL_INTERVAL)
 
 
 class TurboPuffer(VectorDB):
@@ -25,98 +93,398 @@ def __init__(
         self,
         dim: int,
         db_config: dict,
-        db_case_config: TurboPufferIndexConfig,
+        db_case_config: TurboPufferIndexConfig | TurboPufferFtsConfig,
         drop_old: bool = False,
         with_scalar_labels: bool = False,
         **kwargs,
     ):
-        """Initialize wrapper around the milvus vector database."""
         self.api_key = db_config.get("api_key", "")
-        self.api_base_url = db_config.get("api_base_url", "")
+        self.region = db_config.get("region", "")
+        self.api_base_url = db_config.get("api_base_url")
         self.namespace = db_config.get("namespace", "")
+        self.multitenant_namespace_prefix = db_config.get("multitenant_namespace_prefix", "vdbbench_mt_")
+        self.multitenant_tenant_labels: list[str] = kwargs.get("multitenant_tenant_labels", [])
+        self._multitenant_touched_tenants: set[str] = set()
+        self._ns_cache = {}
+        self.pin_namespace = db_config.get("pin_namespace", False)
+        self.pin_replicas = db_config.get("pin_replicas", 1)
+        self.pin_timeout = db_config.get("pin_timeout", PINNING_TIMEOUT)
+        self._pinning_applied = False
         self.db_case_config = db_case_config
-        self.metric = db_case_config.parse_metric()
+        self._is_fts = isinstance(db_case_config, TurboPufferFtsConfig)
+        self.metric = None if self._is_fts else db_case_config.parse_metric()
 
         self._vector_field = "vector"
         self._scalar_id_field = "id"
         self._scalar_label_field = "label"
+        self._scalar_payload_label_field = db_config.get("scalar_payload_label_field", self._scalar_label_field)
+        self._text_field = "text"
+        self._filter_id_field = "filter_id"
 
         self.with_scalar_labels = with_scalar_labels
-
-        # Initialize client with new SDK pattern
-        self.client = tpuf.Turbopuffer(api_key=self.api_key, base_url=self.api_base_url)
+        self.expr = None
 
         if drop_old:
-            log.info(f"Drop old. delete the namespace: {self.namespace}")
-            ns = self.client.namespace(self.namespace)
-            try:
-                ns.delete_all()
-            except Exception as e:
-                log.warning(f"Failed to delete all. Error: {e}")
+            tmp_client = self._create_client()
+            if self.multitenant_tenant_labels:
+                for tenant in self.multitenant_tenant_labels:
+                    try:
+                        tmp_client.namespace(self._namespace_name_for_tenant(tenant)).delete_all()
+                    except Exception as e:
+                        log.warning(f"Failed to delete multitenant namespace {tenant}. Error: {e}")
+            else:
+                log.info(f"Drop old. delete the namespace: {self.namespace}")
+                ns = tmp_client.namespace(self.namespace)
+                try:
+                    ns.delete_all()
+                except Exception as e:
+                    log.warning(f"Failed to delete all. Error: {e}")
+            tmp_client = None
+
+    def _create_client(self) -> tpuf.Turbopuffer:
+        client_kwargs = {"api_key": self.api_key, "region": self.region}
+        max_retries = os.getenv("TURBOPUFFER_MAX_RETRIES")
+        if max_retries is not None:
+            client_kwargs["max_retries"] = int(max_retries)
+        if self.api_base_url:
+            client_kwargs["base_url"] = self.api_base_url
+        return tpuf.Turbopuffer(**client_kwargs)
+
+    def _apply_namespace_pinning(self):
+        if not self.pin_namespace or self._pinning_applied:
+            return
+        for namespace in self._target_namespaces_for_pinning():
+            namespace_metadata_request(
+                self.api_key,
+                self.region,
+                namespace,
+                "PATCH",
+                {"pinning": {"replicas": self.pin_replicas}},
+                self.api_base_url,
+            )
+            meta = wait_for_namespace_pinning(
+                self.api_key,
+                self.region,
+                namespace,
+                self.pin_replicas,
+                self.api_base_url,
+                self.pin_timeout,
+            )
+            pinning = meta.get("pinning", {})
+            status = pinning.get("status", {}) if isinstance(pinning, dict) else {}
+            log.info(
+                "TurboPuffer pinning requested for %s: replicas=%s ready_replicas=%s",
+                namespace,
+                pinning.get("replicas", self.pin_replicas) if isinstance(pinning, dict) else self.pin_replicas,
+                status.get("ready_replicas"),
+            )
+        self._pinning_applied = True
+
+    def _target_namespaces_for_pinning(self) -> list[str]:
+        if self.multitenant_tenant_labels:
+            return [self._namespace_name_for_tenant(tenant) for tenant in self.multitenant_tenant_labels]
+        return [self.namespace]
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state.pop("client", None)
+        state.pop("ns", None)
+        state.pop("_ns_cache", None)
+        return state
+
+    def __setstate__(self, state: dict):
+        self.__dict__.update(state)
+        self.client = self._create_client()
+        self._ns_cache = {}
+        self.ns = None
+
+    @classmethod
+    def supports_full_text_search(cls) -> bool:
+        return True
+
+    def has_text_field(self) -> bool:
+        return bool(getattr(self, "_is_fts", False) and getattr(self, "_text_field", None))
 
     @contextmanager
     def init(self):
+        self.client = self._create_client()
+        self._ns_cache = {}
         self.ns = self.client.namespace(self.namespace)
+        self._apply_namespace_pinning()
         yield
 
+    def supports_multitenant(self) -> bool:
+        return True
+
+    def set_multitenant_context(self, tenant_labels: list[str]) -> None:
+        self.multitenant_tenant_labels = tenant_labels
+
+    def _namespace_name_for_tenant(self, tenant: str | None) -> str:
+        if tenant is None:
+            return self.namespace
+        return f"{self.multitenant_namespace_prefix}{tenant}"
+
+    def _namespace_for_tenant(self, tenant: str | None):
+        name = self._namespace_name_for_tenant(tenant)
+        ns = self._ns_cache.get(name)
+        if ns is None:
+            ns = self.client.namespace(name)
+            self._ns_cache[name] = ns
+        return ns
+
     def optimize(self, data_size: int | None = None):
         # turbopuffer responds to the request
         #   once the cache warming operation has been started.
         # It does not wait for the operation to complete,
         #   which can take multiple minutes for large namespaces.
-        self.ns.hint_cache_warm()
+        warmed_namespaces = self._warmup_target_namespaces()
+        for namespace in warmed_namespaces:
+            self._namespace_for_tenant(namespace).hint_cache_warm()
+        if not warmed_namespaces:
+            log.info("TurboPuffer cache warmup skipped")
+            return
         log.info(f"warming up but no api waiting for complete. just sleep {self.db_case_config.time_wait_warmup}s")
         time.sleep(self.db_case_config.time_wait_warmup)
 
+    def _warmup_target_namespaces(self) -> list[str | None]:
+        if not self.multitenant_tenant_labels:
+            return [None]
+        policy = getattr(
+            self.db_case_config,
+            "multitenant_warmup_policy",
+            TurboPufferMultitenantWarmupPolicy.NONE,
+        )
+        if policy == TurboPufferMultitenantWarmupPolicy.ALL:
+            return self.multitenant_tenant_labels
+        return []
+
     def insert_embeddings(
         self,
         embeddings: list[list[float]],
         metadata: list[int],
         labels_data: list[str] | None = None,
+        tenant_labels_data: list[str] | None = None,
         **kwargs,
     ) -> tuple[int, Exception]:
+        vectors = [embedding.tolist() if hasattr(embedding, "tolist") else embedding for embedding in embeddings]
+        if tenant_labels_data is not None:
+            inserted = 0
+            successful_tenants: dict[str, int] = {}
+            for tenant in sorted(set(tenant_labels_data)):
+                self._multitenant_touched_tenants.add(tenant)
+                idxs = [i for i, label in enumerate(tenant_labels_data) if label == tenant]
+                try:
+                    upsert_columns = {
+                        self._scalar_id_field: [metadata[i] for i in idxs],
+                        self._vector_field: [vectors[i] for i in idxs],
+                    }
+                    if self.with_scalar_labels:
+                        upsert_columns[self._scalar_payload_label_field] = [labels_data[i] for i in idxs]
+                    self._namespace_for_tenant(tenant).write(
+                        upsert_columns=upsert_columns,
+                        distance_metric=self.metric,
+                        disable_backpressure=self.db_case_config.disable_backpressure,
+                    )
+                except Exception as e:
+                    msg = (
+                        "TurboPuffer multitenant insert failed for "
+                        f"tenant={tenant} after writing {inserted} rows; "
+                        f"successful_tenants={successful_tenants}; "
+                        f"failed_tenant_count={len(idxs)}"
+                    )
+                    err = PartialInsertError(
+                        msg,
+                        inserted_count=inserted,
+                        successful_tenants=successful_tenants,
+                        failed_tenant=tenant,
+                        failed_tenant_count=len(idxs),
+                        cause=e,
+                    )
+                    log.warning(f"Failed to insert. Error: {err}")
+                    return inserted, err
+                inserted += len(idxs)
+                successful_tenants[tenant] = len(idxs)
+            return inserted, None
         try:
             if self.with_scalar_labels:
                 self.ns.write(
-                    columns={
+                    upsert_columns={
                         self._scalar_id_field: metadata,
-                        self._vector_field: embeddings,
-                        self._scalar_label_field: labels_data,
+                        self._vector_field: vectors,
+                        self._scalar_payload_label_field: labels_data,
                     },
                     distance_metric=self.metric,
+                    disable_backpressure=self.db_case_config.disable_backpressure,
                 )
             else:
                 self.ns.write(
-                    columns={
+                    upsert_columns={
                         self._scalar_id_field: metadata,
-                        self._vector_field: embeddings,
+                        self._vector_field: vectors,
                     },
                     distance_metric=self.metric,
+                    disable_backpressure=self.db_case_config.disable_backpressure,
                 )
         except Exception as e:
             log.warning(f"Failed to insert. Error: {e}")
+            return 0, e
         return len(embeddings), None
 
+    def supports_payload_profile(self, payload_profile: PayloadProfile) -> bool:
+        return payload_profile in {
+            PayloadProfile.IDS_ONLY,
+            PayloadProfile.SCALAR_LABEL,
+            PayloadProfile.VECTOR,
+        }
+
+    def poll_insert_readiness(self, expected_count: int) -> dict:
+        if getattr(self, "multitenant_tenant_labels", []):
+            unindexed_by_tenant = {}
+            tenant_labels = (
+                sorted(getattr(self, "_multitenant_touched_tenants", set())) or self.multitenant_tenant_labels
+            )
+            for tenant in tenant_labels:
+                metadata = self._namespace_for_tenant(tenant).metadata()
+                if not isinstance(metadata, dict):
+                    metadata = metadata.model_dump() if hasattr(metadata, "model_dump") else vars(metadata)
+                index = metadata.get("index", {})
+                if not isinstance(index, dict):
+                    index = index.model_dump() if hasattr(index, "model_dump") else vars(index)
+                unindexed_by_tenant[tenant] = metadata.get("unindexed_bytes", index.get("unindexed_bytes", 0))
+            max_unindexed_bytes = max(unindexed_by_tenant.values(), default=0)
+            return {
+                "fully_searchable": max_unindexed_bytes <= TURBOPUFFER_SEARCHABLE_UNINDEXED_BYTES,
+                "fully_indexed": max_unindexed_bytes == 0,
+                "additional_parameters": {
+                    "disable_backpressure": self.db_case_config.disable_backpressure,
+                    "max_unindexed_bytes": max_unindexed_bytes,
+                },
+            }
+        metadata = self.ns.metadata()
+        if not isinstance(metadata, dict):
+            metadata = metadata.model_dump() if hasattr(metadata, "model_dump") else vars(metadata)
+        index = metadata.get("index", {})
+        if not isinstance(index, dict):
+            index = index.model_dump() if hasattr(index, "model_dump") else vars(index)
+        unindexed_bytes = metadata.get("unindexed_bytes", index.get("unindexed_bytes", 0))
+        return {
+            "fully_searchable": unindexed_bytes <= TURBOPUFFER_SEARCHABLE_UNINDEXED_BYTES,
+            "fully_indexed": unindexed_bytes == 0,
+            "additional_parameters": {"disable_backpressure": self.db_case_config.disable_backpressure},
+        }
+
+    def insert_documents(
+        self,
+        texts: list[str],
+        doc_ids: list[str],
+        **kwargs,
+    ) -> tuple[int, Exception | None]:
+        if not getattr(self, "_is_fts", False):
+            msg = "TurboPuffer full-text insert requires TurboPufferFtsConfig"
+            raise RuntimeError(msg)
+        assert self.ns is not None, "should self.init() first"
+
+        docs = list(texts)
+        if len(docs) != len(doc_ids):
+            msg = f"Mismatch between texts ({len(docs)}) and doc_ids ({len(doc_ids)}) lengths"
+            raise ValueError(msg)
+        filter_ids = kwargs.get("filter_ids")
+        if filter_ids is not None and len(filter_ids) != len(docs):
+            msg = f"Mismatch between texts ({len(docs)}) and filter_ids ({len(filter_ids)}) lengths"
+            raise ValueError(msg)
+
+        text_field = self._text_field
+        upsert_columns = {
+            self._scalar_id_field: [str(doc_id) for doc_id in doc_ids],
+            text_field: docs,
+        }
+        if filter_ids is not None:
+            upsert_columns[self._filter_id_field] = [int(filter_id) for filter_id in filter_ids]
+        schema = {
+            text_field: {
+                "type": "string",
+                "full_text_search": True,
+            }
+        }
+        if filter_ids is not None:
+            schema[self._filter_id_field] = {
+                "type": "int",
+                "filterable": True,
+            }
+        try:
+            self.ns.write(
+                upsert_columns=upsert_columns,
+                schema=schema,
+                disable_backpressure=self.db_case_config.disable_backpressure,
+            )
+        except Exception as e:
+            log.warning(f"Failed to insert FTS docs. Error: {e}")
+            return 0, e
+        return len(docs), None
+
     def search_embedding(
         self,
         query: list[float],
         k: int = 100,
         timeout: int | None = None,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+        tenant: str | None = None,
     ) -> list[int]:
-        res = self.ns.query(
-            rank_by=("vector", "ANN", query),
-            top_k=k,
-            filters=self.expr,
-        )
-        return [row.id for row in res.rows] if res.rows is not None else []
+        query_kwargs = {
+            "rank_by": ("vector", "ANN", query),
+            "top_k": k,
+            "filters": self.expr,
+        }
+        if payload_profile == PayloadProfile.VECTOR:
+            query_kwargs["include_attributes"] = [self._vector_field]
+        elif payload_profile == PayloadProfile.SCALAR_LABEL:
+            query_kwargs["include_attributes"] = [self._scalar_payload_label_field]
+        res = self._namespace_for_tenant(tenant).query(**query_kwargs)
+        return [int(row.id) for row in res.rows] if res.rows is not None else []
+
+    def search_documents(
+        self,
+        query: str,
+        k: int = 100,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+        **kwargs,
+    ) -> list[str]:
+        if not getattr(self, "_is_fts", False):
+            msg = "TurboPuffer full-text search requires TurboPufferFtsConfig"
+            raise RuntimeError(msg)
+        if not self.supports_document_payload_profile(payload_profile):
+            msg = f"TurboPuffer does not support document payload_profile={payload_profile.value}"
+            raise NotImplementedError(msg)
+        assert self.ns is not None, "should self.init() first"
+
+        query_kwargs = {
+            "rank_by": (self._text_field, "BM25", query),
+            "top_k": k,
+        }
+        if self.expr is not None:
+            query_kwargs["filters"] = self.expr
+        if payload_profile == PayloadProfile.TEXT:
+            query_kwargs["include_attributes"] = [self._text_field]
+        res = self.ns.query(**query_kwargs)
+        rows = getattr(res, "rows", None) or []
+        return [str(row.id) for row in rows if getattr(row, "id", None) is not None]
 
     def prepare_filter(self, filters: Filter):
         if filters.type == FilterOp.NonFilter:
             self.expr = None
         elif filters.type == FilterOp.NumGE:
-            self.expr = (self._scalar_id_field, "Gte", filters.int_value)
+            if self._is_fts:
+                if getattr(filters, "int_field", None) != self._filter_id_field:
+                    msg = f"TurboPuffer FTS filters only support int_field='{self._filter_id_field}'"
+                    raise ValueError(msg)
+                self.expr = (self._filter_id_field, "Gte", filters.int_value)
+            else:
+                self.expr = (self._scalar_id_field, "Gte", filters.int_value)
         elif filters.type == FilterOp.StrEqual:
-            self.expr = (self._scalar_label_field, "Eq", filters.label_value)
+            if self._is_fts:
+                msg = f"Not support Filter for TurboPuffer FTS - {filters}"
+                raise ValueError(msg)
+            self.expr = (self._scalar_payload_label_field, "Eq", filters.label_value)
         else:
             msg = f"Not support Filter for TurboPuffer - {filters}"
             raise ValueError(msg)
diff --git a/vectordb_bench/backend/clients/vectorchord/__init__.py b/vectordb_bench/backend/clients/vectorchord/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/vectordb_bench/backend/clients/vectorchord/cli.py b/vectordb_bench/backend/clients/vectorchord/cli.py
new file mode 100644
index 000000000..2b3e3862b
--- /dev/null
+++ b/vectordb_bench/backend/clients/vectorchord/cli.py
@@ -0,0 +1,267 @@
+import os
+from typing import Annotated, Unpack
+
+import click
+from pydantic import SecretStr
+
+from vectordb_bench.backend.clients import DB
+
+from ....cli.cli import (
+    CommonTypedDict,
+    cli,
+    click_parameter_decorators_from_typed_dict,
+    run,
+)
+
+
+class VectorChordTypedDict(CommonTypedDict):
+    user_name: Annotated[
+        str,
+        click.option("--user-name", type=str, help="Db username", required=True),
+    ]
+    password: Annotated[
+        str,
+        click.option(
+            "--password",
+            type=str,
+            help="Postgres database password",
+            default=lambda: os.environ.get("POSTGRES_PASSWORD", ""),
+            show_default="$POSTGRES_PASSWORD",
+        ),
+    ]
+
+    host: Annotated[str, click.option("--host", type=str, help="Db host", required=True)]
+    port: Annotated[
+        int,
+        click.option(
+            "--port",
+            type=int,
+            help="Postgres database port",
+            default=5432,
+            show_default=True,
+            required=False,
+        ),
+    ]
+    db_name: Annotated[str, click.option("--db-name", type=str, help="Db name", required=True)]
+    max_parallel_workers: Annotated[
+        int | None,
+        click.option(
+            "--max-parallel-workers",
+            type=int,
+            help="Sets the maximum number of parallel workers for index creation",
+            required=False,
+        ),
+    ]
+    quantization_type: Annotated[
+        str | None,
+        click.option(
+            "--quantization-type",
+            type=click.Choice(["vector", "halfvec", "rabitq8", "rabitq4"]),
+            help="Quantization type for vectors",
+            default="vector",
+            show_default=True,
+        ),
+    ]
+
+
+class VectorChordRQTypedDict(VectorChordTypedDict):
+    lists: Annotated[
+        int | None,
+        click.option(
+            "--lists",
+            type=int,
+            help="Number of IVF lists for vchordrq index",
+        ),
+    ]
+    probes: Annotated[
+        int | None,
+        click.option(
+            "--probes",
+            type=int,
+            help="Number of probes during search",
+            default=10,
+            show_default=True,
+        ),
+    ]
+    epsilon: Annotated[
+        float | None,
+        click.option(
+            "--epsilon",
+            type=float,
+            help="Reranking precision factor (0.0-4.0, higher is more accurate but slower)",
+            default=1.9,
+            show_default=True,
+        ),
+    ]
+    residual_quantization: Annotated[
+        bool,
+        click.option(
+            "--residual-quantization/--no-residual-quantization",
+            type=bool,
+            help="Enable residual quantization for improved accuracy",
+            default=False,
+            show_default=True,
+        ),
+    ]
+    rerank_in_table: Annotated[
+        bool,
+        click.option(
+            "--rerank-in-table/--no-rerank-in-table",
+            type=bool,
+            help="Read vectors from table instead of storing in index (saves storage, degrades query performance)",
+            default=False,
+            show_default=True,
+        ),
+    ]
+    spherical_centroids: Annotated[
+        bool,
+        click.option(
+            "--spherical-centroids/--no-spherical-centroids",
+            type=bool,
+            help="L2-normalize centroids during K-means (recommended for cosine/IP)",
+            default=False,
+            show_default=True,
+        ),
+    ]
+    build_threads: Annotated[
+        int | None,
+        click.option(
+            "--build-threads",
+            type=int,
+            help="Number of threads for index building (range: 1-255)",
+        ),
+    ]
+    degree_of_parallelism: Annotated[
+        int | None,
+        click.option(
+            "--degree-of-parallelism",
+            type=int,
+            help="Degree of parallelism for index build (range: 1-256, default: 32)",
+        ),
+    ]
+    max_scan_tuples: Annotated[
+        int | None,
+        click.option(
+            "--max-scan-tuples",
+            type=int,
+            help="Max tuples to scan before stopping (-1 for unlimited)",
+        ),
+    ]
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(VectorChordRQTypedDict)
+def VectorChordRQ(
+    **parameters: Unpack[VectorChordRQTypedDict],
+):
+    from .config import VectorChordConfig, VectorChordRQConfig
+
+    run(
+        db=DB.VectorChord,
+        db_config=VectorChordConfig(
+            db_label=parameters["db_label"],
+            user_name=SecretStr(parameters["user_name"]),
+            password=SecretStr(parameters["password"]),
+            host=parameters["host"],
+            port=parameters["port"],
+            db_name=parameters["db_name"],
+        ),
+        db_case_config=VectorChordRQConfig(
+            quantization_type=parameters["quantization_type"],
+            lists=parameters["lists"],
+            probes=parameters["probes"],
+            epsilon=parameters["epsilon"],
+            residual_quantization=parameters["residual_quantization"],
+            rerank_in_table=parameters["rerank_in_table"],
+            spherical_centroids=parameters["spherical_centroids"],
+            build_threads=parameters["build_threads"],
+            degree_of_parallelism=parameters["degree_of_parallelism"],
+            max_scan_tuples=parameters["max_scan_tuples"],
+            max_parallel_workers=parameters["max_parallel_workers"],
+        ),
+        **parameters,
+    )
+
+
+class VectorChordGraphTypedDict(VectorChordTypedDict):
+    m: Annotated[
+        int | None,
+        click.option(
+            "--m",
+            type=int,
+            help="Max neighbors per vertex (default: 32)",
+        ),
+    ]
+    ef_construction: Annotated[
+        int | None,
+        click.option(
+            "--ef-construction",
+            type=int,
+            help="Dynamic list size during insertion (default: 64)",
+        ),
+    ]
+    bits: Annotated[
+        int | None,
+        click.option(
+            "--bits",
+            type=int,
+            help="RaBitQ quantization ratio (1 or 2, default: 2)",
+        ),
+    ]
+    ef_search: Annotated[
+        int | None,
+        click.option(
+            "--ef-search",
+            type=int,
+            help="Dynamic list size for search (default: 64)",
+            default=64,
+            show_default=True,
+        ),
+    ]
+    beam_search: Annotated[
+        int | None,
+        click.option(
+            "--beam-search",
+            type=int,
+            help="Batch vertex access width during search (default: 1)",
+        ),
+    ]
+    max_scan_tuples: Annotated[
+        int | None,
+        click.option(
+            "--max-scan-tuples",
+            type=int,
+            help="Max tuples to scan before stopping (-1 for unlimited)",
+        ),
+    ]
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(VectorChordGraphTypedDict)
+def VectorChordGraph(
+    **parameters: Unpack[VectorChordGraphTypedDict],
+):
+    from .config import VectorChordConfig, VectorChordGraphConfig
+
+    run(
+        db=DB.VectorChord,
+        db_config=VectorChordConfig(
+            db_label=parameters["db_label"],
+            user_name=SecretStr(parameters["user_name"]),
+            password=SecretStr(parameters["password"]),
+            host=parameters["host"],
+            port=parameters["port"],
+            db_name=parameters["db_name"],
+        ),
+        db_case_config=VectorChordGraphConfig(
+            quantization_type=parameters["quantization_type"],
+            m=parameters["m"],
+            ef_construction=parameters["ef_construction"],
+            bits=parameters["bits"],
+            ef_search=parameters["ef_search"],
+            beam_search=parameters["beam_search"],
+            max_parallel_workers=parameters["max_parallel_workers"],
+            max_scan_tuples=parameters["max_scan_tuples"],
+        ),
+        **parameters,
+    )
diff --git a/vectordb_bench/backend/clients/vectorchord/config.py b/vectordb_bench/backend/clients/vectorchord/config.py
new file mode 100644
index 000000000..95916eb06
--- /dev/null
+++ b/vectordb_bench/backend/clients/vectorchord/config.py
@@ -0,0 +1,196 @@
+from abc import abstractmethod
+from typing import Literal, LiteralString, TypedDict
+
+from pydantic import BaseModel, SecretStr
+
+from ..api import DBCaseConfig, DBConfig, IndexType, MetricType
+
+
+class VectorChordConfigDict(TypedDict):
+    """These keys will be directly used as kwargs in psycopg connection string,
+    so the names must match exactly psycopg API"""
+
+    user: str
+    password: str
+    host: str
+    port: int
+    dbname: str
+
+
+class VectorChordConfig(DBConfig):
+    user_name: SecretStr = SecretStr("postgres")
+    password: SecretStr
+    host: str = "localhost"
+    port: int = 5432
+    db_name: str = "vectordb"
+
+    def to_dict(self) -> VectorChordConfigDict:
+        user_str = self.user_name.get_secret_value()
+        pwd_str = self.password.get_secret_value()
+        return {
+            "host": self.host,
+            "port": self.port,
+            "dbname": self.db_name,
+            "user": user_str,
+            "password": pwd_str,
+        }
+
+
+_METRIC_OPS = {
+    "vector": {
+        MetricType.L2: "vector_l2_ops",
+        MetricType.IP: "vector_ip_ops",
+        MetricType.COSINE: "vector_cosine_ops",
+    },
+    "halfvec": {
+        MetricType.L2: "halfvec_l2_ops",
+        MetricType.IP: "halfvec_ip_ops",
+        MetricType.COSINE: "halfvec_cosine_ops",
+    },
+    "rabitq8": {
+        MetricType.L2: "rabitq8_l2_ops",
+        MetricType.IP: "rabitq8_ip_ops",
+        MetricType.COSINE: "rabitq8_cosine_ops",
+    },
+    "rabitq4": {
+        MetricType.L2: "rabitq4_l2_ops",
+        MetricType.IP: "rabitq4_ip_ops",
+        MetricType.COSINE: "rabitq4_cosine_ops",
+    },
+}
+
+
+class VectorChordIndexConfig(BaseModel, DBCaseConfig):
+    metric_type: MetricType | None = None
+    create_index_before_load: bool = False
+    create_index_after_load: bool = True
+    quantization_type: Literal["vector", "halfvec", "rabitq8", "rabitq4"] = "vector"
+
+    def parse_metric(self) -> str:
+        ops = _METRIC_OPS.get(self.quantization_type, _METRIC_OPS["vector"])
+        return ops.get(self.metric_type, ops[MetricType.COSINE])
+
+    def parse_metric_fun_op(self) -> LiteralString:
+        if self.metric_type == MetricType.L2:
+            return "<->"
+        if self.metric_type == MetricType.IP:
+            return "<#>"
+        return "<=>"
+
+    @abstractmethod
+    def index_param(self) -> dict: ...
+
+    @abstractmethod
+    def search_param(self) -> dict: ...
+
+    @abstractmethod
+    def session_param(self) -> dict: ...
+
+
+class VectorChordRQConfig(VectorChordIndexConfig):
+    index: IndexType = IndexType.VCHORDRQ
+    # Build parameters (top-level options)
+    residual_quantization: bool = False
+    rerank_in_table: bool = False
+    degree_of_parallelism: int | None = None  # default 32, range [1, 256]
+    # Build parameters ([build.internal] section)
+    lists: int | None = None
+    spherical_centroids: bool = False
+    build_threads: int | None = None  # range [1, 255]
+    # PostgreSQL tuning parameter
+    max_parallel_workers: int | None = None  # sets max_parallel_workers & max_parallel_maintenance_workers
+    # Search parameters (GUCs)
+    probes: int | None = 10
+    epsilon: float | None = 1.9  # range [0.0, 4.0]
+    max_scan_tuples: int | None = None  # default -1, range [-1, 2147483647]
+
+    def index_param(self) -> dict:
+        options_parts = []
+        if self.rerank_in_table:
+            options_parts.append("rerank_in_table = true")
+        if self.residual_quantization:
+            options_parts.append("residual_quantization = true")
+        if self.degree_of_parallelism is not None:
+            options_parts.append(f"degree_of_parallelism = {self.degree_of_parallelism}")
+        options_parts.append("[build.internal]")
+        if self.lists is not None:
+            options_parts.append(f"lists = [{self.lists}]")
+        if self.spherical_centroids:
+            options_parts.append("spherical_centroids = true")
+        if self.build_threads is not None:
+            options_parts.append(f"build_threads = {self.build_threads}")
+
+        return {
+            "metric": self.parse_metric(),
+            "index_type": self.index.value,
+            "quantization_type": self.quantization_type,
+            "options": "\n".join(options_parts),
+            "max_parallel_workers": self.max_parallel_workers,
+        }
+
+    def search_param(self) -> dict:
+        return {
+            "metric_fun_op": self.parse_metric_fun_op(),
+        }
+
+    def session_param(self) -> dict:
+        params = {}
+        if self.probes is not None:
+            params["vchordrq.probes"] = str(self.probes)
+        if self.epsilon is not None:
+            params["vchordrq.epsilon"] = str(self.epsilon)
+        if self.max_scan_tuples is not None:
+            params["vchordrq.max_scan_tuples"] = str(self.max_scan_tuples)
+        return params
+
+
+class VectorChordGraphConfig(VectorChordIndexConfig):
+    index: IndexType = IndexType.VCHORDG
+    # Build parameters
+    m: int | None = None  # default 32, max neighbors per vertex
+    ef_construction: int | None = None  # default 64
+    bits: int | None = None  # default 2, quantization ratio (1 or 2)
+    # PostgreSQL tuning parameter
+    max_parallel_workers: int | None = None
+    # Search parameters (GUCs)
+    ef_search: int | None = 64  # range [1, 65535]
+    beam_search: int | None = None  # default 1
+    max_scan_tuples: int | None = None  # default -1, range [-1, 2147483647]
+
+    def index_param(self) -> dict:
+        options_parts = []
+        if self.m is not None:
+            options_parts.append(f"m = {self.m}")
+        if self.ef_construction is not None:
+            options_parts.append(f"ef_construction = {self.ef_construction}")
+        if self.bits is not None:
+            options_parts.append(f"bits = {self.bits}")
+
+        return {
+            "metric": self.parse_metric(),
+            "index_type": self.index.value,
+            "quantization_type": self.quantization_type,
+            "options": "\n".join(options_parts),
+            "max_parallel_workers": self.max_parallel_workers,
+        }
+
+    def search_param(self) -> dict:
+        return {
+            "metric_fun_op": self.parse_metric_fun_op(),
+        }
+
+    def session_param(self) -> dict:
+        params = {}
+        if self.ef_search is not None:
+            params["vchordg.ef_search"] = str(self.ef_search)
+        if self.beam_search is not None:
+            params["vchordg.beam_search"] = str(self.beam_search)
+        if self.max_scan_tuples is not None:
+            params["vchordg.max_scan_tuples"] = str(self.max_scan_tuples)
+        return params
+
+
+_vectorchord_case_config = {
+    IndexType.VCHORDRQ: VectorChordRQConfig,
+    IndexType.VCHORDG: VectorChordGraphConfig,
+}
diff --git a/vectordb_bench/backend/clients/vectorchord/vectorchord.py b/vectordb_bench/backend/clients/vectorchord/vectorchord.py
new file mode 100644
index 000000000..77a462e75
--- /dev/null
+++ b/vectordb_bench/backend/clients/vectorchord/vectorchord.py
@@ -0,0 +1,325 @@
+"""Wrapper around the VectorChord vector database over VectorDB"""
+
+import logging
+from collections.abc import Generator
+from contextlib import contextmanager
+from typing import Any
+
+import numpy as np
+import psycopg
+from pgvector.psycopg import register_vector
+from psycopg import Connection, Cursor, sql
+
+from ...filter import Filter, FilterOp
+from ..api import VectorDB
+from .config import VectorChordConfigDict, VectorChordIndexConfig
+
+log = logging.getLogger(__name__)
+
+
+class VectorChord(VectorDB):
+    """Use psycopg instructions"""
+
+    thread_safe: bool = False
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+    ]
+
+    conn: psycopg.Connection[Any] | None = None
+    cursor: psycopg.Cursor[Any] | None = None
+
+    _search: sql.Composed
+    where_clause: str = ""
+
+    def __init__(
+        self,
+        dim: int,
+        db_config: VectorChordConfigDict,
+        db_case_config: VectorChordIndexConfig,
+        collection_name: str = "vectorchord_collection",
+        drop_old: bool = False,
+        **kwargs,
+    ):
+        self.name = "VectorChord"
+        self.db_config = db_config
+        self.case_config = db_case_config
+        self.table_name = collection_name
+        self.dim = dim
+
+        self._index_name = "vectorchord_index"
+        self._primary_field = "id"
+        self._vector_field = "embedding"
+
+        index_param = self.case_config.index_param()
+        self._quantization_type = index_param["quantization_type"]
+        self._index_method = index_param["index_type"]
+
+        self.conn, self.cursor = self._create_connection(**self.db_config)
+
+        # create vectorchord extension if not exists
+        self.cursor.execute("CREATE EXTENSION IF NOT EXISTS vchord CASCADE")
+        self.conn.commit()
+
+        log.info(f"{self.name} config values: {self.db_config}\n{self.case_config}")
+        if not any(
+            (
+                self.case_config.create_index_before_load,
+                self.case_config.create_index_after_load,
+            ),
+        ):
+            msg = (
+                f"{self.name} config must create an index using create_index_before_load or create_index_after_load"
+                f"{self.name} config values: {self.db_config}\n{self.case_config}"
+            )
+            log.error(msg)
+            raise RuntimeError(msg)
+
+        if drop_old:
+            self._drop_index()
+            self._drop_table()
+            self._create_table(dim)
+            if self.case_config.create_index_before_load:
+                self._create_index()
+
+        self.cursor.close()
+        self.conn.close()
+        self.cursor = None
+        self.conn = None
+
+    @staticmethod
+    def _create_connection(**kwargs) -> tuple[Connection, Cursor]:
+        conn = psycopg.connect(**kwargs)
+        register_vector(conn)
+        conn.autocommit = False
+        cursor = conn.cursor()
+
+        assert conn is not None, "Connection is not initialized"
+        assert cursor is not None, "Cursor is not initialized"
+
+        return conn, cursor
+
+    @contextmanager
+    def init(self) -> Generator[None, None, None]:
+        self.conn, self.cursor = self._create_connection(**self.db_config)
+
+        # index configuration may have commands defined that we should set during each client session
+        session_options: dict[str, Any] = self.case_config.session_param()
+
+        if len(session_options) > 0:
+            for setting_name, setting_val in session_options.items():
+                command = sql.SQL("SET {setting_name} " + "= {setting_val};").format(
+                    setting_name=sql.Identifier(setting_name),
+                    setting_val=sql.Literal(str(setting_val)),
+                )
+                log.debug(command.as_string(self.cursor))
+                self.cursor.execute(command)
+            self.conn.commit()
+
+        try:
+            yield
+        finally:
+            self.cursor.close()
+            self.conn.close()
+            self.cursor = None
+            self.conn = None
+
+    def _drop_table(self):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+        log.info(f"{self.name} client drop table : {self.table_name}")
+
+        self.cursor.execute(
+            sql.SQL("DROP TABLE IF EXISTS public.{table_name}").format(
+                table_name=sql.Identifier(self.table_name),
+            ),
+        )
+        self.conn.commit()
+
+    def optimize(self, data_size: int | None = None):
+        self._post_insert()
+
+    def _post_insert(self):
+        log.info(f"{self.name} post insert before optimize")
+        if self.case_config.create_index_after_load:
+            self._drop_index()
+            self._create_index()
+
+    def _drop_index(self):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+        log.info(f"{self.name} client drop index : {self._index_name}")
+
+        drop_index_sql = sql.SQL("DROP INDEX IF EXISTS {index_name}").format(
+            index_name=sql.Identifier(self._index_name),
+        )
+        log.debug(drop_index_sql.as_string(self.cursor))
+        self.cursor.execute(drop_index_sql)
+        self.conn.commit()
+
+    def _set_parallel_index_build_param(self):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        index_param = self.case_config.index_param()
+
+        if index_param["max_parallel_workers"] is not None:
+            self.cursor.execute(
+                sql.SQL("SET max_parallel_workers TO '{}';").format(
+                    index_param["max_parallel_workers"],
+                ),
+            )
+            self.cursor.execute(
+                sql.SQL("SET max_parallel_maintenance_workers TO '{}';").format(
+                    index_param["max_parallel_workers"],
+                ),
+            )
+            self.cursor.execute(
+                sql.SQL("ALTER TABLE {} SET (parallel_workers = {});").format(
+                    sql.Identifier(self.table_name),
+                    index_param["max_parallel_workers"],
+                ),
+            )
+            self.conn.commit()
+
+        results = self.cursor.execute(sql.SQL("SHOW max_parallel_workers;")).fetchall()
+        results.extend(self.cursor.execute(sql.SQL("SHOW max_parallel_maintenance_workers;")).fetchall())
+        log.info(f"{self.name} parallel index creation parameters: {results}")
+
+    def _create_index(self):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+        log.info(f"{self.name} client create index : {self._index_name}")
+
+        index_param: dict[str, Any] = self.case_config.index_param()
+        self._set_parallel_index_build_param()
+
+        index_create_sql = sql.SQL(
+            """
+            CREATE INDEX IF NOT EXISTS {index_name} ON public.{table_name}
+            USING {index_method} (embedding {embedding_metric})
+            """,
+        ).format(
+            index_name=sql.Identifier(self._index_name),
+            table_name=sql.Identifier(self.table_name),
+            index_method=sql.SQL(self._index_method),
+            embedding_metric=sql.Identifier(index_param["metric"]),
+        )
+
+        options_str = index_param.get("options", "")
+        if options_str:
+            with_clause = sql.SQL(
+                "WITH (options = $vchord$\n{options}\n$vchord$);",
+            ).format(options=sql.SQL(options_str))
+        else:
+            with_clause = sql.SQL(";")
+
+        full_sql = index_create_sql + sql.SQL(" ") + with_clause
+        log.debug(full_sql.as_string(self.cursor))
+        self.cursor.execute(full_sql)
+        self.conn.commit()
+
+    def _create_table(self, dim: int):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        try:
+            log.info(f"{self.name} client create table : {self.table_name}")
+
+            col_type = self._quantization_type
+            if col_type in ("rabitq8", "rabitq4"):
+                # rabitq types need vector column + quantization during insert
+                col_type = "vector"
+
+            self.cursor.execute(
+                sql.SQL(
+                    "CREATE TABLE IF NOT EXISTS public.{table_name} "
+                    "(id BIGINT PRIMARY KEY, embedding {col_type}({dim}));",
+                ).format(
+                    table_name=sql.Identifier(self.table_name),
+                    col_type=sql.SQL(col_type),
+                    dim=dim,
+                ),
+            )
+            self.conn.commit()
+        except Exception as e:
+            log.warning(f"Failed to create vectorchord table: {self.table_name} error: {e}")
+            raise e from None
+
+    def insert_embeddings(
+        self,
+        embeddings: list[list[float]],
+        metadata: list[int],
+        **kwargs: Any,
+    ) -> tuple[int, Exception | None]:
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        try:
+            metadata_arr = np.array(metadata)
+            embeddings_arr = np.array(embeddings)
+
+            if self._quantization_type == "halfvec":
+                with self.cursor.copy(
+                    sql.SQL("COPY public.{table_name} FROM STDIN (FORMAT BINARY)").format(
+                        table_name=sql.Identifier(self.table_name),
+                    ),
+                ) as copy:
+                    copy.set_types(["bigint", "halfvec"])
+                    for i, row in enumerate(metadata_arr):
+                        copy.write_row((row, np.float16(embeddings_arr[i])))
+            else:
+                # vector, rabitq8, rabitq4 all store as vector column
+                with self.cursor.copy(
+                    sql.SQL("COPY public.{table_name} FROM STDIN (FORMAT BINARY)").format(
+                        table_name=sql.Identifier(self.table_name),
+                    ),
+                ) as copy:
+                    copy.set_types(["bigint", "vector"])
+                    for i, row in enumerate(metadata_arr):
+                        copy.write_row((row, embeddings_arr[i]))
+            self.conn.commit()
+
+            return len(metadata), None
+        except Exception as e:
+            log.warning(f"Failed to insert data into vectorchord table ({self.table_name}), error: {e}")
+            return 0, e
+
+    def _generate_search_query(self) -> sql.Composed:
+        # Search query cast type: rabitq8/rabitq4 queries still accept ::vector input
+        cast_type = "vector"
+        return sql.Composed(
+            [
+                sql.SQL("SELECT id FROM public.{table_name} {where_clause} ORDER BY embedding ").format(
+                    table_name=sql.Identifier(self.table_name),
+                    where_clause=sql.SQL(self.where_clause),
+                ),
+                sql.SQL(self.case_config.search_param()["metric_fun_op"]),
+                sql.SQL(f" %s::{cast_type} LIMIT %s::int"),
+            ],
+        )
+
+    def prepare_filter(self, filters: Filter):
+        if filters.type == FilterOp.NonFilter:
+            self.where_clause = ""
+        elif filters.type == FilterOp.NumGE:
+            self.where_clause = f"WHERE {self._primary_field} >= {filters.int_value}"
+        else:
+            msg = f"Not support Filter for VectorChord - {filters}"
+            raise ValueError(msg)
+
+        self._search = self._generate_search_query()
+
+    def search_embedding(
+        self,
+        query: list[float],
+        k: int = 100,
+        timeout: int | None = None,
+        **kwargs: Any,
+    ) -> list[int]:
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        q = np.asarray(query)
+        result = self.cursor.execute(self._search, (q, k), prepare=True, binary=True)
+        return [int(i[0]) for i in result.fetchall()]
diff --git a/vectordb_bench/backend/clients/vespa/config.py b/vectordb_bench/backend/clients/vespa/config.py
index 3d4a1deaf..5eeced0f6 100644
--- a/vectordb_bench/backend/clients/vespa/config.py
+++ b/vectordb_bench/backend/clients/vespa/config.py
@@ -49,3 +49,28 @@ def parse_metric(self, metric_type: MetricType) -> VespaMetric:
                 return "hamming"
             case _:
                 raise NotImplementedError
+
+
+class VespaFtsConfig(BaseModel, DBCaseConfig):
+    metric_type: MetricType = MetricType.BM25
+    bm25_k1: float | None = None
+    bm25_b: float | None = None
+    bm25_avgdl: float | None = None
+    feed_client_command: str = "vespa"
+    feed_client_connections: int | None = None
+
+    def index_param(self) -> dict:
+        return {}
+
+    def search_param(self) -> dict:
+        return {}
+
+    def rank_properties(self) -> list[tuple[str, str]]:
+        properties = []
+        if self.bm25_k1 is not None:
+            properties.append(("bm25(text).k1", str(self.bm25_k1)))
+        if self.bm25_b is not None:
+            properties.append(("bm25(text).b", str(self.bm25_b)))
+        if self.bm25_avgdl is not None:
+            properties.append(("bm25(text).averageFieldLength", str(self.bm25_avgdl)))
+        return properties
diff --git a/vectordb_bench/backend/clients/vespa/vespa.py b/vectordb_bench/backend/clients/vespa/vespa.py
index 5288bc04c..12fa65d5e 100644
--- a/vectordb_bench/backend/clients/vespa/vespa.py
+++ b/vectordb_bench/backend/clients/vespa/vespa.py
@@ -1,24 +1,62 @@
 import datetime
+import json
 import logging
 import math
+import shutil
+import subprocess
+import tempfile
+import threading
+import time
 from collections.abc import Generator
 from contextlib import contextmanager
+from typing import Any
 
 from vespa import application
 
+from vectordb_bench.backend.filter import Filter, FilterOp
+from vectordb_bench.backend.payload import PayloadProfile
+
 from ..api import VectorDB
 from . import util
-from .config import VespaHNSWConfig
+from .config import VespaFtsConfig, VespaHNSWConfig
 
 log = logging.getLogger(__name__)
 
+VESPA_DOC_COUNT_POLL_INTERVAL_SEC = 2
+VESPA_DOC_COUNT_TIMEOUT_SEC = 1800
+VESPA_FEED_OUTPUT_TAIL_CHARS = 4000
+
+
+def _document_id_from_hit(hit: dict) -> str | None:
+    fields = hit.get("fields")
+    if isinstance(fields, dict) and "id" in fields:
+        return str(fields["id"])
+
+    document_id = hit.get("id")
+    if document_id is None:
+        return None
+
+    document_id = str(document_id)
+    if document_id.startswith("id:") and "::" in document_id:
+        return document_id.rsplit("::", 1)[-1]
+    return document_id
+
+
+def _tail_text(value: str, limit: int = VESPA_FEED_OUTPUT_TAIL_CHARS) -> str:
+    return value[-limit:] if len(value) > limit else value
+
 
 class Vespa(VectorDB):
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+    ]
+
     def __init__(
         self,
         dim: int,
         db_config: dict[str, str],
-        db_case_config: VespaHNSWConfig | None = None,
+        db_case_config: VespaHNSWConfig | VespaFtsConfig | None = None,
         collection_name: str = "VectorDBBenchCollection",
         drop_old: bool = False,
         **kwargs,
@@ -26,7 +64,11 @@ def __init__(
         self.dim = dim
         self.db_config = db_config
         self.case_config = db_case_config or VespaHNSWConfig()
+        self._is_fts = isinstance(self.case_config, VespaFtsConfig)
         self.schema_name = collection_name
+        self._text_field = "text"
+        self._filter_id_field = "filter_id"
+        self._filter_expr: str | None = None
 
         client = self.deploy_http()
         client.wait_for_application_up()
@@ -35,8 +77,13 @@ def __init__(
             try:
                 client.delete_all_docs("vectordbbench_content", self.schema_name)
             except Exception:
+                if self._is_fts:
+                    raise
                 drop_old = False
                 log.exception(f"Vespa client drop_old schema: {self.schema_name}")
+            else:
+                if self._is_fts:
+                    self._wait_for_document_count(client, 0, "drop_old")
 
     @contextmanager
     def init(self) -> Generator[None, None, None]:
@@ -54,8 +101,39 @@ def init(self) -> Generator[None, None, None]:
             >>>     self.insert_embeddings()
         """
         self.client = application.Vespa(self.db_config["url"], port=self.db_config["port"])
-        yield
+        self._reset_fts_feed_client()
+        try:
+            yield
+            self._finish_fts_feed_client()
+        finally:
+            self._cleanup_fts_feed_client()
+            self.client = None
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        # Multiprocessing search uses spawn, so DB instances are pickled before
+        # workers run. Vespa client and feed-client handles are process-local.
+        state.pop("client", None)
+        state.pop("_feed_proc", None)
+        state.pop("_feed_stdout_file", None)
+        state.pop("_feed_stderr_file", None)
+        state.pop("_feed_lock", None)
+        return state
+
+    def __setstate__(self, state: dict):
+        self.__dict__.update(state)
         self.client = None
+        self._feed_proc = None
+        self._feed_stdout_file = None
+        self._feed_stderr_file = None
+        self._feed_lock = threading.Lock()
+
+    @classmethod
+    def supports_full_text_search(cls) -> bool:
+        return True
+
+    def has_text_field(self) -> bool:
+        return bool(getattr(self, "_is_fts", False) and getattr(self, "_text_field", None))
 
     def need_normalize_cosine(self) -> bool:
         """Wheather this database need to normalize dataset to support COSINE"""
@@ -84,6 +162,180 @@ def insert_embeddings(
         self.client.feed_iterable(data, self.schema_name)
         return len(embeddings), None
 
+    def insert_documents(
+        self,
+        texts: list[str],
+        doc_ids: list[str],
+        **kwargs,
+    ) -> tuple[int, Exception | None]:
+        if not self._is_fts:
+            msg = "Vespa full-text insert requires VespaFtsConfig"
+            raise RuntimeError(msg)
+        assert self.client is not None
+
+        if len(texts) != len(doc_ids):
+            msg = f"Mismatch between texts ({len(texts)}) and doc_ids ({len(doc_ids)}) lengths"
+            raise ValueError(msg)
+        filter_ids = kwargs.get("filter_ids")
+        if filter_ids is not None and len(filter_ids) != len(texts):
+            msg = f"Mismatch between texts ({len(texts)}) and filter_ids ({len(filter_ids)}) lengths"
+            raise ValueError(msg)
+
+        try:
+            self._write_fts_feed_batch(texts, doc_ids, filter_ids)
+        except Exception as exc:
+            log.warning("Vespa feed failed for schema %s", self.schema_name, exc_info=True)
+            return 0, exc
+
+        return len(texts), None
+
+    def _reset_fts_feed_client(self) -> None:
+        self._feed_proc = None
+        self._feed_stdout_file = None
+        self._feed_stderr_file = None
+        self._feed_written_count = 0
+        self._feed_lock = threading.Lock()
+
+    def _ensure_fts_feed_client(self) -> subprocess.Popen:
+        if self._feed_proc is not None:
+            return self._feed_proc
+        command = self.case_config.feed_client_command
+        if shutil.which(command) is None:
+            msg = (
+                f"Vespa feed client command {command!r} was not found. "
+                "Install the Vespa CLI or set VespaFtsConfig.feed_client_command."
+            )
+            raise RuntimeError(msg)
+
+        connections = self.case_config.feed_client_connections or 8
+        cmd = [
+            command,
+            "feed",
+            "-",
+            "--target",
+            self._feed_target(),
+            "--connections",
+            str(connections),
+            "--inflight",
+            "0",
+            "--progress",
+            "0",
+        ]
+        log.info("Start Vespa feed client: %s", " ".join(cmd))
+
+        # These files must stay open until the feed client exits; Popen writes to them after this method returns.
+        self._feed_stdout_file = tempfile.TemporaryFile(mode="w+", encoding="utf-8")  # noqa: SIM115
+        self._feed_stderr_file = tempfile.TemporaryFile(mode="w+", encoding="utf-8")  # noqa: SIM115
+        self._feed_proc = subprocess.Popen(
+            cmd,
+            stdin=subprocess.PIPE,
+            stdout=self._feed_stdout_file,
+            stderr=self._feed_stderr_file,
+            text=True,
+        )
+        return self._feed_proc
+
+    def _write_fts_feed_batch(self, texts: list[str], doc_ids: list[str], filter_ids: list[int] | None = None) -> None:
+        lines = []
+        for i, (doc_id, text) in enumerate(zip(doc_ids, texts, strict=True)):
+            fields = {"id": str(doc_id), "text": text}
+            if filter_ids is not None:
+                fields[self._filter_id_field] = int(filter_ids[i])
+            operation = {
+                "put": self._vespa_document_id(str(doc_id)),
+                "fields": fields,
+            }
+            lines.append(json.dumps(operation, ensure_ascii=False, separators=(",", ":")))
+
+        with self._feed_lock:
+            proc = self._ensure_fts_feed_client()
+            returncode = proc.poll()
+            if returncode is not None:
+                msg = f"Vespa feed client exited before all documents were written: returncode={returncode}"
+                raise RuntimeError(msg)
+            assert proc.stdin is not None
+            proc.stdin.write("\n".join(lines))
+            proc.stdin.write("\n")
+            proc.stdin.flush()
+            self._feed_written_count += len(lines)
+
+    def _finish_fts_feed_client(self) -> None:
+        if self._feed_proc is None:
+            return
+        with self._feed_lock:
+            proc = self._feed_proc
+            if proc.stdin is not None and not proc.stdin.closed:
+                proc.stdin.close()
+            returncode = proc.wait()
+            assert self._feed_stdout_file is not None
+            assert self._feed_stderr_file is not None
+            self._feed_stdout_file.seek(0)
+            self._feed_stderr_file.seek(0)
+            stdout = self._feed_stdout_file.read()
+            stderr = self._feed_stderr_file.read()
+
+        count = self._feed_written_count
+        metrics = self._parse_feed_metrics(stdout)
+        ok_count = int(metrics.get("feeder.ok.count", count)) if metrics else count
+        error_count = int(metrics.get("feeder.error.count", 0)) if metrics else 0
+        response_error_count = int(metrics.get("http.response.error.count", 0)) if metrics else 0
+
+        if returncode != 0 or error_count or ok_count != count:
+            msg = (
+                "Vespa feed client failed "
+                f"returncode={returncode}, written={count}, ok={ok_count}, "
+                f"feed_errors={error_count}, response_errors={response_error_count}, "
+                f"stdout_tail={_tail_text(stdout)!r}, stderr_tail={_tail_text(stderr)!r}"
+            )
+            raise RuntimeError(msg)
+        if response_error_count:
+            log.warning(
+                "Vespa feed client reported %d HTTP response errors, but all %d documents were accepted",
+                response_error_count,
+                ok_count,
+            )
+
+        log.info("Vespa feed client inserted %d docs; metrics=%s", ok_count, metrics)
+        self._feed_proc = None
+
+    def _cleanup_fts_feed_client(self) -> None:
+        proc = getattr(self, "_feed_proc", None)
+        if proc is not None and proc.poll() is None:
+            proc.kill()
+            proc.wait()
+        for file_attr in ("_feed_stdout_file", "_feed_stderr_file"):
+            file_obj = getattr(self, file_attr, None)
+            if file_obj is not None:
+                file_obj.close()
+                setattr(self, file_attr, None)
+        self._feed_proc = None
+
+    def _vespa_document_id(self, doc_id: str) -> str:
+        return f"id:{self.schema_name}:{self.schema_name}::{doc_id}"
+
+    def _feed_target(self) -> str:
+        target = str(self.db_config["url"]).rstrip("/")
+        port = self.db_config.get("port")
+        if port is not None and f":{port}" not in target.rsplit("/", 1)[-1]:
+            target = f"{target}:{port}"
+        return target
+
+    def _parse_feed_metrics(self, output: str) -> dict[str, Any]:
+        text = output.strip()
+        if not text:
+            return {}
+        try:
+            return json.loads(text)
+        except json.JSONDecodeError:
+            start = text.rfind("\n{")
+            if start >= 0:
+                try:
+                    return json.loads(text[start + 1 :])
+                except json.JSONDecodeError:
+                    pass
+        log.warning("Failed to parse Vespa feed client metrics: %s", _tail_text(output))
+        return {}
+
     def search_embedding(
         self,
         query: list[float],
@@ -107,14 +359,17 @@ def search_embedding(
         embedding_field = "embedding" if self.case_config.quantization_type == "none" else "embedding_binary"
 
         yql = (
-            f"select id from {self.schema_name} where "  # noqa: S608
+            f"select id from {self.schema_name} where "
             f"{{targetHits: {k}, hnsw.exploreAdditionalHits: {extra_ef}}}"
             f"nearestNeighbor({embedding_field}, query_embedding)"
         )
 
+        prepared_filter = getattr(self, "_filter_expr", None)
         if filters:
             id_filter = filters.get("id")
-            yql += f" and id >= {id_filter}"
+            prepared_filter = f"id >= {id_filter}"
+        if prepared_filter:
+            yql += f" and {prepared_filter}"
 
         query_embedding = query if self.case_config.quantization_type == "none" else util.binarize_tensor(query)
 
@@ -123,6 +378,61 @@ def search_embedding(
         result = self.client.query({"yql": yql, "input.query(query_embedding)": query_embedding, "ranking": ranking})
         return [child["fields"]["id"] for child in result.get_json()["root"]["children"]]
 
+    def search_documents(
+        self,
+        query: str,
+        k: int = 100,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+        **kwargs,
+    ) -> list[str]:
+        if not self._is_fts:
+            msg = "Vespa full-text search requires VespaFtsConfig"
+            raise RuntimeError(msg)
+        if not self.supports_document_payload_profile(payload_profile):
+            msg = f"Vespa does not support document payload_profile={payload_profile.value}"
+            raise NotImplementedError(msg)
+        assert self.client is not None
+
+        selected_fields = "id"
+        if payload_profile == PayloadProfile.TEXT:
+            selected_fields = f"id, {self._text_field}"
+        yql = f"select {selected_fields} from {self.schema_name} where userQuery()"
+        if self._filter_expr:
+            yql += f" and {self._filter_expr}"
+        result = self.client.query(
+            {
+                "yql": yql,
+                "query": query,
+                "type": "any",
+                "ranking": "bm25",
+                "hits": k,
+                "default-index": self._text_field,
+            }
+        )
+        children = result.get_json().get("root", {}).get("children", []) or []
+        ids = []
+        for child in children:
+            if not isinstance(child, dict):
+                continue
+            doc_id = _document_id_from_hit(child)
+            if doc_id is not None:
+                ids.append(doc_id)
+        return ids
+
+    def prepare_filter(self, filters: Filter) -> None:
+        if filters.type == FilterOp.NonFilter:
+            self._filter_expr = None
+            return
+        if filters.type == FilterOp.NumGE:
+            expected_field = self._filter_id_field if self._is_fts else "id"
+            if getattr(filters, "int_field", None) != expected_field:
+                msg = f"Vespa filters only support int_field='{expected_field}' in this mode"
+                raise ValueError(msg)
+            self._filter_expr = f"{expected_field} >= {filters.int_value}"
+            return
+        msg = f"Not support Filter for Vespa - {filters}"
+        raise ValueError(msg)
+
     def optimize(self, data_size: int | None = None):
         """optimize will be called between insertion and search in performance cases.
 
