From fc4adae6b812da078d42efff728b8ca30a654098 Mon Sep 17 00:00:00 2001 From: peterxcli Date: Sun, 6 Sep 2026 01:43:07 +0800 Subject: [PATCH 01/13] feat: overlap Parquet row-group I/O and decode --- datafusion/datasource-parquet/Cargo.toml | 1 + .../datasource-parquet/src/opener/mod.rs | 12 +- .../datasource-parquet/src/push_decoder.rs | 513 +++++++++++++++++- datafusion/datasource-parquet/src/source.rs | 30 + 4 files changed, 549 insertions(+), 7 deletions(-) diff --git a/datafusion/datasource-parquet/Cargo.toml b/datafusion/datasource-parquet/Cargo.toml index a2589af19a6e..df30b968b102 100644 --- a/datafusion/datasource-parquet/Cargo.toml +++ b/datafusion/datasource-parquet/Cargo.toml @@ -63,6 +63,7 @@ criterion = { workspace = true } datafusion-functions = { workspace = true } datafusion-functions-nested = { workspace = true } tempfile = { workspace = true } +tokio = { workspace = true, features = ["rt-multi-thread"] } # Note: add additional linter rules in lib.rs. # Rust does not support workspace + new linter rules in subcrates yet diff --git a/datafusion/datasource-parquet/src/opener/mod.rs b/datafusion/datasource-parquet/src/opener/mod.rs index 25c3bc9a7785..e8a8a9c20044 100644 --- a/datafusion/datasource-parquet/src/opener/mod.rs +++ b/datafusion/datasource-parquet/src/opener/mod.rs @@ -29,7 +29,7 @@ use crate::metrics::{ByteProgress, RowFilterSkippedFullyMatchedMetric}; use crate::page_filter::PagePruningAccessPlanFilter; use crate::push_decoder::{ DecoderBuilderConfig, InitialDecoderState, PushDecoderStreamState, RgPlanEntry, - RowGroupPruner, + RowGroupPrefetchOptions, RowGroupPruner, }; use crate::row_group_filter::{RowGroupAccessPlanFilter, row_group_in_range}; use crate::{ @@ -236,6 +236,7 @@ fn validate_predicate_does_not_reference_virtual_columns( /// as an explicit state machine -- see [`ParquetOpenState`] for details. #[derive(Clone)] pub(super) struct ParquetMorselizer { + pub(crate) row_group_prefetch: Option, /// Execution partition index pub(crate) partition_index: usize, /// Projection to apply on top of the table schema (i.e. can reference partition columns). @@ -421,6 +422,7 @@ impl fmt::Debug for ParquetOpenState { } struct PreparedParquetOpen { + row_group_prefetch: Option, partition_index: usize, partitioned_file: PartitionedFile, /// Tracks how much of this file range the scan has finished with. @@ -854,6 +856,7 @@ impl ParquetMorselizer { metrics: self.metrics.clone(), parquet_file_reader_factory: Arc::clone(&self.parquet_file_reader_factory), async_file_reader, + row_group_prefetch: self.row_group_prefetch.clone(), batch_size: self.batch_size, logical_file_schema: Arc::clone(&logical_file_schema), physical_file_schema: logical_file_schema, @@ -1667,7 +1670,11 @@ impl RowGroupsPrunedParquetOpen { decoder: Some(decoder), active_reader: None, rg_plan, - reader: prepared.async_file_reader, + reader: Arc::new(tokio::sync::Mutex::new(prepared.async_file_reader)), + row_group_prefetch: prepared.row_group_prefetch, + parquet_metadata: Arc::clone(reader_metadata.metadata()), + pending_prefetch: None, + prefetch_reservation: None, decoder_projection, arrow_reader_metrics, predicate_cache_inner_records, @@ -2294,6 +2301,7 @@ mod test { )?; Ok(ParquetMorselizer { + row_group_prefetch: None, partition_index: self.partition_index, projection, batch_size: self.batch_size, diff --git a/datafusion/datasource-parquet/src/push_decoder.rs b/datafusion/datasource-parquet/src/push_decoder.rs index 94a0e5049fef..6f60d8e18285 100644 --- a/datafusion/datasource-parquet/src/push_decoder.rs +++ b/datafusion/datasource-parquet/src/push_decoder.rs @@ -35,8 +35,13 @@ //! The opener constructs both halves and hands the state off to //! [`PushDecoderStreamState::into_stream`] for consumption. +use bytes::Bytes; +use datafusion_common_runtime::SpawnedTask; +use datafusion_execution::memory_pool::{MemoryConsumer, MemoryPool, MemoryReservation}; use std::collections::VecDeque; +use std::ops::Range; use std::sync::Arc; +use tokio::sync::Mutex; use arrow::array::RecordBatch; use arrow::datatypes::SchemaRef; @@ -280,6 +285,62 @@ impl RowGroupPruner { } } +/// Execution-local prefetch configuration, supplied by the embedding engine. +#[derive(Clone, Debug)] +pub(crate) struct RowGroupPrefetchOptions { + pub(crate) max_bytes: usize, + pub(crate) memory_pool: Arc, +} + +/// At most one future row group is in flight. The task owns the reservation so +/// cancellation keeps its bytes accounted until the I/O future is actually dropped. +pub(crate) struct PrefetchedRowGroup { + row_group: usize, + ranges: Vec>, + task: SpawnedTask, MemoryReservation)>>, +} + +impl PrefetchedRowGroup { + fn start( + row_group: usize, + metadata: &ParquetMetaData, + projection: &ProjectionMask, + options: &RowGroupPrefetchOptions, + reader: Arc>>, + ) -> Option { + let ranges: Vec<_> = metadata + .row_group(row_group) + .columns() + .iter() + .enumerate() + .filter(|(i, _)| projection.leaf_included(*i)) + .map(|(_, column)| { + let (start, len) = column.byte_range(); + start.checked_add(len).map(|end| start..end) + }) + .collect::>()?; + let bytes = ranges.iter().try_fold(0usize, |total, range| { + total.checked_add(usize::try_from(range.end - range.start).ok()?) + })?; + if bytes == 0 || bytes > options.max_bytes { + return None; + } + let reservation = MemoryConsumer::new("Parquet row-group prefetch") + .register(&options.memory_pool); + reservation.try_grow(bytes).ok()?; + let fetch_ranges = ranges.clone(); + let task = SpawnedTask::spawn(async move { + let data = reader.lock().await.get_byte_ranges(fetch_ranges).await?; + Ok((data, reservation)) + }); + Some(Self { + row_group, + ranges, + task, + }) + } +} + /// State for a stream that decodes a single Parquet file using a push-based decoder. /// /// The [`transition`](Self::transition) method drives the decoder in a loop: it requests @@ -290,7 +351,11 @@ pub(crate) struct PushDecoderStreamState { pub(crate) decoder: Option, pub(crate) active_reader: Option, pub(crate) rg_plan: VecDeque, - pub(crate) reader: Box, + pub(crate) reader: Arc>>, + pub(crate) row_group_prefetch: Option, + pub(crate) parquet_metadata: Arc, + pub(crate) pending_prefetch: Option, + pub(crate) prefetch_reservation: Option, /// Per-file projection: the mask installed on every decoder and the /// per-batch transform applied by [`Self::project_batch`]. pub(crate) decoder_projection: DecoderProjection, @@ -424,10 +489,10 @@ impl PushDecoderStreamState { /// Advances the decoder state machine until the next [`RecordBatch`] is /// produced, the file is fully consumed, or an error occurs. /// - /// On each iteration the decoder is polled via [`ParquetPushDecoder::try_decode`]: + /// At a row-group boundary the decoder is polled via [`ParquetPushDecoder::try_next_reader`]: /// - [`NeedsData`](DecodeResult::NeedsData) – the requested byte ranges are /// fetched from the [`AsyncFileReader`] and fed back into the decoder. - /// - [`Data`](DecodeResult::Data) – a decoded batch is projected and returned. + /// - [`Data`](DecodeResult::Data) – a reader is retained for subsequent batch decoding. /// - [`Finished`](DecodeResult::Finished) – signals end-of-stream (`None`). /// /// Takes `self` by value (rather than `&mut self`) so the generated future @@ -502,12 +567,44 @@ impl PushDecoderStreamState { } } + // Apply speculation only after runtime pruning has chosen the next + // row group. A pruned group's task is aborted and its bytes discarded. + if let Some(prefetch) = self.pending_prefetch.take() { + let decoder = self.decoder.as_mut().expect("decoder present"); + match decoder.peek_next_row_group() { + Ok(Some(next)) if next == prefetch.row_group => { + match prefetch.task.join_unwind().await { + Ok(Ok((data, reservation))) => { + if let Err(e) = decoder.push_ranges(prefetch.ranges, data) + { + return Some((Err(e.into()), self)); + } + self.prefetch_reservation = Some(reservation); + } + // A speculative error must not fail a scan that would + // not need those bytes. Let demand reads retry normally. + Ok(Err(e)) => debug!("Parquet prefetch failed: {e}"), + Err(e) => { + return Some(( + Err(DataFusionError::External(Box::new(e))), + self, + )); + } + } + } + Ok(_) => {} + Err(e) => return Some((Err(e.into()), self)), + } + } + // Step 3: drive the decoder. let decoder = self.decoder.as_mut().expect("decoder present"); match decoder.try_next_reader() { Ok(DecodeResult::NeedsData(ranges)) => { let data = self .reader + .lock() + .await .get_byte_ranges(ranges.clone()) .await .map_err(DataFusionError::from); @@ -539,6 +636,27 @@ impl PushDecoderStreamState { self.byte_progress.credit(entry.bytes); } self.active_reader = Some(reader); + // The extracted reader now owns required bytes. Release any + // unused speculation (e.g. pages removed by a row filter). + if let Some(reservation) = self.prefetch_reservation.take() { + decoder.clear_all_ranges(); + drop(reservation); + } + if let Some(options) = &self.row_group_prefetch { + match decoder.peek_next_row_group() { + Ok(Some(next)) => { + self.pending_prefetch = PrefetchedRowGroup::start( + next, + &self.parquet_metadata, + self.decoder_projection.projection_mask(), + options, + Arc::clone(&self.reader), + ); + } + Ok(None) => {} + Err(e) => return Some((Err(e.into()), self)), + } + } } Ok(DecodeResult::Finished) => return None, Err(e) => { @@ -722,6 +840,11 @@ mod tests { /// column statistics are disjoint: RG0 → 0..1000, RG1 → 1000..2000, /// RG2 → 2000..3000. Returns (metadata, schema). fn build_three_rg_file() -> (Arc, SchemaRef) { + let (_, metadata, schema) = build_three_rg_file_data(); + (metadata, schema) + } + + fn build_three_rg_file_data() -> (Bytes, Arc, SchemaRef) { let schema = Arc::new(Schema::new(vec![Field::new("v", DataType::Int64, false)])); let mut buf = Vec::new(); let props = WriterProperties::builder() @@ -752,12 +875,392 @@ mod tests { reason = "we want a single range covering the whole file" )] let ranges = vec![0..len]; - md.push_ranges(ranges, vec![file]).unwrap(); + md.push_ranges(ranges, vec![file.clone()]).unwrap(); let DecodeResult::Data(meta) = md.try_decode().unwrap() else { panic!("decoding metadata"); }; assert_eq!(meta.num_row_groups(), 3, "test fixture must have 3 RGs"); - (Arc::new(meta), schema) + (file, Arc::new(meta), schema) + } + + #[derive(Debug, Default)] + struct ReadControl { + calls: std::sync::atomic::AtomicUsize, + started: tokio::sync::Notify, + release: tokio::sync::Notify, + block_second: bool, + fail_second: bool, + latency: std::time::Duration, + } + + #[derive(Debug, Clone)] + struct TestReader { + data: Bytes, + metadata: Arc, + control: Arc, + } + + impl AsyncFileReader for TestReader { + fn get_bytes( + &mut self, + range: Range, + ) -> futures::future::BoxFuture<'_, parquet::errors::Result> { + use futures::FutureExt; + async move { Ok(self.data.slice(range.start as usize..range.end as usize)) } + .boxed() + } + + fn get_byte_ranges( + &mut self, + ranges: Vec>, + ) -> futures::future::BoxFuture<'_, parquet::errors::Result>> { + use futures::FutureExt; + use std::sync::atomic::Ordering; + async move { + let call = self.control.calls.fetch_add(1, Ordering::SeqCst); + if call == 1 { + self.control.started.notify_one(); + if self.control.block_second { + self.control.release.notified().await; + } + if self.control.fail_second { + return Err(parquet::errors::ParquetError::General( + "injected prefetch failure".into(), + )); + } + } + tokio::time::sleep(self.control.latency).await; + Ok(ranges + .into_iter() + .map(|range| { + self.data.slice(range.start as usize..range.end as usize) + }) + .collect()) + } + .boxed() + } + + fn get_metadata<'a>( + &'a mut self, + _options: Option<&'a parquet::arrow::arrow_reader::ArrowReaderOptions>, + ) -> futures::future::BoxFuture<'a, parquet::errors::Result>> + { + use futures::FutureExt; + async move { Ok(Arc::clone(&self.metadata)) }.boxed() + } + } + + impl crate::ParquetFileReaderFactory for TestReader { + fn create_reader( + &self, + _partition: usize, + _file: datafusion_datasource::PartitionedFile, + _hint: Option, + _metrics: &ExecutionPlanMetricsSet, + ) -> Result> { + Ok(Box::new(self.clone())) + } + } + + fn prefetch_test_stream( + budget: usize, + pool: Arc, + control: Arc, + limit: Option, + predicate: Option>, + ) -> datafusion_execution::SendableRecordBatchStream { + use datafusion_datasource::file_scan_config::FileScanConfigBuilder; + use datafusion_datasource::source::DataSourceExec; + use datafusion_datasource::{PartitionedFile, file_groups::FileGroup}; + use datafusion_execution::{ + TaskContext, config::SessionConfig, object_store::ObjectStoreUrl, + }; + use datafusion_physical_plan::ExecutionPlan; + let (data, metadata, schema) = build_three_rg_file_data(); + let file = PartitionedFile::new("prefetch.parquet", data.len() as u64); + let mut source = crate::source::ParquetSource::new(schema) + .with_row_group_prefetch(budget, pool) + .with_pushdown_filters(true) + .with_parquet_file_reader_factory(Arc::new(TestReader { + data, + metadata, + control, + })); + if let Some(predicate) = predicate { + source = source.with_predicate(predicate); + } + let config = FileScanConfigBuilder::new( + ObjectStoreUrl::local_filesystem(), + Arc::new(source), + ) + .with_file_group(FileGroup::new(vec![file])) + .with_limit(limit) + .build(); + let task = TaskContext::default() + .with_session_config(SessionConfig::new().with_batch_size(100)); + DataSourceExec::new(Arc::new(config)) + .execute(0, Arc::new(task)) + .unwrap() + } + + async fn assert_pool_released(pool: &Arc) { + tokio::time::timeout(std::time::Duration::from_secs(5), async { + while pool.reserved() != 0 { + tokio::task::yield_now().await; + } + }) + .await + .unwrap(); + } + + #[tokio::test] + async fn prefetch_overlaps_decode_and_preserves_order() { + use datafusion_execution::memory_pool::GreedyMemoryPool; + let pool: Arc = Arc::new(GreedyMemoryPool::new(1 << 20)); + let control = Arc::new(ReadControl { + block_second: true, + ..Default::default() + }); + let mut stream = prefetch_test_stream( + 1 << 20, + Arc::clone(&pool), + Arc::clone(&control), + None, + None, + ); + let first = stream.next().await.unwrap().unwrap(); + assert_eq!(first.num_rows(), 100); + // No further polling of the scan: next-RG I/O must start independently. + tokio::time::timeout( + std::time::Duration::from_secs(5), + control.started.notified(), + ) + .await + .unwrap(); + assert!(pool.reserved() > 0); + let mut batches = vec![first]; + // The current reader must keep producing while the next fetch is blocked. + for _ in 1..10 { + batches.push( + tokio::time::timeout(std::time::Duration::from_secs(5), stream.next()) + .await + .unwrap() + .unwrap() + .unwrap(), + ); + } + control.release.notify_one(); + while let Some(batch) = stream.next().await { + batches.push(batch.unwrap()); + } + let values: Vec = batches + .iter() + .flat_map(|batch| { + batch + .column(0) + .as_any() + .downcast_ref::() + .unwrap() + .values() + .iter() + .copied() + }) + .collect(); + assert_eq!(values, (0..3000).collect::>()); + assert_pool_released(&pool).await; + } + + #[tokio::test] + async fn prefetch_budget_pool_pressure_limit_and_cancellation() { + use datafusion_execution::memory_pool::GreedyMemoryPool; + use std::sync::atomic::Ordering; + for (budget, capacity, limit) in [ + (0, 1 << 20, None), + (1, 1 << 20, None), + (1 << 20, 0, None), + (1 << 20, 1 << 20, Some(100)), + ] { + let pool: Arc = Arc::new(GreedyMemoryPool::new(capacity)); + let control = Arc::new(ReadControl::default()); + let mut stream = prefetch_test_stream( + budget, + Arc::clone(&pool), + Arc::clone(&control), + limit, + None, + ); + assert_eq!(stream.next().await.unwrap().unwrap().num_rows(), 100); + tokio::task::yield_now().await; + assert_eq!(pool.reserved(), 0); + assert_eq!(control.calls.load(Ordering::SeqCst), 1); + drop(stream); + } + let pool: Arc = Arc::new(GreedyMemoryPool::new(1 << 20)); + let control = Arc::new(ReadControl { + block_second: true, + ..Default::default() + }); + let mut stream = prefetch_test_stream( + 1 << 20, + Arc::clone(&pool), + Arc::clone(&control), + None, + None, + ); + stream.next().await.unwrap().unwrap(); + tokio::time::timeout( + std::time::Duration::from_secs(5), + control.started.notified(), + ) + .await + .unwrap(); + assert!(pool.reserved() > 0); + drop(stream); // The blocked read must be aborted without releasing its gate. + assert_pool_released(&pool).await; + } + + #[tokio::test] + async fn prefetch_failure_retries_on_demand() { + use datafusion_execution::memory_pool::GreedyMemoryPool; + use std::sync::atomic::Ordering; + let pool: Arc = Arc::new(GreedyMemoryPool::new(1 << 20)); + let control = Arc::new(ReadControl { + fail_second: true, + ..Default::default() + }); + let mut stream = prefetch_test_stream( + 1 << 20, + Arc::clone(&pool), + Arc::clone(&control), + None, + None, + ); + let mut rows = 0; + while let Some(batch) = stream.next().await { + rows += batch.unwrap().num_rows(); + } + assert_eq!