@@ -132,6 +442,35 @@ def optimize(self, data_size: int | None = None):
         Time(insert the dataset) + Time(optimize) will be recorded as "load_duration" metric
         Optimize's execution time is limited, the limited time is based on cases.
         """
+        if self._is_fts and data_size is not None:
+            assert self.client is not None
+            self._wait_for_document_count(self.client, data_size, "post_insert")
+
+    def _document_count(self, client: application.Vespa) -> int:
+        result = client.query({"yql": f"select id from {self.schema_name} where true", "hits": 0})
+        root = result.get_json().get("root", {})
+        fields = root.get("fields", {})
+        if "totalCount" not in fields:
+            msg = f"Vespa count query did not return totalCount for schema {self.schema_name}: {root}"
+            raise RuntimeError(msg)
+        return int(fields["totalCount"])
+
+    def _wait_for_document_count(self, client: application.Vespa, expected_count: int, stage: str) -> None:
+        deadline = time.monotonic() + VESPA_DOC_COUNT_TIMEOUT_SEC
+        last_count = None
+        while True:
+            last_count = self._document_count(client)
+            if last_count == expected_count:
+                log.info("Vespa %s document count reached %d", stage, expected_count)
+                return
+            if time.monotonic() >= deadline:
+                msg = (
+                    f"Timed out waiting for Vespa {stage} document count to reach "
+                    f"{expected_count}; last_count={last_count}"
+                )
+                raise TimeoutError(msg)
+            log.info("Waiting for Vespa %s document count: current=%d expected=%d", stage, last_count, expected_count)
+            time.sleep(VESPA_DOC_COUNT_POLL_INTERVAL_SEC)
 
     @property
     def application_package(self):
@@ -151,6 +490,39 @@ def _create_application_package(self):
             ValidationID,
         )
 
+        tomorrow = datetime.date.today() + datetime.timedelta(days=1)
+
+        if self._is_fts:
+            fields = [
+                Field("id", "string", indexing=["summary", "attribute"]),
+                Field("filter_id", "int", indexing=["summary", "attribute"]),
+                Field(
+                    "text",
+                    "string",
+                    indexing=["index", "summary"],
+                    index="enable-bm25",
+                    stemming="none",
+                ),
+            ]
+            return ApplicationPackage(
+                "vectordbbench",
+                [
+                    Schema(
+                        self.schema_name,
+                        Document(fields),
+                        rank_profiles=[
+                            RankProfile(
+                                name="bm25",
+                                first_phase="bm25(text)",
+                                inherits="default",
+                                rank_properties=self.case_config.rank_properties(),
+                            ),
+                        ],
+                    )
+                ],
+                validations=[Validation(ValidationID.fieldTypeChange, until=tomorrow)],
+            )
+
         fields = [
             Field(
                 "id",
@@ -185,8 +557,6 @@ def _create_application_package(self):
                 )
             )
 
-        tomorrow = datetime.date.today() + datetime.timedelta(days=1)
-
         return ApplicationPackage(
             "vectordbbench",
             [
diff --git a/vectordb_bench/backend/clients/volc_mysql/cli.py b/vectordb_bench/backend/clients/volc_mysql/cli.py
new file mode 100755
index 000000000..9ce563473
--- /dev/null
+++ b/vectordb_bench/backend/clients/volc_mysql/cli.py
@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+from typing import Annotated, Unpack
+
+import click
+from pydantic import SecretStr
+
+from vectordb_bench.backend.clients import DB
+
+from ....cli.cli import (
+    CommonTypedDict,
+    cli,
+    click_parameter_decorators_from_typed_dict,
+    run,
+)
+
+
+class VolcMySQLTypedDict(CommonTypedDict):
+    user_name: Annotated[
+        str,
+        click.option(
+            "--username",
+            type=str,
+            help="Username",
+            required=True,
+        ),
+    ]
+    password: Annotated[
+        str,
+        click.option(
+            "--password",
+            type=str,
+            help="Password",
+            required=True,
+        ),
+    ]
+
+    host: Annotated[
+        str,
+        click.option(
+            "--host",
+            type=str,
+            help="Db host",
+            default="127.0.0.1",
+        ),
+    ]
+
+    port: Annotated[
+        int,
+        click.option(
+            "--port",
+            type=int,
+            default=3306,
+            help="DB Port",
+        ),
+    ]
+
+
+class VolcMySQLHNSWTypedDict(VolcMySQLTypedDict):
+    m: Annotated[
+        int | None,
+        click.option(
+            "--m",
+            type=int,
+            help="M parameter in HNSW vector indexing",
+            required=False,
+        ),
+    ]
+
+    ef_search: Annotated[
+        int | None,
+        click.option(
+            "--ef-search",
+            type=int,
+            help="Session variable loose_hnsw_ef_search",
+            required=False,
+        ),
+    ]
+
+    ef_construction: Annotated[
+        int | None,
+        click.option(
+            "--ef-construction",
+            type=int,
+            help="HNSW ef_construction",
+            required=False,
+        ),
+    ]
+
+    quant_algorithm: Annotated[
+        str | None,
+        click.option(
+            "--quant-algorithm",
+            type=click.Choice(["NONE", "SQ", "PQ"]),
+            help="Quantization algorithm",
+            required=False,
+        ),
+    ]
+
+    quant_type: Annotated[
+        str | None,
+        click.option(
+            "--quant-type",
+            type=click.Choice(["16_bit", "8_bit", "4_bit", "binary"]),
+            help="Quantization type",
+            required=False,
+        ),
+    ]
+
+
+@cli.command()
+@click_parameter_decorators_from_typed_dict(VolcMySQLHNSWTypedDict)
+def VolcMySQLHNSW(
+    **parameters: Unpack[VolcMySQLHNSWTypedDict],
+):
+    from .config import VolcMySQLConfig, VolcMySQLHNSWConfig
+
+    run(
+        db=DB.VolcMySQL,
+        db_config=VolcMySQLConfig(
+            db_label=parameters["db_label"],
+            user_name=parameters["username"],
+            password=SecretStr(parameters["password"]),
+            host=parameters["host"],
+            port=parameters["port"],
+        ),
+        db_case_config=VolcMySQLHNSWConfig(
+            M=parameters["m"],
+            ef_search=parameters["ef_search"],
+            ef_construction=parameters["ef_construction"],
+            quant_algorithm=parameters["quant_algorithm"],
+            quant_type=parameters["quant_type"],
+        ),
+        **parameters,
+    )
diff --git a/vectordb_bench/backend/clients/volc_mysql/config.py b/vectordb_bench/backend/clients/volc_mysql/config.py
new file mode 100755
index 000000000..aa4224848
--- /dev/null
+++ b/vectordb_bench/backend/clients/volc_mysql/config.py
@@ -0,0 +1,75 @@
+#!/usr/bin/env python3
+from typing import TypedDict
+
+from pydantic import BaseModel, SecretStr
+
+from ..api import DBCaseConfig, DBConfig, IndexType, MetricType
+
+
+class VolcMySQLConfigDict(TypedDict):
+    """Keys used directly as kwargs in mysql.connector.connect;
+    names must match mysql-connector-python's API."""
+
+    user: str
+    password: str
+    host: str
+    port: int
+
+
+class VolcMySQLConfig(DBConfig):
+    user_name: str = "root"
+    password: SecretStr
+    host: str = "127.0.0.1"
+    port: int = 3306
+
+    def to_dict(self) -> VolcMySQLConfigDict:
+        return {
+            "host": self.host,
+            "port": self.port,
+            "user": self.user_name,
+            "password": self.password.get_secret_value(),
+        }
+
+
+class VolcMySQLIndexConfig(BaseModel):
+    """Base index config for VolcMySQL"""
+
+    metric_type: MetricType | None = None
+
+    def parse_metric(self) -> str:
+        if self.metric_type == MetricType.L2:
+            return "l2"
+        if self.metric_type == MetricType.COSINE:
+            return "cosine"
+        msg = f"Metric type {self.metric_type} is not supported!"
+        raise ValueError(msg)
+
+
+class VolcMySQLHNSWConfig(VolcMySQLIndexConfig, DBCaseConfig):
+    M: int | None = None
+    ef_search: int | None = None
+    ef_construction: int | None = None
+    quant_algorithm: str | None = None
+    quant_type: str | None = None
+    index: IndexType = IndexType.HNSW
+
+    def index_param(self) -> dict:
+        return {
+            "metric_type": self.parse_metric(),
+            "index_type": self.index.value,
+            "M": self.M,
+            "ef_construction": self.ef_construction,
+            "quant_algorithm": self.quant_algorithm,
+            "quant_type": self.quant_type,
+        }
+
+    def search_param(self) -> dict:
+        return {
+            "metric_type": self.parse_metric(),
+            "ef_search": self.ef_search,
+        }
+
+
+_volcmysql_case_config = {
+    IndexType.HNSW: VolcMySQLHNSWConfig,
+}
diff --git a/vectordb_bench/backend/clients/volc_mysql/volc_mysql.py b/vectordb_bench/backend/clients/volc_mysql/volc_mysql.py
new file mode 100755
index 000000000..455286019
--- /dev/null
+++ b/vectordb_bench/backend/clients/volc_mysql/volc_mysql.py
@@ -0,0 +1,411 @@
+#!/usr/bin/env python3
+import json
+import logging
+import os
+import struct
+import tempfile
+from contextlib import contextmanager
+from pathlib import Path
+
+import mysql.connector as mysql
+import numpy as np
+
+from vectordb_bench.backend.filter import Filter, FilterOp
+
+from ..api import VectorDB
+from .config import VolcMySQLConfigDict, VolcMySQLIndexConfig
+
+log = logging.getLogger(__name__)
+
+
+def _encode_batch_to_tsv(
+    metadata: list[int],
+    embeddings: list[list[float]],
+    dim: int,
+    tsv_path: str,
+    *,
+    binary: bool = True,
+) -> None:
+    """Sort (id, vector) pairs by id ascending and write them to ``tsv_path``
+    for bulk ``LOAD DATA`` ingestion.
+
+    ``binary=True`` (default) encodes each vector as hex of little-endian
+    float32 bytes -> ``<id>\\t<hex>\\n``, consumed by
+    ``... (id, @h) SET v = UNHEX(@h)``.
+
+    ``binary=False`` (to_vector fallback) writes the vector as a JSON-style
+    ``[f1,f2,...]`` literal -> ``<id>\\t[..]\\n``, consumed by
+    ``... (id, @v) SET v = to_vector(@v)``. The literal contains no tab or
+    newline, so it stays delimiter-safe in the TSV transport.
+    """
+    order = np.argsort(metadata)
+    with Path(tsv_path).open("w", buffering=1 << 20) as f:
+        if binary:
+            pack_fmt = f"<{dim}f"
+            for i in order:
+                hex_str = struct.pack(pack_fmt, *embeddings[i]).hex()
+                f.write(f"{metadata[i]}\t{hex_str}\n")
+        else:
+            for i in order:
+                vec_str = "[" + ",".join(repr(float(x)) for x in embeddings[i]) + "]"
+                f.write(f"{metadata[i]}\t{vec_str}\n")
+
+
+def _build_index_attrs_json(index_param: dict) -> str:
+    """Build the SECONDARY_ENGINE_ATTRIBUTE JSON payload for CREATE VECTOR INDEX
+    from a case-config index_param dict. None values are dropped so the server
+    sees only the attributes the user explicitly set.
+    """
+    attrs = {
+        "algorithm": "hnsw",
+        "distance": index_param.get("metric_type"),
+        "m": index_param.get("M"),
+        "ef_construction": index_param.get("ef_construction"),
+        "quant_algorithm": index_param.get("quant_algorithm"),
+        "quant_type": index_param.get("quant_type"),
+    }
+    attrs = {k: v for k, v in attrs.items() if v is not None}
+    return json.dumps(attrs, ensure_ascii=False, separators=(",", ":"))
+
+
+class VolcMySQL(VectorDB):
+    # mysql.connector is not thread-safe; ConcurrentInsertRunner uses
+    # max_workers=1 when False. rate_runner branches on db.name to give
+    # each worker thread its own connection.
+    thread_safe: bool = False
+
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+    ]
+
+    def __init__(
+        self,
+        dim: int,
+        db_config: VolcMySQLConfigDict,
+        db_case_config: VolcMySQLIndexConfig,
+        collection_name: str = "vec_collection",
+        drop_old: bool = False,
+        **kwargs,
+    ):
+        self.name = "VolcMySQL"
+        self.db_config = db_config
+        self.case_config = db_case_config
+        self.db_name = "vectordbbench"
+        self.table_name = collection_name
+        self.dim = dim
+
+        self.conn = None
+        self.cursor = None
+        self.admin_cursor = None
+
+        # Active filter predicate selected by prepare_filter(). Runners call
+        # prepare_filter() (never search_embedding(filters=...)), so the
+        # filtered WHERE clause is chosen here, not per query.
+        self._filtered = False
+
+        if drop_old:
+            self.conn, self.cursor, self.admin_cursor = self._create_connection()
+            try:
+                self._drop_db()
+                self._create_db_table(dim)
+            finally:
+                self.cursor.close()
+                self.admin_cursor.close()
+                self.conn.close()
+                self.conn = None
+                self.cursor = None
+                self.admin_cursor = None
+
+    def _create_connection(self):
+        conn = mysql.connect(
+            host=self.db_config["host"],
+            user=self.db_config["user"],
+            port=self.db_config["port"],
+            password=self.db_config["password"],
+            allow_local_infile=True,
+        )
+        cursor = conn.cursor()
+        admin_cursor = conn.cursor()
+
+        assert conn is not None, "Connection is not initialized"
+        assert cursor is not None, "Cursor is not initialized"
+        assert admin_cursor is not None, "Admin cursor is not initialized"
+
+        return conn, cursor, admin_cursor
+
+    def _drop_db(self):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.admin_cursor is not None, "Cursor is not initialized"
+        log.info(f"{self.name} client drop db : {self.db_name}")
+
+        # flush tables before dropping database to avoid some locking issue
+        self.admin_cursor.execute("FLUSH TABLES")
+        self.admin_cursor.execute(f"DROP DATABASE IF EXISTS {self.db_name}")
+        self.admin_cursor.execute("COMMIT")
+        self.admin_cursor.execute("FLUSH TABLES")
+
+    def _create_db_table(self, dim: int):
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.admin_cursor is not None, "Cursor is not initialized"
+
+        try:
+            log.info(f"{self.name} client create database : {self.db_name}")
+            self.admin_cursor.execute(f"CREATE DATABASE {self.db_name}")
+
+            log.info(f"{self.name} client create table : {self.table_name}")
+            self.admin_cursor.execute(f"USE {self.db_name}")
+
+            create_table_sql = f"""
+              CREATE TABLE {self.table_name} (
+                id INT PRIMARY KEY,
+                v VECTOR({self.dim}) NOT NULL
+              ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
+            """
+            self.admin_cursor.execute(create_table_sql)
+            self.admin_cursor.execute("COMMIT")
+
+        except Exception as e:
+            log.warning(f"Failed to create table: {self.table_name} error: {e}")
+            raise e from None
+
+    def _probe_binary_support(self) -> bool:
+        """Probe once whether the server accepts the raw little-endian float32
+        binary VECTOR path: ``UNHEX()`` insert and the ``_binary`` query
+        introducer. Returns ``True`` only if both succeed; otherwise ``False``
+        so :meth:`init` selects the ``to_vector()`` text path for insert and
+        search.
+
+        Runs inside a session-local ``TEMPORARY TABLE`` with a throwaway
+        4-dim vector, so it never touches the benchmark table and is
+        independent of the real vector dimension. A probe failure is expected
+        (not an error) on builds without binary VECTOR support and only flips
+        the path; it never raises.
+        """
+        probe = [1.0, 2.0, 3.0, 4.0]
+        blob = struct.pack(f"<{len(probe)}f", *probe)
+        # Qualify with the (already-existing) benchmark schema: the client never
+        # issues USE, so the connection has no default database to host the temp table.
+        tmp = f"`{self.db_name}`._vdbb_binprobe"
+        cur = self.conn.cursor(buffered=True)
+        try:
+            cur.execute(f"CREATE TEMPORARY TABLE {tmp} (id INT PRIMARY KEY, v VECTOR(4))")
+            cur.execute(f"INSERT INTO {tmp} (id, v) VALUES (1, UNHEX(%s))", (blob.hex(),))
+            cur.execute(f"SELECT id FROM {tmp} ORDER BY L2_DISTANCE(v, _binary %s) LIMIT 1", (blob,))
+            cur.fetchall()
+        except mysql.Error as e:
+            log.warning(f"{self.name}: binary VECTOR path unsupported, falling back to to_vector() text path: {e}")
+            return False
+        else:
+            log.info(f"{self.name}: binary VECTOR path supported; using raw-binary insert + query")
+            return True
+        finally:
+            try:
+                cur.execute(f"DROP TEMPORARY TABLE IF EXISTS {tmp}")
+            except mysql.Error:
+                log.debug("Failed to drop binary-probe temp table", exc_info=True)
+            cur.close()
+
+    @contextmanager
+    def init(self):
+        """create and destory connections to database.
+
+        Examples:
+            >>> with self.init():
+            >>>     self.insert_embeddings()
+        """
+        self.conn, self.cursor, self.admin_cursor = self._create_connection()
+        try:
+            # The binary probe creates a TEMPORARY TABLE qualified with self.db_name.
+            # When drop_old=False on a fresh server, the schema may not exist yet --
+            # ensure it does so the probe lands on a real database and doesn't
+            # spuriously fall back to the text path.
+            self.admin_cursor.execute(f"CREATE DATABASE IF NOT EXISTS {self.db_name}")
+
+            # Load-phase session tuning. SESSION-scoped; resets when the
+            # connection closes. No GLOBAL or instance-level changes.
+            try:
+                self.admin_cursor.execute("SET SESSION unique_checks = 0")
+                self.admin_cursor.execute("SET SESSION foreign_key_checks = 0")
+            except mysql.Error as e:
+                log.warning(f"Could not apply load-phase session tuning: {e}")
+
+            # Per-batch TSV file numbering for LOAD DATA bulk load.
+            self._batch_counter = 0
+
+            index_param = self.case_config.index_param()
+            search_param = self.case_config.search_param()
+
+            if search_param.get("ef_search") is not None:
+                try:
+                    self.admin_cursor.execute(f"SET loose_hnsw_ef_search = {int(search_param['ef_search'])}")
+                    self.conn.commit()
+                except mysql.Error:
+                    log.warning(
+                        f"Could not set loose_hnsw_ef_search = {int(search_param['ef_search'])}, "
+                        "using server defaults"
+                    )
+
+            # prebuild SQL strings
+            dist_func = "L2_DISTANCE" if index_param["metric_type"] == "l2" else "COSINE_DISTANCE"
+            # Raw-binary VECTOR path: send float32 vectors as little-endian bytes and let the
+            # server consume them directly -- UNHEX(@h) on insert, the `_binary` introducer on
+            # query -- with no to_vector() text parse and no Python str() formatting (+71% c80
+            # QPS, recall identical). `_binary <literal>` stays constant-foldable so the HNSW
+            # index scan is preserved (UNHEX() is NOT, hence hex is used only on the load path).
+            #
+            # Not every MySQL-compatible build accepts the binary path, so we AUTO-PROBE it once
+            # per connection (see _probe_binary_support) and fall back to the to_vector() text
+            # path -- for BOTH insert and query -- when it is unsupported. VDB_BINARY_VEC overrides
+            # the probe: "1" forces binary (skip probe), "0" forces the to_vector() text path.
+            force = os.environ.get("VDB_BINARY_VEC")
+            if force == "1":
+                self._binary_vec = True
+            elif force == "0":
+                self._binary_vec = False
+            else:
+                self._binary_vec = self._probe_binary_support()
+            vec_expr = "_binary %s" if self._binary_vec else "to_vector(%s)"
+            # No FORCE INDEX hint: optimize() creates idx_v, but read_write streaming
+            # cases run search before optimize, so hinting a not-yet-existing index
+            # would error every pre-optimize search. The optimizer picks idx_v once
+            # it exists; before that, the seq scan is the only valid plan anyway.
+            self.select_sql = (
+                f"SELECT id FROM {self.db_name}.{self.table_name} ORDER BY {dist_func}(v, {vec_expr}) LIMIT %s"
+            )
+            self.select_sql_with_filter = (
+                f"SELECT id FROM {self.db_name}.{self.table_name} WHERE id >= %s ORDER BY "
+                f"{dist_func}(v, {vec_expr}) LIMIT %s"
+            )
+
+            yield
+        finally:
+            try:
+                if self.cursor is not None:
+                    self.cursor.close()
+                if self.admin_cursor is not None:
+                    self.admin_cursor.close()
+                if self.conn is not None:
+                    self.conn.close()
+            finally:
+                self.cursor = None
+                self.admin_cursor = None
+                self.conn = None
+
+    def optimize(self, data_size: int | None = None) -> None:
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.admin_cursor is not None, "Admin cursor is not initialized"
+
+        try:
+            log.info(f"{self.name} client create index : {self.table_name}")
+            self.admin_cursor.execute(f"USE {self.db_name}")
+
+            # Build vector index attributes
+            index_param = self.case_config.index_param()
+
+            attrs_json = _build_index_attrs_json(index_param)
+
+            sql = f"CREATE VECTOR INDEX idx_v ON {self.table_name}(v) SECONDARY_ENGINE_ATTRIBUTE='{attrs_json}'"
+            log.info(f"{self.name} client execute create index: {sql}")
+            self.admin_cursor.execute(sql)
+            self.admin_cursor.execute("COMMIT")
+        except Exception as e:
+            log.warning(f"Failed to create index on {self.table_name}, error: {e}")
+            raise e from None
+
+    def insert_embeddings(
+        self,
+        embeddings: list[list[float]],
+        metadata: list[int],
+        **kwargs,
+    ) -> tuple[int, Exception | None]:
+        """Insert a batch via LOAD DATA LOCAL INFILE with sorted PK. Uses the
+        hex-binary ``UNHEX`` path when the server supports it (probed in
+        :meth:`init`), otherwise the ``to_vector()`` text path. Requires
+        self.init() context.
+        """
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        n = len(metadata)
+        if n == 0:
+            return 0, None
+
+        tsv_path = Path(tempfile.gettempdir()) / (
+            f"vdbb_volc_{self.db_name}_{self.table_name}_{os.getpid()}_{self._batch_counter}.tsv"
+        )
+        self._batch_counter += 1
+
+        try:
+            _encode_batch_to_tsv(metadata, embeddings, self.dim, str(tsv_path), binary=self._binary_vec)
+
+            tsv_literal = str(tsv_path).replace("'", "''")
+            set_clause = "(id, @h) SET v = UNHEX(@h)" if self._binary_vec else "(id, @v) SET v = to_vector(@v)"
+            load_sql = (
+                f"LOAD DATA LOCAL INFILE '{tsv_literal}' "
+                f"INTO TABLE `{self.db_name}`.`{self.table_name}` "
+                "FIELDS TERMINATED BY '\\t' LINES TERMINATED BY '\\n' "
+                f"{set_clause}"
+            )
+            self.cursor.execute(load_sql)
+            self.conn.commit()
+        except Exception as e:
+            log.warning(f"Failed to LOAD DATA into Vector table ({self.table_name}), error: {e}")
+            return 0, e
+        else:
+            actual = self.cursor.rowcount
+            if actual != n:
+                # Return the real count so the runner's already_insert_count tracks
+                # what's actually on disk; on retry it will slice past the
+                # already-loaded prefix instead of duplicate-PK looping on the
+                # same batch. self.conn.commit() ran above, so those rows are
+                # durable.
+                msg = f"LOAD DATA wrote {actual} rows, expected {n}"
+                return actual, RuntimeError(msg)
+            return n, None
+        finally:
+            if tsv_path.exists():
+                try:
+                    tsv_path.unlink()
+                except OSError as e:
+                    log.warning(f"Failed to unlink staging TSV {tsv_path}: {e}")
+
+    def prepare_filter(self, filters: Filter):
+        """Select the filtered vs unfiltered search template for the case.
+
+        Runners apply filters via this hook (called once per case inside the
+        init() context), not by passing ``filters`` to search_embedding. Store
+        the predicate value so search_embedding can bind it.
+        """
+        if filters.type == FilterOp.NonFilter:
+            self._filtered = False
+        elif filters.type == FilterOp.NumGE:
+            self._filtered = True
+            self._filter_value = filters.int_value
+        else:
+            msg = f"Unsupported filter for VolcMySQL: {filters}"
+            raise ValueError(msg)
+
+    def search_embedding(
+        self,
+        query: list[float],
+        k: int = 100,
+        **kwargs,
+    ) -> list[int]:
+        assert self.conn is not None, "Connection is not initialized"
+        assert self.cursor is not None, "Cursor is not initialized"
+
+        try:
+            # Binary path: raw LE float32 bytes (C-level struct.pack, ~us) consumed
+            # by the server as a binary vector via `_binary %s`; avoids the per-query
+            # str() formatting of 1536 floats and the server-side strtof text parse.
+            query_param = struct.pack(f"<{len(query)}f", *query) if self._binary_vec else str(query)
+            if self._filtered:
+                self.cursor.execute(self.select_sql_with_filter, (self._filter_value, query_param, k))
+            else:
+                self.cursor.execute(self.select_sql, (query_param, k))
+            return [row[0] for row in self.cursor.fetchall()]
+
+        except mysql.Error:
+            log.exception("Failed to execute search query")
+            raise
diff --git a/vectordb_bench/backend/clients/weaviate_cloud/weaviate_cloud.py b/vectordb_bench/backend/clients/weaviate_cloud/weaviate_cloud.py
index d6111c8da..453a22296 100644
--- a/vectordb_bench/backend/clients/weaviate_cloud/weaviate_cloud.py
+++ b/vectordb_bench/backend/clients/weaviate_cloud/weaviate_cloud.py
@@ -7,12 +7,20 @@
 import weaviate
 from weaviate.exceptions import WeaviateBaseError
 
+from vectordb_bench.backend.filter import Filter, FilterOp
+
 from ..api import DBCaseConfig, VectorDB
 
 log = logging.getLogger(__name__)
 
 
 class WeaviateCloud(VectorDB):
+    thread_safe: bool = False
+    supported_filter_types: list[FilterOp] = [
+        FilterOp.NonFilter,
+        FilterOp.NumGE,
+    ]
+
     def __init__(
         self,
         dim: int,
@@ -23,6 +31,7 @@ def __init__(
         **kwargs,
     ):
         """Initialize wrapper around the weaviate vector database."""
+        self.name = "WeaviateCloud"
         db_config.update(
             {
                 "auth_client_secret": weaviate.AuthApiKey(
@@ -37,6 +46,7 @@ def __init__(
         self._scalar_field = "key"
         self._vector_field = "vector"
         self._index_name = "vector_idx"
+        self._where_filter: dict | None = None
 
         # If local setup is used, we
         if db_config["no_auth"]:
@@ -145,16 +155,24 @@ def search_embedding(
             .with_near_vector({"vector": query})
             .with_limit(k)
         )
-        if filters:
-            where_filter = {
-                "path": "key",
-                "operator": "GreaterThanEqual",
-                "valueInt": filters.get("id"),
-            }
-            query_obj = query_obj.with_where(where_filter)
+        if self._where_filter is not None:
+            query_obj = query_obj.with_where(self._where_filter)
 
         # Perform the search.
         res = query_obj.do()
 
         # Organize results.
         return [result[self._scalar_field] for result in res["data"]["Get"][self.collection_name]]
+
+    def prepare_filter(self, filters: Filter):
+        if filters.type == FilterOp.NonFilter:
+            self._where_filter = None
+        elif filters.type == FilterOp.NumGE:
+            self._where_filter = {
+                "path": [self._scalar_field],
+                "operator": "GreaterThanEqual",
+                "valueInt": filters.int_value,
+            }
+        else:
+            msg = f"Unsupported Weaviate filter: {filters}"
+            raise ValueError(msg)
diff --git a/vectordb_bench/backend/clients/zilliz_cloud/cli.py b/vectordb_bench/backend/clients/zilliz_cloud/cli.py
index a8d177ee5..030a00661 100644
--- a/vectordb_bench/backend/clients/zilliz_cloud/cli.py
+++ b/vectordb_bench/backend/clients/zilliz_cloud/cli.py
@@ -13,6 +13,13 @@
 )
 
 
+def _use_partition_key(parameters: dict) -> bool:
+    explicit = parameters.get("use_partition_key")
+    if explicit is not None:
+        return explicit
+    return parameters.get("case_type") == "CloudMultiTenantSearchCase"
+
+
 class ZillizTypedDict(CommonTypedDict):
     uri: Annotated[
         str,
@@ -20,7 +27,7 @@ class ZillizTypedDict(CommonTypedDict):
     ]
     user_name: Annotated[
         str,
-        click.option("--user-name", type=str, help="Db username", required=True),
+        click.option("--user-name", type=str, help="Db username", default=""),
     ]
     password: Annotated[
         str,
@@ -32,6 +39,16 @@ class ZillizTypedDict(CommonTypedDict):
             show_default="$ZILLIZ_PASSWORD",
         ),
     ]
+    token: Annotated[
+        str,
+        click.option(
+            "--token",
+            type=str,
+            help="Zilliz API token",
+            default=lambda: os.environ.get("ZILLIZ_TOKEN", ""),
+            show_default="$ZILLIZ_TOKEN",
+        ),
+    ]
     level: Annotated[
         str,
         click.option("--level", type=str, help="Zilliz index level", required=False),
@@ -58,6 +75,17 @@ class ZillizTypedDict(CommonTypedDict):
             show_default=True,
         ),
     ]
+    use_partition_key: Annotated[
+        bool | None,
+        click.option(
+            "--use-partition-key/--no-use-partition-key",
+            default=None,
+            help=(
+                "Use the Zilliz Cloud partition key on the label field. "
+                "Defaults to enabled for CloudMultiTenantSearchCase and disabled otherwise."
+            ),
+        ),
+    ]
 
 
 @cli.command()
@@ -72,12 +100,14 @@ def ZillizAutoIndex(**parameters: Unpack[ZillizTypedDict]):
             uri=SecretStr(parameters["uri"]),
             user=parameters["user_name"],
             password=SecretStr(parameters["password"]),
+            token=SecretStr(parameters["token"]),
             num_shards=parameters["num_shards"],
             collection_name=parameters["collection_name"],
         ),
         db_case_config=AutoIndexConfig(
             level=int(parameters["level"]) if parameters["level"] else 1,
             num_shards=parameters["num_shards"],
+            use_partition_key=_use_partition_key(parameters),
         ),
         **parameters,
     )
diff --git a/vectordb_bench/backend/clients/zilliz_cloud/config.py b/vectordb_bench/backend/clients/zilliz_cloud/config.py
index f0b3fd000..ee5ecf645 100644
--- a/vectordb_bench/backend/clients/zilliz_cloud/config.py
+++ b/vectordb_bench/backend/clients/zilliz_cloud/config.py
@@ -1,21 +1,27 @@
 from pydantic import SecretStr
 
 from ..api import DBCaseConfig, DBConfig
-from ..milvus.config import IndexType, MilvusIndexConfig
+from ..milvus.config import IndexType, MilvusFtsConfig, MilvusIndexConfig
 
 
 class ZillizCloudConfig(DBConfig):
     uri: SecretStr
-    user: str
-    password: SecretStr
+    user: str = ""
+    password: SecretStr = SecretStr("")
+    token: SecretStr = SecretStr("")
     num_shards: int = 1
     collection_name: str = "ZillizCloudVDBBench"
 
+    @staticmethod
+    def common_long_configs() -> list[str]:
+        return [*DBConfig.common_long_configs(), "user", "password", "token"]
+
     def to_dict(self) -> dict:
         return {
             "uri": self.uri.get_secret_value(),
             "user": self.user,
             "password": self.password.get_secret_value(),
+            "token": self.token.get_secret_value(),
             "num_shards": self.num_shards,
             "collection_name": self.collection_name,
         }
@@ -40,3 +46,27 @@ def search_param(self) -> dict:
                 "level": self.level,
             },
         }
+
+
+class ZillizCloudFtsConfig(MilvusFtsConfig):
+    index_type: str = IndexType.AUTOINDEX.value
+    level: int = 1
+
+    def sparse_index_param(self) -> dict:
+        params = {}
+        if self.bm25_k1 is not None:
+            params["bm25_k1"] = self.bm25_k1
+        if self.bm25_b is not None:
+            params["bm25_b"] = self.bm25_b
+
+        return {
+            "index_type": self.index_type,
+            "metric_type": self.metric_type.value,
+            "params": params,
+        }
+
+    def search_param(self) -> dict:
+        return {
+            "metric_type": self.metric_type.value,
+            "params": {"level": self.level},
+        }
diff --git a/vectordb_bench/backend/data_source.py b/vectordb_bench/backend/data_source.py
index 139d2e308..942a80409 100644
--- a/vectordb_bench/backend/data_source.py
+++ b/vectordb_bench/backend/data_source.py
@@ -1,13 +1,23 @@
 import logging
+import os
 import pathlib
 import typing
 from abc import ABC, abstractmethod
 from enum import Enum
 
+import ir_datasets
 from tqdm import tqdm
 
 from vectordb_bench import config
 
+# Set ir_datasets to use tmp directory for both home and temp
+# This ensures all downloaded files and temporary data are stored in /tmp
+ir_datasets_home = pathlib.Path(config.DATASET_LOCAL_DIR) / "ir_datasets"
+ir_datasets_tmp = pathlib.Path(config.DATASET_LOCAL_DIR) / "ir_datasets_tmp"
+os.environ.setdefault("IR_DATASETS_HOME", str(ir_datasets_home))
+os.environ.setdefault("IR_DATASETS_TMP", str(ir_datasets_tmp))
+
+
 logging.getLogger("s3fs").setLevel(logging.CRITICAL)
 
 log = logging.getLogger(__name__)
@@ -18,6 +28,7 @@
 class DatasetSource(Enum):
     S3 = "S3"
     AliyunOSS = "AliyunOSS"
+    IR_DATASETS = "IR_DATASETS"
 
     def reader(self) -> DatasetReader:
         if self == DatasetSource.S3:
@@ -26,6 +37,9 @@ def reader(self) -> DatasetReader:
         if self == DatasetSource.AliyunOSS:
             return AliyunOSSReader()
 
+        if self == DatasetSource.IR_DATASETS:
+            return IRDatasetsReader()
+
         return None
 
 
@@ -155,3 +169,40 @@ def validate_file(self, remote: pathlib.Path, local: pathlib.Path) -> bool:
             return False
 
         return True
+
+
+class IRDatasetsReader(DatasetReader):
+    """Reader for ir_datasets based datasets"""
+
+    source: DatasetSource = DatasetSource.IR_DATASETS
+    remote_root: str = ""  # Not used for ir_datasets
+
+    def __init__(self):
+        self.ir_datasets = ir_datasets
+
+    def read(self, dataset: str, files: list[str], local_ds_root: pathlib.Path):
+        """
+        Download FTS dataset using ir_datasets API
+
+        Args:
+            dataset: ir_datasets dataset name
+            files: Expected output files (ignored, not used)
+            local_ds_root: Local directory (not used, ir_datasets handles its own cache)
+        """
+        log.info(f"Downloading FTS dataset '{dataset}' using ir_datasets")
+
+        try:
+            # Load dataset using ir_datasets - this will download if needed
+            # ir_datasets handles caching automatically
+            # Actual data download happens lazily when iterating
+            self.ir_datasets.load(dataset)
+            log.info(f"Successfully loaded dataset: {dataset}")
+
+        except Exception:
+            log.exception(f"Failed to download FTS dataset '{dataset}'")
+            raise
+
+    def validate_file(self, remote: pathlib.Path, local: pathlib.Path) -> bool:
+        """For ir_datasets, we don't validate against remote files"""
+        # ir_datasets handles its own caching and validation
+        return local.exists() and local.stat().st_size > 0
diff --git a/vectordb_bench/backend/dataset.py b/vectordb_bench/backend/dataset.py
index 12808e420..176c08e17 100644
--- a/vectordb_bench/backend/dataset.py
+++ b/vectordb_bench/backend/dataset.py
@@ -5,14 +5,22 @@
 """
 
 import logging
+import math
 import pathlib
+import types
+import typing
+from abc import ABC, abstractmethod
+from collections.abc import Iterator
+from dataclasses import dataclass
 from enum import Enum
 from typing import Any, ClassVar, NamedTuple
 
+import ir_datasets
 import pandas as pd
 import polars as pl
 from pyarrow.parquet import ParquetFile
-from pydantic import field_validator
+from pydantic import Field as PydanticField
+from pydantic import PrivateAttr, field_validator
 
 from vectordb_bench import config
 from vectordb_bench.base import BaseModel
@@ -59,7 +67,7 @@ class BaseDataset(BaseModel):
 
     @field_validator("size")
     @classmethod
-    def verify_size(cls, v: int) -> int:
+    def verify_size(cls, v: int):
         if v not in cls._size_label:
             msg = f"Size {v} not supported for the dataset, expected: {cls._size_label.keys()}"
             raise ValueError(msg)
@@ -105,7 +113,7 @@ class CustomDataset(BaseDataset):
 
     @field_validator("size")
     @classmethod
-    def verify_size(cls, v: int) -> int:
+    def verify_size(cls, v: int):
         return v
 
     @property
@@ -122,6 +130,8 @@ def file_count(self) -> int:
 
     @property
     def train_files(self) -> list[str]:
+        if ("," not in self.train_file) and self.file_num > 1:
+            return utils.compose_train_files(self.file_num, self.use_shuffled)
         train_file = self.train_file
         prefix = f"{train_file}"
         train_files = []
@@ -138,7 +148,9 @@ class LAION(BaseDataset):
     metric_type: MetricType = MetricType.L2
     use_shuffled: bool = False
     with_gt: bool = True
-    _size_label: ClassVar[dict] = {
+    with_scalar_labels: bool = True
+    scalar_label_percentages: list[float] = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5]
+    _size_label: ClassVar[dict[int, SizeLabel]] = {
         100_000_000: SizeLabel(100_000_000, "LARGE", 100),
     }
 
@@ -148,7 +160,7 @@ class GIST(BaseDataset):
     dim: int = 960
     metric_type: MetricType = MetricType.L2
     use_shuffled: bool = False
-    _size_label: ClassVar[dict] = {
+    _size_label: ClassVar[dict[int, SizeLabel]] = {
         100_000: SizeLabel(100_000, "SMALL", 1),
         1_000_000: SizeLabel(1_000_000, "MEDIUM", 1),
     }
@@ -160,7 +172,7 @@ class Cohere(BaseDataset):
     metric_type: MetricType = MetricType.COSINE
     use_shuffled: bool = config.USE_SHUFFLED_DATA
     with_gt: bool = True
-    _size_label: ClassVar[dict] = {
+    _size_label: ClassVar[dict[int, SizeLabel]] = {
         100_000: SizeLabel(100_000, "SMALL", 1),
         1_000_000: SizeLabel(1_000_000, "MEDIUM", 1),
         10_000_000: SizeLabel(10_000_000, "LARGE", 10),
@@ -198,7 +210,7 @@ class Bioasq(BaseDataset):
     metric_type: MetricType = MetricType.COSINE
     use_shuffled: bool = config.USE_SHUFFLED_DATA
     with_gt: bool = True
-    _size_label: ClassVar[dict] = {
+    _size_label: ClassVar[dict[int, SizeLabel]] = {
         1_000_000: SizeLabel(1_000_000, "MEDIUM", 1),
         10_000_000: SizeLabel(10_000_000, "LARGE", 10),
     }
@@ -234,7 +246,7 @@ class Glove(BaseDataset):
     dim: int = 200
     metric_type: MetricType = MetricType.COSINE
     use_shuffled: bool = False
-    _size_label: ClassVar[dict] = {1_000_000: SizeLabel(1_000_000, "MEDIUM", 1)}
+    _size_label: ClassVar[dict[int, SizeLabel]] = {1_000_000: SizeLabel(1_000_000, "MEDIUM", 1)}
 
 
 class SIFT(BaseDataset):
@@ -242,7 +254,7 @@ class SIFT(BaseDataset):
     dim: int = 128
     metric_type: MetricType = MetricType.L2
     use_shuffled: bool = False
-    _size_label: ClassVar[dict] = {
+    _size_label: ClassVar[dict[int, SizeLabel]] = {
         500_000: SizeLabel(
             500_000,
             "SMALL",
@@ -259,7 +271,7 @@ class OpenAI(BaseDataset):
     metric_type: MetricType = MetricType.COSINE
     use_shuffled: bool = config.USE_SHUFFLED_DATA
     with_gt: bool = True
-    _size_label: ClassVar[dict] = {
+    _size_label: ClassVar[dict[int, SizeLabel]] = {
         50_000: SizeLabel(50_000, "SMALL", 1),
         500_000: SizeLabel(500_000, "MEDIUM", 1),
         5_000_000: SizeLabel(5_000_000, "LARGE", 10),
@@ -338,11 +350,16 @@ def data_dir(self) -> pathlib.Path:
     def __iter__(self):
         return DataSetIterator(self)
 
+    def iter_batches(self, batch_size: int):
+        return DataSetIterator(self, batch_size=batch_size)
+
     # TODO passing use_shuffle from outside
     def prepare(
         self,
         source: DatasetSource = DatasetSource.S3,
         filters: Filter = non_filter,
+        with_train_files: bool = True,
+        with_scalar_labels: bool = False,
     ) -> bool:
         """Download the dataset from DatasetSource
          url = f"{source}/{self.data.dir_name}"
@@ -356,7 +373,7 @@ def prepare(
             bool: whether the dataset is successfully prepared
 
         """
-        self.train_files = self.data.train_files
+        self.train_files = self.data.train_files if with_train_files else []
         gt_file, test_file = None, None
         if self.data.with_gt:
             gt_file, test_file = filters.groundtruth_file, self.data.test_file
@@ -373,12 +390,10 @@ def prepare(
                 local_ds_root=self.data_dir,
             )
 
+        needs_scalar_labels = filters.type == FilterOp.StrEqual or with_scalar_labels
+
         # read scalar_labels_file if separated
-        if (
-            filters.type == FilterOp.StrEqual
-            and self.data.with_scalar_labels
-            and self.data.scalar_labels_file_separated
-        ):
+        if needs_scalar_labels and self.data.with_scalar_labels and self.data.scalar_labels_file_separated:
             self.scalar_labels = self._read_file(self.data.scalar_labels_file)
 
         if gt_file is not None and test_file is not None:
@@ -401,8 +416,9 @@ def _read_file(self, file_name: str) -> pl.DataFrame:
 
 
 class DataSetIterator:
-    def __init__(self, dataset: DatasetManager):
+    def __init__(self, dataset: DatasetManager, batch_size: int = config.NUM_PER_BATCH):
         self._ds = dataset
+        self._batch_size = batch_size
         self._idx = 0  # file number
         self._cur = None
         self._sub_idx = [0 for i in range(len(self._ds.train_files))]  # iter num for each file
@@ -428,7 +444,7 @@ def _get_iter(self, file_name: str):
             msg = f"No such file: {p}"
             log.warning(msg)
             raise IndexError(msg)
-        return ParquetFile(p, memory_map=True, pre_buffer=True).iter_batches(config.NUM_PER_BATCH)
+        return ParquetFile(p, memory_map=True, pre_buffer=True).iter_batches(self._batch_size)
 
     def __next__(self) -> pd.DataFrame:
         """return the data in the next file of the training list"""
@@ -478,6 +494,7 @@ class DatasetWithSizeType(Enum):
     CohereSmall = "Small Cohere (768dim, 100K)"
     CohereMedium = "Medium Cohere (768dim, 1M)"
     CohereLarge = "Large Cohere (768dim, 10M)"
+    LAIONLarge = "Large LAION (768dim, 100M)"
     BioasqMedium = "Medium Bioasq (1024dim, 1M)"
     BioasqLarge = "Large Bioasq (1024dim, 10M)"
     OpenAISmall = "Small OpenAI (1536dim, 50K)"
@@ -491,6 +508,8 @@ def get_manager(self) -> DatasetManager:
         return DatasetWithSizeMap.get(self)
 
     def get_load_timeout(self) -> float:
+        if self is DatasetWithSizeType.LAIONLarge:
+            return config.LOAD_TIMEOUT_768D_100M
         if "small" in self.value.lower():
             return config.LOAD_TIMEOUT_768D_100K
         if "medium" in self.value.lower():
@@ -501,6 +520,8 @@ def get_load_timeout(self) -> float:
         raise KeyError(msg)
 
     def get_optimize_timeout(self) -> float:
+        if self is DatasetWithSizeType.LAIONLarge:
+            return config.OPTIMIZE_TIMEOUT_768D_100M
         if "small" in self.value.lower():
             return config.OPTIMIZE_TIMEOUT_768D_100K
         if "medium" in self.value.lower():
@@ -514,9 +535,668 @@ def get_optimize_timeout(self) -> float:
     DatasetWithSizeType.CohereSmall: Dataset.COHERE.manager(100_000),
     DatasetWithSizeType.CohereMedium: Dataset.COHERE.manager(1_000_000),
     DatasetWithSizeType.CohereLarge: Dataset.COHERE.manager(10_000_000),
+    DatasetWithSizeType.LAIONLarge: Dataset.LAION.manager(100_000_000),
     DatasetWithSizeType.BioasqMedium: Dataset.BIOASQ.manager(1_000_000),
     DatasetWithSizeType.BioasqLarge: Dataset.BIOASQ.manager(10_000_000),
     DatasetWithSizeType.OpenAISmall: Dataset.OPENAI.manager(50_000),
     DatasetWithSizeType.OpenAIMedium: Dataset.OPENAI.manager(500_000),
     DatasetWithSizeType.OpenAILarge: Dataset.OPENAI.manager(5_000_000),
 }
+
+
+# FTS Dataset Translator Pattern
+@dataclass
+class FtsQuery:
+    """Internal representation of an FTS query."""
+
+    query_id: str
+    text: str
+
+
+@dataclass
+class FtsDocument:
+    """Internal representation of an FTS document."""
+
+    doc_id: str
+    text: str
+    filter_id: int | None = None
+
+
+_FTS_FILTER_GOLDEN_RATIO_64 = 0x9E3779B97F4A7C15
+_FTS_FILTER_OFFSET_SEED = 0xD1B54A32D192ED03
+
+
+@dataclass(frozen=True)
+class FtsFilterIdPermutation:
+    """Deterministic bijection that scatters FTS filter IDs across corpus order."""
+
+    size: int
+    multiplier: int
+    offset: int
+
+    @property
+    def algorithm(self) -> str:
+        return "affine_permutation_v1"
+
+    @classmethod
+    def for_size(cls, size: int) -> "FtsFilterIdPermutation":
+        if size <= 0:
+            msg = f"FTS filter ID permutation size must be positive, got {size}"
+            raise ValueError(msg)
+        if size == 1:
+            return cls(size=1, multiplier=1, offset=0)
+
+        multiplier = max(1, (size * _FTS_FILTER_GOLDEN_RATIO_64) >> 64)
+        while math.gcd(multiplier, size) != 1:
+            multiplier += 1
+            if multiplier >= size:
+                multiplier = 1
+
+        return cls(
+            size=size,
+            multiplier=multiplier,
+            offset=_FTS_FILTER_OFFSET_SEED % size,
+        )
+
+    def map(self, ordinal: int) -> int:
+        if ordinal < 0 or ordinal >= self.size:
+            msg = f"FTS filter ID ordinal must be in [0, {self.size}), got {ordinal}"
+            raise ValueError(msg)
+        return (self.multiplier * ordinal + self.offset) % self.size
+
+
+class FtsDatasetTranslator(ABC):
+    """Abstract base class for converting ir_datasets schema to internal format.
+
+    This translator pattern allows easy extension to support new datasets
+    (BEIR, TREC, etc.) without modifying core code.
+    """
+
+    @property
+    @abstractmethod
+    def ir_datasets_name(self) -> str:
+        """Return the ir_datasets dataset name.
+
+        Example: 'msmarco-passage/dev/small'
+        """
+
+    @abstractmethod
+    def translate_query(self, ir_query: typing.Any) -> FtsQuery:
+        """Convert ir_datasets query to internal FtsQuery format."""
+
+    @abstractmethod
+    def translate_document(self, ir_doc: typing.Any) -> FtsDocument:
+        """Convert ir_datasets document to internal FtsDocument format."""
+
+    def load(self) -> typing.Any:
+        """Load ir_datasets dataset."""
+        return ir_datasets.load(self.ir_datasets_name)
+
+    def iter_queries(self, dataset: typing.Any) -> Iterator[FtsQuery]:
+        """Iterate over queries in the dataset."""
+        for q in dataset.queries_iter():
+            yield self.translate_query(q)
+
+    def iter_documents(self, dataset: typing.Any) -> Iterator[FtsDocument]:
+        """Iterate over documents in the dataset."""
+        for doc in dataset.docs_iter():
+            yield self.translate_document(doc)
+
+    def load_ground_truth(self, dataset: typing.Any) -> dict[str, dict[str, int]]:
+        """Load positive semantic qrels keyed by query id.
+
+        ir_datasets qrels may contain non-positive judgments. Those are not
+        relevant documents for recall/MRR/NDCG, so they are ignored here.
+        """
+        qrels: dict[str, dict[str, int]] = {}
+        for qrel in dataset.qrels_iter():
+            relevance = int(getattr(qrel, "relevance", 0))
+            if relevance <= 0:
+                continue
+            query_id = str(qrel.query_id)
+            doc_id = str(qrel.doc_id)
+            qrels.setdefault(query_id, {})[doc_id] = max(
+                relevance,
+                qrels.get(query_id, {}).get(doc_id, 0),
+            )
+        return qrels
+
+
+class MSMarcoTranslator(FtsDatasetTranslator):
+    """Translator for MS MARCO passage retrieval dataset."""
+
+    @property
+    def ir_datasets_name(self) -> str:
+        return "msmarco-passage/dev/small"
+
+    def translate_query(self, ir_query: typing.Any) -> FtsQuery:
+        return FtsQuery(query_id=str(ir_query.query_id), text=ir_query.text)
+
+    def translate_document(self, ir_doc: typing.Any) -> FtsDocument:
+        clean_text = ir_doc.text.replace("\t", " ").replace("\n", " ")
+        return FtsDocument(doc_id=str(ir_doc.doc_id), text=clean_text)
+
+
+class HotpotQATranslator(FtsDatasetTranslator):
+    """Translator for BEIR HotpotQA."""
+
+    @property
+    def ir_datasets_name(self) -> str:
+        return "beir/hotpotqa/test"
+
+    def translate_query(self, ir_query: typing.Any) -> FtsQuery:
+        return FtsQuery(query_id=str(ir_query.query_id), text=ir_query.text)
+
+    def translate_document(self, ir_doc: typing.Any) -> FtsDocument:
+        title = getattr(ir_doc, "title", "") or ""
+        text = getattr(ir_doc, "text", "") or ""
+        clean_text = f"{title} {text}".replace("\t", " ").replace("\n", " ").strip()
+        return FtsDocument(doc_id=str(ir_doc.doc_id), text=clean_text)
+
+
+class FtsBaseDataset(BaseModel):
+    """Base class for FTS datasets - completely independent from BaseDataset.
+
+    FTS datasets are text-based and use TSV files instead of parquet files.
+    They don't have vector dimensions; native full-text search uses BM25.
+
+    """
+
+    name: str
+    size: int
+    metric_type: MetricType = MetricType.BM25
+    with_gt: bool = True
+    with_remote_resource: bool = False
+    gt_neighbors_field: str = "neighbors_id"
+
+    _size_label: ClassVar[dict[int, SizeLabel]]
+
+    @field_validator("size")
+    @classmethod
+    def verify_size(cls, v: int):
+        if v not in cls._size_label:
+            msg = f"Size {v} not supported for the FTS dataset, expected: {cls._size_label.keys()}"
+            raise ValueError(msg)
+        return v
+
+    @property
+    def label(self) -> str:
+        """Get size label (SMALL, MEDIUM, LARGE, etc.)"""
+        return self._size_label.get(self.size).label
+
+    @property
+    def full_name(self) -> str:
+        return f"{self.name} FTS ({self.label})"
+
+    @property
+    def dir_name(self) -> str:
+        return f"{self.name}_{self.label}_{utils.numerize(self.size)}".lower()
+
+
+class MSMarcoFts(FtsBaseDataset):
+    name: str = "MS MARCO"
+    with_gt: bool = True
+    with_remote_resource: bool = False
+
+    _size_label: ClassVar[dict[int, SizeLabel]] = {
+        100_000: SizeLabel(100_000, "SMALL", 1),
+        1_000_000: SizeLabel(1_000_000, "MEDIUM", 1),
+        8_841_823: SizeLabel(8_841_823, "LARGE", 1),
+    }
+
+    @property
+    def dir_name(self) -> str:
+        return f"msmarco_{self.label}_{utils.numerize(self.size)}".lower()
+
+
+class HotpotQAFts(FtsBaseDataset):
+    name: str = "HotpotQA"
+    with_gt: bool = True
+    with_remote_resource: bool = False
+
+    _size_label: ClassVar[dict[int, SizeLabel]] = {
+        100_000: SizeLabel(100_000, "SMALL", 1),
+        1_000_000: SizeLabel(1_000_000, "MEDIUM", 1),
+        5_233_329: SizeLabel(5_233_329, "LARGE", 1),
+    }
+
+
+class FtsDatasetManager(BaseModel):
+    """Manager for FTS datasets - independent from DatasetManager.
+
+    Handles FTS dataset preparation using Translator pattern for extensibility.
+
+    Similar to DatasetManager, but for text-based FTS datasets:
+    - queries_data: loaded queries (similar to test_data in vectors)
+    - gt_data: loaded ground truth (similar to gt_data in vectors)
+    - recall_queries_data: recall-valid queries after optional FTS filter
+    - recall_gt_data: recall-valid ground truth after optional FTS filter
+    - translator: dataset-specific translator for schema conversion
+    - _ir_dataset: ir_datasets dataset object for direct access
+    """
+
+    data: FtsBaseDataset
+    _translator: typing.Any = PrivateAttr()
+
+    queries_data: list[FtsQuery] | None = None
+    gt_data: list[dict[str, int]] | None = None
+    recall_queries_data: list[FtsQuery] | None = None
+    recall_gt_data: list[dict[str, int]] | None = None
+    recall_skipped: bool = False
+    recall_skip_reason: str | None = None
+    qrels_data: dict[str, dict[str, int]] = PydanticField(default_factory=dict)
+    required_doc_ids: set[str] = PydanticField(default_factory=set)
+    selected_doc_ids: set[str] | None = None
+    qrel_filter_ids: dict[str, int] = PydanticField(default_factory=dict)
+    filter_stats: dict[str, int | float | str] = PydanticField(default_factory=dict)
+    _ir_dataset: typing.Any = PrivateAttr(default=None)
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        # Initialize translator based on dataset name
+        if isinstance(self.data, MSMarcoFts):
+            self._translator = MSMarcoTranslator()
+        elif isinstance(self.data, HotpotQAFts):
+            self._translator = HotpotQATranslator()
+        else:
+            msg = f"No translator available for dataset: {self.data.name}"
+            raise TypeError(msg)
+
+    def __eq__(self, obj: any):
+        if isinstance(obj, FtsDatasetManager):
+            return self.data.name == obj.data.name and self.data.size == obj.data.size
+        return False
+
+    def __hash__(self) -> int:
+        return hash((self.data.name, self.data.size))
+
+    @property
+    def data_dir(self) -> pathlib.Path:
+        """Get local data directory for this FTS dataset, following vector dataset structure"""
+        return pathlib.Path(
+            config.DATASET_LOCAL_DIR,
+            self.data.name.lower(),
+            self.data.dir_name,
+        )
+
+    def _validate_cap(self, required_doc_ids: set[str], target_size: int) -> None:
+        if len(required_doc_ids) > target_size:
+            msg = (
+                f"{self.data.full_name} size={target_size} is too small for semantic qrels; "
+                f"requires {len(required_doc_ids)} qrel documents"
+            )
+            raise ValueError(msg)
+
+    def _build_selected_doc_ids(self) -> set[str]:
+        """Select the capped corpus while preserving every positive qrel doc."""
+        if self._ir_dataset is None:
+            msg = "ir_datasets dataset not loaded. Call prepare() first."
+            raise RuntimeError(msg)
+
+        required_doc_ids = set(self.required_doc_ids)
+        self._validate_cap(required_doc_ids=required_doc_ids, target_size=self.data.size)
+
+        selected_doc_ids = set(required_doc_ids)
+        found_required_doc_ids: set[str] = set()
+        for doc in self._translator.iter_documents(self._ir_dataset):
+            doc_id = str(doc.doc_id)
+            if doc_id in required_doc_ids:
+                found_required_doc_ids.add(doc_id)
+
+            if doc_id not in selected_doc_ids and len(selected_doc_ids) < self.data.size:
+                selected_doc_ids.add(doc_id)
+
+            if len(selected_doc_ids) >= self.data.size and found_required_doc_ids == required_doc_ids:
+                break
+
+        missing_doc_ids = required_doc_ids - found_required_doc_ids
+        if missing_doc_ids:
+            preview = ", ".join(sorted(missing_doc_ids)[:10])
+            msg = (
+                f"{self.data.full_name} semantic qrel docs missing from corpus: {preview}"
+                f"{'...' if len(missing_doc_ids) > 10 else ''}"
+            )
+            raise ValueError(msg)
+
+        return selected_doc_ids
+
+    def _iter_selected_documents_with_filter_ids(self) -> Iterator[FtsDocument]:
+        """Yield selected documents with the exact filter IDs used for insertion and qrels."""
+        if self._ir_dataset is None:
+            msg = "ir_datasets dataset not loaded. Call prepare() first."
+            raise RuntimeError(msg)
+
+        permutation = FtsFilterIdPermutation.for_size(self.data.size)
+        documents = iter(self._translator.iter_documents(self._ir_dataset))
+        emitted_count = 0
+        while emitted_count < self.data.size:
+            try:
+                doc = next(documents)
+                doc.doc_id = str(doc.doc_id)
+                if self.selected_doc_ids is not None and doc.doc_id not in self.selected_doc_ids:
+                    continue
+                doc.filter_id = permutation.map(emitted_count)
+            except StopIteration:
+                break
+            except Exception as e:
+                log.debug(f"Skipping malformed document: {e}")
+                continue
+
+            emitted_count += 1
+            yield doc
+
+    def _build_qrel_filter_ids(self) -> dict[str, int]:
+        """Map qrel doc IDs to their deterministic permuted FTS filter ID."""
+        if self.selected_doc_ids is None:
+            msg = "selected_doc_ids is required before building FTS filter IDs"
+            raise RuntimeError(msg)
+
+        qrel_doc_ids = set(self.required_doc_ids)
+        qrel_filter_ids: dict[str, int] = {}
+        for doc in self._iter_selected_documents_with_filter_ids():
+            doc_id = doc.doc_id
+            if doc_id in qrel_doc_ids:
+                qrel_filter_ids[doc_id] = doc.filter_id
+
+        missing_doc_ids = qrel_doc_ids - set(qrel_filter_ids)
+        if missing_doc_ids:
+            preview = ", ".join(sorted(missing_doc_ids)[:10])
+            msg = (
+                f"{self.data.full_name} semantic qrel docs missing filter_id assignment: {preview}"
+                f"{'...' if len(missing_doc_ids) > 10 else ''}"
+            )
+            raise ValueError(msg)
+        return qrel_filter_ids
+
+    def _apply_integer_filter_to_qrels(
+        self,
+        queries: list[FtsQuery],
+        ground_truth: list[dict[str, int]],
+        filters: Filter,
+    ) -> tuple[list[FtsQuery], list[dict[str, int]]]:
+        filter_field = getattr(filters, "int_field", "filter_id")
+        if filter_field != "filter_id":
+            msg = f"FTS integer filters require int_field='filter_id', got {filter_field!r}"
+            raise ValueError(msg)
+
+        filter_value = int(filters.int_value)
+        if filter_value < 0 or filter_value > self.data.size:
+            msg = f"FTS filter_id threshold must be in [0, {self.data.size}], got {filter_value}"
+            raise ValueError(msg)
+
+        self.qrel_filter_ids = self._build_qrel_filter_ids()
+        filtered_queries: list[FtsQuery] = []
+        filtered_gt: list[dict[str, int]] = []
+        for query, qrels in zip(queries, ground_truth, strict=True):
+            filtered_qrels = {
+                doc_id: rel for doc_id, rel in qrels.items() if self.qrel_filter_ids.get(doc_id, -1) >= filter_value
+            }
+            if not filtered_qrels:
+                continue
+            filtered_queries.append(query)
+            filtered_gt.append(filtered_qrels)
+
+        matched_doc_count = self.data.size - filter_value
+        filtered_relevant_doc_ids = {doc_id for qrels in filtered_gt for doc_id in qrels}
+        permutation = FtsFilterIdPermutation.for_size(self.data.size)
+        self.filter_stats = {
+            "filter_type": filters.type.value,
+            "filter_field": filter_field,
+            "filter_value": filter_value,
+            "filter_rate": filters.filter_rate,
+            "filter_id_distribution": permutation.algorithm,
+            "filter_id_multiplier": permutation.multiplier,
+            "filter_id_offset": permutation.offset,
+            "matched_doc_count": matched_doc_count,
+            "matched_doc_ratio": round(matched_doc_count / self.data.size, 6),
+            "original_query_count": len(queries),
+            "filtered_query_count": len(filtered_queries),
+            "filtered_query_ratio": round(len(filtered_queries) / len(queries), 6),
+            "original_relevant_doc_count": len(self.required_doc_ids),
+            "filtered_relevant_doc_count": len(filtered_relevant_doc_ids),
+        }
+        log.info(
+            "Applied FTS integer filter %s >= %s: queries %s/%s, relevant docs %s/%s",
+            filter_field,
+            filter_value,
+            len(filtered_queries),
+            len(queries),
+            len(filtered_relevant_doc_ids),
+            len(self.required_doc_ids),
+        )
+        if not filtered_queries:
+            self.recall_skipped = True
+            self.recall_skip_reason = "no_positive_qrels_after_filter"
+        return filtered_queries, filtered_gt
+
+    def _apply_filters_to_qrels(
+        self,
+        queries: list[FtsQuery],
+        ground_truth: list[dict[str, int]],
+        filters: Filter | None,
+    ) -> tuple[list[FtsQuery], list[dict[str, int]]]:
+        self.filter_stats = {}
+        self.qrel_filter_ids = {}
+        self.recall_skipped = False
+        self.recall_skip_reason = None
+        if filters is None or filters.type == FilterOp.NonFilter:
+            return queries, ground_truth
+        if filters.type == FilterOp.NumGE:
+            return self._apply_integer_filter_to_qrels(queries, ground_truth, filters)
+        msg = f"FTS dataset filtering does not support filter type {filters.type}"
+        raise ValueError(msg)
+
+    def prepare(
+        self,
+        source: DatasetSource | None = None,
+        filters: Filter | None = None,
+    ) -> bool:
+        """Prepare FTS dataset for testing using Translator pattern.
+
+        Directly uses ir_datasets API without generating TSV files:
+        1. Downloads dataset using ir_datasets (if needed)
+        2. Loads dataset object using translator
+        3. Loads queries and semantic qrels from ir_datasets
+
+        Args:
+            source: Data source to download from (should be IR_DATASETS for FTS)
+            filters: Optional filters. FTS supports natural semantic GT
+                filtering for integer filter_id cases.
+
+        Returns:
+            bool: True if preparation successful, False otherwise
+        """
+        log.info(f"Preparing FTS dataset: {self.data.full_name}")
+
+        try:
+            # Download dataset if needed (ir_datasets handles caching)
+            if source is not None:
+                reader = source.reader()
+                if reader is not None:
+                    dataset_name = self._translator.ir_datasets_name
+                    # reader.read() will download the dataset if needed
+                    reader.read(dataset_name, [], self.data_dir)
+
+            # Load dataset using translator
+            self._ir_dataset = self._translator.load()
+            log.info(f"Successfully loaded ir_datasets dataset: {self._translator.ir_datasets_name}")
+
+            # Load queries from ir_datasets and semantic ground truth by query id.
+            if self.data.with_gt:
+                all_queries = list(self._translator.iter_queries(self._ir_dataset))
+                log.info(f"Loaded {len(all_queries)} queries into memory")
+
+                self.qrels_data = self._translator.load_ground_truth(self._ir_dataset)
+                self.queries_data = []
+                self.gt_data = []
+                for query in all_queries:
+                    qrels = self.qrels_data.get(query.query_id)
+                    if not qrels:
+                        continue
+                    self.queries_data.append(
+                        FtsQuery(
+                            query_id=query.query_id,
+                            text=query.text,
+                        )
+                    )
+                    self.gt_data.append(qrels)
+
+                if not self.queries_data:
+                    msg = f"{self.data.full_name} has no queries with positive semantic qrels"
+                    raise ValueError(msg)  # noqa: TRY301
+
+                self.required_doc_ids = {doc_id for qrels in self.gt_data for doc_id in qrels}
+                self.selected_doc_ids = self._build_selected_doc_ids()
+                self.recall_queries_data, self.recall_gt_data = self._apply_filters_to_qrels(
+                    self.queries_data,
+                    self.gt_data,
+                    filters,
+                )
+                log.info(
+                    "Loaded semantic qrels for %s queries; recall uses %s queries; "
+                    "selected %s corpus docs including %s qrel docs",
+                    len(self.gt_data),
+                    len(self.recall_gt_data),
+                    len(self.selected_doc_ids),
+                    len(self.required_doc_ids),
+                )
+            else:
+                self.selected_doc_ids = None
+                self.qrel_filter_ids = {}
+                self.filter_stats = {}
+                self.recall_queries_data = None
+                self.recall_gt_data = None
+                self.recall_skipped = False
+                self.recall_skip_reason = None
+
+        except (TypeError, ValueError):
+            log.exception("Invalid FTS dataset configuration")
+            raise
+        except Exception:
+            log.exception("Failed to prepare FTS dataset")
+            return False
+        else:
+            log.debug(f"{self.data.name}: FTS dataset prepared")
+            log.info(f"FTS dataset preparation completed: {self.data.full_name}")
+            return True
+
+    def iter_batches(self, batch_size: int = config.NUM_PER_BATCH):
+        """Return an iterator for streaming FTS document batches."""
+        return FtsDocumentIterator(self, batch_size=batch_size)
+
+    def __iter__(self):
+        """Return iterator for streaming document batches.
+
+        Similar to DatasetManager.__iter__() which returns DataSetIterator.
+        This enables batch-by-batch processing of documents without loading
+        all documents into memory at once.
+
+        Example:
+            >>> manager = FtsDataset.MSMARCO.manager(100_000)
+            >>> for batch in manager:
+            >>>     print(f"Processing {len(batch)} documents")
+        """
+        return self.iter_batches()
+
+
+class FtsDocumentIterator:
+    """Iterator for streaming FTS document batches using Translator pattern.
+
+    Similar to DataSetIterator for vector datasets, but reads directly from ir_datasets
+    using translator. Yields batches of FtsDocument objects for memory-efficient
+    processing of large datasets.
+    """
+
+    def __init__(self, dataset: FtsDatasetManager, batch_size: int = config.NUM_PER_BATCH):
+        self._ds = dataset
+        self._batch_size = batch_size
+        self._finished = False
+        self._docs_iter = None
+
+    def __iter__(self):
+        return self
+
+    def __next__(self) -> list[FtsDocument]:
+        """Return the next batch of documents.
+
+        Returns:
+            list[FtsDocument]: List of FtsDocument objects
+
+        Raises:
+            StopIteration: When all documents have been read
+        """
+        if self._finished:
+            raise StopIteration
+
+        if self._docs_iter is None:
+            self._docs_iter = self._ds._iter_selected_documents_with_filter_ids()
+
+        batch = []
+        while len(batch) < self._batch_size:
+            try:
+                batch.append(next(self._docs_iter))
+            except StopIteration:
+                self._finished = True
+                if batch:
+                    return batch
+                raise
+        return batch
+
+    def __enter__(self):
+        """Enter context manager."""
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_val: BaseException | None,
+        exc_tb: types.TracebackType | None,
+    ) -> None:
+        """Exit context manager."""
+
+    def __del__(self):
+        """Cleanup when iterator is destroyed."""
+
+
+class FtsDataset(Enum):
+    MSMARCO = MSMarcoFts
+    HOTPOTQA = HotpotQAFts
+
+    def get(self, size: int) -> FtsBaseDataset:
+        return self.value(size=size)
+
+    def manager(self, size: int) -> FtsDatasetManager:
+        return FtsDatasetManager(data=self.get(size))
+
+
+class FtsDatasetWithSizeType(Enum):
+    MSMarcoSmall = "MS MARCO Small (100K documents)"
+    MSMarcoMedium = "MS MARCO Medium (1M documents)"
+    MSMarcoLarge = "MS MARCO Large (8.8M documents)"
+    HotpotQASmall = "HotpotQA Small (100K documents)"
+    HotpotQAMedium = "HotpotQA Medium (1M documents)"
+    HotpotQALarge = "HotpotQA Large (5.2M documents)"
+
+    def get_manager(self) -> FtsDatasetManager:
+        return {
+            FtsDatasetWithSizeType.MSMarcoSmall: FtsDataset.MSMARCO.manager(100_000),
+            FtsDatasetWithSizeType.MSMarcoMedium: FtsDataset.MSMARCO.manager(1_000_000),
+            FtsDatasetWithSizeType.MSMarcoLarge: FtsDataset.MSMARCO.manager(8_841_823),
+            FtsDatasetWithSizeType.HotpotQASmall: FtsDataset.HOTPOTQA.manager(100_000),
+            FtsDatasetWithSizeType.HotpotQAMedium: FtsDataset.HOTPOTQA.manager(1_000_000),
+            FtsDatasetWithSizeType.HotpotQALarge: FtsDataset.HOTPOTQA.manager(5_233_329),
+        }[self]
+
+    def get_load_timeout(self) -> float:
+        if self in {FtsDatasetWithSizeType.MSMarcoSmall, FtsDatasetWithSizeType.HotpotQASmall}:
+            return config.LOAD_TIMEOUT_768D_100K
+        return config.LOAD_TIMEOUT_DEFAULT
+
+    def get_optimize_timeout(self) -> float:
+        if self in {FtsDatasetWithSizeType.MSMarcoSmall, FtsDatasetWithSizeType.HotpotQASmall}:
+            return config.OPTIMIZE_TIMEOUT_768D_100K
+        return config.OPTIMIZE_TIMEOUT_DEFAULT
+
+    @property
+    def is_advanced(self) -> bool:
+        return self in {FtsDatasetWithSizeType.MSMarcoLarge, FtsDatasetWithSizeType.HotpotQALarge}
diff --git a/vectordb_bench/backend/payload.py b/vectordb_bench/backend/payload.py
new file mode 100644
index 000000000..04cba3b85
--- /dev/null
+++ b/vectordb_bench/backend/payload.py
@@ -0,0 +1,25 @@
+from enum import StrEnum
+
+
+class PayloadProfile(StrEnum):
+    IDS_ONLY = "ids_only"
+    VECTOR = "vector"
+    SCALAR_LABEL = "scalar_label"
+    TEXT = "text"
+
+    def estimated_bytes_per_query(self, *, k: int, dim: int) -> int:
+        # Approximate payload size used for cloud leaderboard cost expansion.
+        # ID + distance is about 20 bytes per hit; vector is float32.
+        id_distance_bytes = 20
+        scalar_label_bytes = 16
+        if self == PayloadProfile.IDS_ONLY:
+            return k * id_distance_bytes
+        if self == PayloadProfile.VECTOR:
+            return k * (id_distance_bytes + dim * 4)
+        if self == PayloadProfile.SCALAR_LABEL:
+            return k * (id_distance_bytes + scalar_label_bytes)
+        if self == PayloadProfile.TEXT:
+            # Approximate returned document text. FTS metrics still use IDs only.
+            return k * (id_distance_bytes + 512)
+        msg = f"Unsupported payload profile: {self}"
+        raise ValueError(msg)
diff --git a/vectordb_bench/backend/result_collector.py b/vectordb_bench/backend/result_collector.py
index a4119a5fd..a550df27a 100644
--- a/vectordb_bench/backend/result_collector.py
+++ b/vectordb_bench/backend/result_collector.py
@@ -1,26 +1,128 @@
+import argparse
 import logging
 import pathlib
+import sys
+from datetime import date
+from typing import Literal
 
 from vectordb_bench.models import TestResult
 
 log = logging.getLogger(__name__)
 
+ResultGroupBy = Literal["run_id", "db"]
+
 
 class ResultCollector:
+    @staticmethod
+    def _group_key(file_result: TestResult, group_by: ResultGroupBy) -> str:
+        if group_by == "run_id":
+            return file_result.run_id
+
+        if not file_result.results:
+            return file_result.run_id
+        return file_result.results[0].task_config.db.value
+
     @classmethod
-    def collect(cls, result_dir: pathlib.Path) -> list[TestResult]:
+    def collect(
+        cls,
+        result_dir: pathlib.Path,
+        group_by: ResultGroupBy = "run_id",
+        trans_unit: bool = True,
+    ) -> list[TestResult]:
         reg = "result_*.json"
         results_d = {}
+        if group_by not in {"run_id", "db"}:
+            msg = f"Unsupported result grouping: {group_by}"
+            raise ValueError(msg)
+
         if not result_dir.exists() or len(list(result_dir.rglob(reg))) == 0:
             return []
 
-        for json_file in result_dir.rglob(reg):
-            file_result = TestResult.read_file(json_file, trans_unit=True)
+        for json_file in sorted(result_dir.rglob(reg)):
+            file_result = TestResult.read_file(json_file, trans_unit=trans_unit)
+            key = cls._group_key(file_result, group_by)
 
-            # Group result files of the same run_id into one TestResult
-            if file_result.run_id in results_d:
-                results_d[file_result.run_id].results.extend(file_result.results)
+            # Default behavior groups files from the same run. FTS publish artifacts can
+            # opt into DB grouping so one backend owns one consolidated result file.
+            if key in results_d:
+                results_d[key].results.extend(file_result.results)
             else:
-                results_d[file_result.run_id] = file_result
+                results_d[key] = file_result
 
         return list(results_d.values())
+
+    @classmethod
+    def merge_by_db(
+        cls,
+        result_dir: pathlib.Path,
+        task_label: str = "fts_standard",
+        replace: bool = False,
+        dry_run: bool = False,
+    ) -> list[pathlib.Path]:
+        source_files = sorted(result_dir.rglob("result_*.json"))
+        merged_results = cls.collect(result_dir, group_by="db", trans_unit=False)
+        output_files = []
+
+        for result in merged_results:
+            if not result.results:
+                continue
+
+            db = result.results[0].task_config.db.value
+            db_lower = db.lower()
+            merged = TestResult(
+                run_id=f"{task_label}_{db_lower}",
+                task_label=task_label,
+                results=result.results,
+                timestamp=result.timestamp,
+            )
+            file_name = merged.file_fmt.format(date.today().strftime("%Y%m%d"), task_label, db_lower)
+            output_file = result_dir.joinpath(db, file_name)
+            output_files.append(output_file)
+
+            if dry_run:
+                log.info("Would write %s with %s case results", output_file, len(result.results))
+            else:
+                merged.write_db_file(result_dir.joinpath(db), merged, db_lower)
+
+        if replace and not dry_run:
+            output_file_set = set(output_files)
+            for source_file in source_files:
+                if source_file not in output_file_set and source_file.exists():
+                    source_file.unlink()
+
+        return output_files
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Collect or consolidate VDBBench result JSON files.")
+    parser.add_argument("result_dir", type=pathlib.Path)
+    parser.add_argument("--merge-by-db", action="store_true", help="write one consolidated result file per backend")
+    parser.add_argument(
+        "--replace", action="store_true", help="remove source result files after merged files are written"
+    )
+    parser.add_argument("--task-label", default="fts_standard", help="task label used for merged result files")
+    parser.add_argument("--dry-run", action="store_true", help="show planned output files without writing")
+    return parser.parse_args()
+
+
+def main():
+    args = _parse_args()
+    logging.basicConfig(level=logging.INFO, format="%(levelname)s:%(name)s:%(message)s")
+
+    if args.merge_by_db:
+        output_files = ResultCollector.merge_by_db(
+            args.result_dir,
+            task_label=args.task_label,
+            replace=args.replace,
+            dry_run=args.dry_run,
+        )
+        for output_file in output_files:
+            sys.stdout.write(f"{output_file}\n")
+        return
+
+    for result in ResultCollector.collect(args.result_dir):
+        sys.stdout.write(f"{result.run_id}\t{len(result.results)}\n")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/vectordb_bench/backend/runner/__init__.py b/vectordb_bench/backend/runner/__init__.py
index 4af583773..ddee99554 100644
--- a/vectordb_bench/backend/runner/__init__.py
+++ b/vectordb_bench/backend/runner/__init__.py
@@ -1,8 +1,12 @@
+from .cold_warm_runner import ColdWarmSearchRunner
+from .concurrent_runner import ConcurrentInsertRunner
 from .mp_runner import MultiProcessingSearchRunner
 from .read_write_runner import ReadWriteRunner
 from .serial_runner import SerialInsertRunner, SerialSearchRunner
 
 __all__ = [
+    "ColdWarmSearchRunner",
+    "ConcurrentInsertRunner",
     "MultiProcessingSearchRunner",
     "ReadWriteRunner",
     "SerialInsertRunner",
diff --git a/vectordb_bench/backend/runner/cold_warm_runner.py b/vectordb_bench/backend/runner/cold_warm_runner.py
new file mode 100644
index 000000000..51bde6ff8
--- /dev/null
+++ b/vectordb_bench/backend/runner/cold_warm_runner.py
@@ -0,0 +1,120 @@
+import logging
+import time
+
+import numpy as np
+
+from vectordb_bench.backend.filter import Filter, non_filter
+from vectordb_bench.backend.payload import PayloadProfile
+
+from ... import config
+from ..clients import api
+
+log = logging.getLogger(__name__)
+
+
+class ColdWarmSearchRunner:
+    def __init__(
+        self,
+        db: api.VectorDB,
+        test_data: list[list[float]],
+        k: int = 100,
+        filters: Filter = non_filter,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+        query_count: int = 1000,
+    ):
+        if query_count <= 0:
+            msg = "query_count must be positive"
+            raise ValueError(msg)
+        if len(test_data) < query_count:
+            msg = f"query_count={query_count} exceeds test_data size={len(test_data)}"
+            raise ValueError(msg)
+
+        self.db = db
+        self.k = k
+        self.filters = filters
+        self.payload_profile = payload_profile
+        self.query_count = query_count
+        if not self.db.supports_payload_profile(self.payload_profile):
+            msg = f"{self.db.name} does not support payload_profile={self.payload_profile.value}"
+            raise NotImplementedError(msg)
+
+        self.test_data = [
+            query.tolist() if isinstance(query, np.ndarray) else query for query in test_data[:query_count]
+        ]
+
+    def _search_embedding(self, emb: list[float]) -> list[int]:
+        if self.payload_profile == PayloadProfile.IDS_ONLY:
+            return self.db.search_embedding(emb, self.k)
+        return self.db.search_embedding(emb, self.k, payload_profile=self.payload_profile)
+
+    def _get_db_search_res(self, emb: list[float], retry_idx: int = 0) -> list[int]:
+        try:
+            results = self._search_embedding(emb)
+        except Exception as e:
+            log.warning(f"Cold/warm search failed, retry_idx={retry_idx}, Exception: {e}")
+            if retry_idx < config.MAX_SEARCH_RETRY:
+                return self._get_db_search_res(emb=emb, retry_idx=retry_idx + 1)
+
+            msg = f"Cold/warm search failed and retried more than {config.MAX_SEARCH_RETRY} times"
+            raise RuntimeError(msg) from e
+
+        return results
+
+    @staticmethod
+    def _latency_stats(latencies: list[float]) -> dict[str, float]:
+        return {
+            "first_query_latency": round(float(latencies[0]), 4),
+            "p99_latency": round(float(np.percentile(latencies, 99)), 4),
+            "p95_latency": round(float(np.percentile(latencies, 95)), 4),
+            "avg_latency": round(float(np.mean(latencies)), 4),
+        }
+
+    @staticmethod
+    def _safe_ratio(numerator: float, denominator: float) -> float:
+        if denominator == 0:
+            return 0.0
+        return round(float(numerator / denominator), 4)
+
+    def _ratio_stats(self, cold_stats: dict[str, float], warm_stats: dict[str, float]) -> dict[str, float]:
+        return {
+            "first_query_latency_ratio": self._safe_ratio(
+                cold_stats["first_query_latency"],
+                warm_stats["first_query_latency"],
+            ),
+            "p99_latency_ratio": self._safe_ratio(cold_stats["p99_latency"], warm_stats["p99_latency"]),
+            "p95_latency_ratio": self._safe_ratio(cold_stats["p95_latency"], warm_stats["p95_latency"]),
+            "avg_latency_ratio": self._safe_ratio(cold_stats["avg_latency"], warm_stats["avg_latency"]),
+        }
+
+    def _run_pass(self, pass_name: str) -> dict[str, float]:
+        latencies = []
+        for emb in self.test_data:
+            start = time.perf_counter()
+            self._get_db_search_res(emb)
+            latencies.append(time.perf_counter() - start)
+
+            if len(latencies) % 100 == 0:
+                log.debug(f"{pass_name} search_count={len(latencies):3}, latest_latency={latencies[-1]}")
+
+        stats = self._latency_stats(latencies)
+        log.info(
+            f"{pass_name} search pass: "
+            f"queries={len(latencies)}, "
+            f"first_query_latency={stats['first_query_latency']}, "
+            f"avg_latency={stats['avg_latency']}, "
+            f"p99={stats['p99_latency']}, "
+            f"p95={stats['p95_latency']}"
+        )
+        return stats
+
+    def run(self) -> dict[str, dict[str, float]]:
+        with self.db.init():
+            self.db.prepare_filter(self.filters)
+            cold_stats = self._run_pass("cold")
+            warm_stats = self._run_pass("warm")
+
+        return {
+            "cold_stats": cold_stats,
+            "warm_stats": warm_stats,
+            "cold_warm_ratio": self._ratio_stats(cold_stats, warm_stats),
+        }
diff --git a/vectordb_bench/backend/runner/concurrent_runner.py b/vectordb_bench/backend/runner/concurrent_runner.py
new file mode 100644
index 000000000..e41af22ee
--- /dev/null
+++ b/vectordb_bench/backend/runner/concurrent_runner.py
@@ -0,0 +1,324 @@
+"""Concurrent insert runner with configurable executor backend.
+
+Replaces SerialInsertRunner for faster data loading in performance cases.
+
+Auto-detects thread-unsafe DBs via VectorDB.thread_safe and
+falls back to single-worker mode.
+"""
+
+from __future__ import annotations
+
+import concurrent.futures
+import logging
+import multiprocessing as mp
+import threading
+import time
+from enum import StrEnum
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from vectordb_bench.backend.filter import Filter, FilterOp, non_filter
+from vectordb_bench.backend.utils import kill_proc_tree, time_it, timeout
+from vectordb_bench.backend.workload import WorkloadKind
+
+from ... import config
+from ...models import LoadTimeoutError, PerformanceTimeoutError
+from .executor import AsyncExecutor, ThreadExecutor
+
+if TYPE_CHECKING:
+    from vectordb_bench.backend.clients import api
+    from vectordb_bench.backend.dataset import DatasetManager, FtsDatasetManager
+
+    from .executor import TaskExecutor
+
+log = logging.getLogger(__name__)
+
+
+class ExecutorBackend(StrEnum):
+    THREADING = "threading"
+    ASYNC = "async"
+
+
+class ConcurrentInsertRunner:
+    """Concurrent insert runner with pluggable executor backend.
+
+    Thread-safety: If db.thread_safe is False, max_workers is clamped to 1
+    so the single worker thread uses self.db directly (no deepcopy needed).
+
+    Args:
+        db: VectorDB instance.
+        dataset: DatasetManager for batch iteration.
+        normalize: Whether to L2-normalize embeddings.
+        filters: Filter configuration.
+        timeout: Timeout in seconds for the overall operation.
+        max_workers: Number of concurrent workers (default: min(cpu_count, 4)).
+        backend: Executor backend to use ('threading' or 'async').
+    """
+
+    def __init__(
+        self,
+        db: api.VectorDB,
+        dataset: DatasetManager,
+        normalize: bool,
+        filters: Filter = non_filter,
+        timeout: float | None = None,
+        max_workers: int | None = None,
+        backend: ExecutorBackend = ExecutorBackend.THREADING,
+        batch_size: int = config.NUM_PER_BATCH,
+        duration: float | None = None,
+        with_scalar_labels: bool = False,
+        tenant_case=None,  # noqa: ANN001
+        workload_kind: WorkloadKind = WorkloadKind.VECTOR,
+    ):
+        self.timeout = timeout if isinstance(timeout, int | float) else None
+        self.dataset: DatasetManager | FtsDatasetManager = dataset
+        self.db = db
+        self.normalize = normalize
+        self.filters = filters
+        self.backend = backend
+        self.batch_size = batch_size
+        self.duration = duration if isinstance(duration, int | float) else None
+        self.with_scalar_labels = with_scalar_labels
+        self.tenant_case = tenant_case
+        self.workload_kind = workload_kind
+        self._prefetched_fts_batch = None
+
+        effective_workers = max_workers or min(mp.cpu_count(), 4)
+        if not db.thread_safe:
+            log.info(f"DB {db.name} is not thread-safe, falling back to max_workers=1")
+            effective_workers = 1
+        self.max_workers = effective_workers
+        assert db.thread_safe or self.max_workers == 1, (
+            "Non-thread-safe DBs must use max_workers=1 — "
+            "_get_thread_db() relies on this to avoid concurrent access to self.db"
+        )
+
+    def __getstate__(self):
+        """Exclude unpicklable thread-local state for ProcessPoolExecutor(spawn)."""
+        state = self.__dict__.copy()
+        state.pop("_iter_lock", None)
+        state.pop("_dataset_iter", None)
+        state.pop("_stop_event", None)
+        return state
+
+    def _create_executor(self) -> TaskExecutor:
+        if self.backend == ExecutorBackend.ASYNC:
+            return AsyncExecutor(max_workers=self.max_workers)
+        return ThreadExecutor(max_workers=self.max_workers)
+
+    def _get_thread_db(self) -> api.VectorDB:
+        """Return self.db.
+
+        All workers share the connection opened by task()'s `with self.db.init()`.
+        Thread-safe DBs share it across multiple workers. Non-thread-safe DBs are
+        clamped to max_workers=1, so there is never concurrent access.
+        """
+        return self.db
+
+    def _insert_batch_with_retry(
+        self,
+        db: api.VectorDB,
+        retry_idx: int = 0,
+        **insert_kwargs,
+    ) -> int:
+        """Insert a single batch with retry logic. Returns inserted count."""
+        if getattr(self, "workload_kind", WorkloadKind.VECTOR) == WorkloadKind.FULL_TEXT:
+            insert_count, error = db.insert_documents(**insert_kwargs)
+        else:
+            insert_count, error = db.insert_embeddings(**insert_kwargs)
+        if error is not None:
+            log.warning(f"Insert failed, try_idx={retry_idx}, Exception: {error}")
+            if getattr(error, "non_retryable", False):
+                msg = f"Non-retryable insert failure after {insert_count} inserted rows: {error}"
+                raise RuntimeError(msg) from error
+            retry_idx += 1
+            if retry_idx <= config.MAX_INSERT_RETRY:
+                time.sleep(retry_idx)
+                return self._insert_batch_with_retry(
+                    db,
+                    retry_idx,
+                    **insert_kwargs,
+                )
+            msg = f"Insert failed and retried more than {config.MAX_INSERT_RETRY} times"
+            raise RuntimeError(msg)
+        return insert_count
+
+    def _worker_insert(self, **insert_kwargs) -> int:
+        """Worker function: insert a batch with retry."""
+        db = self._get_thread_db()
+        return self._insert_batch_with_retry(db, **insert_kwargs)
+
+    def _next_batch(self) -> dict | None:
+        if getattr(self, "workload_kind", WorkloadKind.VECTOR) == WorkloadKind.FULL_TEXT:
+            return self._next_fts_batch()
+        return self._next_vector_batch()
+
+    def _next_vector_batch(self) -> dict | None:
+        """Pull the next batch from the shared dataset iterator.
+
+        Thread-safe: only one thread reads from the iterator at a time.
+        Returns None when the iterator is exhausted.
+        """
+        stop_event = getattr(self, "_stop_event", None)
+        if stop_event is not None and stop_event.is_set():
+            return None
+        if self._deadline is not None and time.perf_counter() >= self._deadline:
+            return None
+        with self._iter_lock:
+            stop_event = getattr(self, "_stop_event", None)
+            if stop_event is not None and stop_event.is_set():
+                return None
+            try:
+                data_df = next(self._dataset_iter)
+            except StopIteration:
+                return None
+
+        all_metadata = data_df[self.dataset.data.train_id_field].tolist()
+        emb_np = np.stack(data_df[self.dataset.data.train_vector_field])
+        if self.normalize:
+            all_embeddings = (emb_np / np.linalg.norm(emb_np, axis=1)[:, np.newaxis]).tolist()
+        else:
+            all_embeddings = emb_np.tolist()
+        del emb_np
+
+        labels_data = None
+        if self.filters.type == FilterOp.StrEqual or self.with_scalar_labels:
+            label_field = self.filters.label_field if self.filters.type == FilterOp.StrEqual else "labels"
+            if self.dataset.data.scalar_labels_file_separated:
+                labels_data = self.dataset.scalar_labels[label_field][all_metadata].to_list()
+            else:
+                labels_data = data_df[label_field].tolist()
+
+        tenant_labels_data = None
+        if self.tenant_case is not None and getattr(self.tenant_case, "is_multitenant", False):
+            tenant_labels_data = self.tenant_case.tenant_labels_for_ids(all_metadata)
+
+        insert_kwargs = {
+            "embeddings": all_embeddings,
+            "metadata": all_metadata,
+            "labels_data": labels_data,
+        }
+        if tenant_labels_data is not None:
+            insert_kwargs["tenant_labels_data"] = tenant_labels_data
+        return insert_kwargs
+
+    def _next_fts_batch(self) -> dict | None:
+        stop_event = getattr(self, "_stop_event", None)
+        if stop_event is not None and stop_event.is_set():
+            return None
+        if self._deadline is not None and time.perf_counter() >= self._deadline:
+            return None
+        with self._iter_lock:
+            stop_event = getattr(self, "_stop_event", None)
+            if stop_event is not None and stop_event.is_set():
+                return None
+            batch = self._prefetched_fts_batch
+            if batch is not None:
+                self._prefetched_fts_batch = None
+            else:
+                try:
+                    batch = next(self._dataset_iter)
+                except StopIteration:
+                    return None
+
+        doc_ids = []
+        texts = []
+        filter_ids = []
+        for doc in batch:
+            doc_ids.append(doc.doc_id if hasattr(doc, "doc_id") else str(doc["doc_id"]))
+            texts.append(doc.text if hasattr(doc, "text") else doc["text"])
+            filter_id = doc.filter_id if hasattr(doc, "filter_id") else doc.get("filter_id", None)
+            filter_ids.append(filter_id)
+        insert_kwargs = {"texts": texts, "doc_ids": doc_ids}
+        if any(filter_id is not None for filter_id in filter_ids):
+            insert_kwargs["filter_ids"] = filter_ids
+        return insert_kwargs
+
+    def _worker_loop(self) -> int:
+        """Worker loop: pull batches from the shared iterator and insert them."""
+        total = 0
+        try:
+            while True:
+                batch = self._next_batch()
+                if batch is None:
+                    break
+                total += self._worker_insert(**batch)
+        except Exception:
+            stop_event = getattr(self, "_stop_event", None)
+            if stop_event is not None:
+                stop_event.set()
+            raise
+        return total
+
+    def task(self) -> int:
+        """Insert entire dataset using concurrent executor. Runs in subprocess."""
+        count = 0
+        self._iter_lock = threading.Lock()
+        self._stop_event = threading.Event()
+        self._deadline = None if self.duration is None else time.perf_counter() + self.duration
+        self._dataset_iter = self.dataset.iter_batches(self.batch_size)
+        if getattr(self, "workload_kind", WorkloadKind.VECTOR) == WorkloadKind.FULL_TEXT:
+            # Prime lazy ir_datasets document preparation before timed backend inserts.
+            self._prefetched_fts_batch = next(self._dataset_iter, None)
+
+        with self.db.init():
+            log.info(
+                f"({mp.current_process().name:16}) Start concurrent insert, "
+                f"batch_size={self.batch_size}, max_workers={self.max_workers}, workload={self.workload_kind}"
+            )
+            start = time.perf_counter()
+
+            with self._create_executor() as executor:
+                for _ in range(self.max_workers):
+                    executor.submit(self._worker_loop)
+
+                batch_results = executor.wait_all()
+
+            # Log all errors, then raise the first one
+            errors = [r.error for r in batch_results if r.error is not None]
+            if errors:
+                for err in errors:
+                    log.warning(f"Batch insert error: {err}")
+                raise errors[0]
+
+            count = sum(r.value for r in batch_results)
+
+            log.info(
+                f"({mp.current_process().name:16}) Finish concurrent insert, "
+                f"count={count}, dur={time.perf_counter() - start:.2f}s"
+            )
+        return count
+
+    @time_it
+    def _insert_all_batches(self) -> int:
+        """Performance case only: run task() in subprocess with timeout."""
+        if getattr(self, "workload_kind", WorkloadKind.VECTOR) == WorkloadKind.FULL_TEXT:
+            # FTS datasets come from ir_datasets, whose loaded objects may contain
+            # lambdas or handles that cannot be pickled by ProcessPoolExecutor.
+            # Keep FTS loading in this process and use threads to overlap the
+            # I/O-bound insert RPCs instead of Python CPU-bound work.
+            with timeout(self.timeout, lambda: LoadTimeoutError(self.timeout)):
+                return self.task()
+        with concurrent.futures.ProcessPoolExecutor(
+            mp_context=mp.get_context("spawn"),
+            max_workers=1,
+        ) as executor:
+            future = executor.submit(self.task)
+            try:
+                count = future.result(timeout=self.timeout)
+            except TimeoutError as e:
+                msg = f"VectorDB load dataset timeout in {self.timeout}"
+                log.warning(msg)
+                kill_proc_tree(pids=list(executor._processes.keys()))
+                raise PerformanceTimeoutError(msg) from e
+            except Exception as e:
+                log.warning(f"VectorDB load dataset error: {e}")
+                raise e from e
+            else:
+                return count
+
+    def run(self) -> int:
+        """Insert full dataset concurrently. Returns total inserted count."""
+        count, _ = self._insert_all_batches()
+        return count
diff --git a/vectordb_bench/backend/runner/executor.py b/vectordb_bench/backend/runner/executor.py
new file mode 100644
index 000000000..0bff2dc2a
--- /dev/null
+++ b/vectordb_bench/backend/runner/executor.py
@@ -0,0 +1,170 @@
+"""Task executor abstraction with threading and async backends.
+
+Provides a unified interface for submitting callables with controlled
+concurrency. Two implementations:
+  - ThreadExecutor: backed by ThreadPoolExecutor
+  - AsyncExecutor: backed by asyncio with semaphore-based concurrency control
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from abc import ABC, abstractmethod
+from concurrent.futures import Future, ThreadPoolExecutor
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+log = logging.getLogger(__name__)
+
+
+@dataclass
+class TaskResult:
+    """Result of a single submitted task."""
+
+    value: Any = None
+    error: Exception | None = None
+
+    @property
+    def success(self) -> bool:
+        return self.error is None
+
+
+class TaskExecutor(ABC):
+    """Abstract executor that accepts callables and controls concurrency."""
+
+    @abstractmethod
+    def start(self) -> None:
+        """Initialize executor resources."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def submit(self, fn: Callable[..., Any], *args: Any, **kwargs: Any) -> None:
+        """Submit a task for execution."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def wait_all(self) -> list[TaskResult]:
+        """Block until all submitted tasks complete. Return results in submission order."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def shutdown(self) -> None:
+        """Release executor resources. Safe to call multiple times."""
+        raise NotImplementedError
+
+    def __enter__(self):
+        self.start()
+        return self
+
+    def __exit__(self, exc_type: type[BaseException] | None, exc_val: BaseException | None, exc_tb: object) -> bool:
+        self.shutdown()
+        return False
+
+
+class ThreadExecutor(TaskExecutor):
+    """ThreadPoolExecutor-backed implementation."""
+
+    def __init__(self, max_workers: int):
+        self._max_workers = max(1, max_workers)
+        self._executor: ThreadPoolExecutor | None = None
+        self._futures: list[Future] = []
+
+    def start(self) -> None:
+        self._executor = ThreadPoolExecutor(max_workers=self._max_workers)
+        self._futures = []
+
+    def submit(self, fn: Callable[..., Any], *args: Any, **kwargs: Any) -> None:
+        if self._executor is None:
+            raise RuntimeError("Executor not started. Call start() or use as context manager.")
+        future = self._executor.submit(fn, *args, **kwargs)
+        self._futures.append(future)
+
+    def wait_all(self) -> list[TaskResult]:
+        results = []
+        for future in self._futures:
+            try:
+                value = future.result()
+                results.append(TaskResult(value=value))
+            except Exception as e:
+                results.append(TaskResult(error=e))
+        self._futures = []
+        return results
+
+    def shutdown(self) -> None:
+        if self._executor is not None:
+            self._executor.shutdown(wait=True)
+            self._executor = None
+
+
+class AsyncExecutor(TaskExecutor):
+    """asyncio-backed implementation for async DB clients.
+
+    Accepts coroutine functions (async def), runs them on a single event
+    loop thread with semaphore-based concurrency control. No thread pool.
+    """
+
+    def __init__(self, max_workers: int):
+        self._max_workers = max(1, max_workers)
+        self._loop: asyncio.AbstractEventLoop | None = None
+        self._semaphore: asyncio.Semaphore | None = None
+        self._coros: list = []
+        self._owns_loop = False
+
+    def start(self) -> None:
+        try:
+            self._loop = asyncio.get_running_loop()
+        except RuntimeError:
+            self._loop = asyncio.new_event_loop()
+            self._owns_loop = True
+        self._semaphore = asyncio.Semaphore(self._max_workers)
+        self._coros = []
+
+    def submit(self, fn: Callable[..., Any], *args: Any, **kwargs: Any) -> None:
+        """Submit a callable for execution.
+
+        Accepts both coroutine functions (async def) and regular functions.
+        Sync functions are offloaded to a thread via run_in_executor.
+        """
+        if self._loop is None or self._semaphore is None:
+            raise RuntimeError("Executor not started. Call start() or use as context manager.")
+
+        async def _run():
+            async with self._semaphore:
+                if asyncio.iscoroutinefunction(fn):
+                    return await fn(*args, **kwargs)
+                loop = asyncio.get_running_loop()
+                return await loop.run_in_executor(None, lambda: fn(*args, **kwargs))
+
+        self._coros.append(_run())
+
+    def wait_all(self) -> list[TaskResult]:
+        if not self._coros:
+            return []
+
+        async def _gather():
+            gathered = await asyncio.gather(*self._coros, return_exceptions=True)
+            results = []
+            for item in gathered:
+                if isinstance(item, Exception):
+                    results.append(TaskResult(error=item))
+                else:
+                    results.append(TaskResult(value=item))
+            return results
+
+        if self._owns_loop:
+            results = self._loop.run_until_complete(_gather())
+        else:
+            results = asyncio.run_coroutine_threadsafe(_gather(), self._loop).result()
+
+        self._coros = []
+        return results
+
+    def shutdown(self) -> None:
+        if self._owns_loop and self._loop is not None:
+            self._loop.close()
+        self._loop = None
+        self._semaphore = None
diff --git a/vectordb_bench/backend/runner/mp_runner.py b/vectordb_bench/backend/runner/mp_runner.py
index b7823af37..bf81d2a7e 100644
--- a/vectordb_bench/backend/runner/mp_runner.py
+++ b/vectordb_bench/backend/runner/mp_runner.py
@@ -12,6 +12,8 @@
 from hdrh.histogram import HdrHistogram
 
 from vectordb_bench.backend.filter import Filter, non_filter
+from vectordb_bench.backend.payload import PayloadProfile
+from vectordb_bench.backend.workload import WorkloadKind
 
 from ... import config
 from ...models import ConcurrencySlotTimeoutError
@@ -45,10 +47,31 @@ def __init__(
         concurrencies: Iterable[int] = config.NUM_CONCURRENCY,
         duration: int = config.CONCURRENCY_DURATION,
         concurrency_timeout: int = config.CONCURRENCY_TIMEOUT,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+        tenant_labels: list[str] | None = None,
+        workload_kind: WorkloadKind = WorkloadKind.VECTOR,
     ):
         self.db = db
         self.k = k
         self.filters = filters
+        self.workload_kind = workload_kind
+        self.payload_profile = payload_profile
+        self.tenant_labels = tenant_labels or []
+        if self.workload_kind == WorkloadKind.FULL_TEXT:
+            self._search_func = self.db.search_documents
+        elif self.workload_kind == WorkloadKind.VECTOR:
+            self._search_func = self._search_embedding
+        else:
+            msg = f"Unsupported search workload: {workload_kind}"
+            raise NotImplementedError(msg)
+        if self.workload_kind == WorkloadKind.VECTOR and not self.db.supports_payload_profile(self.payload_profile):
+            msg = f"{self.db.name} does not support payload_profile={self.payload_profile.value}"
+            raise NotImplementedError(msg)
+        if self.workload_kind == WorkloadKind.FULL_TEXT and not self.db.supports_document_payload_profile(
+            self.payload_profile
+        ):
+            msg = f"{self.db.name} does not support document payload_profile={self.payload_profile.value}"
+            raise NotImplementedError(msg)
         self.concurrencies = concurrencies
         self.duration = duration
         self.concurrency_timeout = concurrency_timeout
@@ -56,6 +79,42 @@ def __init__(
         self.test_data = test_data
         log.debug(f"test dataset columns: {len(test_data)}")
 