(rows, 3000); + assert_eq!(control.calls.load(Ordering::SeqCst), 4); + assert_pool_released(&pool).await; + } + + #[tokio::test] + async fn prefetch_cancels_a_row_group_pruned_while_decoding() { + use datafusion_execution::memory_pool::GreedyMemoryPool; + let pool: Arc = Arc::new(GreedyMemoryPool::new(1 << 20)); + let control = Arc::new(ReadControl { + block_second: true, + ..Default::default() + }); + let dynamic = Arc::new(DynamicFilterPhysicalExpr::new( + vec![Arc::new(Column::new("v", 0))], + gt_predicate(-1), + )); + let mut stream = prefetch_test_stream( + 1 << 20, + Arc::clone(&pool), + Arc::clone(&control), + None, + Some(Arc::clone(&dynamic) as _), + ); + stream.next().await.unwrap().unwrap(); + tokio::time::timeout( + std::time::Duration::from_secs(5), + control.started.notified(), + ) + .await + .unwrap(); + dynamic.update(gt_predicate(2500)).unwrap(); + // RG1 is now prunable. Its blocked prefetch must be cancelled, not awaited. + let rows = tokio::time::timeout(std::time::Duration::from_secs(5), async { + let mut rows = 100; + while let Some(batch) = stream.next().await { + rows += batch.unwrap().num_rows(); + } + rows + }) + .await + .unwrap(); + assert_eq!(rows, 1499); // RG0 already active; 499 rows in RG2 pass the filter. + assert_pool_released(&pool).await; + } + + #[tokio::test] + async fn prefetch_with_row_filter_matches_demand_reads() { + use datafusion_execution::memory_pool::GreedyMemoryPool; + // The modulo filter empties RG1 without statistics pruning, exercising + // the decoder advancing past a prefetched group without yielding a reader. + let modulo = Arc::new(BinaryExpr::new( + Arc::new(BinaryExpr::new( + Arc::new(Column::new("v", 0)), + Operator::Modulo, + lit(2000i64), + )), + Operator::Lt, + lit(1000i64), + )) as Arc; + for (predicate, expected) in [ + (gt_predicate(1500), (1501..3000).collect::>()), + (modulo, (0..1000).chain(2000..3000).collect()), + ] { + for budget in [0, 1 << 20] { + let pool: Arc = Arc::new(GreedyMemoryPool::new(1 << 20)); + let mut stream = prefetch_test_stream( + budget, + Arc::clone(&pool), + Arc::new(ReadControl::default()), + None, + Some(Arc::clone(&predicate)), + ); + let mut values = Vec::new(); + while let Some(batch) = stream.next().await { + values.extend_from_slice( + batch + .unwrap() + .column(0) + .as_any() + .downcast_ref::() + .unwrap() + .values(), + ); + } + assert_eq!(values, expected); + assert_pool_released(&pool).await; + } + } + } + + /// Controlled scheduling experiment, not a production throughput benchmark. + #[tokio::test(flavor = "multi_thread", worker_threads = 2)] + #[ignore = "manual benchmark with simulated I/O and batch processing latency"] + async fn prefetch_latency_benchmark() { + use datafusion_common::instant::Instant; + use datafusion_execution::memory_pool::GreedyMemoryPool; + use std::time::Duration; + for budget in [0, 1 << 20] { + let mut times = Vec::new(); + for _ in 0..5 { + let pool: Arc = Arc::new(GreedyMemoryPool::new(1 << 20)); + let control = Arc::new(ReadControl { + latency: Duration::from_millis(40), + ..Default::default() + }); + let mut stream = + prefetch_test_stream(budget, Arc::clone(&pool), control, None, None); + let start = Instant::now(); + let mut rows = 0; + while let Some(batch) = stream.next().await { + rows += batch.unwrap().num_rows(); + // Simulate synchronous downstream processing on this worker. + std::thread::sleep(Duration::from_millis(4)); + } + assert_eq!(rows, 3000); + times.push(start.elapsed()); + assert_pool_released(&pool).await; + } + times.sort(); + println!("prefetch budget={budget}, median={:?}", times[2]); + } } /// Create a fresh `(creation_errors, evaluation_errors)` counter pair diff --git a/datafusion/datasource-parquet/src/source.rs b/datafusion/datasource-parquet/src/source.rs index 4872db9fd332..356138c20e0b 100644 --- a/datafusion/datasource-parquet/src/source.rs +++ b/datafusion/datasource-parquet/src/source.rs @@ -25,6 +25,7 @@ use crate::ParquetFileReaderFactory; use crate::opener::ParquetMorselizer; use crate::opener::build_pruning_predicates; use crate::opener::build_virtual_columns_state; +use crate::push_decoder::RowGroupPrefetchOptions; use crate::row_filter::can_expr_be_pushed_down_with_schemas; use arrow_schema::Fields; use arrow_schema::extension::ExtensionType; @@ -35,6 +36,7 @@ use datafusion_common::config::EncryptionFactoryOptions; use datafusion_datasource::as_file_source; use datafusion_datasource::file_stream::FileOpener; use datafusion_datasource::morsel::Morselizer; +use datafusion_execution::memory_pool::MemoryPool; use arrow::array::timezone::Tz; use arrow::datatypes::TimeUnit; @@ -318,6 +320,7 @@ pub struct ParquetSource { /// Sort order driving `PreparedAccessPlan::reorder_by_statistics` /// in the opener. sort_order_for_reorder: Option, + row_group_prefetch: Option, } impl ParquetSource { @@ -344,9 +347,35 @@ impl ParquetSource { encryption_factory: None, reverse_row_groups: false, sort_order_for_reorder: None, + row_group_prefetch: None, } } + /// Prefetch one upcoming row group's projected column chunks while decoding + /// the current group. Disabled by default; a zero budget disables it. + /// + /// `max_bytes` bounds additional compressed bytes per file stream, not the + /// current reader's memory. Prefetch is skipped if a complete projected row + /// group does not fit or `memory_pool` cannot reserve its bytes. Required + /// reads continue normally. Use the execution's memory pool to account for + /// concurrent scans together. + /// + /// This can read extra bytes when page/row filtering or a later dynamic + /// predicate eliminates prefetched data. Output order is unchanged. Dropping + /// the stream cancels its background I/O. This execution-local option is not + /// serialized in physical plans; set it on the executing ParquetSource. + pub fn with_row_group_prefetch( + mut self, + max_bytes: usize, + memory_pool: Arc, + ) -> Self { + self.row_group_prefetch = (max_bytes > 0).then_some(RowGroupPrefetchOptions { + max_bytes, + memory_pool, + }); + self + } + /// Set the `TableParquetOptions` for this ParquetSource. pub fn with_table_parquet_options( mut self, @@ -634,6 +663,7 @@ impl FileSource for ParquetSource { Ok(Box::new(ParquetMorselizer { partition_index: partition, + row_group_prefetch: self.row_group_prefetch.clone(), projection: self.projection.clone(), batch_size: self .batch_size From 89ebf94c073a7df4c7e3075af0f43650e8d11761 Mon Sep 17 00:00:00 2001 From: peterxcli Date: Wed, 9 Sep 2026 22:58:19 +0800 Subject: [PATCH 02/13] feat: separate Parquet demand I/O policy from prefetch --- .../datasource-parquet/IO_POLICY_BENCHMARK.md | 77 ++++ .../datasource-parquet/examples/io_policy.rs | 342 ++++++++++++++++++ datafusion/datasource-parquet/src/metrics.rs | 23 ++ .../datasource-parquet/src/opener/mod.rs | 15 + .../datasource-parquet/src/push_decoder.rs | 200 +++++++++- .../datasource-parquet/src/row_filter.rs | 2 +- datafusion/datasource-parquet/src/source.rs | 18 +- 7 files changed, 655 insertions(+), 22 deletions(-) create mode 100644 datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md create mode 100644 datafusion/datasource-parquet/examples/io_policy.rs diff --git a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md new file mode 100644 index 000000000000..a43775b8c874 --- /dev/null +++ b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md @@ -0,0 +1,77 @@ + + +# Parquet I/O policy experiment + +This fork experiment targets DataFusion 55.0.0 so Comet can use the patch through +Cargo git dependencies. Both new options are execution-local `ParquetSource` +builders. Progressive fetching remains the default and prefetch is disabled by +default. + +- `with_progressive_io(false)` fetches the output and predicate column chunks + together when a row group first requests data. Filters still determine which + rows are returned, but can no longer save the bytes already fetched. +- `with_row_group_prefetch(bytes, memory_pool)` fetches at most one next row group + while the current reader