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state.pop("_search_func", None)
+        return state
+
+    def __setstate__(self, state: dict):
+        self.__dict__.update(state)
+        if self.workload_kind == WorkloadKind.FULL_TEXT:
+            self._search_func = self.db.search_documents
+        elif self.workload_kind == WorkloadKind.VECTOR:
+            self._search_func = self._search_embedding
+        else:
+            msg = f"Unsupported search workload: {self.workload_kind}"
+            raise NotImplementedError(msg)
+
+    def _search_embedding(self, emb: list[float], tenant: str | None = None) -> list[int]:
+        if tenant is None:
+            if self.payload_profile == PayloadProfile.IDS_ONLY:
+                return self.db.search_embedding(emb, self.k)
+            return self.db.search_embedding(emb, self.k, payload_profile=self.payload_profile)
+        if self.payload_profile == PayloadProfile.IDS_ONLY:
+            return self.db.search_embedding(emb, self.k, tenant=tenant)
+        return self.db.search_embedding(emb, self.k, payload_profile=self.payload_profile, tenant=tenant)
+
+    def _search_once(self, query: list[float] | str, tenant_rng: random.Random | None = None):
+        if self.workload_kind == WorkloadKind.FULL_TEXT:
+            if self.payload_profile == PayloadProfile.IDS_ONLY:
+                return self._search_func(query, self.k)
+            return self._search_func(query, self.k, payload_profile=self.payload_profile)
+        tenant = (
+            self.tenant_labels[tenant_rng.randrange(len(self.tenant_labels))]
+            if tenant_rng is not None and self.tenant_labels
+            else None
+        )
+        return self._search_func(query, tenant=tenant)
+
     def search(
         self,
         test_data: list[list[float]],
@@ -75,6 +134,7 @@ def search(
         with self.db.init():
             self.db.prepare_filter(self.filters)
             num, idx = len(test_data), random.randint(0, len(test_data) - 1)
+            tenant_rng = random.Random(mp.current_process().pid or 0)
 
             start_time = time.perf_counter()
             count = 0
@@ -82,7 +142,7 @@ def search(
             while time.perf_counter() < start_time + self.duration:
                 s = time.perf_counter()
                 try:
-                    self.db.search_embedding(test_data[idx], self.k)
+                    self._search_once(test_data[idx], tenant_rng=tenant_rng)
                     count += 1
                     latencies.append(time.perf_counter() - s)
                 except Exception as e:
@@ -341,7 +401,7 @@ def search_by_dur(
             while time.perf_counter() < start_time + dur:
                 s = time.perf_counter()
                 try:
-                    self.db.search_embedding(test_data[idx], self.k)
+                    self._search_once(test_data[idx])
                     success_count += 1
                     latency_us = int((time.perf_counter() - s) * US_TO_SECONDS)
                     histogram.record_value(min(latency_us, HDR_HISTOGRAM_MAX_US))
diff --git a/vectordb_bench/backend/runner/rate_runner.py b/vectordb_bench/backend/runner/rate_runner.py
index c56c8ca61..91d0bb3ee 100644
--- a/vectordb_bench/backend/runner/rate_runner.py
+++ b/vectordb_bench/backend/runner/rate_runner.py
@@ -3,7 +3,7 @@
 import multiprocessing as mp
 import time
 from concurrent.futures import ThreadPoolExecutor
-from copy import deepcopy
+from copy import copy, deepcopy
 
 from vectordb_bench import config
 from vectordb_bench.backend.clients import api
@@ -65,6 +65,29 @@ def _insert_embeddings(db: api.VectorDB, emb: list[list[float]], metadata: list[
                 log.debug("Failed to reset Doris client or table on thread-local copy", exc_info=True)
             with db_copy.init():
                 _insert_embeddings(db_copy, emb, metadata, retry_idx=0)
+        elif db.name == "SeekDB":
+            # mysql.connector is not thread-safe; do not share one connection across workers.
+            # deepcopy() fails on an open _conn (socket is not picklable / not copy-safe in spawn workers).
+            db_copy = copy(db)
+            try:
+                db_copy._conn = None
+                db_copy._cursor = None
+            except Exception:
+                log.debug("Failed to reset SeekDB connection on thread-local copy", exc_info=True)
+            with db_copy.init():
+                _insert_embeddings(db_copy, emb, metadata, retry_idx=0)
+        elif db.name == "VolcMySQL":
+            # mysql.connector is not thread-safe; do not share one connection across workers.
+            # deepcopy() fails on an open conn (socket is not picklable / not copy-safe in spawn workers).
+            db_copy = copy(db)
+            try:
+                db_copy.conn = None
+                db_copy.cursor = None
+                db_copy.admin_cursor = None
+            except Exception:
+                log.debug("Failed to reset VolcMySQL connection on thread-local copy", exc_info=True)
+            with db_copy.init():
+                _insert_embeddings(db_copy, emb, metadata, retry_idx=0)
         else:
             _insert_embeddings(db, emb, metadata, retry_idx=0)
 
diff --git a/vectordb_bench/backend/runner/serial_runner.py b/vectordb_bench/backend/runner/serial_runner.py
index db3595c49..f671d381a 100644
--- a/vectordb_bench/backend/runner/serial_runner.py
+++ b/vectordb_bench/backend/runner/serial_runner.py
@@ -2,18 +2,20 @@
 import logging
 import math
 import multiprocessing as mp
+import random
 import time
 import traceback
 
 import numpy as np
-import psutil
 
 from vectordb_bench.backend.dataset import DatasetManager
-from vectordb_bench.backend.filter import Filter, FilterOp, non_filter
+from vectordb_bench.backend.filter import Filter, non_filter
+from vectordb_bench.backend.payload import PayloadProfile
+from vectordb_bench.backend.workload import WorkloadKind
 
 from ... import config
-from ...metric import calc_ndcg, calc_recall, get_ideal_dcg
-from ...models import LoadTimeoutError, PerformanceTimeoutError
+from ...metric import calc_mrr_fts, calc_ndcg, calc_ndcg_fts, calc_recall, calc_recall_fts, get_ideal_dcg
+from ...models import LoadTimeoutError
 from .. import utils
 from ..clients import api
 
@@ -24,6 +26,9 @@
 
 
 class SerialInsertRunner:
+    # FTS insert is intentionally not implemented here. FTS performance loading
+    # goes through ConcurrentInsertRunner; serial FTS insert can be added later
+    # if a capacity or serial-load FTS case needs it.
     def __init__(
         self,
         db: api.VectorDB,
@@ -38,66 +43,6 @@ def __init__(
         self.normalize = normalize
         self.filters = filters
 
-    def retry_insert(self, db: api.VectorDB, retry_idx: int = 0, **kwargs):
-        _, error = db.insert_embeddings(**kwargs)
-        if error is not None:
-            log.warning(f"Insert Failed, try_idx={retry_idx}, Exception: {error}")
-            retry_idx += 1
-            if retry_idx <= config.MAX_INSERT_RETRY:
-                time.sleep(retry_idx)
-                self.retry_insert(db, retry_idx=retry_idx, **kwargs)
-            else:
-                msg = f"Insert failed and retried more than {config.MAX_INSERT_RETRY} times"
-                raise RuntimeError(msg) from None
-
-    def task(self) -> int:
-        count = 0
-        with self.db.init():
-            log.info(f"({mp.current_process().name:16}) Start inserting embeddings in batch {config.NUM_PER_BATCH}")
-            start = time.perf_counter()
-            for data_df in self.dataset:
-                all_metadata = data_df[self.dataset.data.train_id_field].tolist()
-
-                emb_np = np.stack(data_df[self.dataset.data.train_vector_field])
-                if self.normalize:
-                    log.debug("normalize the 100k train data")
-                    all_embeddings = (emb_np / np.linalg.norm(emb_np, axis=1)[:, np.newaxis]).tolist()
-                else:
-                    all_embeddings = emb_np.tolist()
-                del emb_np
-                log.debug(f"batch dataset size: {len(all_embeddings)}, {len(all_metadata)}")
-
-                labels_data = None
-                if self.filters.type == FilterOp.StrEqual:
-                    if self.dataset.data.scalar_labels_file_separated:
-                        labels_data = self.dataset.scalar_labels[self.filters.label_field][all_metadata].to_list()
-                    else:
-                        labels_data = data_df[self.filters.label_field].tolist()
-
-                insert_count, error = self.db.insert_embeddings(
-                    embeddings=all_embeddings,
-                    metadata=all_metadata,
-                    labels_data=labels_data,
-                )
-                if error is not None:
-                    self.retry_insert(
-                        self.db,
-                        embeddings=all_embeddings,
-                        metadata=all_metadata,
-                        labels_data=labels_data,
-                    )
-
-                assert insert_count == len(all_metadata)
-                count += insert_count
-                if count % 100_000 == 0:
-                    log.info(f"({mp.current_process().name:16}) Loaded {count} embeddings into VectorDB")
-
-            log.info(
-                f"({mp.current_process().name:16}) Finish loading all dataset into VectorDB, "
-                f"dur={time.perf_counter() - start}"
-            )
-            return count
-
     def endless_insert_data(self, all_embeddings: list, all_metadata: list, left_id: int = 0) -> int:
         with self.db.init():
             # unique id for endlessness insertion
@@ -147,45 +92,6 @@ def endless_insert_data(self, all_embeddings: list, all_metadata: list, left_id:
             )
         return count
 
-    @utils.time_it
-    def _insert_all_batches(self) -> int:
-        """Performance case only"""
-
-        def stop_worker_processes():
-            processes = list((executor._processes or {}).keys())
-            executor.shutdown(wait=False, cancel_futures=True)
-            for pid in processes:
-                try:
-                    proc = psutil.Process(pid)
-                    proc.terminate()
-                    proc.wait(timeout=5)
-                except psutil.NoSuchProcess:
-                    continue
-                except psutil.TimeoutExpired:
-                    psutil.Process(pid).kill()
-
-        executor = concurrent.futures.ProcessPoolExecutor(
-            mp_context=mp.get_context("spawn"),
-            max_workers=1,
-        )
-        try:
-            future = executor.submit(self.task)
-            try:
-                count = future.result(timeout=self.timeout)
-            except TimeoutError as e:
-                msg = f"VectorDB load dataset timeout in {self.timeout}"
-                log.warning(msg)
-                for pid, _ in executor._processes.items():
-                    psutil.Process(pid).kill()
-                raise PerformanceTimeoutError(msg) from e
-            except Exception as e:
-                log.warning(f"VectorDB load dataset error: {e}")
-                raise e from e
-            else:
-                return count
-        finally:
-            stop_worker_processes()
-
     def run_endlessness(self) -> int:
         """run forever util DB raises exception or crash"""
         # datasets for load tests are quite small, can fit into memory
@@ -221,23 +127,44 @@ def run_endlessness(self) -> int:
         else:
             raise LoadTimeoutError(self.timeout)
 
-    def run(self) -> int:
-        count, _ = self._insert_all_batches()
-        return count
-
 
 class SerialSearchRunner:
     def __init__(
         self,
         db: api.VectorDB,
-        test_data: list[list[float]],
-        ground_truth: list[list[int]],
+        test_data: list,
+        ground_truth: list[list[int]] | list[dict[str, int]],
         k: int = 100,
         filters: Filter = non_filter,
+        payload_profile: PayloadProfile = PayloadProfile.IDS_ONLY,
+        tenant_labels: list[str] | None = None,
+        measure_recall: bool = True,
+        workload_kind: WorkloadKind = WorkloadKind.VECTOR,
     ):
         self.db = db
         self.k = k
         self.filters = filters
+        self.workload_kind = workload_kind
+        self.payload_profile = payload_profile
+        self.tenant_labels = tenant_labels or []
+        self.measure_recall = measure_recall
+        if workload_kind == WorkloadKind.FULL_TEXT:
+            self._search_func = self.db.search_documents
+            self._use_fts_metrics = True
+        elif workload_kind == WorkloadKind.VECTOR:
+            self._search_func = self._search_embedding
+            self._use_fts_metrics = False
+        else:
+            msg = f"Unsupported search workload: {workload_kind}"
+            raise NotImplementedError(msg)
+        if self.workload_kind == WorkloadKind.VECTOR and not self.db.supports_payload_profile(self.payload_profile):
+            msg = f"{self.db.name} does not support payload_profile={self.payload_profile.value}"
+            raise NotImplementedError(msg)
+        if self.workload_kind == WorkloadKind.FULL_TEXT and not self.db.supports_document_payload_profile(
+            self.payload_profile
+        ):
+            msg = f"{self.db.name} does not support document payload_profile={self.payload_profile.value}"
+            raise NotImplementedError(msg)
 
         if isinstance(test_data[0], np.ndarray):
             self.test_data = [query.tolist() for query in test_data]
@@ -245,47 +172,80 @@ def __init__(
             self.test_data = test_data
         self.ground_truth = ground_truth
 
-    def _get_db_search_res(self, emb: list[float], retry_idx: int = 0) -> list[int]:
+    def _search_embedding(self, emb: list[float], tenant: str | None = None) -> list[int]:
+        if tenant is None:
+            if self.payload_profile == PayloadProfile.IDS_ONLY:
+                return self.db.search_embedding(emb, self.k)
+            return self.db.search_embedding(emb, self.k, payload_profile=self.payload_profile)
+        if self.payload_profile == PayloadProfile.IDS_ONLY:
+            return self.db.search_embedding(emb, self.k, tenant=tenant)
+        return self.db.search_embedding(emb, self.k, payload_profile=self.payload_profile, tenant=tenant)
+
+    def _get_db_search_res(
+        self,
+        query: list[float] | str,
+        tenant: str | None = None,
+        retry_idx: int = 0,
+    ) -> list[int]:
         try:
-            results = self.db.search_embedding(emb, self.k)
+            if self.workload_kind == WorkloadKind.FULL_TEXT:
+                if self.payload_profile == PayloadProfile.IDS_ONLY:
+                    results = self._search_func(query, self.k)
+                else:
+                    results = self._search_func(query, self.k, payload_profile=self.payload_profile)
+            else:
+                results = self._search_func(query, tenant=tenant)
         except Exception as e:
             log.warning(f"Serial search failed, retry_idx={retry_idx}, Exception: {e}")
             if retry_idx < config.MAX_SEARCH_RETRY:
-                return self._get_db_search_res(emb=emb, retry_idx=retry_idx + 1)
+                return self._get_db_search_res(query=query, tenant=tenant, retry_idx=retry_idx + 1)
 
             msg = f"Serial search failed and retried more than {config.MAX_SEARCH_RETRY} times"
             raise RuntimeError(msg) from e
 
         return results
 
-    def search(self, args: tuple[list, list[list[int]]]) -> tuple[float, float, float, float]:
+    def search(self, args: tuple[list, list[list[int]] | list[dict[str, int]]]) -> tuple[float, ...]:
         log.info(f"{mp.current_process().name:14} start search the entire test_data to get recall and latency")
         with self.db.init():
             self.db.prepare_filter(self.filters)
             test_data, ground_truth = args
-            ideal_dcg = get_ideal_dcg(self.k)
+            ideal_dcg = None if self._use_fts_metrics else get_ideal_dcg(self.k)
 
             log.debug(f"test dataset size: {len(test_data)}")
-            log.debug(f"ground truth size: {len(ground_truth)}")
+            log.debug(f"ground truth size: {len(ground_truth) if ground_truth is not None else 0}")
 
-            latencies, recalls, ndcgs = [], [], []
+            latencies, recalls, ndcgs, mrrs = [], [], [], []
+            tenant_rng = random.Random(0)
             for idx, emb in enumerate(test_data):
+                tenant = (
+                    self.tenant_labels[tenant_rng.randrange(len(self.tenant_labels))]
+                    if self.workload_kind == WorkloadKind.VECTOR and self.tenant_labels
+                    else None
+                )
                 s = time.perf_counter()
                 try:
-                    results = self._get_db_search_res(emb)
+                    results = self._get_db_search_res(emb, tenant=tenant)
                 except Exception as e:
                     log.warning(f"VectorDB search_embedding error: {e}")
                     raise e from None
 
                 latencies.append(time.perf_counter() - s)
 
-                if ground_truth is not None:
+                if self.measure_recall and ground_truth is not None:
                     gt = ground_truth[idx]
-                    recalls.append(calc_recall(self.k, gt[: self.k], results))
-                    ndcgs.append(calc_ndcg(gt[: self.k], results, ideal_dcg))
+                    if self._use_fts_metrics:
+                        recalls.append(calc_recall_fts(self.k, gt, results))
+                        ndcgs.append(calc_ndcg_fts(self.k, gt, results))
+                        mrrs.append(calc_mrr_fts(self.k, gt, results))
+                    else:
+                        recalls.append(calc_recall(self.k, gt[: self.k], results))
+                        ndcgs.append(calc_ndcg(gt[: self.k], results, ideal_dcg))
                 else:
                     recalls.append(0)
                     ndcgs.append(0)
+                    if self._use_fts_metrics:
+                        mrrs.append(0)
 
                 if len(latencies) % 100 == 0:
                     log.debug(
@@ -299,6 +259,21 @@ def search(self, args: tuple[list, list[list[int]]]) -> tuple[float, float, floa
         cost = round(np.sum(latencies), 4)
         p99 = round(np.percentile(latencies, 99), 4)
         p95 = round(np.percentile(latencies, 95), 4)
+        if self._use_fts_metrics:
+            avg_mrr = round(np.mean(mrrs), 4)
+            log.info(
+                f"{mp.current_process().name:14} search entire test_data: "
+                f"cost={cost}s, "
+                f"queries={len(latencies)}, "
+                f"avg_recall={avg_recall}, "
+                f"avg_ndcg={avg_ndcg}, "
+                f"avg_mrr={avg_mrr}, "
+                f"avg_latency={avg_latency}, "
+                f"p99={p99}, "
+                f"p95={p95}"
+            )
+            return (avg_recall, avg_ndcg, avg_mrr, p99, p95)
+
         log.info(
             f"{mp.current_process().name:14} search entire test_data: "
             f"cost={cost}s, "
@@ -311,13 +286,13 @@ def search(self, args: tuple[list, list[list[int]]]) -> tuple[float, float, floa
         )
         return (avg_recall, avg_ndcg, p99, p95)
 
-    def _run_in_subprocess(self) -> tuple[float, float, float, float]:
+    def _run_in_subprocess(self) -> tuple[float, ...]:
         with concurrent.futures.ProcessPoolExecutor(max_workers=1) as executor:
             future = executor.submit(self.search, (self.test_data, self.ground_truth))
             return future.result()
 
     @utils.time_it
-    def run(self) -> tuple[float, float, float, float]:
+    def run(self) -> tuple[float, ...]:
         log.info(f"{mp.current_process().name:14} start serial search")
         if self.test_data is None:
             msg = "empty test_data"
@@ -326,11 +301,11 @@ def run(self) -> tuple[float, float, float, float]:
         return self._run_in_subprocess()
 
     @utils.time_it
-    def run_with_cost(self) -> tuple[tuple[float, float, float, float], float]:
+    def run_with_cost(self) -> tuple[tuple[float, ...], float]:
         """
         Search all test data in serial.
         Returns:
-            tuple[tuple[float, float, float, float], float]: (avg_recall, avg_ndcg, p99_latency, p95_latency), cost
+            tuple[tuple[float, ...], float]: search metrics and cost
         """
         log.info(f"{mp.current_process().name:14} start serial search")
         if self.test_data is None:
diff --git a/vectordb_bench/backend/task_runner.py b/vectordb_bench/backend/task_runner.py
index 89a70d5fb..d66d6f454 100644
--- a/vectordb_bench/backend/task_runner.py
+++ b/vectordb_bench/backend/task_runner.py
@@ -2,12 +2,13 @@
 import hashlib
 import logging
 import re
+import time
 import traceback
 from enum import Enum, auto
 
 import numpy as np
-import psutil
 
+from .. import config
 from ..base import BaseModel
 from ..metric import Metric
 from ..models import PerformanceTimeoutError, TaskConfig, TaskStage
@@ -15,7 +16,16 @@
 from .cases import Case, CaseLabel, StreamingPerformanceCase
 from .clients import DB, MetricType, api
 from .data_source import DatasetSource
-from .runner import MultiProcessingSearchRunner, ReadWriteRunner, SerialInsertRunner, SerialSearchRunner
+from .runner import (
+    ColdWarmSearchRunner,
+    ConcurrentInsertRunner,
+    MultiProcessingSearchRunner,
+    ReadWriteRunner,
+    SerialInsertRunner,
+    SerialSearchRunner,
+)
+from .utils import kill_proc_tree
+from .workload import WorkloadKind
 
 log = logging.getLogger(__name__)
 
@@ -46,46 +56,107 @@ class CaseRunner(BaseModel):
 
     db: api.VectorDB | None = None
     test_emb: list[list[float]] | None = None
+    test_texts: list[str] | None = None
     serial_search_runner: SerialSearchRunner | None = None
     search_runner: MultiProcessingSearchRunner | None = None
     final_search_runner: MultiProcessingSearchRunner | None = None
     read_write_runner: ReadWriteRunner | None = None
+    cold_warm_search_runner: ColdWarmSearchRunner | None = None
 
     def __eq__(self, obj: any):
         if isinstance(obj, CaseRunner):
-            return (
-                self.ca.label == CaseLabel.Performance
-                and self.config.db == obj.config.db
-                and self.config.db_case_config == obj.config.db_case_config
-                and self.ca.dataset == obj.ca.dataset
-            )
+            key = self.load_reuse_key()
+            return key is not None and key == obj.load_reuse_key()
         return False
 
     def __hash__(self) -> int:
         """Hash method to maintain consistency with __eq__ method."""
-        return hash(
-            (
-                self.ca.label,
-                self.config.db,
-                self.config.db_case_config,
-                self.ca.dataset,
-            )
+        return hash(self.load_reuse_key())
+
+    def load_reuse_key(self) -> tuple | None:
+        if self.ca.label != CaseLabel.Performance:
+            return None
+        return (
+            self.config.db.value,
+            self._db_config_hash_key(),
+            self._db_case_config_hash_key(),
+            self._collection_name_hash_key(),
+            self._dataset_hash_key(),
+            self.ca.with_scalar_labels,
+            self.ca.is_multitenant,
+            self._multitenant_routing_hash_key(),
+        )
+
+    @classmethod
+    def _hashable_value(cls, value: object) -> object:
+        if isinstance(value, dict):
+            hashable = tuple(sorted((str(k), cls._hashable_value(v)) for k, v in value.items()))
+        elif isinstance(value, (list, tuple)):
+            hashable = tuple(cls._hashable_value(v) for v in value)
+        elif isinstance(value, (set, frozenset)):
+            hashable = tuple(sorted((cls._hashable_value(v) for v in value), key=repr))
+        elif isinstance(value, Enum):
+            hashable = value.value
+        elif hasattr(value, "model_dump"):
+            hashable = cls._hashable_value(value.model_dump(mode="json"))
+        elif hasattr(value, "get_secret_value"):
+            hashable = value.get_secret_value()
+        else:
+            hashable = value
+        return hashable
+
+    def _db_config_hash_key(self) -> object:
+        db_config = self.config.db_config
+        if hasattr(db_config, "to_dict"):
+            return self._hashable_value(db_config.to_dict())
+        return self._hashable_value(db_config)
+
+    def _db_case_config_hash_key(self) -> object:
+        return self._hashable_value(self.config.db_case_config)
+
+    def _collection_name_hash_key(self) -> str | None:
+        return self._doris_collection_name()
+
+    def _dataset_hash_key(self) -> object:
+        return self._hashable_value(self.ca.dataset.data)
+
+    def _multitenant_routing_hash_key(self) -> tuple | None:
+        if not self.ca.is_multitenant:
+            return None
+        return (
+            getattr(self.ca, "tenant_count", None),
+            getattr(self.ca, "tenant_prefix", None),
+            getattr(self.ca, "tenant_id_width", None),
+            getattr(self.ca, "tenant_distribution", None),
         )
 
+    def _doris_collection_name(self) -> str | None:
+        if self.config.db != DB.Doris:
+            return None
+        case_type_name = self.config.case_config.case_id.name
+        base = f"{case_type_name.lower()}"
+        base = re.sub(r"[^a-z0-9_]+", "_", base).strip("_")
+        if len(base) > 63:
+            h = hashlib.md5(base.encode(), usedforsecurity=False).hexdigest()[:6]
+            base = f"{base[:(63-7)]}_{h}"
+        return base
+
     def display(self) -> dict:
-        c_dict = self.ca.model_dump(
+        dataset_include = {
+            "name": True,
+            "size": True,
+            "label": True,
+            "metric_type": True,
+        }
+        if self.ca.label != CaseLabel.FullTextSearchPerformance:
+            dataset_include["dim"] = True
+        c_dict = self.ca.dict(
             include={
                 "label": True,
                 "name": True,
                 "filters": True,
                 "dataset": {
-                    "data": {
-                        "name": True,
-                        "size": True,
-                        "dim": True,
-                        "metric_type": True,
-                        "label": True,
-                    },
+                    "data": dataset_include,
                 },
             },
         )
@@ -95,24 +166,26 @@ def display(self) -> dict:
     @property
     def normalize(self) -> bool:
         assert self.db
+        if self.is_fts:
+            return False
         return self.db.need_normalize_cosine() and self.ca.dataset.data.metric_type == MetricType.COSINE
 
+    @property
+    def workload_kind(self) -> WorkloadKind:
+        if getattr(self.ca, "label", None) == CaseLabel.FullTextSearchPerformance:
+            return WorkloadKind.FULL_TEXT
+        return WorkloadKind.VECTOR
+
+    @property
+    def is_fts(self) -> bool:
+        return self.workload_kind == WorkloadKind.FULL_TEXT
+
     def init_db(self, drop_old: bool = True) -> None:
         db_cls = self.config.db.init_cls
         # Compose a compact, case-unique collection/table name for Doris to avoid cross-case interference
         collection_name = None
         try:
-            if self.config.db == DB.Doris:
-                # Primary identifier = case-type enum name from CLI (e.g., Performance768D10M)
-                case_type_name = self.config.case_config.case_id.name
-                base = f"{case_type_name.lower()}"
-                # Sanitize to [a-z0-9_]
-                base = re.sub(r"[^a-z0-9_]+", "_", base).strip("_")
-                # Cap to 63 chars; add short hash if truncated
-                if len(base) > 63:
-                    h = hashlib.md5(base.encode(), usedforsecurity=False).hexdigest()[:6]
-                    base = f"{base[:(63-7)]}_{h}"
-                collection_name = base
+            collection_name = self._doris_collection_name()
         except Exception:
             # If anything goes wrong, fall back silently; Doris will use its default name logic
             collection_name = None
@@ -122,23 +195,75 @@ def init_db(self, drop_old: bool = True) -> None:
         if "collection_name" in db_config_dict and not collection_name:
             collection_name = db_config_dict.pop("collection_name")
 
+        extra_db_kwargs = {}
+        if collection_name:
+            extra_db_kwargs["collection_name"] = collection_name
+        if self.ca.is_multitenant:
+            extra_db_kwargs["multitenant_tenant_labels"] = self.ca.tenant_labels()
+
         self.db = db_cls(
-            dim=self.ca.dataset.data.dim,
+            dim=getattr(self.ca.dataset.data, "dim", 0),
             db_config=db_config_dict,
             db_case_config=self.config.db_case_config,
             drop_old=drop_old,
             with_scalar_labels=self.ca.with_scalar_labels,
-            **({"collection_name": collection_name} if collection_name else {}),
+            **extra_db_kwargs,
         )
 
     def _pre_run(self, drop_old: bool = True):
         try:
+            self._validate_cloud_cold_latency_config(drop_old)
+            creates_multitenant_collection = (
+                TaskStage.DROP_OLD in self.config.stages or TaskStage.LOAD in self.config.stages
+            )
+            if (
+                self.ca.is_multitenant
+                and self.config.db in {DB.Milvus, DB.ZillizCloud}
+                and creates_multitenant_collection
+                and not getattr(self.config.db_case_config, "use_partition_key", False)
+            ):
+                msg = "CloudMultiTenantSearchCase requires use_partition_key=True for Milvus/ZillizCloud"
+                raise ValueError(msg)
+
+            if self.is_fts:
+                self.ca.dataset.prepare(
+                    self.dataset_source,
+                    filters=self.ca.filters,
+                )
+                self.init_db(drop_old)
+                return
+
             self.init_db(drop_old)
-            self.ca.dataset.prepare(self.dataset_source, filters=self.ca.filters)
+            if self.ca.is_multitenant and self.db is not None:
+                if not self.db.supports_multitenant():
+                    msg = f"{self.config.db_name} does not support CloudMultiTenantSearchCase"
+                    raise NotImplementedError(msg)
+                self.db.set_multitenant_context(self.ca.tenant_labels())
+                if self.config.db in {DB.Milvus, DB.ZillizCloud} and not creates_multitenant_collection:
+                    self.db.validate_multitenant_schema()
+            self.ca.dataset.prepare(
+                self.dataset_source,
+                filters=self.ca.filters,
+                with_train_files=TaskStage.LOAD in self.config.stages,
+                with_scalar_labels=self.ca.with_scalar_labels,
+            )
         except ModuleNotFoundError as e:
             log.warning(f"pre run case error: please install client for db: {self.config.db}, error={e}")
             raise e from None
 
+    def _validate_cloud_cold_latency_config(self, drop_old: bool) -> None:
+        if getattr(self.ca, "label", None) != CaseLabel.CloudColdLatency:
+            return
+        if drop_old:
+            msg = (
+                "CloudColdLatencyCase requires an existing cold collection. "
+                "Run with --skip-drop-old and --skip-load."
+            )
+            raise ValueError(msg)
+        if TaskStage.LOAD in self.config.stages:
+            msg = "CloudColdLatencyCase is search-only. Run with --skip-load."
+            raise ValueError(msg)
+
     def run(self, drop_old: bool = True) -> Metric:
         log.info("Starting run")
 
@@ -146,10 +271,14 @@ def run(self, drop_old: bool = True) -> Metric:
 
         if self.ca.label == CaseLabel.Load:
             return self._run_capacity_case()
-        if self.ca.label == CaseLabel.Performance:
+        if self.ca.label in {CaseLabel.Performance, CaseLabel.FullTextSearchPerformance}:
             return self._run_perf_case(drop_old)
         if self.ca.label == CaseLabel.Streaming:
             return self._run_streaming_case()
+        if self.ca.label == CaseLabel.CloudInsert:
+            return self._run_cloud_insert_case()
+        if self.ca.label == CaseLabel.CloudColdLatency:
+            return self._run_cloud_cold_latency_case(drop_old)
         msg = f"unknown case type: {self.ca.label}"
         log.warning(msg)
         raise ValueError(msg)
@@ -188,13 +317,28 @@ def _run_perf_case(self, drop_old: bool = True) -> Metric:
         log.info("Start performance case")
         try:
             m = Metric()
+            if self.is_fts and getattr(self.ca.dataset, "filter_stats", None):
+                m.additional_parameters["fts_filter"] = dict(self.ca.dataset.filter_stats)
+                m.additional_parameters["fts_recall"] = {
+                    "skipped": bool(getattr(self.ca.dataset, "recall_skipped", False)),
+                    "reason": getattr(self.ca.dataset, "recall_skip_reason", None),
+                    "serial_query_count": len(getattr(self.ca.dataset, "recall_queries_data", []) or []),
+                    "full_query_count": len(getattr(self.ca.dataset, "queries_data", []) or []),
+                }
             if drop_old:
                 if TaskStage.LOAD in self.config.stages:
-                    _, load_dur = self._load_train_data()
+                    count, load_dur = self._load_data()
                     build_dur = self._optimize()
+                    m.inserted_count = count
                     m.insert_duration = round(load_dur, 4)
                     m.optimize_duration = round(build_dur, 4)
                     m.load_duration = round(load_dur + build_dur, 4)
+                    m.additional_parameters.update(
+                        {
+                            "num_per_batch": config.NUM_PER_BATCH,
+                            "load_concurrency": self.config.load_concurrency,
+                        }
+                    )
                     log.info(
                         f"Finish loading the entire dataset into VectorDB,"
                         f" insert_duration={load_dur}, optimize_duration={build_dur}"
@@ -203,7 +347,7 @@ def _run_perf_case(self, drop_old: bool = True) -> Metric:
                 else:
                     log.info("Data loading skipped")
             if TaskStage.SEARCH_SERIAL in self.config.stages or TaskStage.SEARCH_CONCURRENT in self.config.stages:
-                self._init_search_runner()
+                self._init_search_runners()
                 if TaskStage.SEARCH_CONCURRENT in self.config.stages:
                     search_results = self._conc_search()
                     (
@@ -215,9 +359,22 @@ def _run_perf_case(self, drop_old: bool = True) -> Metric:
                         m.conc_latency_avg_list,
                     ) = search_results
                 if TaskStage.SEARCH_SERIAL in self.config.stages:
+                    cooldown = self.config.case_config.concurrency_search_config.serial_cooldown
+                    if TaskStage.SEARCH_CONCURRENT in self.config.stages and cooldown > 0:
+                        log.info(
+                            f"Cooldown {cooldown}s before serial search to ensure a stable measurement environment"
+                        )
+                        time.sleep(cooldown)
                     search_results = self._serial_search()
-                    m.recall, m.ndcg, m.serial_latency_p99, m.serial_latency_p95 = search_results
-
+                    if self.is_fts:
+                        m.recall, m.ndcg, m.mrr, m.serial_latency_p99, m.serial_latency_p95 = search_results
+                    else:
+                        m.recall, m.ndcg, m.serial_latency_p99, m.serial_latency_p95 = search_results
+            if hasattr(self.ca, "payload_profile"):
+                m.payload_profile = self.ca.payload_profile.value
+                m.payload_estimated_bytes_per_query = self.ca.estimated_payload_bytes_per_query(
+                    self.config.case_config.k
+                )
         except Exception as e:
             log.warning(f"Failed to run performance case, reason = {e}")
             traceback.print_exc()
@@ -239,31 +396,149 @@ def _run_streaming_case(self) -> Metric:
             log.info(f"Streaming case got result: {m}")
             return m
 
+    def _run_cloud_insert_case(self) -> Metric:
+        assert self.db is not None
+        started = time.perf_counter()
+        runner_kwargs = {}
+        if self.ca.is_multitenant:
+            runner_kwargs["tenant_case"] = self.ca
+        runner = ConcurrentInsertRunner(
+            self.db,
+            self.ca.dataset,
+            self.normalize,
+            self.ca.filters,
+            max_workers=self.config.load_concurrency or None,
+            batch_size=self.ca.batch_size,
+            duration=self.ca.duration,
+            **runner_kwargs,
+        )
+        count = runner.task()
+        insert_done = time.perf_counter()
+        readiness_timeout = self.ca.readiness_timeout
+        readiness_poll_interval = self.ca.readiness_poll_interval
+        readiness_deadline = None if readiness_timeout is None else time.perf_counter() + readiness_timeout
+        with self.db.init():
+            status = self.db.poll_insert_readiness(count)
+            searchable_started = time.perf_counter()
+            while not status["fully_searchable"]:
+                if readiness_deadline is not None and time.perf_counter() >= readiness_deadline:
+                    msg = (
+                        "Cloud insert readiness timed out waiting for fully_searchable "
+                        f"after {readiness_timeout}s; last_status={status}"
+                    )
+                    raise TimeoutError(msg)
+                time.sleep(readiness_poll_interval)
+                status = self.db.poll_insert_readiness(count)
+            indexed_started = time.perf_counter()
+            while not status["fully_indexed"]:
+                if readiness_deadline is not None and time.perf_counter() >= readiness_deadline:
+                    msg = (
+                        "Cloud insert readiness timed out waiting for fully_indexed "
+                        f"after {readiness_timeout}s; last_status={status}"
+                    )
+                    raise TimeoutError(msg)
+                time.sleep(readiness_poll_interval)
+                status = self.db.poll_insert_readiness(count)
+        return Metric(
+            inserted_count=count,
+            insert_rows_per_second=round(count / max(insert_done - started, 0.001), 4),
+            insert_completion_seconds=round(insert_done - started, 4),
+            searchable_after_insert_seconds=round(indexed_started - searchable_started, 4),
+            indexed_after_searchable_seconds=round(time.perf_counter() - indexed_started, 4),
+            additional_parameters=status.get("additional_parameters", {}),
+        )
+
+    def _init_cold_warm_search_runner(self) -> None:
+        if self.normalize:
+            test_emb = np.stack(self.ca.dataset.test_data)
+            test_emb = test_emb / np.linalg.norm(test_emb, axis=1)[:, np.newaxis]
+            self.test_emb = test_emb.tolist()
+        else:
+            self.test_emb = self.ca.dataset.test_data
+
+        self.cold_warm_search_runner = ColdWarmSearchRunner(
+            db=self.db,
+            test_data=self.test_emb,
+            filters=self.ca.filters,
+            k=self.config.case_config.k,
+            payload_profile=self.ca.payload_profile,
+            query_count=self.ca.query_count,
+        )
+
+    def _run_cloud_cold_latency_case(self, drop_old: bool = True) -> Metric:
+        log.info("Start cloud cold latency case")
+        try:
+            self._validate_cloud_cold_latency_config(drop_old)
+            m = Metric()
+            if drop_old:
+                if TaskStage.LOAD in self.config.stages:
+                    _, load_dur = self._load_train_data()
+                    build_dur = self._optimize()
+                    m.insert_duration = round(load_dur, 4)
+                    m.optimize_duration = round(build_dur, 4)
+                    m.load_duration = round(load_dur + build_dur, 4)
+                else:
+                    log.info("Data loading skipped")
+
+            self._init_cold_warm_search_runner()
+            m.additional_parameters = {
+                "cold_latency": self.cold_warm_search_runner.run(),
+            }
+            m.payload_profile = self.ca.payload_profile.value
+            m.payload_estimated_bytes_per_query = self.ca.estimated_payload_bytes_per_query(self.config.case_config.k)
+        except Exception as e:
+            log.warning(f"Failed to run cloud cold latency case, reason = {e}")
+            traceback.print_exc()
+            raise e from None
+        else:
+            log.info(f"Cloud cold latency case got result: {m}")
+            return m
+
     @utils.time_it
+    def _load_data(self):
+        return self._load_train_data()
+
     def _load_train_data(self):
-        """Insert train data and get the insert_duration"""
+        """Insert vector or FTS train data concurrently and get insert duration."""
         try:
-            runner = SerialInsertRunner(
+            runner_kwargs = {}
+            if self.ca.is_multitenant:
+                runner_kwargs["tenant_case"] = self.ca
+            runner = ConcurrentInsertRunner(
                 self.db,
                 self.ca.dataset,
                 self.normalize,
                 self.ca.filters,
                 self.ca.load_timeout,
+                max_workers=self.config.load_concurrency or None,
+                with_scalar_labels=self.ca.with_scalar_labels,
+                workload_kind=self.workload_kind,
+                **runner_kwargs,
             )
-            runner.run()
+            return runner.run()
         except Exception as e:
             raise e from None
         finally:
             runner = None
 
-    def _serial_search(self) -> tuple[float, float, float, float]:
+    def _serial_search(self) -> tuple[float, ...]:
         """Performance serial tests, search the entire test data once,
         calculate the recall, serial_latency_p99, serial_latency_p95
 
         Returns:
-            tuple[float, float, float, float]: recall, ndcg, serial_latency_p99, serial_latency_p95
+            tuple[float, ...]: vector cases return recall, ndcg, p99, p95;
+                FTS cases return recall, p99, p95.
         """
         try:
+            if self.serial_search_runner is None:
+                if self.is_fts and getattr(self.ca.dataset, "recall_skipped", False):
+                    log.warning(
+                        "Skipping FTS serial recall: %s",
+                        getattr(self.ca.dataset, "recall_skip_reason", "unknown"),
+                    )
+                    return (0.0, 0.0, 0.0, 0.0, 0.0)
+                msg = "serial search runner is not initialized"
+                raise RuntimeError(msg)  # noqa: TRY301
             results, _ = self.serial_search_runner.run()
         except Exception as e:
             log.warning(f"search error: {e!s}, {e}")
@@ -293,19 +568,31 @@ def _optimize_task(self) -> None:
             self.db.optimize(data_size=self.ca.dataset.data.size)
 
     def _optimize(self) -> float:
+        if self.is_fts:
+            try:
+                with utils.timeout(self.ca.optimize_timeout, PerformanceTimeoutError):
+                    _, duration = self._optimize_task()
+                    return duration
+            except PerformanceTimeoutError:
+                log.warning(f"VectorDB optimize timeout in {self.ca.optimize_timeout}")
+                raise
         with concurrent.futures.ProcessPoolExecutor(max_workers=1) as executor:
             future = executor.submit(self._optimize_task)
             try:
                 return future.result(timeout=self.ca.optimize_timeout)[1]
             except TimeoutError as e:
                 log.warning(f"VectorDB optimize timeout in {self.ca.optimize_timeout}")
-                for pid, _ in executor._processes.items():
-                    psutil.Process(pid).kill()
+                kill_proc_tree(pids=list(executor._processes.keys()))
                 raise PerformanceTimeoutError from e
             except Exception as e:
                 log.warning(f"VectorDB optimize error: {e}")
                 raise e from None
 
+    def _init_search_runners(self):
+        if self.is_fts:
+            return self._init_fts_search_runner()
+        return self._init_search_runner()
+
     def _init_search_runner(self):
         if self.normalize:
             test_emb = np.stack(self.ca.dataset.test_data)
@@ -314,7 +601,9 @@ def _init_search_runner(self):
         else:
             self.test_emb = self.ca.dataset.test_data
 