produces batches. The budget and memory pool bound + additional compressed bytes; they do not bound current-reader or decoded memory. + The full group is skipped when it cannot fit. Dropping the scan cancels pending + work. A speculative read error is retried through the ordinary demand path. + +The two controls are configured independently. Prefetch includes columns used only +by row filters and always fetches full chunks. Consequently, progressive fetching +with prefetch also changes the I/O policy for groups successfully prefetched. To +isolate overlap while holding fetched chunks constant, compare upfront fetching +with prefetch off/on. Compare progressive and upfront with prefetch off to measure +the dependent-read versus extra-bytes tradeoff. + +## Reproduce + +```sh +cargo run --profile release-nonlto -p datafusion-datasource-parquet \ + --example io_policy -- 256 > io-policy.csv +``` + +The example creates four local Snappy Parquet files in a temporary directory, +using 256 row groups of 131,072 rows and eight Int64 columns per file. That is +33,554,432 rows and 2 GiB of logical values per file. Files are generated and +measured one at a time; generation is outside the timed region. + +The six configurations are pushdown off, pushdown with progressive fetching, and +pushdown with upfront fetching, each with prefetch off/on. The prefetch budget is +16 MiB. Every configuration evaluates the same predicate (`c1 < 10000`) and returns +the same columns. Pushdown-off plans retain a physical `FilterExec` before the +output projection. All configurations retain statistics and page-index pruning. + +Each configuration runs against both scattered and page-clustered matches, with +and without column/offset indexes, and with either one or seven output columns. +The filter column is omitted from the output in every case. Writer metadata +assertions verify index presence/absence. Expected row counts and checksums are +calculated during generation and checked on every scan. + +Round zero warms every configuration. The report uses medians of rounds 1–3, +rotating the order of all six configurations each round. The measured region +includes plan execution, decoding, filtering, and summing every output value. +There is no artificial I/O or downstream delay. These are warm local-file results; +they do not establish Spark end-to-end or remote-storage speedups. + +`bytes_scanned` counts requested bytes, including metadata and speculative reads. +`reader_calls` and `reader_ranges` count calls/ranges observed by the reader wrapper, +excluding reads internal to `get_metadata`; they are not physical disk operations +or HTTP request counts. `prefetch_peak` uses DataFusion's existing `PeakRecordingPool` to record peak prefetch reservations. It does not measure current-reader, decoded-buffer, or total process memory. `prefetch_bytes` and `prefetch_row_groups` count completed +speculative reads, including bytes subsequently discarded. diff --git a/datafusion/datasource-parquet/examples/io_policy.rs b/datafusion/datasource-parquet/examples/io_policy.rs new file mode 100644 index 000000000000..12360b13db16 --- /dev/null +++ b/datafusion/datasource-parquet/examples/io_policy.rs @@ -0,0 +1,342 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Local-file I/O policy experiment. Run with a row-group count (default 64). +//! Round zero warms each configuration. No artificial latency is injected. +//! Reader calls/ranges exclude internal footer reads; bytes_scanned includes them. +//! The pool peak covers prefetch reservations, not total scan memory. +use arrow::{ + array::Int64Array, + datatypes::{DataType, Field, Schema}, + record_batch::RecordBatch, +}; +use bytes::Bytes; +use datafusion_common::{Result, instant::Instant}; +use datafusion_datasource::{ + PartitionedFile, file_groups::FileGroup, file_scan_config::FileScanConfigBuilder, + source::DataSourceExec, +}; +use datafusion_datasource_parquet::{ + DefaultParquetFileReaderFactory, ParquetFileReaderFactory, source::ParquetSource, +}; +use datafusion_execution::{ + TaskContext, + config::SessionConfig, + memory_pool::{GreedyMemoryPool, MemoryPool, PeakRecordingPool}, + object_store::ObjectStoreUrl, +}; +use datafusion_expr::Operator; +use datafusion_physical_expr::{ + PhysicalExpr, + expressions::{BinaryExpr, Column, lit}, +}; +use datafusion_physical_plan::{ + ExecutionPlan, filter::FilterExec, metrics::ExecutionPlanMetricsSet, + projection::ProjectionExec, +}; +use futures::{StreamExt, future::BoxFuture}; +use parquet::{ + arrow::{ + ArrowWriter, arrow_reader::ArrowReaderOptions, async_reader::AsyncFileReader, + }, + basic::Compression, + file::{ + metadata::ParquetMetaData, + properties::{EnabledStatistics, WriterProperties}, + }, +}; +use std::{ + ops::Range, + sync::{ + Arc, + atomic::{AtomicUsize, Ordering}, + }, +}; + +#[derive(Debug, Default)] +struct Reads { + calls: AtomicUsize, + ranges: AtomicUsize, +} + +struct Reader { + inner: Box, + reads: Arc, +} +impl AsyncFileReader for Reader { + fn get_bytes( + &mut self, + range: Range, + ) -> BoxFuture<'_, parquet::errors::Result> { + self.reads.calls.fetch_add(1, Ordering::Relaxed); + self.reads.ranges.fetch_add(1, Ordering::Relaxed); + self.inner.get_bytes(range) + } + fn get_byte_ranges( + &mut self, + ranges: Vec>, + ) -> BoxFuture<'_, parquet::errors::Result>> { + self.reads.calls.fetch_add(1, Ordering::Relaxed); + self.reads.ranges.fetch_add(ranges.len(), Ordering::Relaxed); + self.inner.get_byte_ranges(ranges) + } + fn get_metadata<'a>( + &'a mut self, + options: Option<&'a ArrowReaderOptions>, + ) -> BoxFuture<'a, parquet::errors::Result>> { + self.inner.get_metadata(options) + } +} +#[derive(Debug)] +struct Factory { + inner: DefaultParquetFileReaderFactory, + reads: Arc, +} +impl ParquetFileReaderFactory for Factory { + fn create_reader( + &self, + partition: usize, + file: PartitionedFile, + hint: Option, + metrics: &ExecutionPlanMetricsSet, + ) -> Result> { + Ok(Box::new(Reader { + inner: self.inner.create_reader(partition, file, hint, metrics)?, + reads: Arc::clone(&self.reads), + })) + } +} + +#[tokio::main(flavor = "multi_thread", worker_threads = 2)] +async fn main() -> Result<(), Box> { + let groups: usize = std::env::args() + .nth(1) + .unwrap_or_else(|| "64".into()) + .parse()?; + assert!(groups > 0); + let group_rows = 131_072; + let schema = Arc::new(Schema::new( + (0..8) + .map(|i| Field::new(format!("c{i}"), DataType::Int64, false)) + .collect::>(), + )); + let directory = tempfile::tempdir()?; + println!( + "distribution,indexes,projection,mode,prefetch,round,file_bytes,elapsed_ms,rows,checksum,bytes_scanned,reader_calls,reader_ranges,page_rows_pruned,prefetch_peak,prefetch_bytes,prefetch_row_groups" + ); + for clustered in [false, true] { + for indexed in [false, true] { + let path = directory.path().join("scan.parquet"); + let props = WriterProperties::builder() + .set_compression(Compression::SNAPPY) + .set_dictionary_enabled(false) + .set_max_row_group_row_count(Some(group_rows)) + .set_data_page_row_count_limit(1024) + .set_statistics_enabled(if indexed { + EnabledStatistics::Page + } else { + EnabledStatistics::Chunk + }) + .set_offset_index_disabled(!indexed) + .build(); + let mut writer = ArrowWriter::try_new( + std::fs::File::create(&path)?, + Arc::clone(&schema), + Some(props), + )?; + let mut random = 42u64; + let mut expected_rows = 0; + let mut expected_wide = 0i64; + let mut expected_narrow = 0i64; + for group in 0..groups { + let mut columns = Vec::new(); + for column in 0..8 { + columns.push( + (0..group_rows) + .map(|row| { + random ^= random << 13; + random ^= random >> 7; + random ^= random << 17; + if column == 0 { + (group * group_rows + row) as i64 + } else if column == 1 && clustered { + (row * 1_000_000 / group_rows) as i64 + } else { + (random % 1_000_000) as i64 + } + }) + .collect::>(), + ); + } + for row in 0..group_rows { + if columns[1][row] < 10_000 { + expected_rows += 1; + expected_narrow = expected_narrow.wrapping_add(columns[0][row]); + for (i, column) in columns.iter().enumerate() { + if i != 1 { + expected_wide = expected_wide.wrapping_add(column[row]); + } + } + } + } + writer.write(&RecordBatch::try_new( + Arc::clone(&schema), + columns + .into_iter() + .map(|v| Arc::new(Int64Array::from(v)) as _) + .collect(), + )?)?; + writer.flush()?; + } + let metadata = writer.close()?; + assert_eq!(metadata.num_row_groups(), groups); + for group in metadata.row_groups() { + for column in group.columns() { + assert_eq!(column.column_index_offset().is_some(), indexed); + assert_eq!(column.offset_index_offset().is_some(), indexed); + } + } + let size = std::fs::metadata(&path)?.len(); + for narrow in [false, true] { + let output: Vec = if narrow { + vec![0] + } else { + vec![0, 2, 3, 4, 5, 6, 7] + }; + for round in 0..4 { + // Rotate all six configurations to reduce ordering bias. + for offset in 0..6 { + let case = (offset + round) % 6; + let mode = case / 2; + let budget = if case % 2 == 0 { 0 } else { 16 << 20 }; + let recording = Arc::new(PeakRecordingPool::new(Arc::new( + GreedyMemoryPool::new(16 << 20), + ))); + let pool: Arc = Arc::clone(&recording) as _; + let reads = Arc::new(Reads::default()); + let predicate = Arc::new(BinaryExpr::new( + Arc::new(Column::new("c1", 1)), + Operator::Lt, + lit(10_000i64), + )) + as Arc; + let source = ParquetSource::new(Arc::clone(&schema)) + .with_row_group_prefetch(budget, Arc::clone(&pool)) + .with_progressive_io(mode != 2) + .with_pushdown_filters(mode != 0) + .with_predicate(Arc::clone(&predicate)) + .with_parquet_file_reader_factory(Arc::new(Factory { + inner: DefaultParquetFileReaderFactory::new(Arc::new( + object_store::local::LocalFileSystem::new(), + )), + reads: Arc::clone(&reads), + })); + let scan_columns = if mode == 0 && narrow { + vec![0, 1] + } else if mode == 0 { + (0..8).collect() + } else { + output.clone() + }; + let config = FileScanConfigBuilder::new( + ObjectStoreUrl::local_filesystem(), + Arc::new(source), + ) + .with_file_group(FileGroup::new(vec![PartitionedFile::new( + path.to_str().unwrap(), + size, + )])) + .with_projection_indices(Some(scan_columns))? + .build(); + let scan = Arc::new(DataSourceExec::new(Arc::new(config))); + let mut plan: Arc = Arc::clone(&scan) as _; + if mode == 0 { + plan = Arc::new(FilterExec::try_new(predicate, plan)?); + plan = Arc::new(ProjectionExec::try_new( + output.iter().map(|&i| { + ( + Arc::new(Column::new(&format!("c{i}"), i)) + as Arc, + format!("c{i}"), + ) + }), + plan, + )?); + } + let task = TaskContext::default().with_session_config( + SessionConfig::new().with_batch_size(8192), + ); + let start = Instant::now(); + let mut stream = plan.execute(0, Arc::new(task))?; + let (mut rows, mut checksum) = (0usize, 0i64); + while let Some(batch) = stream.next().await { + let batch = batch?; + rows += batch.num_rows(); + for column in batch.columns() { + for value in column + .as_any() + .downcast_ref::() + .unwrap() + .values() + { + checksum = checksum.wrapping_add(*value); + } + } + } + drop(stream); + let peak = recording.peak_reserved(); + let elapsed = start.elapsed().as_secs_f64() * 1000.0; + assert_eq!( + (rows, checksum), + ( + expected_rows, + if narrow { + expected_narrow + } else { + expected_wide + } + ) + ); + assert_eq!(pool.reserved(), 0); + let metrics = scan.metrics().unwrap(); + let metric = |name| { + metrics.sum_by_name(name).map(|v| match v { + datafusion_physical_plan::metrics::MetricValue::PruningMetrics { pruning_metrics, .. } => pruning_metrics.pruned(), + v => v.as_usize(), + }).unwrap_or(0) + }; + println!( + "{},{},{},{},{},{round},{size},{elapsed:.3},{rows},{checksum},{},{},{},{},{peak},{},{}", + if clustered { "clustered" } else { "random" }, + indexed, + if narrow { "narrow" } else { "wide" }, + ["off", "progressive", "upfront"][mode], + budget != 0, + metric("bytes_scanned"), + reads.calls.load(Ordering::Relaxed), + reads.ranges.load(Ordering::Relaxed), + metric("page_index_rows_pruned"), + metric("prefetch_bytes"), + metric("prefetch_row_groups") + ); + } + } + } + } + } + Ok(()) +} diff --git a/datafusion/datasource-parquet/src/metrics.rs b/datafusion/datasource-parquet/src/metrics.rs index 4ec6cf3477fd..85b6b289281c 100644 --- a/datafusion/datasource-parquet/src/metrics.rs +++ b/datafusion/datasource-parquet/src/metrics.rs @@ -445,3 +445,26 @@ impl RowFilterSkippedFullyMatchedMetric { count.add(1); } } + +/// Per-stream counters registered in the scan's existing metric set. +#[derive(Clone)] +pub(crate) struct PrefetchMetrics { + pub bytes: Count, + pub row_groups: Count, + pub budget_skips: Count, + pub wait_time: Time, +} + +impl PrefetchMetrics { + pub fn new(metrics: &ExecutionPlanMetricsSet, partition: usize) -> Self { + Self { + bytes: MetricBuilder::new(metrics).counter("prefetch_bytes", partition), + row_groups: MetricBuilder::new(metrics) + .counter("prefetch_row_groups", partition), + budget_skips: MetricBuilder::new(metrics) + .counter("prefetch_budget_skips", partition), + wait_time: MetricBuilder::new(metrics) + .subset_time("prefetch_wait_time", partition), + } + } +} diff --git a/datafusion/datasource-parquet/src/opener/mod.rs b/datafusion/datasource-parquet/src/opener/mod.rs index e8a8a9c20044..bf6bf3f1719c 100644 --- a/datafusion/datasource-parquet/src/opener/mod.rs +++ b/datafusion/datasource-parquet/src/opener/mod.rs @@ -237,6 +237,7 @@ fn validate_predicate_does_not_reference_virtual_columns( #[derive(Clone)] pub(super) struct ParquetMorselizer { pub(crate) row_group_prefetch: Option, + pub(crate) progressive_io: bool, /// Execution partition index pub(crate) partition_index: usize, /// Projection to apply on top of the table schema (i.e. can reference partition columns). @@ -423,6 +424,7 @@ impl fmt::Debug for ParquetOpenState { struct PreparedParquetOpen { row_group_prefetch: Option, + progressive_io: bool, partition_index: usize, partitioned_file: PartitionedFile, /// Tracks how much of this file range the scan has finished with. @@ -857,6 +859,7 @@ impl ParquetMorselizer { parquet_file_reader_factory: Arc::clone(&self.parquet_file_reader_factory), async_file_reader, row_group_prefetch: self.row_group_prefetch.clone(), + progressive_io: self.progressive_io, batch_size: self.batch_size, logical_file_schema: Arc::clone(&logical_file_schema), physical_file_schema: logical_file_schema, @@ -1463,6 +1466,7 @@ impl RowGroupsPrunedParquetOpen { prepared.virtual_state.as_deref(), )?; + let mut fetch_projection = decoder_projection.projection_mask().clone(); // Lazily-registered suppression counter shared by the open-time first-RG // skip below and the stream's per-RG toggle (registered on first use so // scans that never suppress don't carry a zero-valued counter). @@ -1479,6 +1483,7 @@ impl RowGroupsPrunedParquetOpen { filter_installed, row_filter_context, } = { + let pushdown_predicate = prepared .pushdown_filters .then_some(prepared.predicate.as_ref()) @@ -1563,6 +1568,7 @@ impl RowGroupsPrunedParquetOpen { decoder_config.build(prepared_access_plan, reader_metadata.clone()); let mut filter_installed = false; if let Some(ctx) = row_filter_context.as_ref() { + ctx.extend_projection(&mut fetch_projection); if first_rg_fully_matched { // The first RG is fully matched: install an empty filter // and count the suppression, exactly as the per-RG toggle @@ -1583,6 +1589,7 @@ impl RowGroupsPrunedParquetOpen { builder = builder .with_max_predicate_cache_size(max_predicate_cache_size); } + } } @@ -1672,8 +1679,15 @@ impl RowGroupsPrunedParquetOpen { rg_plan, reader: Arc::new(tokio::sync::Mutex::new(prepared.async_file_reader)), row_group_prefetch: prepared.row_group_prefetch, + progressive_io: prepared.progressive_io, + fetch_projection, + upfront_row_group: None, parquet_metadata: Arc::clone(reader_metadata.metadata()), pending_prefetch: None, + prefetch_metrics: crate::metrics::PrefetchMetrics::new( + &prepared.metrics, + prepared.partition_index, + ), prefetch_reservation: None, decoder_projection, arrow_reader_metrics, @@ -2302,6 +2316,7 @@ mod test { Ok(ParquetMorselizer { row_group_prefetch: None, + progressive_io: true, partition_index: self.partition_index, projection, batch_size: self.batch_size, diff --git a/datafusion/datasource-parquet/src/push_decoder.rs b/datafusion/datasource-parquet/src/push_decoder.rs index 6f60d8e18285..1bcd675733ed 100644 --- a/datafusion/datasource-parquet/src/push_decoder.rs +++ b/datafusion/datasource-parquet/src/push_decoder.rs @@ -292,6 +292,25 @@ pub(crate) struct RowGroupPrefetchOptions { pub(crate) memory_pool: Arc, } +/// Full chunks shared by upfront demand reads and next-group prefetch. +fn column_chunk_ranges( + metadata: &ParquetMetaData, + row_group: usize, + projection: &ProjectionMask, +) -> Option>> { + metadata + .row_group(row_group) + .columns() + .iter() + .enumerate() + .filter(|(i, _)| projection.leaf_included(*i)) + .map(|(_, column)| { + let (start, len) = column.byte_range(); + start.checked_add(len).map(|end| start..end) + }) + .collect() +} + /// At most one future row group is in flight. The task owns the reservation so /// cancellation keeps its bytes accounted until the I/O future is actually dropped. pub(crate) struct PrefetchedRowGroup { @@ -307,30 +326,28 @@ impl PrefetchedRowGroup { projection: &ProjectionMask, options: &RowGroupPrefetchOptions, reader: Arc>>, + metrics: &crate::metrics::PrefetchMetrics, ) -> Option { - let ranges: Vec<_> = metadata - .row_group(row_group) - .columns() - .iter() - .enumerate() - .filter(|(i, _)| projection.leaf_included(*i)) - .map(|(_, column)| { - let (start, len) = column.byte_range(); - start.checked_add(len).map(|end| start..end) - }) - .collect::>()?; + let ranges = column_chunk_ranges(metadata, row_group, projection)?; let bytes = ranges.iter().try_fold(0usize, |total, range| { total.checked_add(usize::try_from(range.end - range.start).ok()?) })?; if bytes == 0 || bytes > options.max_bytes { + metrics.budget_skips.add(1); return None; } let reservation = MemoryConsumer::new("Parquet row-group prefetch") .register(&options.memory_pool); - reservation.try_grow(bytes).ok()?; + if reservation.try_grow(bytes).is_err() { + metrics.budget_skips.add(1); + return None; + } + let metrics = metrics.clone(); let fetch_ranges = ranges.clone(); let task = SpawnedTask::spawn(async move { let data = reader.lock().await.get_byte_ranges(fetch_ranges).await?; + metrics.bytes.add(data.iter().map(Bytes::len).sum()); + metrics.row_groups.add(1); Ok((data, reservation)) }); Some(Self { @@ -353,8 +370,12 @@ pub(crate) struct PushDecoderStreamState { pub(crate) rg_plan: VecDeque, pub(crate) reader: Arc>>, pub(crate) row_group_prefetch: Option, + pub(crate) progressive_io: bool, + pub(crate) fetch_projection: ProjectionMask, + pub(crate) upfront_row_group: Option, pub(crate) parquet_metadata: Arc, pub(crate) pending_prefetch: Option, + pub(crate) prefetch_metrics: crate::metrics::PrefetchMetrics, pub(crate) prefetch_reservation: Option, /// Per-file projection: the mask installed on every decoder and the /// per-batch transform applied by [`Self::project_batch`]. @@ -430,6 +451,12 @@ pub(crate) struct RowFilterContext { } impl RowFilterContext { + pub(crate) fn extend_projection(&self, projection: &mut ProjectionMask) { + for candidate in self.prebuilt.as_slice() { + projection.union(&candidate.projection_mask); + } + } + /// Precompute the candidate list from the raw predicate + file schema + /// metadata. Returns `None` when the predicate has no push-downable /// conjuncts (mirrors the file-open path behaviour). @@ -573,12 +600,17 @@ impl PushDecoderStreamState { let decoder = self.decoder.as_mut().expect("decoder present"); match decoder.peek_next_row_group() { Ok(Some(next)) if next == prefetch.row_group => { - match prefetch.task.join_unwind().await { + let result = { + let _timer = self.prefetch_metrics.wait_time.timer(); + prefetch.task.join_unwind().await + }; + match result { Ok(Ok((data, reservation))) => { if let Err(e) = decoder.push_ranges(prefetch.ranges, data) { return Some((Err(e.into()), self)); } + self.upfront_row_group = Some(prefetch.row_group); self.prefetch_reservation = Some(reservation); } // A speculative error must not fail a scan that would @@ -600,7 +632,54 @@ impl PushDecoderStreamState { // Step 3: drive the decoder. let decoder = self.decoder.as_mut().expect("decoder present"); match decoder.try_next_reader() { - Ok(DecodeResult::NeedsData(ranges)) => { + Ok(DecodeResult::NeedsData(mut ranges)) => { + if !self.progressive_io { + // The decoder can skip fully filtered groups internally. + // Locate its current group from the demand range rather + // than assuming the previous group yielded a reader. + let row_group = self + .rg_plan + .iter() + .find(|entry| { + self.parquet_metadata + .row_group(entry.rg_index) + .columns() + .iter() + .any(|column| { + let (start, len) = column.byte_range(); + ranges.iter().any(|r| { + r.start >= start + && r.end <= start.saturating_add(len) + }) + }) + }) + .map(|entry| entry.rg_index); + if let Some(row_group) = row_group { + if let Err(e) = + Self::advance_rg_plan_to(&mut self.rg_plan, row_group) + { + return Some((Err(e), self)); + } + if self.upfront_row_group != Some(row_group) { + decoder.clear_all_ranges(); + self.prefetch_reservation = None; + let Some(chunks) = column_chunk_ranges( + &self.parquet_metadata, + row_group, + &self.fetch_projection, + ) else { + return Some(( + internal_err!( + "Parquet column byte range overflows u64" + ), + self, + )); + }; + ranges = chunks; + self.upfront_row_group = Some(row_group); + } + } + } let data = self .reader .lock() @@ -638,9 +717,9 @@ impl PushDecoderStreamState { self.active_reader = Some(reader); // The extracted reader now owns required bytes. Release any // unused speculation (e.g. pages removed by a row filter). - if let Some(reservation) = self.prefetch_reservation.take() { + if !self.progressive_io || self.prefetch_reservation.is_some() { decoder.clear_all_ranges(); - drop(reservation); + self.prefetch_reservation = None; } if let Some(options) = &self.row_group_prefetch { match decoder.peek_next_row_group() { @@ -648,9 +727,10 @@ impl PushDecoderStreamState { self.pending_prefetch = PrefetchedRowGroup::start( next, &self.parquet_metadata, - self.decoder_projection.projection_mask(), + &self.fetch_projection, options, Arc::clone(&self.reader), + &self.prefetch_metrics, ); } Ok(None) => {} @@ -845,7 +925,10 @@ mod tests { } fn build_three_rg_file_data() -> (Bytes, Arc, SchemaRef) { - let schema = Arc::new(Schema::new(vec![Field::new("v", DataType::Int64, false)])); + let schema = Arc::new(Schema::new(vec![ + Field::new("v", DataType::Int64, false), + Field::new("w", DataType::Int64, false), + ])); let mut buf = Vec::new(); let props = WriterProperties::builder() .set_max_row_group_row_count(Some(1000)) @@ -857,7 +940,10 @@ mod tests { let vals: Vec = (base..base + 1000).collect(); let batch = RecordBatch::try_new( Arc::clone(&schema), - vec![Arc::new(Int64Array::from(vals))], + vec![ + Arc::new(Int64Array::from(vals.clone())), + Arc::new(Int64Array::from(vals)), + ], ) .unwrap(); writer.write(&batch).unwrap(); @@ -968,6 +1054,17 @@ mod tests { control: Arc, limit: Option, predicate: Option>, + ) -> datafusion_execution::SendableRecordBatchStream { + io_test_stream(budget, pool, control, limit, predicate, true) + } + + fn io_test_stream( + budget: usize, + pool: Arc, + control: Arc, + limit: Option, + predicate: Option>, + progressive: bool, ) -> datafusion_execution::SendableRecordBatchStream { use datafusion_datasource::file_scan_config::FileScanConfigBuilder; use datafusion_datasource::source::DataSourceExec; @@ -979,6 +1076,7 @@ mod tests { let (data, metadata, schema) = build_three_rg_file_data(); let file = PartitionedFile::new("prefetch.parquet", data.len() as u64); let mut source = crate::source::ParquetSource::new(schema) + .with_progressive_io(progressive) .with_row_group_prefetch(budget, pool) .with_pushdown_filters(true) .with_parquet_file_reader_factory(Arc::new(TestReader { @@ -995,6 +1093,8 @@ mod tests { ) .with_file_group(FileGroup::new(vec![file])) .with_limit(limit) + .with_projection_indices(Some(vec![0])) + .unwrap() .build(); let task = TaskContext::default() .with_session_config(SessionConfig::new().with_batch_size(100)); @@ -1230,6 +1330,68 @@ mod tests { } } + #[tokio::test] + async fn upfront_reads_include_predicate_only_columns_and_skip_empty_groups() { + use datafusion_execution::memory_pool::GreedyMemoryPool; + use std::sync::atomic::Ordering; + // A predicate-only column empties the first, middle, or last group + // without row-group statistics pruning. Compare every returned value. + for empty_group in 0..3i64 { + for progressive in [false, true] { + for budget in [0, 1 << 20] { + let predicate = Arc::new(BinaryExpr::new( + Arc::new(BinaryExpr::new( + Arc::new(Column::new("w", 1)), + Operator::Divide, + lit(1000i64), + )), + Operator::NotEq, + lit(empty_group), + )) as Arc; + let pool: Arc = + Arc::new(GreedyMemoryPool::new(1 << 20)); + let control = Arc::new(ReadControl::default()); + let mut stream = io_test_stream( + budget, + Arc::clone(&pool), + Arc::clone(&control), + None, + Some(predicate), + progressive, + ); + let mut values = Vec::new(); + while let Some(batch) = stream.next().await { + let batch = batch.unwrap(); + assert_eq!