-        gt_df = self.ca.dataset.gt_data
+        tenant_labels = self.ca.tenant_labels() if self.ca.is_multitenant else None
+        measure_recall = getattr(self.ca, "measure_recall", True)
+        gt_df = self.ca.dataset.gt_data if measure_recall else None
 
         if TaskStage.SEARCH_SERIAL in self.config.stages:
             self.serial_search_runner = SerialSearchRunner(
@@ -323,6 +612,10 @@ def _init_search_runner(self):
                 ground_truth=gt_df,
                 filters=self.ca.filters,
                 k=self.config.case_config.k,
+                payload_profile=self.ca.payload_profile,
+                tenant_labels=tenant_labels,
+                measure_recall=measure_recall,
+                workload_kind=WorkloadKind.VECTOR,
             )
         if TaskStage.SEARCH_CONCURRENT in self.config.stages:
             self.search_runner = MultiProcessingSearchRunner(
@@ -333,6 +626,65 @@ def _init_search_runner(self):
                 duration=self.config.case_config.concurrency_search_config.concurrency_duration,
                 concurrency_timeout=self.config.case_config.concurrency_search_config.concurrency_timeout,
                 k=self.config.case_config.k,
+                payload_profile=self.ca.payload_profile,
+                tenant_labels=tenant_labels,
+                workload_kind=WorkloadKind.VECTOR,
+            )
+
+    def _init_fts_search_runner(self):
+        fts_dataset = self.ca.dataset
+
+        if fts_dataset.queries_data is None or fts_dataset.gt_data is None:
+            msg = "FTS dataset is missing queries or ground truth. Call prepare() before initializing search."
+            raise ValueError(msg)
+        test_texts = [q.text for q in fts_dataset.queries_data]
+        if len(test_texts) != len(fts_dataset.gt_data):
+            msg = f"FTS query count {len(test_texts)} does not match ground truth row count {len(fts_dataset.gt_data)}"
+            raise ValueError(msg)
+
+        log.info(f"FTS concurrent test will use {len(test_texts)} queries for testing")
+        self.test_texts = test_texts
+
+        if TaskStage.SEARCH_SERIAL in self.config.stages:
+            recall_queries = fts_dataset.recall_queries_data
+            recall_ground_truth = fts_dataset.recall_gt_data
+            if recall_queries is None or recall_ground_truth is None:
+                msg = (
+                    "FTS dataset is missing recall queries or ground truth. Call prepare() before initializing search."
+                )
+                raise ValueError(msg)
+            if len(recall_queries) != len(recall_ground_truth):
+                msg = (
+                    f"FTS recall query count {len(recall_queries)} does not match "
+                    f"ground truth row count {len(recall_ground_truth)}"
+                )
+                raise ValueError(msg)
+            if fts_dataset.recall_skipped:
+                log.warning("FTS serial recall will be skipped: %s", fts_dataset.recall_skip_reason)
+                self.serial_search_runner = None
+            else:
+                recall_test_texts = [q.text for q in recall_queries]
+                log.info(f"FTS serial recall will use {len(recall_test_texts)} queries")
+                self.serial_search_runner = SerialSearchRunner(
+                    db=self.db,
+                    test_data=recall_test_texts,
+                    ground_truth=recall_ground_truth,
+                    filters=self.ca.filters,
+                    k=self.config.case_config.k,
+                    payload_profile=self.ca.payload_profile,
+                    workload_kind=WorkloadKind.FULL_TEXT,
+                )
+        if TaskStage.SEARCH_CONCURRENT in self.config.stages:
+            self.search_runner = MultiProcessingSearchRunner(
+                db=self.db,
+                test_data=test_texts,
+                filters=self.ca.filters,
+                concurrencies=self.config.case_config.concurrency_search_config.num_concurrency,
+                duration=self.config.case_config.concurrency_search_config.concurrency_duration,
+                concurrency_timeout=self.config.case_config.concurrency_search_config.concurrency_timeout,
+                k=self.config.case_config.k,
+                payload_profile=self.ca.payload_profile,
+                workload_kind=WorkloadKind.FULL_TEXT,
             )
 
     def _init_read_write_runner(self):
diff --git a/vectordb_bench/backend/utils.py b/vectordb_bench/backend/utils.py
index 86c4faf5e..8c9c99a1f 100644
--- a/vectordb_bench/backend/utils.py
+++ b/vectordb_bench/backend/utils.py
@@ -1,6 +1,50 @@
+import contextlib
+import logging
+import signal
+import threading
 import time
+from collections.abc import Callable
+from contextlib import contextmanager
 from functools import wraps
 
+import psutil
+
+log = logging.getLogger(__name__)
+
+
+def kill_proc_tree(pids: list[int] | None = None, grace: float = 2, timeout: float = 3):
+    """Kill child processes with SIGTERM, then SIGKILL for survivors.
+
+    Args:
+        pids: Specific PIDs to kill. If None, kills all children of the
+              current process (recursive).
+        grace: Seconds to wait after SIGTERM before sending SIGKILL.
+        timeout: Seconds to wait for processes to fully exit after SIGKILL.
+    """
+    if pids is not None:
+        targets = []
+        for pid in pids:
+            with contextlib.suppress(psutil.NoSuchProcess):
+                targets.append(psutil.Process(pid))
+    else:
+        targets = psutil.Process().children(recursive=True)
+
+    for p in targets:
+        try:
+            log.warning(f"sending SIGTERM to child process: {p}")
+            p.send_signal(signal.SIGTERM)
+        except psutil.NoSuchProcess:
+            pass
+
+    _, alive = psutil.wait_procs(targets, timeout=grace)
+    for p in alive:
+        try:
+            log.warning(f"force killing child process: {p}")
+            p.kill()
+        except psutil.NoSuchProcess:
+            pass
+    psutil.wait_procs(alive, timeout=timeout)
+
 
 def numerize(n: int) -> str:
     """display positive number n for readability
@@ -47,6 +91,29 @@ def inner(*args, **kwargs):
     return inner
 
 
+@contextmanager
+def timeout(timeout_seconds: float | None, exc_factory: Callable[[], Exception]):
+    if (
+        timeout_seconds is None
+        or not hasattr(signal, "SIGALRM")
+        or threading.current_thread() is not threading.main_thread()
+    ):
+        yield
+        return
+
+    def _handle_timeout(signum, frame):  # noqa: ANN001, ARG001
+        raise exc_factory()
+
+    previous_handler = signal.getsignal(signal.SIGALRM)
+    signal.signal(signal.SIGALRM, _handle_timeout)
+    previous_timer = signal.setitimer(signal.ITIMER_REAL, float(timeout_seconds))
+    try:
+        yield
+    finally:
+        signal.setitimer(signal.ITIMER_REAL, *previous_timer)
+        signal.signal(signal.SIGALRM, previous_handler)
+
+
 def compose_train_files(train_count: int, use_shuffled: bool) -> list[str]:
     prefix = "shuffle_train" if use_shuffled else "train"
     middle = f"of-{train_count}"
diff --git a/vectordb_bench/backend/workload.py b/vectordb_bench/backend/workload.py
new file mode 100644
index 000000000..e3c958b20
--- /dev/null
+++ b/vectordb_bench/backend/workload.py
@@ -0,0 +1,6 @@
+from enum import StrEnum
+
+
+class WorkloadKind(StrEnum):
+    VECTOR = "vector"
+    FULL_TEXT = "full_text"
diff --git a/vectordb_bench/base.py b/vectordb_bench/base.py
index 694308d90..401d2086d 100644
--- a/vectordb_bench/base.py
+++ b/vectordb_bench/base.py
@@ -3,4 +3,4 @@
 
 
 class BaseModel(PydanticBaseModel):
-    model_config = ConfigDict(arbitrary_types_allowed=True, validate_default=True)
+    model_config = ConfigDict(arbitrary_types_allowed=True)
diff --git a/vectordb_bench/cli/cli.py b/vectordb_bench/cli/cli.py
index 12bb4be9b..abcb91607 100644
--- a/vectordb_bench/cli/cli.py
+++ b/vectordb_bench/cli/cli.py
@@ -1,7 +1,6 @@
 import logging
 import time
 from collections.abc import Callable
-from concurrent.futures import wait
 from datetime import datetime
 from pathlib import Path
 from pprint import pformat
@@ -15,12 +14,16 @@
 )
 
 import click
+from click.core import ParameterSource
 from yaml import load
 
 from .. import config
+from ..backend.cases import FTS_FILTER_RATES
 from ..backend.clients import DB
-from ..backend.clients.api import MetricType
-from ..interface import benchmark_runner, global_result_future
+from ..backend.clients.api import IndexType, MetricType
+from ..backend.dataset import DatasetWithSizeType, FtsDatasetWithSizeType
+from ..backend.payload import PayloadProfile
+from ..interface import benchmark_runner
 from ..models import (
     CaseConfig,
     CaseType,
@@ -36,6 +39,22 @@
 except ImportError:
     from yaml import Loader
 
+DEFAULT_DATASET_WITH_SIZE_TYPE = DatasetWithSizeType.CohereMedium.value
+SUPPORTED_DATASET_WITH_SIZE_TYPES = "|".join(dataset.value for dataset in DatasetWithSizeType)
+SUPPORTED_FTS_DATASET_WITH_SIZE_TYPES = "|".join(dataset.value for dataset in FtsDatasetWithSizeType)
+SUPPORTED_FTS_FILTER_RATES = "|".join(f"{rate:g}" for rate in FTS_FILTER_RATES)
+
+
+def copy_if_not_none(
+    custom_case_config: dict[str, Any],
+    parameters: dict[str, Any],
+    key: str,
+    target_key: str | None = None,
+) -> None:
+    value = parameters[key]
+    if value is not None:
+        custom_case_config[target_key or key] = value
+
 
 def click_get_defaults_from_file(ctx, param, value):  # noqa: ANN001, ARG001
     if value:
@@ -51,6 +70,26 @@ def click_get_defaults_from_file(ctx, param, value):  # noqa: ANN001, ARG001
     return value
 
 
+def resolve_db_note(note: str, note_file: Path | None) -> str:
+    ctx = click.get_current_context()
+    note_source = ctx.get_parameter_source("note")
+    note_file_source = ctx.get_parameter_source("note_file")
+    note_supplied = note_source not in {None, ParameterSource.DEFAULT}
+    note_file_supplied = note_file_source not in {None, ParameterSource.DEFAULT}
+
+    if note_supplied and note_file_supplied:
+        raise click.UsageError("--note and --note-file cannot be used together")
+    if note_file is None:
+        return note
+    try:
+        content = note_file.read_text(encoding="utf-8").rstrip("\r\n")
+    except UnicodeDecodeError as e:
+        raise click.BadParameter("Note file is not valid UTF-8", param_hint="--note-file") from e
+    if not content.strip():
+        raise click.BadParameter("Note file is empty", param_hint="--note-file")
+    return content
+
+
 def click_parameter_decorators_from_typed_dict(
     typed_dict: type,
 ) -> Callable[[click.decorators.FC], click.decorators.FC]:
@@ -166,6 +205,7 @@ def check_custom_case_parameters(ctx: any, param: any, value: any):  # noqa: ARG
 
 def get_custom_case_config(parameters: dict) -> dict:
     custom_case_config = {}
+    dataset_with_size_type = parameters["dataset_with_size_type"] or DEFAULT_DATASET_WITH_SIZE_TYPE
     if parameters["case_type"] == "PerformanceCustomDataset":
         custom_case_config = {
             "name": parameters["custom_case_name"],
@@ -185,17 +225,120 @@ def get_custom_case_config(parameters: dict) -> dict:
         }
     elif parameters["case_type"] == "NewIntFilterPerformanceCase":
         custom_case_config = {
-            "dataset_with_size_type": parameters["dataset_with_size_type"],
+            "dataset_with_size_type": dataset_with_size_type,
             "filter_rate": parameters["filter_rate"],
         }
     elif parameters["case_type"] == "LabelFilterPerformanceCase":
         custom_case_config = {
-            "dataset_with_size_type": parameters["dataset_with_size_type"],
+            "dataset_with_size_type": dataset_with_size_type,
             "label_percentage": parameters["label_percentage"],
         }
+    elif parameters["case_type"] == "CloudPayloadSearchCase":
+        custom_case_config = {
+            "payload_profile": parameters["payload_profile"],
+        }
+        copy_if_not_none(custom_case_config, parameters, "dataset_with_size_type")
+        if parameters["cloud_filter_rate"] is not None:
+            custom_case_config["filter_rate"] = parameters["cloud_filter_rate"]
+        if parameters["cloud_label_percentage"] is not None:
+            custom_case_config["label_percentage"] = parameters["cloud_label_percentage"]
+    elif parameters["case_type"] == "CloudColdLatencyCase":
+        custom_case_config = {
+            "payload_profile": parameters["payload_profile"],
+            "query_count": parameters["cloud_cold_query_count"],
+        }
+        copy_if_not_none(custom_case_config, parameters, "dataset_with_size_type")
+        copy_if_not_none(custom_case_config, parameters, "cloud_filter_rate", "filter_rate")
+        copy_if_not_none(custom_case_config, parameters, "cloud_label_percentage", "label_percentage")
+    elif parameters["case_type"] == "CloudInsertCase":
+        custom_case_config = {
+            "batch_size": parameters["cloud_insert_batch_size"],
+            "duration": parameters["cloud_insert_duration"],
+            "dataset_with_size_type": dataset_with_size_type,
+        }
+        copy_if_not_none(custom_case_config, parameters, "cloud_insert_readiness_timeout", "readiness_timeout")
+        copy_if_not_none(
+            custom_case_config,
+            parameters,
+            "cloud_insert_readiness_poll_interval",
+            "readiness_poll_interval",
+        )
+    elif parameters["case_type"] == "CloudMultiTenantSearchCase":
+        custom_case_config = {
+            "tenant_count": parameters["tenant_count"],
+            "tenant_prefix": parameters["tenant_prefix"],
+            "tenant_id_width": parameters["tenant_id_width"],
+            "payload_profile": parameters["payload_profile"],
+        }
+        copy_if_not_none(custom_case_config, parameters, "dataset_with_size_type")
+        if parameters["cloud_filter_rate"] is not None:
+            custom_case_config["filter_rate"] = parameters["cloud_filter_rate"]
+        if parameters["cloud_label_percentage"] is not None:
+            custom_case_config["label_percentage"] = parameters["cloud_label_percentage"]
+    elif parameters["case_type"] == "FTSBm25Performance":
+        dataset_with_size_type = parameters["dataset_with_size_type"]
+        if dataset_with_size_type not in {dataset.value for dataset in FtsDatasetWithSizeType}:
+            dataset_with_size_type = FtsDatasetWithSizeType.MSMarcoSmall.value
+        custom_case_config = {
+            "dataset_with_size_type": dataset_with_size_type,
+            "payload_profile": parameters.get("payload_profile", PayloadProfile.IDS_ONLY.value),
+        }
+        copy_if_not_none(custom_case_config, parameters, "fts_filter_rate", "filter_rate")
     return custom_case_config
 
 
+def copy_fts_compatible_db_case_fields(source: DBCaseConfig, target: DBCaseConfig) -> DBCaseConfig:
+    """Copy CLI fields that remain meaningful when routing a backend to its FTS config."""
+    preserved_fields = (
+        "number_of_shards",
+        "number_of_replicas",
+        "refresh_interval",
+        "use_force_merge",
+        "force_merge_enabled",
+        "disable_backpressure",
+        "level",
+    )
+    updates = {
+        field: getattr(source, field) for field in preserved_fields if hasattr(source, field) and hasattr(target, field)
+    }
+    if not updates:
+        return target
+    return target.model_copy(update=updates)
+
+
+def apply_fts_cli_db_case_params(
+    db_case_config: DBCaseConfig,
+    parameters: dict[str, Any] | None,
+) -> DBCaseConfig:
+    if not parameters:
+        return db_case_config
+
+    updates = {
+        field: parameters[field]
+        for field in ("bm25_k1", "bm25_b")
+        if parameters.get(field) is not None and hasattr(db_case_config, field)
+    }
+    if not updates:
+        return db_case_config
+    return db_case_config.model_copy(update=updates)
+
+
+def select_cli_db_case_config(
+    db: DB,
+    db_case_config: DBCaseConfig,
+    case_type: str,
+    parameters: dict[str, Any] | None = None,
+) -> DBCaseConfig:
+    if case_type != CaseType.FTSBm25Performance.name:
+        return db_case_config
+
+    fts_case_config_cls = db.case_config_cls(IndexType.FTS)
+    if isinstance(db_case_config, fts_case_config_cls):
+        return apply_fts_cli_db_case_params(db_case_config, parameters)
+    fts_db_case_config = copy_fts_compatible_db_case_fields(db_case_config, fts_case_config_cls())
+    return apply_fts_cli_db_case_params(fts_db_case_config, parameters)
+
+
 log = logging.getLogger(__name__)
 
 
@@ -231,6 +374,16 @@ class CommonTypedDict(TypedDict):
             show_default=True,
         ),
     ]
+    load_concurrency: Annotated[
+        int,
+        click.option(
+            "--load-concurrency",
+            type=int,
+            default=config.LOAD_CONCURRENCY,
+            show_default=True,
+            help="Number of concurrent workers for data loading in performance cases (0 = cpu_count)",
+        ),
+    ]
     search_serial: Annotated[
         bool,
         click.option(
@@ -271,6 +424,25 @@ class CommonTypedDict(TypedDict):
             default=datetime.now().isoformat(),
         ),
     ]
+    note: Annotated[
+        str,
+        click.option(
+            "--note",
+            type=str,
+            help="Run context stored with each result",
+            default="",
+            show_default=True,
+        ),
+    ]
+    note_file: Annotated[
+        Path | None,
+        click.option(
+            "--note-file",
+            type=click.Path(exists=True, dir_okay=False, readable=True, path_type=Path),
+            help="Read run context from a UTF-8 text file",
+            default=None,
+        ),
+    ]
     dry_run: Annotated[
         bool,
         click.option(
@@ -323,6 +495,16 @@ class CommonTypedDict(TypedDict):
             "Set to a negative value to wait indefinitely.",
         ),
     ]
+    serial_cooldown: Annotated[
+        float,
+        click.option(
+            "--serial-cooldown",
+            type=float,
+            default=config.SERIAL_COOLDOWN,
+            show_default=True,
+            help="Cooldown in seconds between concurrent and serial search phases",
+        ),
+    ]
     custom_case_name: Annotated[
         str,
         click.option(
@@ -427,14 +609,15 @@ class CommonTypedDict(TypedDict):
     ]
     task_label: Annotated[str, click.option("--task-label", help="Task label")]
     dataset_with_size_type: Annotated[
-        str,
+        str | None,
         click.option(
             "--dataset-with-size-type",
-            help="Dataset with size type for NewIntFilterPerformanceCase/LabelFilterPerformanceCase, you can use "
-            "Medium Cohere (768dim, 1M)|Large Cohere (768dim, 10M)|Medium Bioasq (1024dim, 1M)|"
-            "Large Bioasq (1024dim, 10M)|Large OpenAI (1536dim, 5M)|Medium OpenAI (1536dim, 500K)",
-            default="Medium Cohere (768dim, 1M)",
-            show_default=True,
+            help="Dataset with size type. When omitted, filter/insert cases use Medium Cohere (768dim, 1M), "
+            "CloudPayloadSearchCase and CloudColdLatencyCase use LAION 100M, and CloudMultiTenantSearchCase "
+            f"uses Large Cohere (768dim, 10M). Supported vector values include "
+            f"{SUPPORTED_DATASET_WITH_SIZE_TYPES}. For FTSBm25Performance, supported datasets include "
+            f"{SUPPORTED_FTS_DATASET_WITH_SIZE_TYPES}.",
+            default=None,
         ),
     ]
     filter_rate: Annotated[
@@ -455,6 +638,141 @@ class CommonTypedDict(TypedDict):
             show_default=True,
         ),
     ]
+    payload_profile: Annotated[
+        str,
+        click.option(
+            "--payload-profile",
+            type=click.Choice([profile.value for profile in PayloadProfile]),
+            help="Response payload profile for payload and FTS cases",
+            default="ids_only",
+            show_default=True,
+        ),
+    ]
+    bm25_k1: Annotated[
+        float | None,
+        click.option(
+            "--bm25-k1",
+            type=float,
+            default=None,
+            help="Optional BM25 k1 override for FTS cases. Omit to use the backend default.",
+        ),
+    ]
+    bm25_b: Annotated[
+        float | None,
+        click.option(
+            "--bm25-b",
+            type=float,
+            default=None,
+            help="Optional BM25 b override for FTS cases. Omit to use the backend default.",
+        ),
+    ]
+    fts_filter_rate: Annotated[
+        float | None,
+        click.option(
+            "--fts-filter-rate",
+            type=float,
+            default=None,
+            help=(
+                "Optional FTS integer filter rate for FTSBm25Performance. "
+                f"Only valid for large FTS datasets. Supported values: {SUPPORTED_FTS_FILTER_RATES}."
+            ),
+        ),
+    ]
+    cloud_filter_rate: Annotated[
+        float | None,
+        click.option(
+            "--cloud-filter-rate",
+            type=float,
+            default=None,
+            help="Optional int filter rate for CloudPayloadSearchCase and CloudColdLatencyCase",
+        ),
+    ]
+    cloud_label_percentage: Annotated[
+        float | None,
+        click.option(
+            "--cloud-label-percentage",
+            type=float,
+            default=None,
+            help="Optional label percentage for CloudPayloadSearchCase and CloudColdLatencyCase",
+        ),
+    ]
+    cloud_cold_query_count: Annotated[
+        int,
+        click.option(
+            "--cloud-cold-query-count",
+            type=int,
+            default=1000,
+            show_default=True,
+            help="Number of serial queries per cold/warm pass for CloudColdLatencyCase",
+        ),
+    ]
+    cloud_insert_batch_size: Annotated[
+        int,
+        click.option(
+            "--cloud-insert-batch-size",
+            type=int,
+            default=5000,
+            show_default=True,
+            help="Insert batch size for CloudInsertCase",
+        ),
+    ]
+    cloud_insert_duration: Annotated[
+        float | None,
+        click.option(
+            "--cloud-insert-duration",
+            type=float,
+            default=None,
+            help="Optional insert duration in seconds for CloudInsertCase",
+        ),
+    ]
+    cloud_insert_readiness_timeout: Annotated[
+        float | None,
+        click.option(
+            "--cloud-insert-readiness-timeout",
+            type=float,
+            default=None,
+            help="Optional readiness polling timeout in seconds for CloudInsertCase",
+        ),
+    ]
+    cloud_insert_readiness_poll_interval: Annotated[
+        float | None,
+        click.option(
+            "--cloud-insert-readiness-poll-interval",
+            type=float,
+            default=None,
+            help="Optional readiness polling interval in seconds for CloudInsertCase",
+        ),
+    ]
+    tenant_count: Annotated[
+        int,
+        click.option(
+            "--tenant-count",
+            type=int,
+            default=1000,
+            show_default=True,
+            help="Tenant count for CloudMultiTenantSearchCase",
+        ),
+    ]
+    tenant_prefix: Annotated[
+        str,
+        click.option(
+            "--tenant-prefix",
+            type=str,
+            default="tenant_",
+            show_default=True,
+            help="Tenant label prefix for CloudMultiTenantSearchCase",
+        ),
+    ]
+    tenant_id_width: Annotated[
+        int,
+        click.option(
+            "--tenant-id-width",
+            type=int,
+            default=4,
+            show_default=True,
+            help="Zero-padding width for CloudMultiTenantSearchCase tenant IDs",
+        ),
+    ]
 
 
 class HNSWBaseTypedDict(TypedDict):
@@ -623,10 +941,14 @@ def run(
         **parameters: expects keys from CommonTypedDict
     """
 
+    db_config = db_config.model_copy(
+        update={"note": resolve_db_note(parameters["note"], parameters["note_file"])},
+    )
+
     task = TaskConfig(
         db=db,
         db_config=db_config,
-        db_case_config=db_case_config,
+        db_case_config=select_cli_db_case_config(db, db_case_config, parameters["case_type"], parameters),
         case_config=CaseConfig(
             case_id=CaseType[parameters["case_type"]],
             k=parameters["k"],
@@ -634,6 +956,7 @@ def run(
                 concurrency_duration=parameters["concurrency_duration"],
                 num_concurrency=[int(s) for s in parameters["num_concurrency"]],
                 concurrency_timeout=parameters["concurrency_timeout"],
+                serial_cooldown=parameters["serial_cooldown"],
             ),
             custom_case=get_custom_case_config(parameters),
         ),
@@ -643,15 +966,16 @@ def run(
             parameters["search_serial"],
             parameters["search_concurrent"],
         ),
+        load_concurrency=parameters["load_concurrency"],
     )
     task_label = parameters["task_label"]
 
     log.info(f"Task:\n{pformat(task)}\n")
     if not parameters["dry_run"]:
         benchmark_runner.run([task], task_label)
-        time.sleep(5)
-        if global_result_future:
-            wait([global_result_future])
-
-        while benchmark_runner.has_running():
-            time.sleep(1)
+        try:
+            while benchmark_runner.has_running():
+                time.sleep(1)
+        except KeyboardInterrupt:
+            log.warning("Ctrl+C received, stopping benchmark...")
+            benchmark_runner.stop_running()
diff --git a/vectordb_bench/cli/vectordbbench.py b/vectordb_bench/cli/vectordbbench.py
index 751eaed66..261f3ebff 100644
--- a/vectordb_bench/cli/vectordbbench.py
+++ b/vectordb_bench/cli/vectordbbench.py
@@ -1,3 +1,4 @@
+from ..backend.clients.adbpg.cli import AdbpgNova
 from ..backend.clients.alisql.cli import AliSQLHNSW
 from ..backend.clients.alloydb.cli import AlloyDBScaNN
 from ..backend.clients.antfly.cli import AntflyAKNN
@@ -14,11 +15,17 @@
 )
 from ..backend.clients.endee.cli import Endee
 from ..backend.clients.hologres.cli import HologresHGraph
-from ..backend.clients.lancedb.cli import LanceDB
+from ..backend.clients.lancedb.cli import (
+    LanceDB,
+    LanceDBAutoIndex,
+    LanceDBIVFHNSWPQ,
+    LanceDBIVFHNSWSQ,
+    LanceDBIVFPQ,
+)
 from ..backend.clients.lindorm.cli import LindormHNSW, LindormIVFBQ, LindormIVFPQ
 from ..backend.clients.mariadb.cli import MariaDBHNSW
 from ..backend.clients.memorydb.cli import MemoryDB
-from ..backend.clients.milvus.cli import MilvusAutoIndex
+from ..backend.clients.milvus.cli import MilvusAutoIndex, MilvusFTS
 from ..backend.clients.oceanbase.cli import OceanBaseHNSW, OceanBaseIVF
 from ..backend.clients.oss_opensearch.cli import OSSOpenSearch
 from ..backend.clients.pgdiskann.cli import PgDiskAnn
@@ -26,21 +33,31 @@
 from ..backend.clients.pgvector.cli import PgVectorHNSW
 from ..backend.clients.pgvectorscale.cli import PgVectorScaleDiskAnn
 from ..backend.clients.pinecone.cli import Pinecone
+from ..backend.clients.pinot.cli import Pinot
+from ..backend.clients.polardb.cli import (
+    PolarDBHNSWFlat,
+    PolarDBHNSWPQ,
+    PolarDBHNSWSQ,
+)
 from ..backend.clients.qdrant_cloud.cli import QdrantCloud
 from ..backend.clients.qdrant_local.cli import QdrantLocal
 from ..backend.clients.redis.cli import Redis
 from ..backend.clients.s3_vectors.cli import S3Vectors
+from ..backend.clients.seekdb.cli import SeekDBHNSW
 from ..backend.clients.tencent_elasticsearch.cli import TencentElasticsearch
 from ..backend.clients.test.cli import Test
 from ..backend.clients.tidb.cli import TiDB
-from ..backend.clients.turbopuffer.cli import TurboPuffer
+from ..backend.clients.turbopuffer.cli import TurboPuffer, TurboPufferUnpin
+from ..backend.clients.vectorchord.cli import VectorChordGraph, VectorChordRQ
 from ..backend.clients.vespa.cli import Vespa
+from ..backend.clients.volc_mysql.cli import VolcMySQLHNSW
 from ..backend.clients.weaviate_cloud.cli import Weaviate
 from ..backend.clients.zilliz_cloud.cli import ZillizAutoIndex
 from ..backend.clients.zvec.cli import Zvec
 from .batch_cli import BatchCli
 from .cli import cli
 
+cli.add_command(AdbpgNova)
 cli.add_command(PgVectorHNSW)
 cli.add_command(PgVectoRSHNSW)
 cli.add_command(PgVectoRSIVFFlat)
@@ -50,6 +67,7 @@
 cli.add_command(Test)
 cli.add_command(ZillizAutoIndex)
 cli.add_command(MilvusAutoIndex)
+cli.add_command(MilvusFTS)
 cli.add_command(AWSOpenSearch)
 cli.add_command(OSSOpenSearch)
 cli.add_command(PgVectorScaleDiskAnn)
@@ -63,6 +81,10 @@
 cli.add_command(Clickhouse)
 cli.add_command(Vespa)
 cli.add_command(LanceDB)
+cli.add_command(LanceDBAutoIndex)
+cli.add_command(LanceDBIVFPQ)
+cli.add_command(LanceDBIVFHNSWSQ)
+cli.add_command(LanceDBIVFHNSWPQ)
 cli.add_command(HologresHGraph)
 cli.add_command(QdrantCloud)
 cli.add_command(QdrantLocal)
@@ -76,6 +98,7 @@
 cli.add_command(AliSQLHNSW)
 cli.add_command(Doris)
 cli.add_command(TurboPuffer)
+cli.add_command(TurboPufferUnpin)
 cli.add_command(Chroma)
 cli.add_command(Zvec)
 cli.add_command(Endee)
@@ -83,6 +106,14 @@
 cli.add_command(LindormHNSW)
 cli.add_command(LindormIVFBQ)
 cli.add_command(Pinecone)
+cli.add_command(VectorChordRQ)
+cli.add_command(VectorChordGraph)
+cli.add_command(Pinot)
+cli.add_command(PolarDBHNSWFlat)
+cli.add_command(PolarDBHNSWPQ)
+cli.add_command(PolarDBHNSWSQ)
+cli.add_command(SeekDBHNSW)
+cli.add_command(VolcMySQLHNSW)
 cli.add_command(AntflyAKNN)
 
 
diff --git a/vectordb_bench/fig/homepage/full_text_search.png b/vectordb_bench/fig/homepage/full_text_search.png
new file mode 100644
index 000000000..fa9b19af8
Binary files /dev/null and b/vectordb_bench/fig/homepage/full_text_search.png differ
diff --git a/vectordb_bench/frontend/components/check_results/charts.py b/vectordb_bench/frontend/components/check_results/charts.py
index 0e74d2752..d36cc5fc6 100644
--- a/vectordb_bench/frontend/components/check_results/charts.py
+++ b/vectordb_bench/frontend/components/check_results/charts.py
@@ -153,4 +153,4 @@ def drawMetricChart(data, metric, st, key: str):
         ),
     )
 
-    chart.plotly_chart(fig, use_container_width=True, key=key)
+    chart.plotly_chart(fig, width="stretch", key=key)
diff --git a/vectordb_bench/frontend/components/check_results/filters.py b/vectordb_bench/frontend/components/check_results/filters.py
index 6016c0040..e42a63fd6 100644
--- a/vectordb_bench/frontend/components/check_results/filters.py
+++ b/vectordb_bench/frontend/components/check_results/filters.py
@@ -1,4 +1,4 @@
-from vectordb_bench.backend.cases import Case
+from vectordb_bench.backend.cases import Case, CaseLabel
 from vectordb_bench.backend.dataset import DatasetWithSizeType
 from vectordb_bench.backend.filter import FilterOp
 from vectordb_bench.frontend.components.check_results.data import getChartData
@@ -75,6 +75,10 @@ def getShowDbsAndCases(st, result: list[CaseResult], filter_type: FilterOp) -> t
     )
     showCaseNames = []
 
+    # Handle FTS cases separately
+    fts_cases = [case for case in allCases if case.label == CaseLabel.FullTextSearchPerformance]
+    non_fts_cases = [case for case in allCases if case.label != CaseLabel.FullTextSearchPerformance]
+
     if filter_type == FilterOp.NonFilter:
         allCaseNameSet = set({case.name for case in allCases})
         allCaseNames = [case_name for case_name in CASE_NAME_ORDER if case_name in allCaseNameSet] + [
@@ -101,7 +105,10 @@ def getShowDbsAndCases(st, result: list[CaseResult], filter_type: FilterOp) -> t
             optionLables=[v.value for v in datasetWithSizeTypes],
         )
         datasets = [dataset_with_size_type.get_manager() for dataset_with_size_type in showDatasetWithSizeTypes]
-        showCaseNames = list(set([case.name for case in allCases if case.dataset in datasets]))
+        showCaseNames = list(set([case.name for case in non_fts_cases if case.dataset in datasets]))
+        # Add FTS cases
+        fts_case_names = [case.name for case in fts_cases]
+        showCaseNames.extend(fts_case_names)
 
     return showDBNames, showCaseNames
 
diff --git a/vectordb_bench/frontend/components/check_results/nav.py b/vectordb_bench/frontend/components/check_results/nav.py
index ba4fa99c7..a6515ac24 100644
--- a/vectordb_bench/frontend/components/check_results/nav.py
+++ b/vectordb_bench/frontend/components/check_results/nav.py
@@ -1,25 +1,22 @@
-from streamlit_extras.switch_page_button import switch_page
-
-
 def NavToRunTest(st):
     st.subheader("Run your test")
     st.write("You can set the configs and run your own test.")
     navClick = st.button("Run Your Test &nbsp;&nbsp;>")
     if navClick:
-        switch_page("run test")
+        st.switch_page("pages/run_test.py")
 
 
 def NavToQuriesPerDollar(st):
     st.subheader("Compare qps with price.")
     navClick = st.button("QP$ (Quries per Dollar) &nbsp;&nbsp;>")
     if navClick:
-        switch_page("quries_per_dollar")
+        st.switch_page("pages/quries_per_dollar.py")
 
 
 def NavToResults(st, key="nav-to-results"):
     navClick = st.button("< &nbsp;&nbsp;Back to Results", key=key)
     if navClick:
-        switch_page("results")
+        st.switch_page("pages/results.py")
 
 
 def NavToPages(st):
@@ -27,6 +24,7 @@ def NavToPages(st):
         {"name": "Run Test", "link": "run_test"},
         {"name": "Results", "link": "results"},
         {"name": "Qps & Recall", "link": "qps_recall"},
+        {"name": "Full Text Search", "link": "full_text_search"},
         {"name": "Quries Per Dollar", "link": "quries_per_dollar"},
         {"name": "Concurrent", "link": "concurrent"},
         {"name": "Label Filter", "link": "label_filter"},
diff --git a/vectordb_bench/frontend/components/check_results/priceTable.py b/vectordb_bench/frontend/components/check_results/priceTable.py
index f2c0ae001..f34f70872 100644
--- a/vectordb_bench/frontend/components/check_results/priceTable.py
+++ b/vectordb_bench/frontend/components/check_results/priceTable.py
@@ -27,7 +27,7 @@ def priceTable(container, data):
     expander = container.expander("Price List (Editable).")
     editTable = expander.data_editor(
         table,
-        use_container_width=True,
+        width="stretch",
         hide_index=True,
         height=height,
         disabled=("DB", "Label"),
diff --git a/vectordb_bench/frontend/components/concurrent/charts.py b/vectordb_bench/frontend/components/concurrent/charts.py
index 004fcb261..5369d5912 100644
--- a/vectordb_bench/frontend/components/concurrent/charts.py
+++ b/vectordb_bench/frontend/components/concurrent/charts.py
@@ -94,4 +94,4 @@ def drawChart(data, st, key: str, x_metric: str = "latency_p99", y_metric: str =
     fig.update_yaxes(range=yrange, title_text=gen_title(y_metric))
     fig.update_traces(textposition="bottom right", texttemplate="conc-%{text:,.4~r}")
 
-    st.plotly_chart(fig, use_container_width=True, key=key)
+    st.plotly_chart(fig, width="stretch", key=key)
diff --git a/vectordb_bench/frontend/components/int_filter/charts.py b/vectordb_bench/frontend/components/int_filter/charts.py
index 881681031..5c32a089e 100644
--- a/vectordb_bench/frontend/components/int_filter/charts.py
+++ b/vectordb_bench/frontend/components/int_filter/charts.py
@@ -57,4 +57,4 @@ def drawChart(st, data: list[object], metric):
         margin=dict(l=0, r=0, t=40, b=0, pad=8),
         legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="right", x=1, title=""),
     )
-    st.plotly_chart(fig, use_container_width=True)
+    st.plotly_chart(fig, width="stretch")
diff --git a/vectordb_bench/frontend/components/label_filter/charts.py b/vectordb_bench/frontend/components/label_filter/charts.py
index 881681031..5c32a089e 100644
--- a/vectordb_bench/frontend/components/label_filter/charts.py
+++ b/vectordb_bench/frontend/components/label_filter/charts.py
@@ -57,4 +57,4 @@ def drawChart(st, data: list[object], metric):
         margin=dict(l=0, r=0, t=40, b=0, pad=8),
         legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="right", x=1, title=""),
     )
-    st.plotly_chart(fig, use_container_width=True)
+    st.plotly_chart(fig, width="stretch")
diff --git a/vectordb_bench/frontend/components/qps_recall/charts.py b/vectordb_bench/frontend/components/qps_recall/charts.py
index ab57dd0ce..a44c51a36 100644
--- a/vectordb_bench/frontend/components/qps_recall/charts.py
+++ b/vectordb_bench/frontend/components/qps_recall/charts.py
@@ -115,4 +115,4 @@ def drawlinechart(st, data: list[object], metric, key: str):
         margin=dict(l=0, r=0, t=40, b=0, pad=8),
         legend=dict(orientation="h", yanchor="bottom", y=1, xanchor="right", x=1, title=""),
     )
-    st.plotly_chart(fig, use_container_width=True, key=key)
+    st.plotly_chart(fig, width="stretch", key=key)
diff --git a/vectordb_bench/frontend/components/run_test/autoRefresh.py b/vectordb_bench/frontend/components/run_test/autoRefresh.py
deleted file mode 100644
index 034ab5017..000000000
--- a/vectordb_bench/frontend/components/run_test/autoRefresh.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from streamlit_autorefresh import st_autorefresh
-from vectordb_bench.frontend.config.styles import *
-
-
-def autoRefresh():
-    auto_refresh_count = st_autorefresh(
-        interval=MAX_AUTO_REFRESH_INTERVAL,
-        limit=MAX_AUTO_REFRESH_COUNT,
-        key="streamlit-auto-refresh",
-    )
diff --git a/vectordb_bench/frontend/components/run_test/caseSelector.py b/vectordb_bench/frontend/components/run_test/caseSelector.py
index 2e104ce54..685a38ec2 100644
--- a/vectordb_bench/frontend/components/run_test/caseSelector.py
+++ b/vectordb_bench/frontend/components/run_test/caseSelector.py
@@ -8,6 +8,7 @@
     get_case_config_inputs,
     get_custom_case_cluter,
     get_custom_streaming_case_cluster,
+    get_selectable_case_items,
 )
 from vectordb_bench.frontend.config.styles import (
     CASE_CONFIG_SETTING_COLUMNS,
@@ -47,7 +48,7 @@ def caseSelector(st, activedDbList: list[DB]):
 def caseClusterExpander(st, caseCluster: UICaseItemCluster, dbToCaseClusterConfigs, activedDbList: list[DB]):
     expander = st.expander(caseCluster.label, False)
     activedCases: list[CaseConfig] = []
-    for uiCaseItem in caseCluster.uiCaseItems:
+    for uiCaseItem in get_selectable_case_items(caseCluster, activedDbList):
         if uiCaseItem.isLine:
             addHorizontalLine(expander)
         else:
diff --git a/vectordb_bench/frontend/components/run_test/dbConfigSetting.py b/vectordb_bench/frontend/components/run_test/dbConfigSetting.py
index a2d2de77f..85167fb66 100644
--- a/vectordb_bench/frontend/components/run_test/dbConfigSetting.py
+++ b/vectordb_bench/frontend/components/run_test/dbConfigSetting.py
@@ -11,19 +11,18 @@ def dbConfigSettings(st, activedDbList: list[DB]):
     isAllValid = True
     for activeDb in activedDbList:
         dbConfigSettingItemContainer = expander.container()
-        dbConfig = dbConfigSettingItem(dbConfigSettingItemContainer, activeDb)
         try:
+            dbConfig = dbConfigSettingItem(dbConfigSettingItemContainer, activeDb)
             dbConfigs[activeDb] = activeDb.config_cls(**dbConfig)
+            # Probe client module so missing optional deps surface now, not on Run.
+            _ = activeDb.init_cls
+        except ModuleNotFoundError as e:
+            isAllValid = False
+            dbConfigSettingItemContainer.error(f"{activeDb.value} needs `{e.name}` but it is not installed.")
         except ValidationError as e:
             isAllValid = False
-            errTexts = []
-            for err in e.raw_errors:
-                errLocs = err.loc_tuple()
-                errInfo = err.exc
-                errText = f"{', '.join(errLocs)} - {errInfo}"
-                errTexts.append(errText)
-
-            dbConfigSettingItemContainer.error(f"{'; '.join(errTexts)}")
+            errTexts = [f"{', '.join(str(x) for x in err['loc'])} - {err['msg']}" for err in e.errors()]
+            dbConfigSettingItemContainer.error("; ".join(errTexts))
 
     return dbConfigs, isAllValid
 
diff --git a/vectordb_bench/frontend/components/run_test/generateTasks.py b/vectordb_bench/frontend/components/run_test/generateTasks.py
index 725dea769..5d848bb94 100644
--- a/vectordb_bench/frontend/components/run_test/generateTasks.py
+++ b/vectordb_bench/frontend/components/run_test/generateTasks.py
@@ -1,4 +1,5 @@
 from vectordb_bench.backend.clients import DB
+from vectordb_bench.backend.cases import CaseLabel
 from vectordb_bench.models import CaseConfig, CaseConfigParamType, TaskConfig
 
 
@@ -6,18 +7,34 @@ def generate_tasks(activedDbList: list[DB], dbConfigs, activedCaseList: list[Cas
     tasks = []
     for db in activedDbList:
         for case in activedCaseList:
+            # Get the index type for this case
+            index_type = allCaseConfigs[db][case].get(CaseConfigParamType.IndexType, None)
+
+            # Special handling for FTS cases
+            if case.case.label == CaseLabel.FullTextSearchPerformance:
+                from vectordb_bench.backend.clients.api import IndexType
+
+                index_type = IndexType.FTS
+            elif index_type is None:
+                # Default to AUTOINDEX for cases without specific index type
+                from vectordb_bench.backend.clients.api import IndexType
+
+                index_type = IndexType.AUTOINDEX
+
+            # Create the database case config
             cfg = {key.value: value for key, value in allCaseConfigs[db][case].items()}
             # Many DBCaseConfig models require an `index` field, while the UI stores the selection under `IndexType`.
             # Passing both keeps backwards-compatibility (extra fields are ignored) and enables strict models (e.g. OceanBase).
             if CaseConfigParamType.IndexType in allCaseConfigs[db][case] and "index" not in cfg:
                 cfg["index"] = allCaseConfigs[db][case][CaseConfigParamType.IndexType]
+
+            db_case_config = db.case_config_cls(index_type)(**cfg)
+
             task = TaskConfig(
                 db=db.value,
                 db_config=dbConfigs[db],
                 case_config=case,
-                db_case_config=db.case_config_cls(allCaseConfigs[db][case].get(CaseConfigParamType.IndexType, None))(
-                    **cfg
-                ),
+                db_case_config=db_case_config,
             )
             tasks.append(task)
 
diff --git a/vectordb_bench/frontend/components/run_test/submitTask.py b/vectordb_bench/frontend/components/run_test/submitTask.py
index 01d0c5876..93fd280c3 100644
--- a/vectordb_bench/frontend/components/run_test/submitTask.py
+++ b/vectordb_bench/frontend/components/run_test/submitTask.py
@@ -1,71 +1,76 @@
 from datetime import datetime
+
+import streamlit as st
+
 from vectordb_bench import config
 from vectordb_bench.frontend.config import styles
 from vectordb_bench.interface import benchmark_runner
 from vectordb_bench.models import TaskConfig
 
 
-def submitTask(st, tasks, isAllValid):
-    st.markdown(
+def submitTask(container, tasks, isAllValid):
+    container.markdown(
         "<div style='height: 24px;'></div>",
         unsafe_allow_html=True,
     )
-    st.subheader("STEP 3: Task Label")
-    st.markdown(
+    container.subheader("STEP 3: Task Label")
+    container.markdown(
         "<div style='color: #647489; margin-bottom: 20px; margin-top: -12px;'>This description is used to mark the result. </div>",
         unsafe_allow_html=True,
     )
 
-    taskLabel = taskLabelInput(st)
+    taskLabel = taskLabelInput(container)
 
-    st.markdown(
+    container.markdown(
         "<div style='height: 24px;'></div>",
         unsafe_allow_html=True,
     )
 
-    controlPanelContainer = st.container()
-    controlPanel(controlPanelContainer, tasks, taskLabel, isAllValid)
+    controlPanel(container.container(), tasks, taskLabel, isAllValid)
 
 
-def taskLabelInput(st):
+def taskLabelInput(container):
     defaultTaskLabel = datetime.now().strftime("%Y%m%d%H")
-    columns = st.columns(styles.TASK_LABEL_INPUT_COLUMNS)
-    taskLabel = columns[0].text_input("task_label", defaultTaskLabel, label_visibility="collapsed")
-    return taskLabel
+    cols = container.columns(styles.TASK_LABEL_INPUT_COLUMNS)
+    return cols[0].text_input("task_label", defaultTaskLabel, label_visibility="collapsed")
 
 
-def advancedSettings(st):
-    container = st.columns([1, 2])
-    index_already_exists = container[0].checkbox("Index already exists", value=False)
-    container[1].caption("if selected, inserting and building will be skipped.")
+def advancedSettings(container):
+    cols = container.columns([1, 2])
+    index_already_exists = cols[0].checkbox("Index already exists", value=False)
+    cols[1].caption("if selected, inserting and building will be skipped.")
 
-    container = st.columns([1, 2])
-    use_aliyun = container[0].checkbox("Dataset from Aliyun (Shanghai)", value=False)
-    container[1].caption(
-        "if selected, the dataset will be downloaded from Aliyun OSS shanghai, default AWS S3 aws-us-west."
-    )
+    cols = container.columns([1, 2])
+    use_aliyun = cols[0].checkbox("Dataset from Aliyun (Shanghai)", value=False)
+    cols[1].caption("if selected, the dataset will be downloaded from Aliyun OSS shanghai, default AWS S3 aws-us-west.")
 
-    container = st.columns([1, 2])
-    k = container[0].number_input("k", min_value=1, value=100, label_visibility="collapsed")
-    container[1].caption("K value for number of nearest neighbors to search")
+    cols = container.columns([1, 2])
+    k = cols[0].number_input("k", min_value=1, value=100, label_visibility="collapsed")
+    cols[1].caption("K value for number of nearest neighbors to search")
 
-    container = st.columns([1, 2])
+    cols = container.columns([1, 2])
     defaultconcurrentInput = ",".join(map(str, config.NUM_CONCURRENCY))
-    concurrentInput = container[0].text_input(
-        "Concurrent Input", value=defaultconcurrentInput, label_visibility="collapsed"
-    )
-    container[1].caption("num of concurrencies for search tests to get max-qps")
+    concurrentInput = cols[0].text_input("Concurrent Input", value=defaultconcurrentInput, label_visibility="collapsed")
+    cols[1].caption("num of concurrencies for search tests to get max-qps")
 
-    container = st.columns([1, 2])
-    concurrency_duration = container[0].number_input(
+    cols = container.columns([1, 2])
+    concurrency_duration = cols[0].number_input(
         "Concurrency Duration", value=config.CONCURRENCY_DURATION, label_visibility="collapsed"
     )
-    container[1].caption("concurrency duration for each concurrency search test")
-    return index_already_exists, use_aliyun, k, concurrentInput, concurrency_duration
+    cols[1].caption("concurrency duration for each concurrency search test")
+
+    cols = container.columns([1, 2])
+    load_concurrency = cols[0].number_input(
+        "Load Concurrency", min_value=0, value=config.LOAD_CONCURRENCY, label_visibility="collapsed"
+    )
+    cols[1].caption("number of concurrent workers for data loading in performance cases (0 = cpu_count)")
+    return index_already_exists, use_aliyun, k, concurrentInput, concurrency_duration, load_concurrency
 
 
-def controlPanel(st, tasks: list[TaskConfig], taskLabel, isAllValid):
-    index_already_exists, use_aliyun, k, concurrentInput, concurrency_duration = advancedSettings(st)
+def controlPanel(container, tasks: list[TaskConfig], taskLabel, isAllValid):
+    index_already_exists, use_aliyun, k, concurrentInput, concurrency_duration, load_concurrency = advancedSettings(
+        container
+    )
 
     def runHandler():
         benchmark_runner.set_drop_old(not index_already_exists)
@@ -73,54 +78,57 @@ def runHandler():
         try:
             concurrentInput_list = [int(item.strip()) for item in concurrentInput.split(",")]
         except ValueError:
-            st.write("please input correct number")
+            container.write("please input correct number")
             return None
 
         for task in tasks:
             task.case_config.k = k
             task.case_config.concurrency_search_config.num_concurrency = concurrentInput_list
             task.case_config.concurrency_search_config.concurrency_duration = concurrency_duration
+            task.load_concurrency = load_concurrency
         benchmark_runner.set_download_address(use_aliyun)
         benchmark_runner.run(tasks, taskLabel)
 
     def stopHandler():
         benchmark_runner.stop_running()
 
-    isRunning = benchmark_runner.has_running()
-
-    if isRunning:
-        currentTaskId = benchmark_runner.get_current_task_id()
-        tasksCount = benchmark_runner.get_tasks_count()
-        text = f":running: Running Task {currentTaskId} / {tasksCount}"
-
-        if tasksCount > 0:
-            st.progress(currentTaskId / tasksCount, text=text)
-
-        columns = st.columns(6)
-        columns[0].button(
-            "Run Your Test",
-            disabled=True,
-            on_click=runHandler,
-            type="primary",
-        )
-        columns[1].button(
-            "Stop",
-            on_click=stopHandler,
-            type="primary",
-        )
-
-    else:
-        errorText = benchmark_runner.latest_error or ""
-        if len(errorText) > 0:
-            st.error(errorText)
-        disabled = True if len(tasks) == 0 or not isAllValid else False
-        if not isAllValid:
-            st.error("Make sure all config is valid.")
-        elif len(tasks) == 0:
-            st.warning("No tests to run.")
-        st.button(
-            "Run Your Test",
-            disabled=disabled,
-            on_click=runHandler,
-            type="primary",
-        )
+    @st.fragment(run_every=f"{styles.MAX_AUTO_REFRESH_INTERVAL / 1000}s")
+    def _renderLiveStatus():
+        if benchmark_runner.has_running():
+            currentTaskId = benchmark_runner.get_current_task_id()
+            tasksCount = benchmark_runner.get_tasks_count()
+            text = f":running: Running Task {currentTaskId} / {tasksCount}"
+            if tasksCount > 0:
+                st.progress(currentTaskId / tasksCount, text=text)
+            cols = st.columns(6)
+            cols[0].button(
+                "Run Your Test",
+                disabled=True,
+                on_click=runHandler,
+                type="primary",
+                key="run-disabled",
+            )
+            cols[1].button(
+                "Stop",
+                on_click=stopHandler,
+                type="primary",
+                key="stop-btn",
+            )
+        else:
+            errorText = benchmark_runner.latest_error or ""
+            if len(errorText) > 0:
+                st.error(errorText)
+            disabled = len(tasks) == 0 or not isAllValid
+            if not isAllValid:
+                st.error("Make sure all config is valid.")
+            elif len(tasks) == 0:
+                st.warning("No tests to run.")
+            st.button(
+                "Run Your Test",
+                disabled=disabled,
+                on_click=runHandler,
+                type="primary",
+                key="run-btn",
+            )
+
+    _renderLiveStatus()
diff --git a/vectordb_bench/frontend/components/streaming/charts.py b/vectordb_bench/frontend/components/streaming/charts.py
index a05da9b25..09357cf44 100644
--- a/vectordb_bench/frontend/components/streaming/charts.py
+++ b/vectordb_bench/frontend/components/streaming/charts.py
@@ -119,7 +119,7 @@ def drawLineChart(
     if x_metric == DisplayedMetric.search_time:
         x_title = "Actual Time (s)"
     fig.update_layout(xaxis_title=x_title)
-    st.plotly_chart(fig, use_container_width=True, key=key)
+    st.plotly_chart(fig, width="stretch", key=key)
 
 
 def get_normal_scatter(
@@ -234,7 +234,7 @@ def drawBarChart(
     fig.update_layout(xaxis_title="time (s)")
     fig.update_layout(barmode="stack")
     fig.update_traces(width=0.15)
-    st.plotly_chart(fig, use_container_width=True, key=key)
+    st.plotly_chart(fig, width="stretch", key=key)
 
 
 def get_bar(
diff --git a/vectordb_bench/frontend/components/streaming/concurrent_detail.py b/vectordb_bench/frontend/components/streaming/concurrent_detail.py
index 0580bad14..37852a7ab 100644
--- a/vectordb_bench/frontend/components/streaming/concurrent_detail.py
+++ b/vectordb_bench/frontend/components/streaming/concurrent_detail.py
@@ -124,7 +124,7 @@ def drawQPSLatencyChart(container, qps_values, latencies_ms, stage, metric_name,
         showlegend=False,
     )
 
-    container.plotly_chart(fig, use_container_width=True, key=f"{case_name}-chart-{stage}")
+    container.plotly_chart(fig, width="stretch", key=f"{case_name}-chart-{stage}")
 
 
 def drawMetricsTable(container, qps_values, conc_nums, p99_list, p95_list, avg_list, stage):
@@ -267,7 +267,7 @@ def drawComparisonChart(container, case_data, selected_stages, metric_name, case
         legend=dict(yanchor="top", y=0.99, xanchor="right", x=0.99),
     )
 
-    container.plotly_chart(fig, use_container_width=True, key=f"{case_name}-compare-chart")
+    container.plotly_chart(fig, width="stretch", key=f"{case_name}-compare-chart")
 
     # Add insight
     container.info("**Insight:** Compare curves across stages to understand how performance scales with data growth.")
diff --git a/vectordb_bench/frontend/components/welcome/welcomePrams.py b/vectordb_bench/frontend/components/welcome/welcomePrams.py
index 48bf5995c..6a7cf48e0 100644
--- a/vectordb_bench/frontend/components/welcome/welcomePrams.py
+++ b/vectordb_bench/frontend/components/welcome/welcomePrams.py
@@ -80,6 +80,16 @@ def welcomePrams(st):
             "image": "fig/homepage/table.png",
             "link": "tables",
         },
+        {
+            "title": "FullTextSearch Performance",
+            "description": (
+                "<span style='font-size: 17px;'>"
+                "To view BM25 full text search performance across datasets, payload modes, and backends."
+                "</span>"
+            ),
+            "image": "fig/homepage/full_text_search.png",
+            "link": "full_text_search",
+        },
         {
             "title": "Concurrent Performance",
             "description": (
@@ -148,7 +158,7 @@ def welcomePrams(st):
     for option in options:
         option["image"] = get_image_as_base64(option["image"])
 
-    for option in options[:7]:
+    for option in options[:8]:
         html_content += f"""
         <a href="/{option['link']}" target="_self" style="text-decoration: none;">
             <div class="section-card">
@@ -167,7 +177,7 @@ def welcomePrams(st):
     <div class="last-row">
     """
 
-    for option in options[7:9]:
+    for option in options[8:10]:
         html_content += f"""
         <a href="/{option['link']}" target="_self" style="text-decoration: none;">
             <div class="section-card">
diff --git a/vectordb_bench/frontend/config/dbCaseConfigs.py b/vectordb_bench/frontend/config/dbCaseConfigs.py
index 825f89893..daacae26a 100644
--- a/vectordb_bench/frontend/config/dbCaseConfigs.py
+++ b/vectordb_bench/frontend/config/dbCaseConfigs.py
@@ -4,7 +4,7 @@
 from vectordb_bench.backend.cases import CaseLabel, CaseType
 from vectordb_bench.backend.clients import DB
 from vectordb_bench.backend.clients.api import IndexType, MetricType, SQType
-from vectordb_bench.backend.dataset import DatasetWithSizeType
+from vectordb_bench.backend.dataset import DatasetWithSizeType, FtsDatasetWithSizeType
 from vectordb_bench.frontend.components.custom.getCustomConfig import get_custom_configs
 
 from vectordb_bench.models import CaseConfig, CaseConfigParamType
@@ -12,6 +12,7 @@
 MAX_STREAMLIT_INT = (1 << 53) - 1
 
 DB_LIST = [d for d in DB if d != DB.Test]
+FTS_SUPPORTED_DBS = {DB.Milvus, DB.ZillizCloud, DB.ElasticCloud, DB.OSSOpenSearch, DB.Vespa, DB.TurboPuffer}
 
 
 class Delimiter(Enum):
@@ -52,6 +53,7 @@ class UICaseItem(BaseModel):
     description: str = ""
     cases: list[CaseConfig] = []
     caseLabel: CaseLabel = CaseLabel.Performance
+    supportedDbs: list[DB] | None = None
     extra_custom_case_config_inputs: list[ConfigInput] = []
     tmp_custom_config: dict = dict()
 
@@ -104,12 +106,21 @@ def get_cases(self) -> list[CaseConfig]:
         ]
         return cases
 
+    def supports_dbs(self, dbs: list[DB]) -> bool:
+        if self.supportedDbs is None:
+            return True
+        return all(db in self.supportedDbs for db in dbs)
+
 
 class UICaseItemCluster(BaseModel):
     label: str = ""
     uiCaseItems: list[UICaseItem] = []
 
 
+def get_selectable_case_items(caseCluster: UICaseItemCluster, activedDbList: list[DB]) -> list[UICaseItem]:
+    return [uiCaseItem for uiCaseItem in caseCluster.uiCaseItems if uiCaseItem.supports_dbs(activedDbList)]
+
+
 def get_custom_case_items() -> list[UICaseItem]:
     custom_configs = get_custom_configs()
     return [
@@ -157,6 +168,30 @@ def generate_normal_cases(case_id: CaseType, custom_case: dict | None = None) ->
     return [CaseConfig(case_id=case_id, custom_case=custom_case)]
 
 
+def generate_fts_case(dataset_with_size_type: FtsDatasetWithSizeType) -> CaseConfig:
+    return CaseConfig(
+        case_id=CaseType.FTSBm25Performance,
+        custom_case={"dataset_with_size_type": dataset_with_size_type.value},
+    )
+
+
+def get_fts_case_items() -> list[UICaseItem]:
+    dataset_with_size_types = list(FtsDatasetWithSizeType)
+    return [
+        UICaseItem(
+            label=f"FTS BM25 Performance - {dataset_with_size_type.value}",
+            description=(
+                f"This case tests native BM25 full-text search performance on {dataset_with_size_type.value}. "
+                "It measures index building time, recall, serial latency, and search QPS."
+            ),
+            cases=[generate_fts_case(dataset_with_size_type)],
+            caseLabel=CaseLabel.FullTextSearchPerformance,
+            supportedDbs=list(FTS_SUPPORTED_DBS),
+        )
+        for dataset_with_size_type in dataset_with_size_types
+    ]
+
+
 def get_custom_case_cluter() -> UICaseItemCluster:
     return UICaseItemCluster(label="Custom Search Performance Test", uiCaseItems=get_custom_case_items())
 
@@ -358,6 +393,10 @@ def generate_int_filter_cases(dataset_with_size_type: DatasetWithSizeType) -> li
             )
         ],
     ),
+    UICaseItemCluster(
+        label="Full-Text Search (FTS) Test",
+        uiCaseItems=get_fts_case_items(),
+    ),
 ]
 
 # DIVIDER = "DIVIDER"
@@ -372,6 +411,7 @@ def generate_int_filter_cases(dataset_with_size_type: DatasetWithSizeType) -> li
     CaseType.Performance1024D10M,
     CaseType.CapacityDim960,
     CaseType.CapacityDim128,
+    CaseType.FTSBm25Performance,
 ]
 CASE_NAME_ORDER = [case.case_cls().name for case in DISPLAY_CASE_ORDER]
 
@@ -379,15 +419,6 @@ def generate_int_filter_cases(dataset_with_size_type: DatasetWithSizeType) -> li
 #     item for item in CASE_LIST_WITH_DIVIDER if isinstance(item, CaseType)]
 
 
-class InputType(IntEnum):
-    Text = 20001
-    Number = 20002
-    Option = 20003
-    Float = 20004
-    Bool = 20005
-    Select = 20006
-
-
 class CaseConfigInput(BaseModel):
     label: CaseConfigParamType
     inputType: InputType = InputType.Text
@@ -517,7 +548,7 @@ class CaseConfigInput(BaseModel):
     displayLabel="Reranking Metric",
     inputType=InputType.Option,
     inputConfig={
-        "options": [metric.value for metric in MetricType if metric.value not in ["HAMMING", "JACCARD", "DP"]],
+        "options": [metric.value for metric in MetricType if metric.value not in ["HAMMING", "JACCARD", "DP", "BM25"]],
     },
     isDisplayed=lambda config: config.get(CaseConfigParamType.reranking, False),
 )
@@ -1467,7 +1498,7 @@ class CaseConfigInput(BaseModel):
     label=CaseConfigParamType.rerankingMetric,
     inputType=InputType.Option,
     inputConfig={
-        "options": [metric.value for metric in MetricType if metric.value not in ["HAMMING", "JACCARD"]],
+        "options": [metric.value for metric in MetricType if metric.value not in ["HAMMING", "JACCARD", "BM25"]],
     },
     isDisplayed=lambda config: config.get(CaseConfigParamType.quantizationType, None) == "bit"
     and config.get(CaseConfigParamType.reranking, False),
@@ -2119,6 +2150,113 @@ class CaseConfigInput(BaseModel):
     ),
 )
 
+CaseConfigParamInput_IndexType_FTS = CaseConfigInput(
+    label=CaseConfigParamType.IndexType,
+    inputHelp="FTS Index Type (currently only AUTOINDEX supported)",
+    inputType=InputType.Option,
+    inputConfig={
+        "options": [
+            IndexType.FTS.value,
+        ],
+    },
+    isDisplayed=lambda config: False,  # Hidden since FTS only has one index type currently
+)
+
+CaseConfigParamInput_FTS_inverted_index_algo = CaseConfigInput(
+    label=CaseConfigParamType.inverted_index_algo,
+    displayLabel="Inverted Index Algorithm",
+    inputHelp="Algorithm for building and querying sparse inverted index.",
+    inputType=InputType.Option,
+    inputConfig={
+        "value": "DAAT_MAXSCORE",
+        "options": ["DAAT_MAXSCORE", "DAAT_WAND", "TAAT_NAIVE"],
+    },
+)
+
+CaseConfigParamInput_FTS_bm25_k1 = CaseConfigInput(
+    label=CaseConfigParamType.bm25_k1,
+    displayLabel="BM25 k1",
+    inputHelp="BM25 k1 parameter controls term frequency saturation [1.2, 2.0]. Higher values emphasize term frequency.",
+    inputType=InputType.Float,
+    inputConfig={
+        "value": 1.5,
+        "step": 0.1,
+        "min": 1.2,
+        "max": 2.0,
+    },
+)
+
+CaseConfigParamInput_FTS_bm25_b = CaseConfigInput(
+    label=CaseConfigParamType.bm25_b,
+    displayLabel="BM25 b",
+    inputHelp="BM25 b parameter controls document length normalization [0.0, 1.0]. 0.75 is commonly used.",
+    inputType=InputType.Float,
+    inputConfig={
+        "value": 0.75,
+        "step": 0.05,
+        "min": 0.0,
+        "max": 1.0,
+    },
+)
+
+CaseConfigParamInput_FTS_analyzer_tokenizer = CaseConfigInput(
+    label=CaseConfigParamType.analyzer_tokenizer,
+    displayLabel="Analyzer Tokenizer",
+    inputHelp="Text tokenizer type for BM25 analysis.",
+    inputType=InputType.Option,
+    inputConfig={
+        "value": "standard",
+        "options": ["standard", "whitespace", "keyword"],
+    },
+)
+
+CaseConfigParamInput_FTS_analyzer_enable_lowercase = CaseConfigInput(
+    label=CaseConfigParamType.analyzer_enable_lowercase,
+    displayLabel="Enable Lowercase Filter",
+    inputHelp="Convert text to lowercase during analysis.",
+    inputType=InputType.Bool,
+    inputConfig={
+        "value": True,
+    },
+)
+
+CaseConfigParamInput_FTS_analyzer_max_token_length = CaseConfigInput(
+    label=CaseConfigParamType.analyzer_max_token_length,
+    displayLabel="Max Token Length",
+    inputHelp="Maximum length of individual tokens (optional, leave empty to disable).",
+    inputType=InputType.Number,
+    inputConfig={
+        "value": None,
+        "min": 1,
+        "max": 100,
+    },
+)
+
+CaseConfigParamInput_FTS_analyzer_stop_words = CaseConfigInput(
+    label=CaseConfigParamType.analyzer_stop_words,
+    displayLabel="Stop Words",
+    inputHelp="Comma-separated list of stop words to filter out.",
+    inputType=InputType.Text,
+    inputConfig={
+        "value": "",
+        "placeholder": "of,to,the,and,or",
+    },
+)
+
+CaseConfigParamInput_FTS_drop_ratio_search = CaseConfigInput(
+    label=CaseConfigParamType.drop_ratio_search,
+    displayLabel="Drop Ratio (search)",
+    inputHelp="Search-time sparsification ratio. 0.0 keeps best recall; larger is faster.",
+    inputType=InputType.Float,
+    inputConfig={
+        "value": 0.0,
+        "step": 0.1,
+        "min": 0.0,
+        "max": 0.99,
+    },
+)
+
+
 MilvusLoadConfig = [
     CaseConfigParamInput_IndexType,
     CaseConfigParamInput_M,
@@ -2168,6 +2306,29 @@ class CaseConfigInput(BaseModel):
     CaseConfigParamInput_Milvus_use_partition_key,
 ]
 
+
+MilvusFtsConfig = [
+    CaseConfigParamInput_IndexType_FTS,
+    CaseConfigParamInput_FTS_inverted_index_algo,
+    CaseConfigParamInput_FTS_bm25_k1,
+    CaseConfigParamInput_FTS_bm25_b,
+    CaseConfigParamInput_FTS_analyzer_tokenizer,
+    CaseConfigParamInput_FTS_analyzer_enable_lowercase,
+    CaseConfigParamInput_FTS_analyzer_max_token_length,
+    CaseConfigParamInput_FTS_analyzer_stop_words,
+    CaseConfigParamInput_FTS_drop_ratio_search,
+]
+
+ZillizCloudFtsConfig = [
+    CaseConfigParamInput_IndexType_FTS,
+    CaseConfigParamInput_ZillizLevel,
+]
+
+ElasticCloudFtsConfig = []
+VespaFtsConfig = []
+OSSOpenSearchFtsConfig = []
+TurboPufferFtsConfig = []
+
 WeaviateLoadConfig = [
     CaseConfigParamInput_MaxConnections,
     CaseConfigParamInput_EFConstruction_Weaviate,
@@ -2691,9 +2852,10 @@ class FilterType(Enum):
     inputConfig={
         "min": 1,
         "max": 200,
-        "value": 10,
+        "value": 2,
     },
-    isDisplayed=lambda config: config[CaseConfigParamType.IndexType] == IndexType.IVFPQ.value
+    isDisplayed=lambda config: config[CaseConfigParamType.IndexType] == IndexType.HNSW.value
+    or config[CaseConfigParamType.IndexType] == IndexType.IVFPQ.value
     or config[CaseConfigParamType.IndexType] == IndexType.IVFBQ.value,
     inputHelp="Reorder factor",
 )
@@ -2847,15 +3009,256 @@ class FilterType(Enum):
     CaseConfigParamInput_NumberOfRegions_Lindorm,
 ]
 
+# PolarDB configs
+CaseConfigParamInput_IndexType_PolarDB = CaseConfigInput(
+    label=CaseConfigParamType.IndexType,
+    inputHelp="Select Index Type",
+    inputType=InputType.Option,
+    inputConfig={
+        "options": [
+            IndexType.HNSW.value,
+            IndexType.HNSW_PQ.value,
+            IndexType.HNSW_SQ.value,
+        ],
+    },
+)
+
+CaseConfigParamInput_M_PolarDB = CaseConfigInput(
+    label=CaseConfigParamType.M,
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 2,
+        "max": 1024,
+        "value": 16,
+    },
+    inputHelp="HNSW M parameter",
+)
+
+CaseConfigParamInput_EFConstruction_PolarDB = CaseConfigInput(
+    label=CaseConfigParamType.ef_construction,
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 1,
+        "max": 8192,
+        "value": 200,
+    },
+    inputHelp="ef_construction",
+)
+
+CaseConfigParamInput_EFSearch_PolarDB = CaseConfigInput(
+    label=CaseConfigParamType.ef_search,
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 1,
+        "max": 8192,
+        "value": 200,
+    },
+    inputHelp="ef_search",
+)
+
+CaseConfigParamInput_InsertWorkers_PolarDB = CaseConfigInput(
+    label=CaseConfigParamType.insert_workers,
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 1,
+        "max": 1024,
+        "value": 10,
+    },
+    inputHelp="Number of insert workers",
+)
+
+CaseConfigParamInput_PostLoadIndex_PolarDB = CaseConfigInput(
+    label=CaseConfigParamType.post_load_index,
+    inputType=InputType.Bool,
+    inputConfig={
+        "value": True,
+    },
+    inputHelp="Create index after data load via ALTER TABLE",
+)
+
+CaseConfigParamInput_PQM_PolarDB = CaseConfigInput(
+    label=CaseConfigParamType.pq_m,
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 1,
+        "max": 16383,
+        "value": 1,
+    },
+    isDisplayed=lambda config: config.get(CaseConfigParamType.IndexType, None) == IndexType.HNSW_PQ.value,
+    inputHelp="PQ M parameter",
+)
+
+CaseConfigParamInput_PQNbits_PolarDB = CaseConfigInput(
+    label=CaseConfigParamType.pq_nbits,
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 1,
+        "max": 24,
+        "value": 8,
+    },
+    isDisplayed=lambda config: config.get(CaseConfigParamType.IndexType, None) == IndexType.HNSW_PQ.value,
+    inputHelp="PQ nbits parameter",
+)
+
+CaseConfigParamInput_SQType_PolarDB = CaseConfigInput(
+    label=CaseConfigParamType.sq_type,
+    inputType=InputType.Option,
+    inputConfig={
+        "options": [
+            "8bit",
+            "4bit",
+            "8bit_uniform",
+            "4bit_uniform",
+            "8bit_direct",
+            "8bit_direct_signed",
+            "fp16",
+            "bf16",
+            "6bit",
+        ],
+    },
+    isDisplayed=lambda config: config.get(CaseConfigParamType.IndexType, None) == IndexType.HNSW_SQ.value,
+    inputHelp="Scalar quantizer type",
+)
+
+PolarDBConfig = [
+    CaseConfigParamInput_IndexType_PolarDB,
+    CaseConfigParamInput_M_PolarDB,
+    CaseConfigParamInput_EFConstruction_PolarDB,
+    CaseConfigParamInput_EFSearch_PolarDB,
+    CaseConfigParamInput_InsertWorkers_PolarDB,
+    CaseConfigParamInput_PostLoadIndex_PolarDB,
+    CaseConfigParamInput_PQM_PolarDB,
+    CaseConfigParamInput_PQNbits_PolarDB,
+    CaseConfigParamInput_SQType_PolarDB,
+]
+
+# ADBPG (Aliyun AnalyticDB for PostgreSQL) configs
+CaseConfigParamInput_Algorithm_Adbpg = CaseConfigInput(
+    label=CaseConfigParamType.algorithm,
+    inputHelp="Select Nova algorithm variant",
+    inputType=InputType.Option,
+    inputConfig={
+        "options": ["novamr", "novad"],
+    },
+)
+
+CaseConfigParamInput_HnswM_Adbpg = CaseConfigInput(
+    label=CaseConfigParamType.hnsw_m,
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 1,
+        "max": 256,
+        "value": 16,
+    },
+    inputHelp="HNSW M parameter",
+)
+
+CaseConfigParamInput_EFConstruction_Adbpg = CaseConfigInput(
+    label=CaseConfigParamType.ef_construction,
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 1,
+        "max": 2000,
+        "value": 200,
+    },
+    inputHelp="HNSW ef_construction",
+)
+
+CaseConfigParamInput_RabitqBits_Adbpg = CaseConfigInput(
+    label=CaseConfigParamType.rabitq_bits,
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 1,
+        "max": 8,
+        "value": 7,
+    },
+    inputHelp="RaBitQ quantization bits",
+)
+
+CaseConfigParamInput_AutoReduction_Adbpg = CaseConfigInput(
+    label=CaseConfigParamType.auto_reduction,
+    inputType=InputType.Bool,
+    inputConfig={"value": False},
+    inputHelp="Enable auto_reduction=on for index build",
+)
+
+CaseConfigParamInput_EFSearch_Adbpg = CaseConfigInput(
+    label=CaseConfigParamType.ef_search,
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 1,
+        "max": 2000,
+        "value": 100,
+    },
+    inputHelp="fastann.hnsw_ef_search",
+)
+
+CaseConfigParamInput_MaxScanPoints_Adbpg = CaseConfigInput(
+    label=CaseConfigParamType.max_scan_points,
+    inputType=InputType.Number,
+    inputConfig={
+        "min": 1,
+        "max": 100000,
+        "value": 2000,
+    },
+    inputHelp="fastann.hnsw_max_scan_points",
+)
+
+CaseConfigParamInput_QuantizeRescoreAmp_Adbpg = CaseConfigInput(
+    label=CaseConfigParamType.quantize_rescore_amp,
+    inputType=InputType.Float,
+    inputConfig={
+        "min": 0.0,
+        "max": 100.0,
+        "value": 1.0,
+        "step": 0.1,
+    },
+    inputHelp="fastann.quantize_rescore_amp",
+)
+
+CaseConfigParamInput_NovaAdaptiveGamma_Adbpg = CaseConfigInput(
+    label=CaseConfigParamType.nova_adaptive_gamma,
+    inputType=InputType.Float,
+    inputConfig={
+        "min": 0.0,
+        "max": 1.0,
+        "value": 0.0,
+        "step": 0.05,
+    },
+    inputHelp="fastann.nova_adaptive_gamma",
+)
+
+AdbpgLoadConfig = [
+    CaseConfigParamInput_Algorithm_Adbpg,
+    CaseConfigParamInput_HnswM_Adbpg,
+    CaseConfigParamInput_EFConstruction_Adbpg,
+    CaseConfigParamInput_RabitqBits_Adbpg,
+    CaseConfigParamInput_AutoReduction_Adbpg,
+]
+
+AdbpgPerformanceConfig = [
+    CaseConfigParamInput_Algorithm_Adbpg,
+    CaseConfigParamInput_HnswM_Adbpg,
+    CaseConfigParamInput_EFConstruction_Adbpg,
+    CaseConfigParamInput_RabitqBits_Adbpg,
+    CaseConfigParamInput_AutoReduction_Adbpg,
+    CaseConfigParamInput_EFSearch_Adbpg,
+    CaseConfigParamInput_MaxScanPoints_Adbpg,
+    CaseConfigParamInput_QuantizeRescoreAmp_Adbpg,
+    CaseConfigParamInput_NovaAdaptiveGamma_Adbpg,
+]
+
 # Map DB to config
 CASE_CONFIG_MAP = {
     DB.Milvus: {
         CaseLabel.Load: MilvusLoadConfig,
         CaseLabel.Performance: MilvusPerformanceConfig,
         CaseLabel.Streaming: MilvusPerformanceConfig,
+        CaseLabel.FullTextSearchPerformance: MilvusFtsConfig,
     },
     DB.ZillizCloud: {
         CaseLabel.Performance: ZillizCloudPerformanceConfig,
+        CaseLabel.FullTextSearchPerformance: ZillizCloudFtsConfig,
     },
     DB.WeaviateCloud: {
         CaseLabel.Load: WeaviateLoadConfig,
@@ -2864,6 +3267,7 @@ class FilterType(Enum):
     DB.ElasticCloud: {
         CaseLabel.Load: ESLoadingConfig,
         CaseLabel.Performance: ESPerformanceConfig,
+        CaseLabel.FullTextSearchPerformance: ElasticCloudFtsConfig,
     },
     DB.AWSOpenSearch: {
         CaseLabel.Load: AWSOpensearchLoadingConfig,
@@ -2872,6 +3276,7 @@ class FilterType(Enum):
     DB.OSSOpenSearch: {
         CaseLabel.Load: OSSOpensearchLoadingConfig,
         CaseLabel.Performance: OSSOpenSearchPerformanceConfig,
+        CaseLabel.FullTextSearchPerformance: OSSOpenSearchFtsConfig,
     },
     DB.PgVector: {
         CaseLabel.Load: PgVectorLoadingConfig,
@@ -2912,6 +3317,7 @@ class FilterType(Enum):
     DB.Vespa: {
         CaseLabel.Load: VespaLoadingConfig,
         CaseLabel.Performance: VespaPerformanceConfig,
+        CaseLabel.FullTextSearchPerformance: VespaFtsConfig,
     },
     DB.LanceDB: {
         CaseLabel.Load: LanceDBLoadConfig,
@@ -2937,14 +3343,26 @@ class FilterType(Enum):
         CaseLabel.Load: LindormLoadConfig,
         CaseLabel.Performance: LindormPerformanceConfig,
     },
+    DB.PolarDB: {
+        CaseLabel.Load: PolarDBConfig,
+        CaseLabel.Performance: PolarDBConfig,
+    },
+    DB.TurboPuffer: {
+        CaseLabel.FullTextSearchPerformance: TurboPufferFtsConfig,
+    },
+    DB.Adbpg: {
+        CaseLabel.Load: AdbpgLoadConfig,
+        CaseLabel.Performance: AdbpgPerformanceConfig,
+    },
 }
 
 
 def get_case_config_inputs(db: DB, case_label: CaseLabel) -> list[CaseConfigInput]:
-    if db not in CASE_CONFIG_MAP:
-        return []
+    db_case_config_map = CASE_CONFIG_MAP.get(db, {})
     if case_label == CaseLabel.Load:
-        return CASE_CONFIG_MAP[db][CaseLabel.Load]
-    elif case_label == CaseLabel.Performance or case_label == CaseLabel.Streaming:
-        return CASE_CONFIG_MAP[db][CaseLabel.Performance]
+        return db_case_config_map.get(CaseLabel.Load, [])
+    if case_label == CaseLabel.Performance or case_label == CaseLabel.Streaming:
+        return db_case_config_map.get(CaseLabel.Performance, [])
+    elif case_label == CaseLabel.FullTextSearchPerformance:
+        return db_case_config_map.get(CaseLabel.FullTextSearchPerformance, [])
     return []
diff --git a/vectordb_bench/frontend/config/styles.py b/vectordb_bench/frontend/config/styles.py
index fabe9bdcf..4b162e9ed 100644
--- a/vectordb_bench/frontend/config/styles.py
+++ b/vectordb_bench/frontend/config/styles.py
@@ -74,6 +74,8 @@ def getPatternShape(i):
     DB.Zvec: "https://zvec.org/img/zvec-logo-light.svg",
     DB.Endee: "data:image/svg+xml,%3c?xml%20version=%271.0%27%20encoding=%27UTF-8%27?%3e%3csvg%20id=%27Layer_1%27%20xmlns=%27http://www.w3.org/2000/svg%27%20version=%271.1%27%20viewBox=%270%200%20600%20600%27%3e%3c!--%20Generator:%20Adobe%20Illustrator%2030.0.0,%20SVG%20Export%20Plug-In%20.%20SVG%20Version:%202.1.1%20Build%20123)%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%233266a4;%20}%20%3c/style%3e%3c/defs%3e%3cpath%20class=%27st0%27%20d=%27M106.22,490.02H10.36l-.04-184.85c11.19-163.31,232.1-211.74,306.42-61.94,23.96,48.3,15.59,99.83,17,152.01h61.62c15.25,0,42.7-13.75,54.75-23.2,66.11-51.86,57.28-158.2-16.28-198.49-9.65-5.28-33.16-14.19-43.74-14.19h-154.31v-91.09c0-.87,2.55-1.86,3.63-1.63,104.05,4.23,201.15-21.64,284.48,55.84,119.18,110.8,69.12,325.47-91.33,362.6-28.65,6.63-85.47,7.76-115.02,4.8-19.71-1.97-43.29-16.57-55.97-31.45-37.98-44.56-20.77-98.07-24.7-151.16-5.18-69.99-100-85.31-125.9-20.47-1.16,2.92-4.76,13.88-4.76,16.3v186.91Z%27/%3e%3c/svg%3e",
     DB.Lindorm: "data:image/png;base64,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",
 }
 
 # RedisCloud color: #0D6EFD
diff --git a/vectordb_bench/frontend/pages/full_text_search.py b/vectordb_bench/frontend/pages/full_text_search.py
new file mode 100644
index 000000000..d6023c0c8
--- /dev/null
+++ b/vectordb_bench/frontend/pages/full_text_search.py
@@ -0,0 +1,557 @@
+import json
+import re
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+
+from vectordb_bench import config
+from vectordb_bench.frontend.components.check_results.footer import footer
+from vectordb_bench.frontend.components.check_results.headerIcon import drawHeaderIcon
+from vectordb_bench.frontend.components.check_results.nav import NavToPages
+from vectordb_bench.frontend.config.styles import FAVICON
+
+RESULT_DIR = config.RESULTS_LOCAL_DIR / "FullTextSearch"
+DATASET_ORDER = [
+    "MS MARCO Small",
+    "MS MARCO Medium",
+    "MS MARCO Large",
+    "HotpotQA Small",
+    "HotpotQA Medium",
+    "HotpotQA Large",
+]
+# Published FTS results currently cover this cloud/service backend subset.
+BACKEND_ORDER = ["ZillizCloud", "ElasticSearch", "OSSOpenSearch", "TurboPuffer"]
+BACKEND_COLORS = {
+    "ZillizCloud": "#0D6EFD",
+    "ElasticSearch": "#04D6C8",
+    "OSSOpenSearch": "#61D790",
+    "TurboPuffer": "#FF6B2C",
+}
+SIZE_ORDER = ["Small", "Medium", "Large"]
+FILTER_RATE_LABEL_ORDER = ["50%", "75%", "90%", "95%", "99%"]
+CHART_TABS = ["QPS", "Recall", "NDCG", "MRR", "Load", "Filtered QPS"]
+FILTERED_TAB = "Filtered QPS"
+
+
+def _normalize_backend(db: str, result_file: Path) -> str:
+    if db == "ElasticCloud":
+        return "ElasticSearch"
+    if db:
+        return db
+    return result_file.parent.name
+
+
+def _dataset_parts(dataset_label: str) -> tuple[str, str, str]:
+    if dataset_label.startswith("MS MARCO"):
+        family = "MS MARCO"
+    elif dataset_label.startswith("HotpotQA"):
+        family = "HotpotQA"
+    else:
+        family = dataset_label.split(" ", 1)[0]
+
+    size = next((name for name in SIZE_ORDER if name in dataset_label), "")
+    return family, size, f"{family} {size}".strip()
+
+
+def _dataset_doc_count(dataset_label: str) -> str:
+    match = re.search(r"\(([^)]+)\)", dataset_label)
+    return match.group(1) if match else ""
+
+
+def _dataset_axis_label(dataset: str, doc_count: str) -> str:
+    return f"{dataset}<br>{doc_count}" if doc_count else dataset
+
+
+def _dataset_axis_order(data: pd.DataFrame) -> list[str]:
+    labels = []
+    for dataset in DATASET_ORDER:
+        matches = data[data["dataset"].astype(str) == dataset]
+        if not matches.empty:
+            labels.append(matches["dataset_axis_label"].iloc[0])
+    return labels
+
+
+def _filter_rate_label(value: Any) -> str:
+    if value is None or pd.isna(value):
+        return "Unfiltered"
+    return f"{float(value):.0%}"
+
+
+def _backend_metric_order(data: pd.DataFrame, metric: str, ascending: bool) -> list[str]:
+    if data.empty or metric not in data:
+        return BACKEND_ORDER
+
+    metric_data = data[["backend", metric]].dropna().copy()
+    if metric_data.empty:
+        return BACKEND_ORDER
+
+    metric_data["backend"] = metric_data["backend"].astype(str)
+    scores = metric_data.groupby("backend")[metric].mean()
+    ordered = scores.sort_values(ascending=ascending).index.tolist()
+    return ordered + [backend for backend in BACKEND_ORDER if backend not in ordered]
+
+
+def _run_context(task_label: str) -> str:
+    if "mathgt" in task_label:
+        return "Math GT"
+    return "Recorded"
+
+
+def _parse_result_file(result_file: Path) -> list[dict[str, Any]]:
+    with result_file.open() as f:
+        test_result = json.load(f)
+
+    task_label = test_result.get("task_label") or result_file.stem
+    rows = []
+    for case_result in test_result.get("results", []):
+        metrics = case_result.get("metrics", {})
+        task_config = case_result.get("task_config", {})
+        case_config = task_config.get("case_config", {})
+        custom_case = case_config.get("custom_case") or {}
+        additional_parameters = metrics.get("additional_parameters") or {}
+        fts_filter = additional_parameters.get("fts_filter") or {}
+        filter_rate = fts_filter.get("filter_rate", custom_case.get("filter_rate"))
+        dataset_label = custom_case.get("dataset_with_size_type", "")
+        dataset_family, dataset_size, dataset_key = _dataset_parts(dataset_label)
+        dataset_doc_count = _dataset_doc_count(dataset_label)
+        dataset_axis_label = _dataset_axis_label(dataset_key, dataset_doc_count)
+        backend = _normalize_backend(task_config.get("db", ""), result_file)
+        payload = metrics.get("payload_profile") or custom_case.get("payload_profile") or "ids_only"
+        row_task_label = task_config.get("task_label") or task_label
+
+        rows.append(
+            {
+                "backend": backend,
+                "dataset_family": dataset_family,
+                "dataset_size": dataset_size,
+                "dataset": dataset_key,
+                "dataset_doc_count": dataset_doc_count,
+                "dataset_axis_label": dataset_axis_label,
+                "payload": payload,
+                "context": _run_context(task_label),
+                "task_label": row_task_label,
+                "filter_rate": filter_rate,
+                "filter_rate_label": _filter_rate_label(filter_rate),
+                "is_filtered": filter_rate is not None,
+                "load_s": metrics.get("load_duration", 0.0),
+                "qps": metrics.get("qps", 0.0),
+                "recall": metrics.get("recall", 0.0),
+                "ndcg": metrics.get("ndcg", 0.0),
+                "mrr": metrics.get("mrr", 0.0),
+                "p95_s": metrics.get("serial_latency_p95", 0.0),
+                "p99_s": metrics.get("serial_latency_p99", 0.0),
+                "concurrency": metrics.get("conc_num_list") or [],
+                "concurrent_qps": metrics.get("conc_qps_list") or [],
+            }
+        )
+
+    return rows
+
+
+def _result_files(result_dir: Path) -> list[Path]:
+    return sorted(result_dir.rglob("result_*.json"))
+
+
+def load_full_text_search_rows(result_dir: Path = RESULT_DIR) -> pd.DataFrame:
+    if not result_dir.exists():
+        return pd.DataFrame()
+
+    rows = []
+    for result_file in _result_files(result_dir):
+        rows.extend(_parse_result_file(result_file))
+
+    data = pd.DataFrame(rows)
+    if data.empty:
+        return data
+
+    data = data[data["backend"] != "Milvus"].copy()
+    if data.empty:
+        return data
+
+    data["dataset"] = pd.Categorical(data["dataset"], DATASET_ORDER, ordered=True)
+    data["backend"] = pd.Categorical(data["backend"], BACKEND_ORDER, ordered=True)
+    data["dataset_size"] = pd.Categorical(data["dataset_size"], SIZE_ORDER, ordered=True)
+    data["filter_rate"] = pd.to_numeric(data["filter_rate"], errors="coerce")
+    return data.sort_values(["is_filtered", "dataset", "filter_rate", "backend", "payload"]).reset_index(drop=True)
+
+
+def _filter_data(st: Any, data: pd.DataFrame) -> pd.DataFrame:
+    with st.sidebar:
+        st.header("Filters")
+        selected_datasets = st.multiselect(
+            "Dataset",
+            [dataset for dataset in DATASET_ORDER if dataset in set(data["dataset"].astype(str))],
+            default=[dataset for dataset in DATASET_ORDER if dataset in set(data["dataset"].astype(str))],
+            key="fts-standard-datasets",
+        )
+        backend_options = [backend for backend in BACKEND_ORDER if backend in set(data["backend"].astype(str))]
+        selected_backends = st.multiselect(
+            "Backend", backend_options, default=backend_options, key="fts-standard-backends"
+        )
+        payloads = sorted(data["payload"].dropna().unique().tolist())
+        default_payloads = ["ids_only"] if "ids_only" in payloads else payloads
+        selected_payloads = st.multiselect("Payload", payloads, default=default_payloads, key="fts-standard-payloads")
+
+    filters = (
+        data["dataset"].astype(str).isin(selected_datasets)
+        & data["backend"].astype(str).isin(selected_backends)
+        & data["payload"].isin(selected_payloads)
+    )
+
+    return data[filters].copy()
+
+
+def _filter_filtered_data(st: Any, data: pd.DataFrame) -> pd.DataFrame:
+    with st.sidebar:
+        st.header("Filters")
+        dataset_options = [
+            family for family in ["MS MARCO", "HotpotQA"] if family in set(data["dataset_family"].astype(str))
+        ]
+        selected_dataset = st.selectbox("Dataset", dataset_options, key="fts-filtered-dataset-family")
+        backend_options = [backend for backend in BACKEND_ORDER if backend in set(data["backend"].astype(str))]
+        selected_backends = st.multiselect(
+            "Backend",
+            backend_options,
+            default=backend_options,
+            key="fts-filtered-backends",
+        )
+    filters = data["dataset_family"].astype(str).eq(selected_dataset) & data["backend"].astype(str).isin(
+        selected_backends
+    )
+    return data[filters].copy()
+
+
+def _draw_summary_table(st: Any, data: pd.DataFrame) -> None:
+    columns = [
+        "dataset",
+        "backend",
+        "payload",
+        "load_s",
+        "qps",
+        "recall",
+        "ndcg",
+        "mrr",
+        "p95_s",
+        "p99_s",
+    ]
+    st.dataframe(
+        data[columns],
+        hide_index=True,
+        width="stretch",
+        column_config={
+            "load_s": st.column_config.NumberColumn("Load s", format="%.4f"),
+            "qps": st.column_config.NumberColumn("QPS", format="%.4f"),
+            "recall": st.column_config.NumberColumn("Recall", format="%.4f"),
+            "ndcg": st.column_config.NumberColumn("NDCG", format="%.4f"),
+            "mrr": st.column_config.NumberColumn("MRR", format="%.4f"),
+            "p95_s": st.column_config.NumberColumn("p95 s", format="%.4f"),
+            "p99_s": st.column_config.NumberColumn("p99 s", format="%.4f"),
+        },
+    )
+
+
+def _draw_filtered_summary_table(st: Any, data: pd.DataFrame) -> None:
+    concurrency_data = _concurrency_rows(data)
+    if concurrency_data.empty:
+        st.info("No filtered concurrency QPS rows found.")
+        return
+
+    columns = [
+        "dataset",
+        "backend",
+        "filter_rate_label",
+        "concurrency",
+        "qps",
+        "task_label",
+    ]
+    st.dataframe(
+        concurrency_data[columns],
+        hide_index=True,
+        width="stretch",
+        column_config={
+            "filter_rate_label": "Filter",
+            "concurrency": st.column_config.NumberColumn("Concurrency", format="%d"),
+            "qps": st.column_config.NumberColumn("QPS", format="%.4f"),
+        },
+    )
+
+
+def _chart_label(value: Any, metric: str) -> str:
+    if pd.isna(value):
+        return ""
+
+    value = float(value)
+    abs_value = abs(value)
+    if metric in {"recall", "ndcg", "mrr"}:
+        return f"{value:.3f}"
+    if metric == "qps":
+        return f"{value / 1000:.1f}k" if abs_value >= 1000 else f"{value:.0f}"
+    if metric == "load_s":
+        if abs_value >= 3600:
+            return f"{value / 3600:.1f}h"
+        if abs_value >= 60:
+            return f"{value / 60:.1f}m"
+        return f"{value:.1f}s"
+    return f"{value:.1f}"
+
+
+def _draw_metric_chart(
+    st: Any,
+    data: pd.DataFrame,
+    metric: str,
+    title: str,
+    backend_order: list[str] | None = None,
+) -> None:
+    if backend_order is None:
+        backend_order = BACKEND_ORDER
+
+    show_text = data["payload"].nunique() <= 1
+    chart_data = data.copy()
+    if show_text:
+        chart_data["_chart_label"] = chart_data[metric].apply(lambda value: _chart_label(value, metric))
+
+    fig = px.bar(
+        chart_data,
+        x="dataset_axis_label",
+        y=metric,
+        color="backend",
+        pattern_shape="payload",
+        barmode="group",
+        category_orders={"dataset_axis_label": _dataset_axis_order(data), "backend": backend_order},
+        color_discrete_map=BACKEND_COLORS,
+        hover_data=["dataset_doc_count", "payload", "context", "task_label"],
+        text="_chart_label" if show_text else None,
+        title=title,
+    )
+    if show_text:
+        fig.update_traces(
+            texttemplate="%{text}",
+            textposition="outside",
+            textangle=0,
+            textfont={"size": 10},
+            cliponaxis=False,
+        )
+    fig.update_layout(
+        margin={"l": 0, "r": 0, "t": 56, "b": 12, "pad": 8},
+        legend={"orientation": "h", "yanchor": "bottom", "y": 1, "xanchor": "right", "x": 1, "title": ""},
+        xaxis_title="",
+        xaxis={"tickfont": {"size": 12}},
+        uniformtext={"minsize": 10, "mode": "show"},
+    )
+    st.plotly_chart(fig, width="stretch", key=f"fts-{metric}")
+
+
+def _concurrency_rows(data: pd.DataFrame) -> pd.DataFrame:
+    rows = []
+    for row in data.to_dict("records"):
+        for concurrency, qps in zip(row["concurrency"], row["concurrent_qps"], strict=True):
+            rows.append(
+                {
+                    "dataset": row["dataset"],
+                    "dataset_family": row["dataset_family"],
+                    "dataset_axis_label": row["dataset_axis_label"],
+                    "dataset_doc_count": row["dataset_doc_count"],
+                    "backend": row["backend"],
+                    "payload": row["payload"],
+                    "context": row["context"],
+                    "filter_rate": row["filter_rate"],
+                    "filter_rate_label": row["filter_rate_label"],
+                    "concurrency": concurrency,
+                    "qps": qps,
+                    "task_label": row["task_label"],
+                }
+            )
+    return pd.DataFrame(rows)
+
+
+def _peak_filtered_qps_rows(data: pd.DataFrame) -> pd.DataFrame:
+    concurrency_data = _concurrency_rows(data)
+    if concurrency_data.empty:
+        return concurrency_data
+
+    concurrency_data["qps"] = pd.to_numeric(concurrency_data["qps"], errors="coerce")
+    concurrency_data = concurrency_data.dropna(subset=["qps"])
+    if concurrency_data.empty:
+        return concurrency_data
+
+    peak_indices = concurrency_data.groupby(["backend", "filter_rate_label"], sort=False, observed=True)["qps"].idxmax()
+    return concurrency_data.loc[peak_indices].reset_index(drop=True)
+
+
+def _draw_concurrency_chart(st: Any, data: pd.DataFrame) -> None:
+    concurrency_data = _concurrency_rows(data)
+    if concurrency_data.empty:
+        return
+
+    fig = px.line(
+        concurrency_data,
+        x="concurrency",
+        y="qps",
+        color="backend",
+        line_dash="dataset_axis_label",
+        symbol="payload",
+        markers=True,
+        category_orders={"dataset_axis_label": _dataset_axis_order(data), "backend": BACKEND_ORDER},
+        color_discrete_map=BACKEND_COLORS,
+        hover_data=["dataset", "dataset_doc_count", "payload", "context", "task_label"],
+        title="Concurrent Search QPS",
+    )
+    fig.update_layout(
+        margin={"l": 0, "r": 0, "t": 48, "b": 12, "pad": 8},
+        legend={"orientation": "h", "yanchor": "bottom", "y": 1, "xanchor": "right", "x": 1, "title": ""},
+    )
+    fig.update_xaxes(title_text="Concurrency")
+    fig.update_yaxes(title_text="QPS")
+    st.plotly_chart(fig, width="stretch", key="fts-concurrency-qps")
+
+
+def _draw_filtered_qps_tab(st: Any, data: pd.DataFrame) -> None:
+    filtered_data = data[(data["payload"] == "ids_only") & data["filter_rate"].notna()].copy()
+    peak_data = _peak_filtered_qps_rows(filtered_data)
+    if peak_data.empty:
+        st.info("No filtered concurrency QPS rows found.")
+        return
+
+    selected_family = str(peak_data["dataset_family"].iloc[0])
+    peak_data["_chart_label"] = peak_data["qps"].apply(lambda value: _chart_label(value, "qps"))
+    backend_order = _backend_metric_order(peak_data, "qps", ascending=False)
+    filter_rate_order = [label for label in FILTER_RATE_LABEL_ORDER if label in set(peak_data["filter_rate_label"])]
+
+    fig = px.bar(
+        peak_data,
+        x="filter_rate_label",
+        y="qps",
+        color="backend",
+        barmode="group",
+        category_orders={
+            "filter_rate_label": filter_rate_order,
+            "backend": backend_order,
+        },
+        color_discrete_map=BACKEND_COLORS,
+        hover_data=[
+            "dataset",
+            "dataset_doc_count",
+            "payload",
+            "concurrency",
+            "task_label",
+        ],
+        text="_chart_label",
+        title=f"{selected_family} Peak Filtered Concurrent Search QPS",
+    )
+    fig.update_traces(
+        texttemplate="%{text}",
+        textposition="outside",
+        textangle=0,
+        textfont={"size": 10},
+        cliponaxis=False,
+    )
+    fig.update_layout(
+        margin={"l": 0, "r": 0, "t": 56, "b": 12, "pad": 8},
+        legend={"orientation": "h", "yanchor": "bottom", "y": 1, "xanchor": "right", "x": 1, "title": ""},
+        xaxis_title="Filter rate",
+        yaxis_title="QPS",
+        uniformtext={"minsize": 10, "mode": "show"},
+    )
+    fig.update_xaxes(type="category", categoryorder="array", categoryarray=filter_rate_order)
+    st.plotly_chart(fig, width="stretch", key=f"fts-filtered-concurrency-qps-{selected_family}")
+
+
+def main():
+    st.set_page_config(
+        page_title="Full Text Search Cloud Results",
+        page_icon=FAVICON,
+        layout="wide",
+    )
+
+    drawHeaderIcon(st)
+    NavToPages(st)
+
+    st.title("Full Text Search Cloud Results")
+    st.caption("Published FTS results for Zilliz Cloud, ElasticSearch, OpenSearch, and TurboPuffer.")
+
+    data = load_full_text_search_rows()
+    if data.empty:
+        st.warning("No FullTextSearch result JSONs found.")
+        footer(st.container())
+        return
+
+    normal_data = data[~data["is_filtered"]].copy()
+    filtered_data = data[data["is_filtered"]].copy()
+    active_tab = st.session_state.get("fts-chart-tabs", CHART_TABS[0])
+    if active_tab == FILTERED_TAB:
+        shown_data = normal_data
+        shown_filtered_data = _filter_filtered_data(st, filtered_data) if not filtered_data.empty else filtered_data
+    else:
+        shown_data = _filter_data(st, normal_data) if not normal_data.empty else normal_data
+        shown_filtered_data = filtered_data
+
+    if shown_data.empty and shown_filtered_data.empty:
+        st.warning("No rows match the selected filters.")
+        footer(st.container())
+        return
+
+    if active_tab == FILTERED_TAB and not shown_filtered_data.empty:
+        _draw_filtered_summary_table(st, shown_filtered_data)
+    elif not shown_data.empty:
+        _draw_summary_table(st, shown_data)
+
+    chart_tabs = st.tabs(CHART_TABS, default=active_tab, key="fts-chart-tabs", on_change="rerun")
+    with chart_tabs[0]:
+        qps_data = shown_data
+        if qps_data.empty:
+            st.info("No standard FTS rows match the selected filters.")
+        else:
+            _draw_metric_chart(
+                st,
+                qps_data,
+                "qps",
+                "Search QPS",
+                _backend_metric_order(qps_data, "qps", ascending=False),
+            )
+    with chart_tabs[1]:
+        recall_data = shown_data[shown_data["payload"] == "ids_only"]
+        _draw_metric_chart(
+            st,
+            recall_data,
+            "recall",
+            "Semantic Recall",
+            _backend_metric_order(recall_data, "recall", ascending=False),
+        )
+    with chart_tabs[2]:
+        ndcg_data = shown_data[shown_data["payload"] == "ids_only"]
+        _draw_metric_chart(
+            st,
+            ndcg_data,
+            "ndcg",
+            "NDCG",
+            _backend_metric_order(ndcg_data, "ndcg", ascending=False),
+        )
+    with chart_tabs[3]:
+        mrr_data = shown_data[shown_data["payload"] == "ids_only"]
+        _draw_metric_chart(
+            st,
+            mrr_data,
+            "mrr",
+            "MRR",
+            _backend_metric_order(mrr_data, "mrr", ascending=False),
+        )
+    with chart_tabs[4]:
+        load_data = shown_data[shown_data["payload"] == "ids_only"]
+        _draw_metric_chart(
+            st,
+            load_data,
+            "load_s",
+            "Load Duration",
+            _backend_metric_order(load_data, "load_s", ascending=True),
+        )
+    with chart_tabs[5]:
+        _draw_filtered_qps_tab(st, shown_filtered_data)
+
+    footer(st.container())
+
+
+if __name__ == "__main__":
+    main()
diff --git a/vectordb_bench/frontend/pages/qps_recall.py b/vectordb_bench/frontend/pages/qps_recall.py
index 27f9c4691..75dcd570b 100644
--- a/vectordb_bench/frontend/pages/qps_recall.py
+++ b/vectordb_bench/frontend/pages/qps_recall.py
@@ -41,9 +41,15 @@ def main():
 
     def case_results_filter(case_result: CaseResult) -> bool:
         case = case_result.task_config.case_config.case
-        return case.label == CaseLabel.Performance and case.filters.type == FilterOp.NonFilter
-
-    default_selected_task_labels = ["standard_2025"]
+        # Include both vector performance cases and FTS cases
+        return (
+            case.label == CaseLabel.Performance and case.filters.type == FilterOp.NonFilter
+        ) or case.label == CaseLabel.FullTextSearchPerformance
+
+    default_selected_task_labels = ["standard_20260403", "standard_20250519"]
+    # Filter defaults to only include labels that exist in results
+    available_labels = {r.task_label for r in allResults}
+    default_selected_task_labels = [l for l in default_selected_task_labels if l in available_labels]
     shownData, failedTasks, showCaseNames = getshownData(
         resultSelectorContainer,
         allResults,
diff --git a/vectordb_bench/frontend/pages/results.py b/vectordb_bench/frontend/pages/results.py
index a146f2fdc..216029bb1 100644
--- a/vectordb_bench/frontend/pages/results.py
+++ b/vectordb_bench/frontend/pages/results.py
@@ -32,7 +32,7 @@ def main():
     st.caption(
         "Choose your desired test results to display from the sidebar. "
         "For your reference, we've included two standard benchmarks tested by our team. "
-        "Note that `standard_2025` was tested in 2025; the others in 2023. "
+        "Note that `standard_20260403` is the latest benchmark; the others were tested in 2023-2025. "
         "Unless explicitly labeled as distributed multi-node, test with single-node mode by default."
     )
     st.caption("We welcome community contributions for better results, parameter configurations, and optimizations.")
diff --git a/vectordb_bench/frontend/pages/run_test.py b/vectordb_bench/frontend/pages/run_test.py
index e4472e767..64115ff17 100644
--- a/vectordb_bench/frontend/pages/run_test.py
+++ b/vectordb_bench/frontend/pages/run_test.py
@@ -1,5 +1,4 @@
 import streamlit as st
-from vectordb_bench.frontend.components.run_test.autoRefresh import autoRefresh
 from vectordb_bench.frontend.components.run_test.caseSelector import caseSelector
 from vectordb_bench.frontend.components.run_test.dbConfigSetting import dbConfigSettings
 from vectordb_bench.frontend.components.run_test.dbSelector import dbSelector
@@ -57,9 +56,6 @@ def main():
     # nav to results
     NavToResults(st, key="footer-nav-to-results")
 
-    # autofresh
-    autoRefresh()
-
 
 if __name__ == "__main__":
     main()
diff --git a/vectordb_bench/frontend/vdbbench.py b/vectordb_bench/frontend/vdbbench.py
index 860467734..90769019c 100644
--- a/vectordb_bench/frontend/vdbbench.py
+++ b/vectordb_bench/frontend/vdbbench.py
@@ -1,10 +1,33 @@
 import streamlit as st
+from tornado.iostream import StreamClosedError
+from tornado.websocket import WebSocketClosedError, WebSocketProtocol13
+
 from vectordb_bench.frontend.components.check_results.headerIcon import drawHeaderIcon
 from vectordb_bench.frontend.components.custom.initStyle import initStyle
 from vectordb_bench.frontend.components.welcome.explainPrams import explainPrams
 from vectordb_bench.frontend.components.welcome.welcomePrams import welcomePrams
 from vectordb_bench.frontend.config.styles import FAVICON, PAGE_TITLE
 
+# Consume expected WS-close errors on streamlit's fire-and-forget writes
+# (streamlit#9787, unfixed upstream).
+_orig_write_message = WebSocketProtocol13.write_message
+
+
+def _write_message_with_consumer(self, message, binary=False):
+    task = _orig_write_message(self, message, binary=binary)
+
+    def _consume(t):
+        exc = t.exception()
+        if exc is None or isinstance(exc, (WebSocketClosedError, StreamClosedError)):
+            return
+        t.get_loop().call_exception_handler({"message": "websocket write failed", "exception": exc, "task": t})
+
+    task.add_done_callback(_consume)
+    return task
+
+
+WebSocketProtocol13.write_message = _write_message_with_consumer
+
 
 def main():
     st.set_page_config(
diff --git a/vectordb_bench/interface.py b/vectordb_bench/interface.py
index 42dc876b0..78e6958a2 100644
--- a/vectordb_bench/interface.py
+++ b/vectordb_bench/interface.py
@@ -2,20 +2,17 @@
 import logging
 import multiprocessing as mp
 import pathlib
-import signal
 import traceback
 import uuid
-from collections.abc import Callable
 from enum import Enum
 from multiprocessing.connection import Connection
 
-import psutil
-
 from . import config
 from .backend.assembler import Assembler, FilterNotSupportedError
 from .backend.data_source import DatasetSource
 from .backend.result_collector import ResultCollector
 from .backend.task_runner import TaskRunner
+from .backend.utils import kill_proc_tree
 from .metric import Metric
 from .models import (
     CaseResult,
@@ -46,14 +43,18 @@ def __init__(self):
         # set default data source by ENV
         if config.DATASET_SOURCE.upper() == "ALIYUNOSS":
             self.dataset_source: DatasetSource = DatasetSource.AliyunOSS
+        elif config.DATASET_SOURCE.upper() == "IR_DATASETS":
+            self.dataset_source: DatasetSource = DatasetSource.IR_DATASETS
         else:
             self.dataset_source: DatasetSource = DatasetSource.S3
 
     def set_drop_old(self, drop_old: bool):
         self.drop_old = drop_old
 
-    def set_download_address(self, use_aliyun: bool):
-        if use_aliyun:
+    def set_download_address(self, use_aliyun: bool, use_ir_datasets: bool = False):
+        if use_ir_datasets:
+            self.dataset_source = DatasetSource.IR_DATASETS
+        elif use_aliyun:
             self.dataset_source = DatasetSource.AliyunOSS
         else:
             self.dataset_source = DatasetSource.S3
@@ -166,7 +167,7 @@ def _async_task_v2(self, running_task: TaskRunner, send_conn: Connection) -> Non
                 return
 
             c_results = []
-            latest_runner, cached_load_duration = None, None
+            latest_loaded_reuse_key, cached_load_duration = None, None
             for idx, runner in enumerate(running_task.case_runners):
                 case_res = CaseResult(
                     metrics=Metric(),
@@ -174,7 +175,10 @@ def _async_task_v2(self, running_task: TaskRunner, send_conn: Connection) -> Non
                 )
 
                 drop_old = TaskStage.DROP_OLD in runner.config.stages
-                if (latest_runner and runner == latest_runner) or not self.drop_old:
+                reuse_key = runner.load_reuse_key()
+                if reuse_key is not None and reuse_key == latest_loaded_reuse_key:
+                    drop_old = False
+                if not self.drop_old:
                     drop_old = False
                 num_cases = running_task.num_cases()
                 try:
@@ -185,14 +189,12 @@ def _async_task_v2(self, running_task: TaskRunner, send_conn: Connection) -> Non
                         f"result={case_res.metrics}, label={case_res.label}"
                     )
 
-                    # cache the latest succeeded runner
-                    latest_runner = runner
-
-                    # cache the latest drop_old=True load_duration of the latest succeeded runner
-                    cached_load_duration = case_res.metrics.load_duration if drop_old else cached_load_duration
+                    if drop_old and TaskStage.LOAD in runner.config.stages and reuse_key is not None:
+                        latest_loaded_reuse_key = reuse_key
+                        cached_load_duration = case_res.metrics.load_duration
 
                     # use the cached load duration if this case didn't drop the existing collection
-                    if not drop_old:
+                    if not drop_old and reuse_key is not None and reuse_key == latest_loaded_reuse_key:
                         case_res.metrics.load_duration = cached_load_duration if cached_load_duration else 0.0
                 except (LoadTimeoutError, PerformanceTimeoutError) as e:
                     log.warning(f"[{idx+1}/{num_cases}] case {runner.display()} failed to run, reason={e}")
@@ -240,7 +242,7 @@ def _clear_running_task(self):
             for r in self.running_task.case_runners:
                 r.stop()
 
-            self.kill_proc_tree(timeout=5)
+            kill_proc_tree()
             self.running_task = None
 
         if self.receive_conn:
@@ -261,29 +263,5 @@ def _run_async(self, conn: Connection) -> bool:
 
         return True
 
-    def kill_proc_tree(
-        self,
-        sig: int = signal.SIGTERM,
-        timeout: float | None = None,
-        on_terminate: Callable | None = None,
-    ):
-        """Kill a process tree (including grandchildren) with signal
-        "sig" and return a (gone, still_alive) tuple.
-        "on_terminate", if specified, is a callback function which is
-        called as soon as a child terminates.
-        """
-        children = psutil.Process().children(recursive=True)
-        for p in children:
-            try:
-                log.warning(f"sending SIGTERM to child process: {p}")
-                p.send_signal(sig)
-            except psutil.NoSuchProcess:
-                pass
-        _, alive = psutil.wait_procs(children, timeout=timeout, callback=on_terminate)
-
-        for p in alive:
-            log.warning(f"force killing child process: {p}")
-            p.kill()
-
 
 benchmark_runner = BenchMarkRunner()
diff --git a/vectordb_bench/metric.py b/vectordb_bench/metric.py
index 3634b2114..540cefd28 100644
--- a/vectordb_bench/metric.py
+++ b/vectordb_bench/metric.py
@@ -24,11 +24,21 @@ class Metric:
     serial_latency_p95: float = 0.0
     recall: float = 0.0
     ndcg: float = 0.0
+    mrr: float = 0.0
     conc_num_list: list[int] = field(default_factory=list)
     conc_qps_list: list[float] = field(default_factory=list)
     conc_latency_p99_list: list[float] = field(default_factory=list)
     conc_latency_p95_list: list[float] = field(default_factory=list)
     conc_latency_avg_list: list[float] = field(default_factory=list)
+    payload_profile: str = "ids_only"
+    payload_estimated_bytes_per_query: int = 0
+
+    inserted_count: int = 0
+    insert_rows_per_second: float = 0.0
+    insert_completion_seconds: float = 0.0
+    searchable_after_insert_seconds: float = 0.0
+    indexed_after_searchable_seconds: float = 0.0
+    additional_parameters: dict = field(default_factory=dict)
 
     # for streaming cases
     st_ideal_insert_duration: int = 0
@@ -110,3 +120,52 @@ def calc_ndcg(ground_truth: list[int], got: list[int], ideal_dcg: float) -> floa
             idx = ground_truth.index(got_id)
             dcg += 1 / np.log2(idx + 2)
     return dcg / ideal_dcg
+
+
+def _positive_fts_qrels(ground_truth: dict[str, int] | list[int] | list[str]) -> dict[str, int]:
+    if isinstance(ground_truth, dict):
+        return {str(doc_id): int(rel) for doc_id, rel in ground_truth.items() if int(rel) > 0}
+    return {str(doc_id): 1 for doc_id in ground_truth}
+
+
+def calc_recall_fts(k: int, ground_truth: dict[str, int] | list[int] | list[str], got: list[int] | list[str]) -> float:
+    gt = _positive_fts_qrels(ground_truth)
+    if not gt or k <= 0:
+        return 0.0
+    got_set = {str(doc_id) for doc_id in got[:k]}
+    return len(set(gt) & got_set) / len(gt)
+
+
+def calc_mrr_fts(k: int, ground_truth: dict[str, int] | list[int] | list[str], got: list[int] | list[str]) -> float:
+    gt = _positive_fts_qrels(ground_truth)
+    if not gt or k <= 0:
+        return 0.0
+    for rank, doc_id in enumerate(got[:k], start=1):
+        if str(doc_id) in gt:
+            return 1 / rank
+    return 0.0
+
+
+def calc_ndcg_fts(k: int, ground_truth: dict[str, int] | list[int] | list[str], got: list[int] | list[str]) -> float:
+    gt = _positive_fts_qrels(ground_truth)
+    if not gt or k <= 0:
+        return 0.0
+
+    dcg = 0.0
+    seen = set()
+    for rank, raw_doc_id in enumerate(got[:k], start=1):
+        doc_id = str(raw_doc_id)
+        if doc_id in seen:
+            continue
+        seen.add(doc_id)
+        rel = gt.get(doc_id, 0)
+        if rel > 0:
+            dcg += rel / np.log2(rank + 1)
+
+    ideal_dcg = 0.0
+    for rank, rel in enumerate(sorted(gt.values(), reverse=True)[:k], start=1):
+        ideal_dcg += rel / np.log2(rank + 1)
+
+    if ideal_dcg == 0:
+        return 0.0
+    return dcg / ideal_dcg
diff --git a/vectordb_bench/models.py b/vectordb_bench/models.py
index 88b162ae3..d427d4f58 100644
--- a/vectordb_bench/models.py
+++ b/vectordb_bench/models.py
@@ -1,8 +1,9 @@
 import logging
 import pathlib
+from dataclasses import asdict
 from datetime import date, datetime
 from enum import Enum, StrEnum
-from typing import Self
+from typing import Any, ClassVar, Self
 
 import ujson
 
@@ -17,6 +18,7 @@
     DBConfig,
     EmptyDBCaseConfig,
 )
+from .backend.clients.api import IndexType
 from .base import BaseModel
 from .metric import Metric
 
@@ -44,6 +46,16 @@ class CaseConfigParamType(Enum):
     """
 
     IndexType = "IndexType"
+    drop_ratio_search = "drop_ratio_search"
+    drop_ratio_build = "drop_ratio_build"
+    bm25_k1 = "bm25_k1"
+    bm25_b = "bm25_b"
+    inverted_index_algo = "inverted_index_algo"
+    analyzer_tokenizer = "analyzer_tokenizer"
+    analyzer_enable_lowercase = "analyzer_enable_lowercase"
+    analyzer_max_len = "analyzer_max_len"
+    analyzer_max_token_length = "analyzer_max_token_length"  # noqa: S105
+    analyzer_stop_words = "analyzer_stop_words"
     index = "index"
     M = "M"
     EFConstruction = "efConstruction"
@@ -149,12 +161,18 @@ class CaseConfigParamType(Enum):
 
     dataset_with_size_type = "dataset_with_size_type"
     filter_rate = "filter_rate"
+    payload_profile = "payload_profile"
     insert_rate = "insert_rate"
     search_stages = "search_stages"
     concurrencies = "concurrencies"
     optimize_after_write = "optimize_after_write"
     read_dur_after_write = "read_dur_after_write"
 
+    # PolarDB parameters
+    insert_workers = "insert_workers"
+    post_load_index = "post_load_index"
+    pq_nbits = "pq_nbits"
+
     # Lindorm parameters
     efSearch = "efSearch"
     pq_m = "pq_m"
@@ -168,6 +186,15 @@ class CaseConfigParamType(Enum):
     exbits = "exbits"
     number_of_regions = "number_of_regions"
 
+    # ADBPG parameters
+    hnsw_m = "hnsw_m"
+    algorithm = "algorithm"
+    rabitq_bits = "rabitq_bits"
+    quantize_rescore_amp = "quantize_rescore_amp"
+    nova_adaptive_gamma = "nova_adaptive_gamma"
+    max_scan_points = "max_scan_points"
+    auto_reduction = "auto_reduction"
+
 
 class CustomizedCase(BaseModel):
     pass
@@ -177,6 +204,7 @@ class ConcurrencySearchConfig(BaseModel):
     num_concurrency: list[int] = config.NUM_CONCURRENCY
     concurrency_duration: int = config.CONCURRENCY_DURATION
     concurrency_timeout: int = config.CONCURRENCY_TIMEOUT
+    serial_cooldown: float = config.SERIAL_COOLDOWN
 
 
 class CaseConfig(BaseModel):
@@ -238,6 +266,7 @@ class TaskConfig(BaseModel):
     db_case_config: DBCaseConfig
     case_config: CaseConfig
     stages: list[TaskStage] = ALL_TASK_STAGES
+    load_concurrency: int = config.LOAD_CONCURRENCY
 
     @property
     def db_name(self):
@@ -270,6 +299,68 @@ class TestResult(BaseModel):
 
     file_fmt: str = "result_{}_{}_{}.json"  # result_20230718_statndard_milvus.json
     timestamp: float = 0.0
+    sensitive_output_fields: ClassVar[set[str]] = {"api_key", "password", "token"}
+
+    @classmethod
+    def _redact_sensitive_fields(cls, value: Any) -> Any:
+        if isinstance(value, dict):
+            return {
+                key: (
+                    "**********"
+                    if key.lower() in cls.sensitive_output_fields and item
+                    else cls._redact_sensitive_fields(item)
+                )
+                for key, item in value.items()
+            }
+        if isinstance(value, list):
+            return [cls._redact_sensitive_fields(item) for item in value]
+        return value
+
+    @staticmethod
+    def _output_metrics_for_case(case_result: CaseResult) -> dict:
+        metrics = asdict(case_result.metrics)
+        case_id = case_result.task_config.case_config.case_id
+
+        if case_id == CaseType.CloudInsertCase:
+            return {
+                "inserted_count": metrics["inserted_count"],
+                "insert_rows_per_second": metrics["insert_rows_per_second"],
+                "insert_completion_seconds": metrics["insert_completion_seconds"],
+                "searchable_after_insert_seconds": metrics["searchable_after_insert_seconds"],
+                "indexed_after_searchable_seconds": metrics["indexed_after_searchable_seconds"],
+                "additional_parameters": metrics["additional_parameters"],
+            }
+
+        if case_id == CaseType.CloudColdLatencyCase:
+            return {
+                "insert_duration": metrics["insert_duration"],
+                "optimize_duration": metrics["optimize_duration"],
+                "load_duration": metrics["load_duration"],
+                "payload_profile": metrics["payload_profile"],
+                "payload_estimated_bytes_per_query": metrics["payload_estimated_bytes_per_query"],
+                "cold_latency": metrics["additional_parameters"].get("cold_latency", {}),
+            }
+
+        return metrics
+
+    @staticmethod
+    def _output_case_config_for_case(case_result: CaseResult) -> dict:
+        case_config = case_result.task_config.case_config
+
+        if case_config.case_id in {CaseType.CloudInsertCase, CaseType.CloudColdLatencyCase}:
+            return {
+                "case_id": case_config.case_id.value,
+                "custom_case": case_config.custom_case,
+            }
+
+        return case_config.model_dump(mode="json")
+
+    def model_dump_for_output(self) -> dict:
+        output = self.model_dump(mode="json", serialize_as_any=True)
+        for idx, case_result in enumerate(self.results):
+            output["results"][idx]["metrics"] = self._output_metrics_for_case(case_result)
+            output["results"][idx]["task_config"]["case_config"] = self._output_case_config_for_case(case_result)
+        return self._redact_sensitive_fields(output)
 
     def flush(self):
         db2case = self.get_db_results()
@@ -308,8 +399,8 @@ def write_db_file(self, result_dir: pathlib.Path, partial: Self, db: str):
 
         log.info(f"write results to disk {result_file}")
         with pathlib.Path(result_file).open("w") as f:
-            b = partial.model_dump_json(exclude={"db_config": {"password", "api_key"}})
-            f.write(b)
+            f.write(ujson.dumps(partial.model_dump_for_output(), indent=2))
+            f.write("\n")
 
     def get_case_config(case_config: CaseConfig) -> dict[CaseConfig]:
         if case_config["case_id"] in {6, 7, 8, 9, 12, 13, 14, 15}:
@@ -340,12 +431,14 @@ def read_file(cls, full_path: pathlib.Path, trans_unit: bool = False) -> Self:
                 task_config = case_result.get("task_config")
                 case_config = task_config.get("case_config")
                 db = DB(task_config.get("db"))
-
                 task_config["db_config"] = db.config_cls(**task_config["db_config"])
-
                 # Safely instantiate DBCaseConfig (fallback to EmptyDBCaseConfig on None)
                 raw_case_cfg = task_config.get("db_case_config") or {}
                 index_value = raw_case_cfg.get("index", None)
+
+                # Handle FTS cases
+                if case_config.get("case_id") == CaseType.FTSBm25Performance.value:
+                    index_value = IndexType.FTS
                 try:
                     task_config["db_case_config"] = db.case_config_cls(index_type=index_value)(**raw_case_cfg)
                 except Exception:
@@ -354,28 +447,32 @@ def read_file(cls, full_path: pathlib.Path, trans_unit: bool = False) -> Self:
 
                 task_config["case_config"] = cls.get_case_config(case_config=case_config)
                 case_result["task_config"] = task_config
-
-                if trans_unit:
-                    cur_max_count = case_result["metrics"]["max_load_count"]
-                    case_result["metrics"]["max_load_count"] = (
-                        cur_max_count / 1000 if int(cur_max_count) > 0 else cur_max_count
-                    )
-
-                    cur_latency = case_result["metrics"]["serial_latency_p99"]
-                    case_result["metrics"]["serial_latency_p99"] = (
-                        cur_latency * 1000 if cur_latency > 0 else cur_latency
-                    )
-
-                    # Handle P95 latency for backward compatibility with existing result files
-                    if "serial_latency_p95" in case_result["metrics"]:
-                        cur_latency_p95 = case_result["metrics"]["serial_latency_p95"]
-                        case_result["metrics"]["serial_latency_p95"] = (
+                metrics = case_result.get("metrics")
+                if (
+                    metrics
+                    and CaseType(case_config.get("case_id")) == CaseType.CloudColdLatencyCase
+                    and "cold_latency" in metrics
+                ):
+                    metrics.setdefault("additional_parameters", {})["cold_latency"] = metrics.pop("cold_latency")
+
+                if trans_unit and metrics:
+                    if "max_load_count" in metrics:
+                        cur_max_count = metrics["max_load_count"]
+                        metrics["max_load_count"] = cur_max_count / 1000 if int(cur_max_count) > 0 else cur_max_count
+
+                    if "serial_latency_p99" in metrics:
+                        cur_latency = metrics["serial_latency_p99"]
+                        metrics["serial_latency_p99"] = cur_latency * 1000 if cur_latency > 0 else cur_latency
+
+                    # Handle P95 latency for backward compatibility with existing result files.
+                    if "serial_latency_p95" in metrics:
+                        cur_latency_p95 = metrics["serial_latency_p95"]
+                        metrics["serial_latency_p95"] = (
                             cur_latency_p95 * 1000 if cur_latency_p95 > 0 else cur_latency_p95
                         )
-                    else:
-                        # Default to 0 for older result files that don't have P95 data
-                        case_result["metrics"]["serial_latency_p95"] = 0.0
-            return TestResult.validate(test_result)
+                    elif "serial_latency_p99" in metrics:
+                        metrics["serial_latency_p95"] = 0.0
+            return TestResult.model_validate(test_result)
 
     def display(self, dbs: list[DB] | None = None):
         filter_list = dbs if dbs and isinstance(dbs, list) else None
diff --git a/vectordb_bench/restful/format_res.py b/vectordb_bench/restful/format_res.py
index 326986319..c4af0d579 100644
--- a/vectordb_bench/restful/format_res.py
+++ b/vectordb_bench/restful/format_res.py
@@ -31,11 +31,14 @@ class FormatResult(BaseModel):
     load_duration: int = 0
     qps: float = 0
     serial_latency_p99: float = 0
+    serial_latency_p95: float = 0
     recall: float = 0
     ndcg: float = 0
+    mrr: float = 0
     conc_num_list: list[int] = []
     conc_qps_list: list[float] = []
     conc_latency_p99_list: list[float] = []
+    conc_latency_p95_list: list[float] = []
     conc_latency_avg_list: list[float] = []
 
 
@@ -66,7 +69,7 @@ def format_results(test_results: list[TestResult], task_label: str) -> list[dict
                         params=task_config.db_case_config.model_dump(),
                         case_name=case.name,
                         dataset=dataset.full_name,
-                        dim=dataset.dim,
+                        dim=getattr(dataset, "dim", 0),
                         filter_type=filter_.type.name,
                         filter_rate=filter_.filter_rate,
                         k=task_config.case_config.k,
diff --git a/vectordb_bench/results/ElasticCloud/result_20260403_standard_elasticcloud.json b/vectordb_bench/results/ElasticCloud/result_20260403_standard_elasticcloud.json
new file mode 100644
index 000000000..2ec557926
--- /dev/null
+++ b/vectordb_bench/results/ElasticCloud/result_20260403_standard_elasticcloud.json
@@ -0,0 +1,1556 @@
+{
+  "run_id": "c11e83b51ff14060a08f06d58f801214",
+  "task_label": "standard_20260403",
+  "results": [
+    {
+      "metrics": {
+        "max_load_count": 0,
+        "insert_duration": 3846.0365,
+        "optimize_duration": 1110.4687,
+        "load_duration": 4956.5052,
+        "qps": 2030.4249,
+        "serial_latency_p99": 0.0106,
+        "serial_latency_p95": 0.0073,
+        "recall": 0.925,
+        "ndcg": 0.9306,
+        "conc_num_list": [
+          1,
+          5,
+          10,
+          20,
+          30,
+          40,
+          60,
+          80
+        ],
+        "conc_qps_list": [
+          139.9315,
+          742.8751,
+          1246.354,
+          1773.6358,
+          1944.4702,
+          1997.4895,
+          2030.4249,
+          2030.061
+        ],
+        "conc_latency_p99_list": [
+          0.011573358500827451,
+          0.01055288627227128,
+          0.013919824328950206,
+          0.021638741448914516,
+          0.035741090469564335,
+          0.045256564997544046,
+          0.06317665028094777,
+          0.08284782179980536
+        ],
+        "conc_latency_p95_list": [
+          0.00789401560141414,
+          0.007500608301234024,
+          0.010187717052576773,
+          0.017150824350028415,
+          0.026996734849308254,
+          0.03459080500033451,
+          0.04788784860138549,
+          0.0629504940006882
+        ],
+        "conc_latency_avg_list": [
+          0.0071435065296698895,
+          0.006726900525604191,
+          0.008019201478369918,
+          0.011268319014980386,
+          0.01541493476050156,
+          0.02000300411234457,
+          0.029459945652882687,
+          0.039275078762117686
+        ],
+        "st_ideal_insert_duration": 0,
+        "st_search_stage_list": [],
+        "st_search_time_list": [],
+        "st_max_qps_list_list": [],
+        "st_recall_list": [],
+        "st_ndcg_list": [],
+        "st_serial_latency_p99_list": [],
+        "st_serial_latency_p95_list": [],
+        "st_conc_failed_rate_list": [],
+        "st_conc_num_list_list": [],
+        "st_conc_qps_list_list": [],
+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": []
+      },
+      "task_config": {
+        "db": "ElasticCloud",
+        "db_config": {
+          "db_label": "8c60g-force_merge",
+          "version": "8.17",
+          "note": "",
+          "cloud_id": "**********",
+          "password": "**********"
+        },
+        "db_case_config": {
+          "element_type": "float",
+          "index": "hnsw",
+          "number_of_shards": 1,
+          "number_of_replicas": 0,
+          "refresh_interval": "30s",
+          "merge_max_thread_count": 8,
+          "use_rescore": false,
+          "oversample_ratio": 2.0,
+          "use_routing": false,
+          "use_force_merge": true,
+          "metric_type": "COSINE",
+          "efConstruction": 256,
+          "M": 16,
+          "num_candidates": 200
+        },
+        "case_config": {
+          "case_id": 5,
+          "custom_case": {},
+          "k": 100,
+          "concurrency_search_config": {
+            "num_concurrency": [
+              1,
+              5,
+              10,
+              20,
+              30,
+              40,
+              60,
+              80
+            ],
+            "concurrency_duration": 30,
+            "concurrency_timeout": 3600
+          }
+        },
+        "stages": [
+          "drop_old",
+          "load",
+          "search_serial",
+          "search_concurrent"
+        ]
+      },
+      "label": ":)"
+    },
+    {
+      "metrics": {
+        "max_load_count": 0,
+        "insert_duration": 3846.0365,
+        "optimize_duration": 1110.4687,
+        "load_duration": 4956.5052,
+        "qps": 1804.8996,
+        "serial_latency_p99": 0.0123,
+        "serial_latency_p95": 0.0079,
+        "recall": 0.9365,
+        "ndcg": 0.9405,
+        "conc_num_list": [
+          1,
+          5,
+          10,
+          20,
+          30,
+          40,
+          60,
+          80
+        ],
+        "conc_qps_list": [
+          128.0906,
+          646.8272,
+          1197.2061,
+          1629.7064,
+          1728.435,
+          1774.0467,
+          1804.8996,
+          1802.14
+        ],
+        "conc_latency_p99_list": [
+          0.0134131232793152,
+          0.012170731302467179,
+          0.01435177448063773,
+          0.023948891401232696,
+          0.038592138251187846,
+          0.04927967675830586,
+          0.06325175963895165,
+          0.08297362611905544
+        ],
+        "conc_latency_p95_list": [
+          0.008920073449553456,
+          0.008726374499019585,
+          0.011022335800225845,
+          0.019354423999175197,
+          0.029856205349278752,
+          0.03790496999936294,
+          0.05230563919976702,
+          0.06659373179936665
+        ],
+        "conc_latency_avg_list": [
+          0.007804807226078364,
+          0.007726593180097387,
+          0.008348314213725788,
+          0.01226467845434359,
+          0.017345920270812453,
+          0.022531855009387067,
+          0.033063582489267676,
+          0.04425664062925568
+        ],
+        "st_ideal_insert_duration": 0,
+        "st_search_stage_list": [],
+        "st_search_time_list": [],
+        "st_max_qps_list_list": [],
+        "st_recall_list": [],
+        "st_ndcg_list": [],
+        "st_serial_latency_p99_list": [],
+        "st_serial_latency_p95_list": [],
+        "st_conc_failed_rate_list": [],
+        "st_conc_num_list_list": [],
+        "st_conc_qps_list_list": [],
+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": []
+      },
+      "task_config": {
+        "db": "ElasticCloud",
+        "db_config": {
+          "db_label": "8c60g-force_merge",
+          "version": "8.17",
+          "note": "",
+          "cloud_id": "**********",
+          "password": "**********"
+        },
+        "db_case_config": {
+          "element_type": "float",
+          "index": "hnsw",
+          "number_of_shards": 1,
+          "number_of_replicas": 0,
+          "refresh_interval": "30s",
+          "merge_max_thread_count": 8,
+          "use_rescore": false,
+          "oversample_ratio": 2.0,
+          "use_routing": false,
+          "use_force_merge": true,
+          "metric_type": "COSINE",
+          "efConstruction": 256,
+          "M": 16,
+          "num_candidates": 250
+        },
+        "case_config": {
+          "case_id": 5,
+          "custom_case": {},
+          "k": 100,
+          "concurrency_search_config": {
+            "num_concurrency": [
+              1,
+              5,
+              10,
+              20,
+              30,
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+            "concurrency_timeout": 3600
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+        },
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+      "label": ":)"
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+        "st_serial_latency_p99_list": [],
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+        "st_conc_num_list_list": [],
+        "st_conc_qps_list_list": [],
+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": []
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+      "task_config": {
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+        "case_config": {
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+          "concurrency_search_config": {
+            "num_concurrency": [
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+              5,
+              10,
+              20,
+              30,
+              40,
+              60,
+              80
+            ],
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+            "concurrency_timeout": 3600
+          }
+        },
+        "stages": [
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+          "load",
+          "search_serial",
+          "search_concurrent"
+        ]
+      },
+      "label": ":)"
+    }
+  ],
+  "file_fmt": "result_{}_{}_{}.json",
+  "timestamp": 1770595200.0
+}
\ No newline at end of file
diff --git a/vectordb_bench/results/FullTextSearch/ElasticCloud/result_20260626_fts_standard_elasticcloud.json b/vectordb_bench/results/FullTextSearch/ElasticCloud/result_20260626_fts_standard_elasticcloud.json
new file mode 100644
index 000000000..1ac7e9773
--- /dev/null
+++ b/vectordb_bench/results/FullTextSearch/ElasticCloud/result_20260626_fts_standard_elasticcloud.json
@@ -0,0 +1,2326 @@
+{
+  "run_id": "fts_standard_elasticcloud",
+  "task_label": "fts_standard",
+  "results": [
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+        "insert_duration": 92.7146,
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+        "additional_parameters": {
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+              "b": 0.75,
+              "avgdl": 47.43239876568051
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+          "applied_analyzer_params": {},
+          "unapplied_analyzer_params": {
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+              "lowercase"
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+            "tokenizer": "standard"
+          }
+        },
+        "st_ideal_insert_duration": 0,
+        "st_search_stage_list": [],
+        "st_search_time_list": [],
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+        "st_serial_latency_p99_list": [],
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+        "st_conc_qps_list_list": [],
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+        "st_conc_latency_p95_list_list": [],
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+      "task_config": {
+        "db": "ElasticCloud",
+        "db_config": {
+          "db_label": "2026-06-23T17:49:51.080771",
+          "version": "",
+          "note": "{\n  \"schema\": \"vdbbench-run-context/v1\",\n  \"profile\": \"elasticsearch/v1\",\n  \"backend_version\": \"9.4.3\",\n  \"deployment\": {\n    \"method\": \"docker\",\n    \"config\": {\n      \"mode\": \"single_node\"\n    }\n  },\n  \"server_groups\": [\n    {\n      \"role\": \"elasticsearch-data-master\",\n      \"count\": 1,\n      \"jvm_heap_gib\": 30,\n      \"hardware\": {\n        \"machine_type\": \"i8g.4xlarge\",\n        \"cpu_count\": 16,\n        \"memory_gib\": 123.54,\n        \"storage\": {\n          \"type\": \"local_nvme\",\n          \"device_count\": 1,\n          \"total_gib\": 3492.46,\n          \"model\": \"Amazon EC2 NVMe Instance Storage\"\n        }\n      }\n    }\n  ],\n  \"client\": {\n    \"machine_type\": \"i8g.2xlarge\",\n    \"cpu_count\": 8,\n    \"memory_gib\": 61.65,\n    \"storage\": {\n      \"type\": \"ebs\",\n      \"device_count\": 1,\n      \"total_gib\": 500,\n      \"model\": \"Amazon Elastic Block Store\"\n    }\n  }\n}",
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+}
diff --git a/vectordb_bench/results/FullTextSearch/OpenSearch/result_20260708_fts_standard_opensearch.json b/vectordb_bench/results/FullTextSearch/OpenSearch/result_20260708_fts_standard_opensearch.json
new file mode 100644
index 000000000..d30aa6227
--- /dev/null
+++ b/vectordb_bench/results/FullTextSearch/OpenSearch/result_20260708_fts_standard_opensearch.json
@@ -0,0 +1,2100 @@
+{
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+          "user": "",
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+            "payload_profile": "ids_only",
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+              80
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+          "search_concurrent",
+          "search_serial"
+        ],
+        "load_concurrency": 0
+      },
+      "label": ":)"
+    }
+  ],
+  "file_fmt": "result_{}_{}_{}.json",
+  "timestamp": 1783468800.0
+}
diff --git a/vectordb_bench/results/FullTextSearch/TurboPuffer/result_20260626_fts_standard_turbopuffer.json b/vectordb_bench/results/FullTextSearch/TurboPuffer/result_20260626_fts_standard_turbopuffer.json
new file mode 100644
index 000000000..76dd16e30
--- /dev/null
+++ b/vectordb_bench/results/FullTextSearch/TurboPuffer/result_20260626_fts_standard_turbopuffer.json
@@ -0,0 +1,2312 @@
+{
+  "run_id": "fts_standard_turbopuffer",
+  "task_label": "fts_standard",
+  "results": [
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+        "insert_duration": 1464.6515,
+        "optimize_duration": 60.0477,
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+        "serial_latency_p99": 0.044,
+        "serial_latency_p95": 0.0329,
+        "recall": 0.7745,
+        "ndcg": 0.6371,
+        "conc_num_list": [
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+          60,
+          80
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+        "inserted_count": 5233329,
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+              "avgdl": 47.43239876568051
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+            "avgdl": 47.43239876568051
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+              "lowercase"
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+        "st_conc_num_list_list": [],
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+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": [],
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+        "db_config": {
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+          "version": "",
+          "note": "{\n  \"schema\": \"vdbbench-turbopuffer-context/v1\",\n  \"backend\": {\n    \"name\": \"TurboPuffer\",\n    \"deployment\": \"managed_service\",\n    \"region\": \"aws-us-west-2\",\n    \"api_base_url\": \"https://aws-us-west-2.turbopuffer.com\",\n    \"service_version\": \"not_exposed\"\n  },\n  \"provenance\": {\n    \"capture\": \"retrospective\",\n    \"basis\": \"User confirmed filtered and unfiltered FTS cases used the same backend setup.\"\n  }\n}",
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+          "region": "aws-us-west-2",
+          "api_base_url": null,
+          "namespace": "vdbbench-hotpotqa-large-c20-b1000-20260624T105757Z",
+          "multitenant_namespace_prefix": "vdbbench_mt_",
+          "scalar_payload_label_field": "label",
+          "pin_namespace": false,
+          "pin_namespace_requested": false,
+          "pin_replicas": 1,
+          "pin_timeout": 2700,
+          "pin_target_namespace_count": 0
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+        "db_case_config": {
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+          "time_wait_warmup": 60,
+          "disable_backpressure": false
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+        "case_config": {
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+          "custom_case": {
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+          "search_serial",
+          "search_concurrent"
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+            "filter_rate": 0.9
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+            "concurrency_timeout": 3600,
+            "serial_cooldown": 0.0
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+            "filter_rate": 0.95,
+            "filter_id_distribution": "affine_permutation_v1",
+            "filter_id_multiplier": 5464547,
+            "filter_id_offset": 1684563,
+            "matched_doc_count": 442092,
+            "matched_doc_ratio": 0.05,
+            "original_query_count": 6980,
+            "filtered_query_count": 347,
+            "filtered_query_ratio": 0.049713,
+            "original_relevant_doc_count": 7433,
+            "filtered_relevant_doc_count": 347
+          },
+          "fts_recall": {
+            "skipped": false,
+            "reason": null,
+            "serial_query_count": 347,
+            "full_query_count": 6980
+          },
+          "serial_measurement": {
+            "composed_from_separate_run": true,
+            "metric_fields": [
+              "serial_latency_p99",
+              "serial_latency_p95",
+              "recall",
+              "ndcg",
+              "mrr"
+            ],
+            "source_run_id": "6a289aff37334e4b8146e6279ed4e0e0",
+            "source_task_label": "fts_filtered_serial_turbopuffer_msmarco-large_r95_permuted",
+            "source_timestamp": 1785801600.0,
+            "source_file": "result_20260804_fts_filtered_serial_turbopuffer_msmarco-large_r95_permuted_turbopuffer.json",
+            "source_sha256": "4ba5221ba7b9294eef13e5adde96d3d16f65e278938e3b44e6b160cd2bd7b25f",
+            "source_db_label": "turbopuffer-aws-us-west-2-filtered-permuted-serial",
+            "source_stages": [
+              "search_serial"
+            ],
+            "source_note": "{\n  \"schema\": \"vdbbench-turbopuffer-context/v1\",\n  \"backend\": {\n    \"name\": \"TurboPuffer\",\n    \"deployment\": \"managed_service\",\n    \"region\": \"aws-us-west-2\",\n    \"api_base_url\": \"https://aws-us-west-2.turbopuffer.com\",\n    \"service_version\": \"not_exposed\",\n    \"sdk_version\": \"2.6.0\"\n  },\n  \"client\": {\n    \"branch\": \"fts_v2_backend\",\n    \"commit\": \"235ac46886e58d48f9561fb8c39ef913fa3e71d7\",\n    \"machine_type\": \"i8g.2xlarge\",\n    \"cpu_count\": 8,\n    \"memory_kib\": 64648904,\n    \"working_tree_clean\": false,\n    \"working_tree_scope\": \"pre-existing FullTextSearch result JSON note edits only\"\n  },\n  \"execution\": {\n    \"credential_delivery\": \"ephemeral mode-600 config outside logs and report bundle; removed by trap\",\n    \"namespace_strategy\": \"create_new_per_dataset_and_reuse_across_filter_rates\",\n    \"retain_namespace_after_run\": true,\n    \"pin_namespace\": false,\n    \"pin_replicas\": 1,\n    \"sdk_max_retries\": 4,\n    \"write_backpressure_enabled\": true,\n    \"warmup_seconds_after_load\": 60,\n    \"insert_batch_size\": 1000,\n    \"load_concurrency\": 4\n  },\n  \"validation\": {\n    \"credentials_stored\": false,\n    \"namespace_list_http_status\": 200,\n    \"target_namespace_precondition\": \"must_not_exist\",\n    \"target_namespaces_absent\": true,\n    \"verified_at\": \"2026-08-03T12:05:22Z\",\n    \"datasets\": [\n      \"HotpotQA Large (5.2M documents)\",\n      \"MS MARCO Large (8.8M documents)\"\n    ]\n  }\n}"
+          }
+        },
+        "st_ideal_insert_duration": 0,
+        "st_search_stage_list": [],
+        "st_search_time_list": [],
+        "st_max_qps_list_list": [],
+        "st_recall_list": [],
+        "st_ndcg_list": [],
+        "st_serial_latency_p99_list": [],
+        "st_serial_latency_p95_list": [],
+        "st_conc_failed_rate_list": [],
+        "st_conc_num_list_list": [],
+        "st_conc_qps_list_list": [],
+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": []
+      },
+      "task_config": {
+        "db": "TurboPuffer",
+        "db_config": {
+          "db_label": "turbopuffer-aws-us-west-2-filtered-permuted",
+          "version": "",
+          "note": "{\n  \"schema\": \"vdbbench-turbopuffer-context/v1\",\n  \"backend\": {\n    \"name\": \"TurboPuffer\",\n    \"deployment\": \"managed_service\",\n    \"service_version\": \"not_exposed\",\n    \"region\": \"aws-us-west-2\",\n    \"api_base_url\": \"https://aws-us-west-2.turbopuffer.com\",\n    \"sdk_version\": \"2.6.0\"\n  },\n  \"client\": {\n    \"machine_type\": \"i8g.2xlarge\",\n    \"cpu_count\": 8,\n    \"memory_kib\": 64648904,\n    \"branch\": \"fts_v2_backend\",\n    \"commit\": \"07bcefdbd5b04f51faebfc857219061d46148b6b\",\n    \"working_tree_clean\": true\n  },\n  \"execution\": {\n    \"pin_namespace\": false,\n    \"pin_replicas\": 1,\n    \"write_backpressure_enabled\": true,\n    \"sdk_max_retries\": 4,\n    \"warmup_seconds_after_load\": 60,\n    \"credential_delivery\": \"ephemeral mode-600 config outside logs and report bundle; removed by trap\"\n  },\n  \"validation\": {\n    \"namespace_list_http_status\": 200,\n    \"existing_namespace_metadata_http_status\": 200,\n    \"historical_capacity_evidence\": {\n      \"datasets\": [\n        \"HotpotQA Large (5.2M documents)\",\n        \"MS MARCO Large (8.8M documents)\"\n      ],\n      \"batch_size\": 1000\n    },\n    \"credentials_stored\": false\n  }\n}",
+          "api_key": "**********",
+          "region": "aws-us-west-2",
+          "api_base_url": null,
+          "namespace": "vdbbench-fts-filter-tpuf-msmarco-large-permuted-20260802",
+          "multitenant_namespace_prefix": "vdbbench_mt_",
+          "scalar_payload_label_field": "label",
+          "pin_namespace": false,
+          "pin_namespace_requested": false,
+          "pin_replicas": 1,
+          "pin_timeout": 2700,
+          "pin_target_namespace_count": 0
+        },
+        "db_case_config": {
+          "metric_type": "BM25",
+          "time_wait_warmup": 60,
+          "disable_backpressure": false
+        },
+        "case_config": {
+          "case_id": 503,
+          "custom_case": {
+            "dataset_with_size_type": "MS MARCO Large (8.8M documents)",
+            "payload_profile": "ids_only",
+            "filter_rate": 0.95
+          },
+          "k": 100,
+          "concurrency_search_config": {
+            "num_concurrency": [
+              60,
+              80
+            ],
+            "concurrency_duration": 30,
+            "concurrency_timeout": 3600,
+            "serial_cooldown": 0.0
+          }
+        },
+        "stages": [
+          "search_concurrent",
+          "search_serial"
+        ],
+        "load_concurrency": 0
+      },
+      "label": ":)"
+    },
+    {
+      "metrics": {
+        "max_load_count": 0,
+        "insert_duration": 0.0,
+        "optimize_duration": 0.0,
+        "load_duration": 0.0,
+        "qps": 292.0949,
+        "serial_latency_p99": 0.0592,
+        "serial_latency_p95": 0.0491,
+        "recall": 0.9403,
+        "ndcg": 0.7061,
+        "mrr": 0.6404,
+        "conc_num_list": [
+          60,
+          80
+        ],
+        "conc_qps_list": [
+          292.0949,
+          288.0392
+        ],
+        "conc_latency_p99_list": [
+          2.2982839281494307,
+          2.3517081442788914
+        ],
+        "conc_latency_p95_list": [
+          1.097375179249866,
+          1.1113469616489966
+        ],
+        "conc_latency_avg_list": [
+          0.20162277745392373,
+          0.26459405379449835
+        ],
+        "payload_profile": "ids_only",
+        "payload_estimated_bytes_per_query": 2000,
+        "inserted_count": 0,
+        "insert_rows_per_second": 0.0,
+        "insert_completion_seconds": 0.0,
+        "searchable_after_insert_seconds": 0.0,
+        "indexed_after_searchable_seconds": 0.0,
+        "additional_parameters": {
+          "fts_filter": {
+            "filter_type": "NumGE",
+            "filter_field": "filter_id",
+            "filter_value": 8753404,
+            "filter_rate": 0.99,
+            "filter_id_distribution": "affine_permutation_v1",
+            "filter_id_multiplier": 5464547,
+            "filter_id_offset": 1684563,
+            "matched_doc_count": 88419,
+            "matched_doc_ratio": 0.01,
+            "original_query_count": 6980,
+            "filtered_query_count": 67,
+            "filtered_query_ratio": 0.009599,
+            "original_relevant_doc_count": 7433,
+            "filtered_relevant_doc_count": 67
+          },
+          "fts_recall": {
+            "skipped": false,
+            "reason": null,
+            "serial_query_count": 67,
+            "full_query_count": 6980
+          },
+          "serial_measurement": {
+            "composed_from_separate_run": true,
+            "metric_fields": [
+              "serial_latency_p99",
+              "serial_latency_p95",
+              "recall",
+              "ndcg",
+              "mrr"
+            ],
+            "source_run_id": "753531e38a8b4fae91339d21ba00d7eb",
+            "source_task_label": "fts_filtered_serial_turbopuffer_msmarco-large_r99_permuted",
+            "source_timestamp": 1785801600.0,
+            "source_file": "result_20260804_fts_filtered_serial_turbopuffer_msmarco-large_r99_permuted_turbopuffer.json",
+            "source_sha256": "fa8e826ecb4e227835341e63ca2fc006ba68df6f6e2721887ba680db935ba319",
+            "source_db_label": "turbopuffer-aws-us-west-2-filtered-permuted-serial",
+            "source_stages": [
+              "search_serial"
+            ],
+            "source_note": "{\n  \"schema\": \"vdbbench-turbopuffer-context/v1\",\n  \"backend\": {\n    \"name\": \"TurboPuffer\",\n    \"deployment\": \"managed_service\",\n    \"region\": \"aws-us-west-2\",\n    \"api_base_url\": \"https://aws-us-west-2.turbopuffer.com\",\n    \"service_version\": \"not_exposed\",\n    \"sdk_version\": \"2.6.0\"\n  },\n  \"client\": {\n    \"branch\": \"fts_v2_backend\",\n    \"commit\": \"235ac46886e58d48f9561fb8c39ef913fa3e71d7\",\n    \"machine_type\": \"i8g.2xlarge\",\n    \"cpu_count\": 8,\n    \"memory_kib\": 64648904,\n    \"working_tree_clean\": false,\n    \"working_tree_scope\": \"pre-existing FullTextSearch result JSON note edits only\"\n  },\n  \"execution\": {\n    \"credential_delivery\": \"ephemeral mode-600 config outside logs and report bundle; removed by trap\",\n    \"namespace_strategy\": \"create_new_per_dataset_and_reuse_across_filter_rates\",\n    \"retain_namespace_after_run\": true,\n    \"pin_namespace\": false,\n    \"pin_replicas\": 1,\n    \"sdk_max_retries\": 4,\n    \"write_backpressure_enabled\": true,\n    \"warmup_seconds_after_load\": 60,\n    \"insert_batch_size\": 1000,\n    \"load_concurrency\": 4\n  },\n  \"validation\": {\n    \"credentials_stored\": false,\n    \"namespace_list_http_status\": 200,\n    \"target_namespace_precondition\": \"must_not_exist\",\n    \"target_namespaces_absent\": true,\n    \"verified_at\": \"2026-08-03T12:05:22Z\",\n    \"datasets\": [\n      \"HotpotQA Large (5.2M documents)\",\n      \"MS MARCO Large (8.8M documents)\"\n    ]\n  }\n}"
+          }
+        },
+        "st_ideal_insert_duration": 0,
+        "st_search_stage_list": [],
+        "st_search_time_list": [],
+        "st_max_qps_list_list": [],
+        "st_recall_list": [],
+        "st_ndcg_list": [],
+        "st_serial_latency_p99_list": [],
+        "st_serial_latency_p95_list": [],
+        "st_conc_failed_rate_list": [],
+        "st_conc_num_list_list": [],
+        "st_conc_qps_list_list": [],
+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": []
+      },
+      "task_config": {
+        "db": "TurboPuffer",
+        "db_config": {
+          "db_label": "turbopuffer-aws-us-west-2-filtered-permuted",
+          "version": "",
+          "note": "{\n  \"schema\": \"vdbbench-turbopuffer-context/v1\",\n  \"backend\": {\n    \"name\": \"TurboPuffer\",\n    \"deployment\": \"managed_service\",\n    \"service_version\": \"not_exposed\",\n    \"region\": \"aws-us-west-2\",\n    \"api_base_url\": \"https://aws-us-west-2.turbopuffer.com\",\n    \"sdk_version\": \"2.6.0\"\n  },\n  \"client\": {\n    \"machine_type\": \"i8g.2xlarge\",\n    \"cpu_count\": 8,\n    \"memory_kib\": 64648904,\n    \"branch\": \"fts_v2_backend\",\n    \"commit\": \"07bcefdbd5b04f51faebfc857219061d46148b6b\",\n    \"working_tree_clean\": true\n  },\n  \"execution\": {\n    \"pin_namespace\": false,\n    \"pin_replicas\": 1,\n    \"write_backpressure_enabled\": true,\n    \"sdk_max_retries\": 4,\n    \"warmup_seconds_after_load\": 60,\n    \"credential_delivery\": \"ephemeral mode-600 config outside logs and report bundle; removed by trap\"\n  },\n  \"validation\": {\n    \"namespace_list_http_status\": 200,\n    \"existing_namespace_metadata_http_status\": 200,\n    \"historical_capacity_evidence\": {\n      \"datasets\": [\n        \"HotpotQA Large (5.2M documents)\",\n        \"MS MARCO Large (8.8M documents)\"\n      ],\n      \"batch_size\": 1000\n    },\n    \"credentials_stored\": false\n  }\n}",
+          "api_key": "**********",
+          "region": "aws-us-west-2",
+          "api_base_url": null,
+          "namespace": "vdbbench-fts-filter-tpuf-msmarco-large-permuted-20260802",
+          "multitenant_namespace_prefix": "vdbbench_mt_",
+          "scalar_payload_label_field": "label",
+          "pin_namespace": false,
+          "pin_namespace_requested": false,
+          "pin_replicas": 1,
+          "pin_timeout": 2700,
+          "pin_target_namespace_count": 0
+        },
+        "db_case_config": {
+          "metric_type": "BM25",
+          "time_wait_warmup": 60,
+          "disable_backpressure": false
+        },
+        "case_config": {
+          "case_id": 503,
+          "custom_case": {
+            "dataset_with_size_type": "MS MARCO Large (8.8M documents)",
+            "payload_profile": "ids_only",
+            "filter_rate": 0.99
+          },
+          "k": 100,
+          "concurrency_search_config": {
+            "num_concurrency": [
+              60,
+              80
+            ],
+            "concurrency_duration": 30,
+            "concurrency_timeout": 3600,
+            "serial_cooldown": 0.0
+          }
+        },
+        "stages": [
+          "search_concurrent",
+          "search_serial"
+        ],
+        "load_concurrency": 0
+      },
+      "label": ":)"
+    }
+  ],
+  "file_fmt": "result_{}_{}_{}.json",
+  "timestamp": 1782259200.0
+}
diff --git a/vectordb_bench/results/FullTextSearch/ZillizCloud/result_20260626_fts_standard_zillizcloud.json b/vectordb_bench/results/FullTextSearch/ZillizCloud/result_20260626_fts_standard_zillizcloud.json
new file mode 100644
index 000000000..6bf69df97
--- /dev/null
+++ b/vectordb_bench/results/FullTextSearch/ZillizCloud/result_20260626_fts_standard_zillizcloud.json
@@ -0,0 +1,2060 @@
+{
+  "run_id": "fts_standard_zillizcloud",
+  "task_label": "fts_standard",
+  "results": [
+    {
+      "metrics": {
+        "max_load_count": 0,
+        "insert_duration": 92.5443,
+        "optimize_duration": 81.9515,
+        "load_duration": 174.4958,
+        "qps": 1763.3502,
+        "serial_latency_p99": 0.0331,
+        "serial_latency_p95": 0.0222,
+        "recall": 0.7674,
+        "ndcg": 0.6265,
+        "mrr": 0.7574,
+        "conc_num_list": [
+          40,
+          60,
+          80
+        ],
+        "conc_qps_list": [
+          1738.3381,
+          1760.0975,
+          1763.3502
+        ],
+        "conc_latency_p99_list": [
+          0.05301692362816539,
+          0.06365443888615119,
+          0.07476101434847807
+        ],
+        "conc_latency_p95_list": [
+          0.03960793959267899,
+          0.05068928290565963,
+          0.061392078457720343
+        ],
+        "conc_latency_avg_list": [
+          0.02283351154340615,
+          0.0336278670182451,
+          0.044479376343180034
+        ],
+        "payload_profile": "ids_only",
+        "payload_estimated_bytes_per_query": 2000,
+        "inserted_count": 5233329,
+        "insert_rows_per_second": 0.0,
+        "insert_completion_seconds": 0.0,
+        "searchable_after_insert_seconds": 0.0,
+        "indexed_after_searchable_seconds": 0.0,
+        "additional_parameters": {},
+        "st_ideal_insert_duration": 0,
+        "st_search_stage_list": [],
+        "st_search_time_list": [],
+        "st_max_qps_list_list": [],
+        "st_recall_list": [],
+        "st_ndcg_list": [],
+        "st_serial_latency_p99_list": [],
+        "st_serial_latency_p95_list": [],
+        "st_conc_failed_rate_list": [],
+        "st_conc_num_list_list": [],
+        "st_conc_qps_list_list": [],
+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": []
+      },
+      "task_config": {
+        "db": "ZillizCloud",
+        "db_config": {
+          "db_label": "task15919-perf-8cu-oneseg-26-retest",
+          "version": "",
+          "note": "{\n  \"schema\": \"vdbbench-zilliz-cloud-context/v1\",\n  \"backend\": {\n    \"name\": \"ZillizCloud\",\n    \"region\": \"aws-us-west-2\",\n    \"cu\": 8,\n    \"replica_count\": 1,\n    \"instance_type\": \"performance\",\n    \"deployment_method\": \"kubernetes\",\n    \"deployment_mode\": \"standalone\",\n    \"server_role\": \"milvus-standalone-r1\",\n    \"server_count\": 1\n  },\n  \"client\": {\n    \"machine_type\": \"i8g.2xlarge\"\n  },\n  \"evidence\": {\n    \"source\": \"User-confirmed Task 15919 deployment shape; one persistent segment per benchmark collection validated by run preflight and post-run inventory\",\n    \"credential_storage\": \"external mode-600 secret file removed by runner cleanup\"\n  }\n}",
+          "uri": "**********",
+          "user": "root",
+          "password": "**********",
+          "token": "",
+          "num_shards": 1,
+          "collection_name": "vdbbench_fts_serial_hotpotqa_permuted_20260803"
+        },
+        "db_case_config": {
+          "index_type": "AUTOINDEX",
+          "metric_type": "BM25",
+          "inverted_index_algo": "DAAT_MAXSCORE",
+          "bm25_k1": null,
+          "bm25_b": null,
+          "analyzer_tokenizer": "standard",
+          "analyzer_enable_lowercase": true,
+          "analyzer_max_token_length": null,
+          "analyzer_stop_words": null,
+          "drop_ratio_search": null,
+          "level": 1
+        },
+        "case_config": {
+          "case_id": 503,
+          "custom_case": {
+            "dataset_with_size_type": "HotpotQA Large (5.2M documents)",
+            "payload_profile": "ids_only"
+          },
+          "k": 100,
+          "concurrency_search_config": {
+            "num_concurrency": [
+              40,
+              60,
+              80
+            ],
+            "concurrency_duration": 30,
+            "concurrency_timeout": 3600,
+            "serial_cooldown": 0.0
+          }
+        },
+        "stages": [
+          "search_serial",
+          "search_concurrent"
+        ],
+        "load_concurrency": 0
+      },
+      "label": ":)"
+    },
+    {
+      "metrics": {
+        "max_load_count": 0,
+        "insert_duration": 33.6028,
+        "optimize_duration": 171.902,
+        "load_duration": 205.5048,
+        "qps": 4913.8932,
+        "serial_latency_p99": 0.0107,
+        "serial_latency_p95": 0.0079,
+        "recall": 0.8421,
+        "ndcg": 0.7319,
+        "mrr": 0.8636,
+        "conc_num_list": [
+          40,
+          60,
+          80
+        ],
+        "conc_qps_list": [
+          4444.7978,
+          4823.2308,
+          4913.8932
+        ],
+        "conc_latency_p99_list": [
+          0.018735603306413395,
+          0.024391331560909748,
+          0.02949425330734808
+        ],
+        "conc_latency_p95_list": [
+          0.01459028425233555,
+          0.019086839698138645,
+          0.023468223997042514
+        ],
+        "conc_latency_avg_list": [
+          0.00892458813977406,
+          0.012230295266112242,
+          0.01590514856506513
+        ],
+        "payload_profile": "ids_only",
+        "payload_estimated_bytes_per_query": 2000,
+        "inserted_count": 1000000,
+        "insert_rows_per_second": 0.0,
+        "insert_completion_seconds": 0.0,
+        "searchable_after_insert_seconds": 0.0,
+        "indexed_after_searchable_seconds": 0.0,
+        "additional_parameters": {
+          "num_per_batch": 1000,
+          "load_concurrency": 4
+        },
+        "st_ideal_insert_duration": 0,
+        "st_search_stage_list": [],
+        "st_search_time_list": [],
+        "st_max_qps_list_list": [],
+        "st_recall_list": [],
+        "st_ndcg_list": [],
+        "st_serial_latency_p99_list": [],
+        "st_serial_latency_p95_list": [],
+        "st_conc_failed_rate_list": [],
+        "st_conc_num_list_list": [],
+        "st_conc_qps_list_list": [],
+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": []
+      },
+      "task_config": {
+        "db": "ZillizCloud",
+        "db_config": {
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+}
diff --git a/vectordb_bench/results/Milvus/result_20260403_standard_milvus.json b/vectordb_bench/results/Milvus/result_20260403_standard_milvus.json
new file mode 100644
index 000000000..56219eb1d
--- /dev/null
+++ b/vectordb_bench/results/Milvus/result_20260403_standard_milvus.json
@@ -0,0 +1,4074 @@
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+}
\ No newline at end of file
diff --git a/vectordb_bench/results/TurboPuffer/result_20260331_standard_2025_turbopuffer.json b/vectordb_bench/results/TurboPuffer/result_20260331_standard_2025_turbopuffer.json
new file mode 100644
index 000000000..3f6294f67
--- /dev/null
+++ b/vectordb_bench/results/TurboPuffer/result_20260331_standard_2025_turbopuffer.json
@@ -0,0 +1,3072 @@
+{
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+}
\ No newline at end of file
diff --git a/vectordb_bench/results/ZillizCloud/result_20260403_standard_zillizcloud.json b/vectordb_bench/results/ZillizCloud/result_20260403_standard_zillizcloud.json
new file mode 100644
index 000000000..1f18a729a
--- /dev/null
+++ b/vectordb_bench/results/ZillizCloud/result_20260403_standard_zillizcloud.json
@@ -0,0 +1,2150 @@
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+          "user": "root",
+          "password": "**********",
+          "collection_name": "ZillizCloudVDBBench"
+        },
+        "db_case_config": {
+          "index": "AUTOINDEX",
+          "metric_type": "COSINE",
+          "use_partition_key": false,
+          "level": 4,
+          "num_shards": 1
+        },
+        "case_config": {
+          "case_id": 4,
+          "custom_case": {},
+          "k": 100,
+          "concurrency_search_config": {
+            "num_concurrency": [
+              1,
+              5,
+              10,
+              20,
+              30,
+              40,
+              60,
+              80
+            ],
+            "concurrency_duration": 30,
+            "concurrency_timeout": 3600
+          }
+        },
+        "stages": [
+          "search_serial",
+          "search_concurrent"
+        ]
+      }
+    },
+    {
+      "metrics": {
+        "max_load_count": 0,
+        "insert_duration": 2451.8899,
+        "optimize_duration": 4862.256,
+        "load_duration": 7314.1459,
+        "qps": 5183.5843,
+        "serial_latency_p99": 0.0024,
+        "serial_latency_p95": 0.0023,
+        "recall": 0.9784,
+        "ndcg": 0.981,
+        "conc_num_list": [
+          1,
+          5,
+          10,
+          20,
+          30,
+          40,
+          60,
+          80
+        ],
+        "conc_qps_list": [
+          456.5329,
+          1821.263,
+          2677.2938,
+          3617.961,
+          4097.4679,
+          4447.7515,
+          4863.2316,
+          5183.5843
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+        "conc_latency_p99_list": [
+          0.002395828692242503,
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+          0.004636971018044278,
+          0.009431733234669086,
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+          0.01654091664124283,
+          0.022408935175044466,
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+        "conc_latency_p95_list": [
+          0.00232212619157508,
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+          0.0043217132915742695,
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+          0.01404695180244743,
+          0.01901522108237258,
+          0.022979849949479103
+        ],
+        "conc_latency_avg_list": [
+          0.0021874608272499383,
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+          0.003728350102575746,
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+          0.0072920914670729624,
+          0.008931282929280606,
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+        ],
+        "st_ideal_insert_duration": 0,
+        "st_search_stage_list": [],
+        "st_search_time_list": [],
+        "st_max_qps_list_list": [],
+        "st_recall_list": [],
+        "st_ndcg_list": [],
+        "st_serial_latency_p99_list": [],
+        "st_serial_latency_p95_list": [],
+        "st_conc_failed_rate_list": [],
+        "st_conc_num_list_list": [],
+        "st_conc_qps_list_list": [],
+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": []
+      },
+      "task_config": {
+        "db": "ZillizCloud",
+        "db_config": {
+          "db_label": "8cu-perf",
+          "version": "v2026.4",
+          "note": "",
+          "uri": "**********",
+          "user": "root",
+          "password": "**********",
+          "collection_name": "ZillizCloudVDBBench"
+        },
+        "db_case_config": {
+          "index": "AUTOINDEX",
+          "metric_type": "COSINE",
+          "use_partition_key": false,
+          "level": 5,
+          "num_shards": 1
+        },
+        "case_config": {
+          "case_id": 4,
+          "custom_case": {},
+          "k": 100,
+          "concurrency_search_config": {
+            "num_concurrency": [
+              1,
+              5,
+              10,
+              20,
+              30,
+              40,
+              60,
+              80
+            ],
+            "concurrency_duration": 30,
+            "concurrency_timeout": 3600
+          }
+        },
+        "stages": [
+          "search_serial",
+          "search_concurrent"
+        ]
+      }
+    },
+    {
+      "metrics": {
+        "max_load_count": 0,
+        "insert_duration": 2451.8899,
+        "optimize_duration": 4862.256,
+        "load_duration": 7314.1459,
+        "qps": 4259.5572,
+        "serial_latency_p99": 0.0026,
+        "serial_latency_p95": 0.0026,
+        "recall": 0.9845,
+        "ndcg": 0.9866,
+        "conc_num_list": [
+          1,
+          5,
+          10,
+          20,
+          30,
+          40,
+          60,
+          80
+        ],
+        "conc_qps_list": [
+          424.077,
+          1676.9478,
+          2434.6516,
+          3184.2011,
+          3571.9199,
+          3767.5544,
+          4061.1698,
+          4259.5572
+        ],
+        "conc_latency_p99_list": [
+          0.002595295326318592,
+          0.003621646617539228,
+          0.005367588647641237,
+          0.011171487247338519,
+          0.0155316284415312,
+          0.019644083841703817,
+          0.02703630503267048,
+          0.03310978163208347
+        ],
+        "conc_latency_p95_list": [
+          0.002518506458727643,
+          0.0034135113819502294,
+          0.004873151995707303,
+          0.009142277223872952,
+          0.013012332818470895,
+          0.016382047021761527,
+          0.022542749025160444,
+          0.027544378107995725
+        ],
+        "conc_latency_avg_list": [
+          0.00235500177392571,
+          0.0029770619258879757,
+          0.00410082317131261,
+          0.006266042699984648,
+          0.008367804647869575,
+          0.010561791085210772,
+          0.01460435324245386,
+          0.018471206884305532
+        ],
+        "st_ideal_insert_duration": 0,
+        "st_search_stage_list": [],
+        "st_search_time_list": [],
+        "st_max_qps_list_list": [],
+        "st_recall_list": [],
+        "st_ndcg_list": [],
+        "st_serial_latency_p99_list": [],
+        "st_serial_latency_p95_list": [],
+        "st_conc_failed_rate_list": [],
+        "st_conc_num_list_list": [],
+        "st_conc_qps_list_list": [],
+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": []
+      },
+      "task_config": {
+        "db": "ZillizCloud",
+        "db_config": {
+          "db_label": "8cu-perf",
+          "version": "v2026.4",
+          "note": "",
+          "uri": "**********",
+          "user": "root",
+          "password": "**********",
+          "collection_name": "ZillizCloudVDBBench"
+        },
+        "db_case_config": {
+          "index": "AUTOINDEX",
+          "metric_type": "COSINE",
+          "use_partition_key": false,
+          "level": 6,
+          "num_shards": 1
+        },
+        "case_config": {
+          "case_id": 4,
+          "custom_case": {},
+          "k": 100,
+          "concurrency_search_config": {
+            "num_concurrency": [
+              1,
+              5,
+              10,
+              20,
+              30,
+              40,
+              60,
+              80
+            ],
+            "concurrency_duration": 30,
+            "concurrency_timeout": 3600
+          }
+        },
+        "stages": [
+          "search_serial",
+          "search_concurrent"
+        ]
+      }
+    },
+    {
+      "metrics": {
+        "max_load_count": 0,
+        "insert_duration": 2451.8899,
+        "optimize_duration": 4862.256,
+        "load_duration": 7314.1459,
+        "qps": 3614.3118,
+        "serial_latency_p99": 0.0029,
+        "serial_latency_p95": 0.0028,
+        "recall": 0.9877,
+        "ndcg": 0.9894,
+        "conc_num_list": [
+          1,
+          5,
+          10,
+          20,
+          30,
+          40,
+          60,
+          80
+        ],
+        "conc_qps_list": [
+          397.2298,
+          1557.1023,
+          2236.3239,
+          2845.1058,
+          3146.0016,
+          3289.0762,
+          3520.8116,
+          3614.3118
+        ],
+        "conc_latency_p99_list": [
+          0.0027946092444472016,
+          0.003967489110655152,
+          0.00615017178060953,
+          0.0128463427053066,
+          0.017453623053152116,
+          0.022870610732934445,
+          0.029518100013956432,
+          0.03869990034552756
+        ],
+        "conc_latency_p95_list": [
+          0.0027128490241011606,
+          0.003724151346250437,
+          0.0054281810007523745,
+          0.01044328572170343,
+          0.01436160156154074,
+          0.01821845376980491,
+          0.024413908016867936,
+          0.031903167749987915
+        ],
+        "conc_latency_avg_list": [
+          0.0025143936207342324,
+          0.003206745084255346,
+          0.004464954355791834,
+          0.007013879446545796,
+          0.009503234791544602,
+          0.012096279585844553,
+          0.01685092675726034,
+          0.021781507836163158
+        ],
+        "st_ideal_insert_duration": 0,
+        "st_search_stage_list": [],
+        "st_search_time_list": [],
+        "st_max_qps_list_list": [],
+        "st_recall_list": [],
+        "st_ndcg_list": [],
+        "st_serial_latency_p99_list": [],
+        "st_serial_latency_p95_list": [],
+        "st_conc_failed_rate_list": [],
+        "st_conc_num_list_list": [],
+        "st_conc_qps_list_list": [],
+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": []
+      },
+      "task_config": {
+        "db": "ZillizCloud",
+        "db_config": {
+          "db_label": "8cu-perf",
+          "version": "v2026.4",
+          "note": "",
+          "uri": "**********",
+          "user": "root",
+          "password": "**********",
+          "collection_name": "ZillizCloudVDBBench"
+        },
+        "db_case_config": {
+          "index": "AUTOINDEX",
+          "metric_type": "COSINE",
+          "use_partition_key": false,
+          "level": 7,
+          "num_shards": 1
+        },
+        "case_config": {
+          "case_id": 4,
+          "custom_case": {},
+          "k": 100,
+          "concurrency_search_config": {
+            "num_concurrency": [
+              1,
+              5,
+              10,
+              20,
+              30,
+              40,
+              60,
+              80
+            ],
+            "concurrency_duration": 30,
+            "concurrency_timeout": 3600
+          }
+        },
+        "stages": [
+          "search_serial",
+          "search_concurrent"
+        ]
+      }
+    },
+    {
+      "metrics": {
+        "max_load_count": 0,
+        "insert_duration": 2451.8899,
+        "optimize_duration": 4862.256,
+        "load_duration": 7314.1459,
+        "qps": 3181.0537,
+        "serial_latency_p99": 0.003,
+        "serial_latency_p95": 0.0029,
+        "recall": 0.9892,
+        "ndcg": 0.9908,
+        "conc_num_list": [
+          1,
+          5,
+          10,
+          20,
+          30,
+          40,
+          60,
+          80
+        ],
+        "conc_qps_list": [
+          373.2168,
+          1461.7612,
+          2063.4765,
+          2583.7979,
+          2781.1167,
+          2908.4205,
+          3073.9858,
+          3181.0537
+        ],
+        "conc_latency_p99_list": [
+          0.003002641891944222,
+          0.004314229479641651,
+          0.007060362810152582,
+          0.01453221907839178,
+          0.01971446685201954,
+          0.024677592392545203,
+          0.032532848112750784,
+          0.04069755139702464
+        ],
+        "conc_latency_p95_list": [
+          0.0029138700163457544,
+          0.00403262600011658,
+          0.0060169674427015705,
+          0.011576333804987366,
+          0.01587381720310077,
+          0.019931912398897106,
+          0.027404746599495412,
+          0.03406435703218449
+        ],
+        "conc_latency_avg_list": [
+          0.002676176823972926,
+          0.0034160777606854604,
+          0.004838368703871575,
+          0.0077237947842559935,
+          0.010749740541723023,
+          0.013688260722494459,
+          0.01930447747175536,
+          0.024747977200758154
+        ],
+        "st_ideal_insert_duration": 0,
+        "st_search_stage_list": [],
+        "st_search_time_list": [],
+        "st_max_qps_list_list": [],
+        "st_recall_list": [],
+        "st_ndcg_list": [],
+        "st_serial_latency_p99_list": [],
+        "st_serial_latency_p95_list": [],
+        "st_conc_failed_rate_list": [],
+        "st_conc_num_list_list": [],
+        "st_conc_qps_list_list": [],
+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": []
+      },
+      "task_config": {
+        "db": "ZillizCloud",
+        "db_config": {
+          "db_label": "8cu-perf",
+          "version": "v2026.4",
+          "note": "",
+          "uri": "**********",
+          "user": "root",
+          "password": "**********",
+          "collection_name": "ZillizCloudVDBBench"
+        },
+        "db_case_config": {
+          "index": "AUTOINDEX",
+          "metric_type": "COSINE",
+          "use_partition_key": false,
+          "level": 8,
+          "num_shards": 1
+        },
+        "case_config": {
+          "case_id": 4,
+          "custom_case": {},
+          "k": 100,
+          "concurrency_search_config": {
+            "num_concurrency": [
+              1,
+              5,
+              10,
+              20,
+              30,
+              40,
+              60,
+              80
+            ],
+            "concurrency_duration": 30,
+            "concurrency_timeout": 3600
+          }
+        },
+        "stages": [
+          "search_serial",
+          "search_concurrent"
+        ]
+      }
+    },
+    {
+      "metrics": {
+        "max_load_count": 0,
+        "insert_duration": 2451.8899,
+        "optimize_duration": 4862.256,
+        "load_duration": 7314.1459,
+        "qps": 2804.8357,
+        "serial_latency_p99": 0.0033,
+        "serial_latency_p95": 0.0032,
+        "recall": 0.9903,
+        "ndcg": 0.9918,
+        "conc_num_list": [
+          1,
+          5,
+          10,
+          20,
+          30,
+          40,
+          60,
+          80
+        ],
+        "conc_qps_list": [
+          352.7729,
+          1367.945,
+          1917.0391,
+          2338.0313,
+          2510.7421,
+          2617.9646,
+          2727.8716,
+          2804.8357
+        ],
+        "conc_latency_p99_list": [
+          0.00323100520123262,
+          0.004680905669229106,
+          0.00799012375646271,
+          0.01590309410821647,
+          0.021047461275593433,
+          0.025625948917586353,
+          0.036803203278686886,
+          0.0467973963287659
+        ],
+        "conc_latency_p95_list": [
+          0.0031083543435670435,
+          0.0043611170200165365,
+          0.0067188970482675355,
+          0.0125031745119486,
+          0.017026124306721607,
+          0.021185590000823137,
+          0.03006786260521039,
+          0.03819325279910117
+        ],
+        "conc_latency_avg_list": [
+          0.002831514758067566,
+          0.003649909442997153,
+          0.00520858526610239,
+          0.00853725560951756,
+          0.011906267087115343,
+          0.015190065559178532,
+          0.021789805178996344,
+          0.0280490530042168
+        ],
+        "st_ideal_insert_duration": 0,
+        "st_search_stage_list": [],
+        "st_search_time_list": [],
+        "st_max_qps_list_list": [],
+        "st_recall_list": [],
+        "st_ndcg_list": [],
+        "st_serial_latency_p99_list": [],
+        "st_serial_latency_p95_list": [],
+        "st_conc_failed_rate_list": [],
+        "st_conc_num_list_list": [],
+        "st_conc_qps_list_list": [],
+        "st_conc_latency_p99_list_list": [],
+        "st_conc_latency_p95_list_list": [],
+        "st_conc_latency_avg_list_list": []
+      },
+      "task_config": {
+        "db": "ZillizCloud",
+        "db_config": {
+          "db_label": "8cu-perf",
+          "version": "v2026.4",
+          "note": "",
+          "uri": "**********",
+          "user": "root",
+          "password": "**********",
+          "collection_name": "ZillizCloudVDBBench"
+        },
+        "db_case_config": {
+          "index": "AUTOINDEX",
+          "metric_type": "COSINE",
+          "use_partition_key": false,
+          "level": 9,
+          "num_shards": 1
+        },
+        "case_config": {
+          "case_id": 4,
+          "custom_case": {},
+          "k": 100,
+          "concurrency_search_config": {
+            "num_concurrency": [
+              1,
+              5,
+              10,
+              20,
+              30,
+              40,
+              60,
+              80
+            ],
+            "concurrency_duration": 30,
+            "concurrency_timeout": 3600
+          }
+        },
+        "stages": [
+          "search_serial",
+          "search_concurrent"
+        ]
+      }
+    }
+  ],
+  "file_fmt": "result_{}_{}_{}.json",
+  "timestamp": 1770595200.0
+}
\ No newline at end of file
diff --git a/vectordb_bench/results/getLeaderboardData.py b/vectordb_bench/results/getLeaderboardData.py
index aef024bdc..1650a6f87 100644
--- a/vectordb_bench/results/getLeaderboardData.py
+++ b/vectordb_bench/results/getLeaderboardData.py
@@ -1,14 +1,16 @@
-from vectordb_bench import config
-import ujson
 import pathlib
+from datetime import datetime
+
+import ujson
+
+from vectordb_bench import config
 from vectordb_bench.backend.cases import CaseType
 from vectordb_bench.backend.clients import DB
 from vectordb_bench.frontend.config.dbPrices import DB_DBLABEL_TO_PRICE
 from vectordb_bench.interface import benchMarkRunner
 from vectordb_bench.models import ResultLabel, TestResult
-from datetime import datetime
 
-taskLabelToCode = {
+task_label_to_code = {
     ResultLabel.FAILED: -1,
     ResultLabel.OUTOFRANGE: -2,
     ResultLabel.NORMAL: 1,
@@ -18,15 +20,14 @@
 def format_time(ts: float) -> str:
     default_standard_test_time = datetime(2023, 8, 1)
     t = datetime.fromtimestamp(ts)
-    if t < default_standard_test_time:
-        t = default_standard_test_time
+    t = max(t, default_standard_test_time)
     return t.strftime("%Y-%m")
 
 
 def main():
-    allResults: list[TestResult] = benchMarkRunner.get_results()
+    all_results: list[TestResult] = benchMarkRunner.get_results()
 
-    if allResults is not None:
+    if all_results is not None:
         data = [
             {
                 "db": d.task_config.db.value,
@@ -36,18 +37,16 @@ def main():
                 "qps": d.metrics.qps,
                 "latency": d.metrics.serial_latency_p99,
                 "recall": d.metrics.recall,
-                "label": taskLabelToCode[d.label],
+                "label": task_label_to_code[d.label],
                 "note": d.task_config.db_config.note,
                 "version": d.task_config.db_config.version,
                 "test_time": format_time(test_result.timestamp),
             }
-            for test_result in allResults
+            for test_result in all_results
             if "standard" in test_result.task_label
             for d in test_result.results
-            if d.task_config.case_config.case_id != CaseType.CapacityDim128
-            and d.task_config.case_config.case_id != CaseType.CapacityDim960
-            if d.task_config.db != DB.ZillizCloud
-            or test_result.timestamp >= datetime(2024, 1, 1).timestamp()
+            if d.task_config.case_config.case_id not in {CaseType.CapacityDim128, CaseType.CapacityDim960}
+            if d.task_config.db != DB.ZillizCloud or test_result.timestamp >= datetime(2024, 1, 1).timestamp()
         ]
 
         # compute qp$
@@ -58,7 +57,7 @@ def main():
             price = DB_DBLABEL_TO_PRICE.get(db, {}).get(db_label, 0)
             d["qp$"] = (qps / price * 3600) if price > 0 else 0.0
 
-        with open(pathlib.Path(config.RESULTS_LOCAL_DIR, "leaderboard.json"), "w") as f:
+        with pathlib.Path(config.RESULTS_LOCAL_DIR, "leaderboard.json").open("w") as f:
             ujson.dump(data, f)
 
 
diff --git a/vectordb_bench/results/getLeaderboardDataV2.py b/vectordb_bench/results/getLeaderboardDataV2.py
index 62440886f..188d9876f 100644
--- a/vectordb_bench/results/getLeaderboardDataV2.py
+++ b/vectordb_bench/results/getLeaderboardDataV2.py
@@ -1,17 +1,14 @@
 import json
 import logging
+import pathlib
 
-
+from vectordb_bench import config
 from vectordb_bench.backend.cases import CaseType, StreamingPerformanceCase
-from vectordb_bench.backend.clients import DB
+from vectordb_bench.interface import BenchMarkRunner
 from vectordb_bench.models import CaseResult
-from vectordb_bench import config
-import numpy as np
 
 logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
 
-from vectordb_bench.interface import BenchMarkRunner
-
 
 def get_standard_2025_results() -> list[CaseResult]:
     all_results = BenchMarkRunner.get_results()
@@ -23,7 +20,7 @@ def get_standard_2025_results() -> list[CaseResult]:
 
 
 def save_to_json(data: list[dict], file_name: str):
-    with open(file_name, "w") as f:
+    with pathlib.Path(file_name).open("w") as f:
         json.dump(data, f, indent=4)
 
 
@@ -56,11 +53,11 @@ def main():
                 }
             )
         else:
-            case: StreamingPerformanceCase = case
+            streaming_case: StreamingPerformanceCase = case
             # use 90p search stage results to represent streaming performance
             qps_90p = metrics.st_max_qps_list_list[metrics.st_search_stage_list.index(90)]
             latency_90p = metrics.st_serial_latency_p99_list[metrics.st_search_stage_list.index(90)]
-            insert_rate = case.insert_rate
+            insert_rate = streaming_case.insert_rate
             streaming_data.append(
                 {
                     "dataset": dataset,
diff --git a/vectordb_bench/results/leaderboard_v2.json b/vectordb_bench/results/leaderboard_v2.json
index e4f8dece5..c987ac9e7 100644
--- a/vectordb_bench/results/leaderboard_v2.json
+++ b/vectordb_bench/results/leaderboard_v2.json
@@ -1,172 +1,12 @@
 [
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 597.3641,
-        "latency": 12.1,
-        "recall": 0.9221,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 551.1757,
-        "latency": 10.7,
-        "recall": 0.9327,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 492.9765,
-        "latency": 13.4,
-        "recall": 0.9443,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 431.0155,
-        "latency": 15.2,
-        "recall": 0.954,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 377.3634,
-        "latency": 16.2,
-        "recall": 0.9615,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 337.5819,
-        "latency": 15.0,
-        "recall": 0.9666,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 279.7257,
-        "latency": 18.1,
-        "recall": 0.9729,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g-routing-64shard",
-        "db_name": "ElasticCloud-8c60g-routing-64shard",
-        "qps": 3033.786,
-        "latency": 8.7,
-        "recall": 0.9934,
-        "filter_ratio": 0.999
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g-routing-64shard",
-        "db_name": "ElasticCloud-8c60g-routing-64shard",
-        "qps": 3019.2416,
-        "latency": 9.5,
-        "recall": 0.9765,
-        "filter_ratio": 0.998
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g-routing-64shard",
-        "db_name": "ElasticCloud-8c60g-routing-64shard",
-        "qps": 2890.9523,
-        "latency": 9.4,
-        "recall": 0.9625,
-        "filter_ratio": 0.995
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g-routing-64shard",
-        "db_name": "ElasticCloud-8c60g-routing-64shard",
-        "qps": 2789.7212,
-        "latency": 8.2,
-        "recall": 0.9538,
-        "filter_ratio": 0.99
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g-routing-64shard",
-        "db_name": "ElasticCloud-8c60g-routing-64shard",
-        "qps": 2457.2628,
-        "latency": 9.0,
-        "recall": 0.9378,
-        "filter_ratio": 0.98
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g-routing-64shard",
-        "db_name": "ElasticCloud-8c60g-routing-64shard",
-        "qps": 2209.4973,
-        "latency": 13.7,
-        "recall": 0.9228,
-        "filter_ratio": 0.95
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g-routing-64shard",
-        "db_name": "ElasticCloud-8c60g-routing-64shard",
-        "qps": 1960.388,
-        "latency": 11.0,
-        "recall": 0.9076,
-        "filter_ratio": 0.9
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g-routing-64shard",
-        "db_name": "ElasticCloud-8c60g-routing-64shard",
-        "qps": 1725.092,
-        "latency": 11.7,
-        "recall": 0.8969,
-        "filter_ratio": 0.8
-    },
-    {
-        "dataset": "Cohere (Medium)",
-        "db": "ElasticCloud",
-        "label": "8c60g-routing-64shard",
-        "db_name": "ElasticCloud-8c60g-routing-64shard",
-        "qps": 1307.419,
-        "latency": 12.3,
-        "recall": 0.8925,
-        "filter_ratio": 0.5
-    },
     {
         "dataset": "Cohere (Large)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 1520.4145,
-        "latency": 12.5,
-        "recall": 0.9028,
+        "qps": 2181.3939,
+        "latency": 9.4,
+        "recall": 0.8501,
         "filter_ratio": 0.0
     },
     {
@@ -174,9 +14,9 @@
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 1273.3452,
-        "latency": 12.3,
-        "recall": 0.9242,
+        "qps": 1721.5416,
+        "latency": 9.6,
+        "recall": 0.8855,
         "filter_ratio": 0.0
     },
     {
@@ -184,9 +24,9 @@
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 1011.7943,
-        "latency": 15.2,
-        "recall": 0.945,
+        "qps": 1452.1536,
+        "latency": 10.8,
+        "recall": 0.9042,
         "filter_ratio": 0.0
     },
     {
@@ -194,9 +34,9 @@
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 824.5097,
-        "latency": 15.5,
-        "recall": 0.9558,
+        "qps": 1295.5543,
+        "latency": 11.2,
+        "recall": 0.9176,
         "filter_ratio": 0.0
     },
     {
@@ -204,50 +44,50 @@
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 350.0132,
-        "latency": 29.7,
-        "recall": 1.0,
-        "filter_ratio": 0.999
+        "qps": 1150.4393,
+        "latency": 13.4,
+        "recall": 0.9265,
+        "filter_ratio": 0.0
     },
     {
         "dataset": "Cohere (Large)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 179.5204,
-        "latency": 51.4,
-        "recall": 1.0,
-        "filter_ratio": 0.998
+        "qps": 1032.9696,
+        "latency": 14.8,
+        "recall": 0.933,
+        "filter_ratio": 0.0
     },
     {
         "dataset": "Cohere (Large)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 72.99,
-        "latency": 111.4,
-        "recall": 1.0,
-        "filter_ratio": 0.995
+        "qps": 125.6164,
+        "latency": 69.8,
+        "recall": 0.8746,
+        "filter_ratio": 0.5
     },
     {
         "dataset": "Cohere (Large)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 42.9877,
-        "latency": 201.9,
-        "recall": 0.9912,
-        "filter_ratio": 0.99
+        "qps": 229.0379,
+        "latency": 43.0,
+        "recall": 0.8908,
+        "filter_ratio": 0.8
     },
     {
         "dataset": "Cohere (Large)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 96.4987,
-        "latency": 113.1,
-        "recall": 0.9296,
-        "filter_ratio": 0.98
+        "qps": 246.7071,
+        "latency": 45.1,
+        "recall": 0.9018,
+        "filter_ratio": 0.9
     },
     {
         "dataset": "Cohere (Large)",
@@ -264,129 +104,59 @@
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 246.7071,
-        "latency": 45.1,
-        "recall": 0.9018,
-        "filter_ratio": 0.9
+        "qps": 96.4987,
+        "latency": 113.1,
+        "recall": 0.9296,
+        "filter_ratio": 0.98
     },
     {
         "dataset": "Cohere (Large)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 229.0379,
-        "latency": 43.0,
-        "recall": 0.8908,
-        "filter_ratio": 0.8
+        "qps": 42.9877,
+        "latency": 201.9,
+        "recall": 0.9912,
+        "filter_ratio": 0.99
     },
     {
         "dataset": "Cohere (Large)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 125.6164,
-        "latency": 69.8,
-        "recall": 0.8746,
-        "filter_ratio": 0.5
-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 376.3752,
-        "latency": 14.5,
-        "recall": 0.9039,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 341.2325,
-        "latency": 13.4,
-        "recall": 0.9136,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 300.7678,
-        "latency": 12.8,
-        "recall": 0.922,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 257.0398,
-        "latency": 15.4,
-        "recall": 0.9303,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 228.7734,
-        "latency": 16.2,
-        "recall": 0.9374,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 204.3654,
-        "latency": 18.2,
-        "recall": 0.9424,
-        "filter_ratio": 0.0
-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 167.5075,
-        "latency": 18.0,
-        "recall": 0.9501,
-        "filter_ratio": 0.0
+        "qps": 72.99,
+        "latency": 111.4,
+        "recall": 1.0,
+        "filter_ratio": 0.995
     },
     {
         "dataset": "Cohere (Large)",
         "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 146.339,
-        "latency": 20.9,
-        "recall": 0.9557,
-        "filter_ratio": 0.0
+        "label": "8c60g-force_merge",
+        "db_name": "ElasticCloud-8c60g-force_merge",
+        "qps": 179.5204,
+        "latency": 51.4,
+        "recall": 1.0,
+        "filter_ratio": 0.998
     },
     {
         "dataset": "Cohere (Large)",
         "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
-        "qps": 129.0705,
-        "latency": 24.3,
-        "recall": 0.96,
-        "filter_ratio": 0.0
+        "label": "8c60g-force_merge",
+        "db_name": "ElasticCloud-8c60g-force_merge",
+        "qps": 350.0132,
+        "latency": 29.7,
+        "recall": 1.0,
+        "filter_ratio": 0.999
     },
     {
         "dataset": "Cohere (Medium)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 2095.7067,
-        "latency": 12.4,
-        "recall": 0.8961,
+        "qps": 2808.2421,
+        "latency": 9.5,
+        "recall": 0.8815,
         "filter_ratio": 0.0
     },
     {
@@ -394,9 +164,9 @@
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 1925.3019,
-        "latency": 11.3,
-        "recall": 0.9141,
+        "qps": 2353.8935,
+        "latency": 17.1,
+        "recall": 0.9143,
         "filter_ratio": 0.0
     },
     {
@@ -404,9 +174,9 @@
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 1707.8841,
-        "latency": 10.0,
-        "recall": 0.9314,
+        "qps": 2030.4249,
+        "latency": 10.6,
+        "recall": 0.9306,
         "filter_ratio": 0.0
     },
     {
@@ -414,9 +184,9 @@
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 1442.0638,
-        "latency": 10.1,
-        "recall": 0.9482,
+        "qps": 1804.8996,
+        "latency": 12.3,
+        "recall": 0.9405,
         "filter_ratio": 0.0
     },
     {
@@ -424,9 +194,9 @@
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 1115.106,
-        "latency": 13.1,
-        "recall": 0.9662,
+        "qps": 1623.8421,
+        "latency": 11.8,
+        "recall": 0.9479,
         "filter_ratio": 0.0
     },
     {
@@ -434,9 +204,9 @@
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 910.4322,
-        "latency": 14.2,
-        "recall": 0.9748,
+        "qps": 1482.3772,
+        "latency": 12.1,
+        "recall": 0.9546,
         "filter_ratio": 0.0
     },
     {
@@ -444,40 +214,40 @@
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 2175.2694,
-        "latency": 9.8,
-        "recall": 1.0,
-        "filter_ratio": 0.999
+        "qps": 899.3114,
+        "latency": 14.9,
+        "recall": 0.9072,
+        "filter_ratio": 0.5
     },
     {
         "dataset": "Cohere (Medium)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 1430.0244,
-        "latency": 12.6,
-        "recall": 1.0,
-        "filter_ratio": 0.998
+        "qps": 669.9441,
+        "latency": 19.1,
+        "recall": 0.9227,
+        "filter_ratio": 0.8
     },
     {
         "dataset": "Cohere (Medium)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 692.5751,
-        "latency": 18.7,
-        "recall": 1.0,
-        "filter_ratio": 0.995
+        "qps": 437.8735,
+        "latency": 27.1,
+        "recall": 0.9364,
+        "filter_ratio": 0.9
     },
     {
         "dataset": "Cohere (Medium)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 364.3516,
-        "latency": 26.4,
-        "recall": 1.0,
-        "filter_ratio": 0.99
+        "qps": 249.3519,
+        "latency": 44.7,
+        "recall": 0.9446,
+        "filter_ratio": 0.95
     },
     {
         "dataset": "Cohere (Medium)",
@@ -494,470 +264,520 @@
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 249.3519,
-        "latency": 44.7,
-        "recall": 0.9446,
-        "filter_ratio": 0.95
+        "qps": 364.3516,
+        "latency": 26.4,
+        "recall": 1.0,
+        "filter_ratio": 0.99
     },
     {
         "dataset": "Cohere (Medium)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 437.8735,
-        "latency": 27.1,
-        "recall": 0.9364,
-        "filter_ratio": 0.9
+        "qps": 692.5751,
+        "latency": 18.7,
+        "recall": 1.0,
+        "filter_ratio": 0.995
     },
     {
         "dataset": "Cohere (Medium)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
-        "qps": 669.9441,
-        "latency": 19.1,
-        "recall": 0.9227,
-        "filter_ratio": 0.8
+        "qps": 1430.0244,
+        "latency": 12.6,
+        "recall": 1.0,
+        "filter_ratio": 0.998
     },
     {
         "dataset": "Cohere (Medium)",
         "db": "ElasticCloud",
         "label": "8c60g-force_merge",
         "db_name": "ElasticCloud-8c60g-force_merge",
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+        "label": "8cu-perf",
+        "db_name": "ZillizCloud-8cu-perf",
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     {
         "dataset": "Cohere (Medium)",
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+        "label": "8cu-perf",
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+        "db": "ZillizCloud",
+        "label": "8cu-perf",
+        "db_name": "ZillizCloud-8cu-perf",
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+        "db": "ZillizCloud",
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+        "db": "ZillizCloud",
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+        "db": "ZillizCloud",
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+        "db": "ZillizCloud",
+        "label": "8cu-perf-partition_key",
+        "db_name": "ZillizCloud-8cu-perf-partition_key",
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         "dataset": "Cohere (Large)",
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+        "db": "ZillizCloud",
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+        "db": "ZillizCloud",
+        "label": "8cu-perf-partition_key",
+        "db_name": "ZillizCloud-8cu-perf-partition_key",
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+        "db": "ZillizCloud",
+        "label": "8cu-perf-partition_key",
+        "db_name": "ZillizCloud-8cu-perf-partition_key",
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     },
     {
         "dataset": "Cohere (Large)",
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     },
     {
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+        "dataset": "Cohere (Medium)",
+        "db": "ZillizCloud",
+        "label": "8cu-perf-partition_key",
+        "db_name": "ZillizCloud-8cu-perf-partition_key",
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+        "latency": 2.8,
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+        "filter_ratio": 0.9
+    },
+    {
+        "dataset": "Cohere (Medium)",
+        "db": "ZillizCloud",
+        "label": "8cu-perf-partition_key",
+        "db_name": "ZillizCloud-8cu-perf-partition_key",
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+    },
+    {
+        "dataset": "Cohere (Medium)",
+        "db": "ZillizCloud",
+        "label": "8cu-perf-partition_key",
+        "db_name": "ZillizCloud-8cu-perf-partition_key",
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+    },
+    {
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+        "db": "ZillizCloud",
+        "label": "8cu-perf-partition_key",
+        "db_name": "ZillizCloud-8cu-perf-partition_key",
+        "qps": 10020.5299,
+        "latency": 2.6,
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+    },
+    {
+        "dataset": "Cohere (Medium)",
+        "db": "ZillizCloud",
+        "label": "8cu-perf-partition_key",
+        "db_name": "ZillizCloud-8cu-perf-partition_key",
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+        "recall": 0.9257,
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+    },
+    {
+        "dataset": "Cohere (Medium)",
+        "db": "ZillizCloud",
+        "label": "8cu-perf-partition_key",
+        "db_name": "ZillizCloud-8cu-perf-partition_key",
+        "qps": 10557.4373,
+        "latency": 2.7,
+        "recall": 0.9393,
+        "filter_ratio": 0.998
+    },
+    {
+        "dataset": "Cohere (Medium)",
+        "db": "ZillizCloud",
+        "label": "8cu-perf-partition_key",
+        "db_name": "ZillizCloud-8cu-perf-partition_key",
+        "qps": 10089.4308,
+        "latency": 2.6,
+        "recall": 0.9934,
+        "filter_ratio": 0.999
     }
 ]
\ No newline at end of file
diff --git a/vectordb_bench/results/leaderboard_v2_streaming.json b/vectordb_bench/results/leaderboard_v2_streaming.json
index 224c78d12..73075e8a8 100644
--- a/vectordb_bench/results/leaderboard_v2_streaming.json
+++ b/vectordb_bench/results/leaderboard_v2_streaming.json
@@ -1,128 +1,146 @@
 [
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-        "dataset": "Cohere (Large)",
-        "db": "ElasticCloud",
-        "label": "8c60g",
-        "db_name": "ElasticCloud-8c60g",
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-        "streaming_latency": 0.0794
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-    {
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-        "db_name": "ElasticCloud-8c60g",
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-        "streaming_latency": 0.2223
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-    {
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-        "db_name": "Milvus-16c64g-sq8",
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-        "streaming_qps": 305.9971,
-        "streaming_latency": 0.005
-    },
-    {
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-        "db": "Milvus",
-        "label": "16c64g-sq8",
-        "db_name": "Milvus-16c64g-sq8",
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-        "streaming_latency": 0.0203
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-    {
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-        "db": "Pinecone",
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-    {
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-        "db": "Pinecone",
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-        "streaming_latency": 5.992
-    },
-    {
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-        "db": "QdrantCloud",
-        "label": "16c64g",
-        "db_name": "QdrantCloud-16c64g",
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-        "streaming_latency": 0.0162
-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "QdrantCloud",
-        "label": "16c64g",
-        "db_name": "QdrantCloud-16c64g",
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-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "OpenSearch",
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-        "db_name": "OpenSearch-16c128g",
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-        "streaming_latency": 0.098
-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "OpenSearch",
-        "label": "16c128g",
-        "db_name": "OpenSearch-16c128g",
-        "insert_rate": 500,
-        "streaming_qps": 161.6694,
-        "streaming_latency": 0.052
-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "ZillizCloud",
-        "label": "8cu-perf",
-        "db_name": "ZillizCloud-8cu-perf",
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-        "streaming_latency": 0.0068
-    },
-    {
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-        "db": "ZillizCloud",
-        "label": "8cu-perf",
-        "db_name": "ZillizCloud-8cu-perf",
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-        "streaming_qps": 1860.2575,
-        "streaming_latency": 0.0101
-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "S3Vectors",
-        "label": "",
-        "db_name": "S3Vectors",
-        "insert_rate": 500,
-        "streaming_qps": 180.9549,
-        "streaming_latency": 0.4204
-    },
-    {
-        "dataset": "Cohere (Large)",
-        "db": "S3Vectors",
-        "label": "",
-        "db_name": "S3Vectors",
-        "insert_rate": 1000,
-        "streaming_qps": 167.2689,
-        "streaming_latency": 0.5048
-    }
+  {
+    "dataset": "Cohere (Large)",
+    "db": "ElasticCloud",
+    "label": "8c60g-force_merge",
+    "db_name": "ElasticCloud-8c60g-force_merge",
+    "insert_rate": 500,
+    "streaming_qps": 61.6708,
+    "streaming_latency": 0.0794
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "ElasticCloud",
+    "label": "8c60g-force_merge",
+    "db_name": "ElasticCloud-8c60g-force_merge",
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+    "streaming_qps": 61.8172,
+    "streaming_latency": 0.2223
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "Milvus",
+    "label": "16c64g-sq8-force_merge",
+    "db_name": "Milvus-16c64g-sq8-force_merge",
+    "insert_rate": 500,
+    "streaming_qps": 305.9971,
+    "streaming_latency": 0.005
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "Milvus",
+    "label": "16c64g-sq8-force_merge",
+    "db_name": "Milvus-16c64g-sq8-force_merge",
+    "insert_rate": 1000,
+    "streaming_qps": 155.9613,
+    "streaming_latency": 0.0203
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "Pinecone",
+    "label": "p2.x8-1node",
+    "db_name": "Pinecone-p2.x8-1node",
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+    "streaming_qps": 367.4299,
+    "streaming_latency": 1.8286
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "Pinecone",
+    "label": "p2.x8-1node",
+    "db_name": "Pinecone-p2.x8-1node",
+    "insert_rate": 1000,
+    "streaming_qps": 369.6771,
+    "streaming_latency": 5.992
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "QdrantCloud",
+    "label": "16c64g",
+    "db_name": "QdrantCloud-16c64g",
+    "insert_rate": 500,
+    "streaming_qps": 393.753,
+    "streaming_latency": 0.0162
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "QdrantCloud",
+    "label": "16c64g",
+    "db_name": "QdrantCloud-16c64g",
+    "insert_rate": 1000,
+    "streaming_qps": 347.5774,
+    "streaming_latency": 0.0118
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "OpenSearch",
+    "label": "16c128g",
+    "db_name": "OpenSearch-16c128g",
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+    "streaming_qps": 149.7168,
+    "streaming_latency": 0.098
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "OpenSearch",
+    "label": "16c128g",
+    "db_name": "OpenSearch-16c128g",
+    "insert_rate": 500,
+    "streaming_qps": 161.6694,
+    "streaming_latency": 0.052
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "ZillizCloud",
+    "label": "8cu-perf",
+    "db_name": "ZillizCloud-8cu-perf",
+    "insert_rate": 500,
+    "streaming_qps": 2118.7516,
+    "streaming_latency": 0.0068
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "ZillizCloud",
+    "label": "8cu-perf",
+    "db_name": "ZillizCloud-8cu-perf",
+    "insert_rate": 1000,
+    "streaming_qps": 1860.2575,
+    "streaming_latency": 0.0101
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "S3Vectors",
+    "label": "",
+    "db_name": "S3Vectors",
+    "insert_rate": 500,
+    "streaming_qps": 180.9549,
+    "streaming_latency": 0.4204
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "S3Vectors",
+    "label": "",
+    "db_name": "S3Vectors",
+    "insert_rate": 1000,
+    "streaming_qps": 167.2689,
+    "streaming_latency": 0.5048
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "TurboPuffer",
+    "label": "2026-03-31",
+    "db_name": "TurboPuffer",
+    "insert_rate": 500,
+    "streaming_qps": 536.0198,
+    "streaming_latency": 0.3132
+  },
+  {
+    "dataset": "Cohere (Large)",
+    "db": "TurboPuffer",
+    "label": "2026-03-31",
+    "db_name": "TurboPuffer",
+    "insert_rate": 1000,
+    "streaming_qps": 442.5824,
+    "streaming_latency": 0.0724
+  }
 ]
\ No newline at end of file