(batch.num_columns(), 1); + values.extend_from_slice( + batch + .column(0) + .as_any() + .downcast_ref::() + .unwrap() + .values(), + ); + } + assert_eq!( + values, + (0..3000) + .filter(|v| v / 1000 != empty_group) + .collect::>() + ); + let expected_calls = if !progressive { + 3 + } else if budget > 0 && empty_group != 1 { + 4 + } else { + 5 + }; + assert_eq!(control.calls.load(Ordering::SeqCst), expected_calls); + assert_pool_released(&pool).await; + } + } + } + } + /// Controlled scheduling experiment, not a production throughput benchmark. #[tokio::test(flavor = "multi_thread", worker_threads = 2)] #[ignore = "manual benchmark with simulated I/O and batch processing latency"] diff --git a/datafusion/datasource-parquet/src/row_filter.rs b/datafusion/datasource-parquet/src/row_filter.rs index 833764ba0809..d788e2fc35b6 100644 --- a/datafusion/datasource-parquet/src/row_filter.rs +++ b/datafusion/datasource-parquet/src/row_filter.rs @@ -460,7 +460,7 @@ pub(crate) struct PrebuiltRowFilterCandidate { physical_expr: Arc, /// Projection mask over the parquet leaf columns needed to evaluate this /// predicate. - projection_mask: ProjectionMask, + pub(crate) projection_mask: ProjectionMask, /// Precomputed sum-of-compressed-bytes for the referenced columns across /// all row groups in the file. Used to sort predicates when /// `reorder_predicates` is enabled. Stable across row groups within a diff --git a/datafusion/datasource-parquet/src/source.rs b/datafusion/datasource-parquet/src/source.rs index 356138c20e0b..a129def4a74e 100644 --- a/datafusion/datasource-parquet/src/source.rs +++ b/datafusion/datasource-parquet/src/source.rs @@ -321,6 +321,7 @@ pub struct ParquetSource { /// in the opener. sort_order_for_reorder: Option, row_group_prefetch: Option, + progressive_io: bool, } impl ParquetSource { @@ -348,14 +349,15 @@ impl ParquetSource { reverse_row_groups: false, sort_order_for_reorder: None, row_group_prefetch: None, + progressive_io: true, } } - /// Prefetch one upcoming row group's projected column chunks while decoding + /// Prefetch one upcoming row group's output and predicate column chunks while decoding /// the current group. Disabled by default; a zero budget disables it. /// /// `max_bytes` bounds additional compressed bytes per file stream, not the - /// current reader's memory. Prefetch is skipped if a complete projected row + /// current reader's memory. Prefetch is skipped if a complete fetched row /// group does not fit or `memory_pool` cannot reserve its bytes. Required /// reads continue normally. Use the execution's memory pool to account for /// concurrent scans together. @@ -376,6 +378,17 @@ impl ParquetSource { self } + /// Fetch column chunks progressively as decoding and row filtering require + /// them (the default). When false, the first demand read for each row group + /// fetches all output and predicate column chunks together. This reduces + /// dependent I/O rounds but can read pages that filtering would skip. + /// Controls demand reads; enabled prefetch always fetches full chunks. + /// This execution-local option is not serialized in physical plans. + pub fn with_progressive_io(mut self, progressive_io: bool) -> Self { + self.progressive_io = progressive_io; + self + } + /// Set the `TableParquetOptions` for this ParquetSource. pub fn with_table_parquet_options( mut self, @@ -664,6 +677,7 @@ impl FileSource for ParquetSource { Ok(Box::new(ParquetMorselizer { partition_index: partition, row_group_prefetch: self.row_group_prefetch.clone(), + progressive_io: self.progressive_io, projection: self.projection.clone(), batch_size: self .batch_size From 21284472b6639ea86ef0dfa1f471495cbf51d0f8 Mon Sep 17 00:00:00 2001 From: peterxcli Date: Wed, 9 Sep 2026 23:12:09 +0800 Subject: [PATCH 03/13] Document Parquet I/O policy matrix and DF55 controls --- .../datasource-parquet/IO_POLICY_BENCHMARK.md | 72 +++++++++++++++++++ 1 file changed, 72 insertions(+) diff --git a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md index a43775b8c874..f831e1f248f3 100644 --- a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md +++ b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md @@ -75,3 +75,75 @@ they do not establish Spark end-to-end or remote-storage speedups. excluding reads internal to `get_metadata`; they are not physical disk operations or HTTP request counts. `prefetch_peak` uses DataFusion's existing `PeakRecordingPool` to record peak prefetch reservations. It does not measure current-reader, decoded-buffer, or total process memory. `prefetch_bytes` and `prefetch_row_groups` count completed speculative reads, including bytes subsequently discarded. + +## Measurements (2026-09-09) + +Apple M4, 10 CPU cores, 24 GiB RAM, Rust 1.97.0, `release-nonlto`, two Tokio worker threads. Implementation: [`943ddfc20`](https://github.com/peterxcli/datafusion/commit/943ddfc209147ebd8022853482009c2827d77b59). The four files are approximately 1.29–1.32 GB compressed each. All 384 scans across two complete patched runs and all 64 baseline scans passed the independent row-count and checksum assertions, including warmups. + +The two tables report milliseconds, each cell a median of three measured scans. `+PF` means a 16 MiB prefetch budget. Both runs are shown because timings moved substantially even though no other build from this experiment ran during measurement. Other desktop activity was uncontrolled. These measurements support the I/O tradeoff; they do not establish a stable overall speedup or zero overhead with the options disabled. + +### First run + +| Data | Off | Off +PF | Progressive | Progressive +PF | Upfront | Upfront +PF | +| ----------------------------- | -----: | ------: | ----------: | --------------: | ------: | ----------: | +| random, no indexes, wide | 1856.0 | 1693.7 | 1845.9 | 1646.1 | 1717.3 | 1551.5 | +| random, no indexes, narrow | 365.9 | 337.5 | 379.3 | 334.8 | 360.2 | 331.5 | +| random, indexes, wide | 1654.3 | 1548.9 | 1772.4 | 1459.7 | 1586.5 | 1460.5 | +| random, indexes, narrow | 436.3 | 366.2 | 489.2 | 362.6 | 399.5 | 415.8 | +| clustered, no indexes, wide | 1807.7 | 1627.7 | 375.3 | 353.2 | 357.0 | 315.4 | +| clustered, no indexes, narrow | 351.3 | 327.5 | 217.3 | 213.2 | 210.5 | 210.8 | +| clustered, indexes, wide | 46.1 | 117.8 | 48.5 | 118.9 | 132.8 | 116.0 | +| clustered, indexes, narrow | 21.3 | 35.8 | 25.8 | 39.9 | 38.9 | 39.8 | + +### Repeat run + +| Data | Off | Off +PF | Progressive | Progressive +PF | Upfront | Upfront +PF | +| ----------------------------- | -----: | ------: | ----------: | --------------: | ------: | ----------: | +| random, no indexes, wide | 1729.8 | 1499.5 | 1655.6 | 1498.1 | 1642.4 | 1542.2 | +| random, no indexes, narrow | 416.2 | 371.6 | 404.0 | 347.8 | 411.2 | 371.0 | +| random, indexes, wide | 1841.6 | 1698.7 | 2374.9 | 1620.4 | 1798.1 | 1638.5 | +| random, indexes, narrow | 528.4 | 473.3 | 556.2 | 483.0 | 508.3 | 480.6 | +| clustered, no indexes, wide | 2099.7 | 1916.2 | 450.0 | 466.4 | 445.9 | 462.4 | +| clustered, no indexes, narrow | 369.0 | 353.3 | 298.8 | 275.6 | 245.6 | 255.0 | +| clustered, indexes, wide | 48.0 | 131.5 | 50.6 | 131.8 | 152.2 | 133.7 | +| clustered, indexes, narrow | 22.5 | 40.3 | 26.3 | 44.1 | 41.2 | 43.2 | + +### Unmodified DataFusion 55 control + +The baseline is [`d55523420`](https://github.com/peterxcli/datafusion/commit/d55523420), the fork's `codex/df55-base`. It uses the same benchmark harness, compiler/profile, dependency lock versions, and two-worker runtime, with only the new builder calls removed and the configurations restricted to off/progressive without prefetch. It ran after the first patched matrix and before the repeat. The table compares the progressive configuration without prefetch in all three runs. + +| Data | DF 55 | Patched first | Patched repeat | +| ----------------------------- | -----: | ------------: | -------------: | +| random, no indexes, wide | 1537.6 | 1845.9 | 1655.6 | +| random, no indexes, narrow | 358.5 | 379.3 | 404.0 | +| random, indexes, wide | 1763.0 | 1772.4 | 2374.9 | +| random, indexes, narrow | 380.9 | 489.2 | 556.2 | +| clustered, no indexes, wide | 308.7 | 375.3 | 450.0 | +| clustered, no indexes, narrow | 210.8 | 217.3 | 298.8 | +| clustered, indexes, wide | 50.4 | 48.5 | 50.6 | +| clustered, indexes, narrow | 25.6 | 25.8 | 26.3 | + +Several disabled-option controls were slower than unmodified DF 55, and the repeat also moved substantially. This experiment cannot distinguish code overhead from machine/load drift. A stable machine with interleaved baseline/patch trials is required before claiming an end-to-end improvement or promoting defaults. + +### Reader I/O and memory + +The following counts are from the first run, without prefetch. Byte counts include metadata. Calls and ranges have the wrapper scope described above. + +| Data | Progressive bytes | Upfront bytes | Progressive calls / ranges | Upfront calls / ranges | +| ----------------------------- | ----------------: | ------------: | -------------------------: | ---------------------: | +| random, no indexes, wide | 1,309,357,880 | 1,309,357,880 | 512 / 2048 | 256 / 2048 | +| random, no indexes, narrow | 303,126,105 | 303,126,105 | 512 / 512 | 256 / 512 | +| random, indexes, wide | 1,318,274,373 | 1,318,274,373 | 513 / 229633 | 257 / 2049 | +| random, indexes, narrow | 312,042,598 | 312,042,598 | 513 / 33025 | 257 / 513 | +| clustered, no indexes, wide | 1,278,042,437 | 1,278,042,437 | 512 / 2048 | 256 / 2048 | +| clustered, no indexes, narrow | 271,810,662 | 271,810,662 | 512 / 512 | 256 / 512 | +| clustered, indexes, wide | 28,883,141 | 1,286,958,261 | 513 / 4097 | 257 / 2049 | +| clustered, indexes, narrow | 13,160,491 | 280,726,486 | 513 / 1025 | 257 / 513 | + +For random matches without indexes, upfront reads reduce 512 reader calls to 256 while fetching the same bytes. With indexes, batching also removes the many small selected-page ranges. For indexed clustered matches, progressive fetching reads only selected pages: upfront reads instead fetch **44.6×** as many bytes for wide output and **21.3×** for narrow output. The wide upfront configurations take approximately 2.4–3.0× the progressive time across these runs. + +To isolate overlap, compare **Upfront** with **Upfront +PF**, which fetch the same chunks. For random wide output without indexes, prefetch reduced the median from 1717.3 to 1551.5 ms (9.7%) in the first run and from 1642.4 to 1542.2 ms (6.1%) in the repeat. This is evidence of potential overlap benefit in this local workload, with the baseline and timing caveats above. Some cases regress: clustered wide output without indexes improved by 11.6% in the first run but regressed by 3.7% in the repeat. + +All groups fit the 16 MiB prefetch budget in this fixture; 255 future groups were prefetched. Peak compressed prefetch reservations were about 5.1 MB for wide output and 1.1 MB for narrow output. These are exact reservation peaks from `PeakRecordingPool`, not total reader/process memory. Upfront demand allocations are outside that prefetch reservation. + +The full-chunk prefetch strategy loses much of page pruning's byte savings even when progressive demand reads are selected. Both options therefore remain opt-in. A remote-object-store benchmark with real latency, bandwidth limits, backend request counters, and concurrent scans is still needed before an upstream performance claim. From 8fd1d23d36d4d9346ecf4118d0dfd1a7062dfd3f Mon Sep 17 00:00:00 2001 From: peterxcli Date: Thu, 10 Sep 2026 01:57:08 +0800 Subject: [PATCH 04/13] docs: visualize Parquet I/O benchmark comparisons --- .../datasource-parquet/IO_POLICY_BENCHMARK.md | 27 +++++++++++++++++- .../parquet-policy-baseline.png | Bin 0 -> 158534 bytes .../benchmark-charts/parquet-policy-io.png | Bin 0 -> 143641 bytes .../parquet-policy-timings.png | Bin 0 -> 399568 bytes 4 files changed, 26 insertions(+), 1 deletion(-) create mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-policy-baseline.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-policy-io.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-policy-timings.png diff --git a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md index f831e1f248f3..60da19c7114f 100644 --- a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md +++ b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md @@ -80,7 +80,16 @@ speculative reads, including bytes subsequently discarded. Apple M4, 10 CPU cores, 24 GiB RAM, Rust 1.97.0, `release-nonlto`, two Tokio worker threads. Implementation: [`943ddfc20`](https://github.com/peterxcli/datafusion/commit/943ddfc209147ebd8022853482009c2827d77b59). The four files are approximately 1.29–1.32 GB compressed each. All 384 scans across two complete patched runs and all 64 baseline scans passed the independent row-count and checksum assertions, including warmups. -The two tables report milliseconds, each cell a median of three measured scans. `+PF` means a 16 MiB prefetch budget. Both runs are shown because timings moved substantially even though no other build from this experiment ran during measurement. Other desktop activity was uncontrolled. These measurements support the I/O tradeoff; they do not establish a stable overall speedup or zero overhead with the options disabled. +Each plotted value is a median of three measured scans. `+PF` means a 16 MiB prefetch budget. Both runs are shown because timings moved substantially even though no other build from this experiment ran during measurement. Other desktop activity was uncontrolled. These measurements support the I/O tradeoff; they do not establish a stable overall speedup or zero overhead with the options disabled. + +### Timing comparison + +Each panel uses the same logarithmic scale. **1× is progressive fetching without prefetch in that workload and run**; points to the left are faster, points to the right are slower. Normalizing each run makes policy differences visible across workloads, but hides absolute timing drift; the unmodified DF55 comparison below shows that separately. + +![Six-policy elapsed-time comparison across eight workloads. Blue circles show the first run and orange triangles the repeat, relative to progressive fetching without prefetch.](benchmark-charts/parquet-policy-timings.png) + +
+Exact timing medians in milliseconds: first and repeat runs ### First run @@ -108,10 +117,17 @@ The two tables report milliseconds, each cell a median of three measured scans. | clustered, indexes, wide | 48.0 | 131.5 | 50.6 | 131.8 | 152.2 | 133.7 | | clustered, indexes, narrow | 22.5 | 40.3 | 26.3 | 44.1 | 41.2 | 43.2 | +
+ ### Unmodified DataFusion 55 control The baseline is [`d55523420`](https://github.com/peterxcli/datafusion/commit/d55523420), the fork's `codex/df55-base`. It uses the same benchmark harness, compiler/profile, dependency lock versions, and two-worker runtime, with only the new builder calls removed and the configurations restricted to off/progressive without prefetch. It ran after the first patched matrix and before the repeat. The table compares the progressive configuration without prefetch in all three runs. +![Patched progressive reads without prefetch compared with unmodified DataFusion 55. Most points exceed 1x, and first-run and repeat ratios differ.](benchmark-charts/parquet-policy-baseline.png) + +
+Exact baseline timing medians in milliseconds + | Data | DF 55 | Patched first | Patched repeat | | ----------------------------- | -----: | ------------: | -------------: | | random, no indexes, wide | 1537.6 | 1845.9 | 1655.6 | @@ -123,12 +139,19 @@ The baseline is [`d55523420`](https://github.com/peterxcli/datafusion/commit/d55 | clustered, indexes, wide | 50.4 | 48.5 | 50.6 | | clustered, indexes, narrow | 25.6 | 25.8 | 26.3 | +
+ Several disabled-option controls were slower than unmodified DF 55, and the repeat also moved substantially. This experiment cannot distinguish code overhead from machine/load drift. A stable machine with interleaved baseline/patch trials is required before claiming an end-to-end improvement or promoting defaults. ### Reader I/O and memory The following counts are from the first run, without prefetch. Byte counts include metadata. Calls and ranges have the wrapper scope described above. +![Progressive and upfront requested bytes for all eight workloads. Indexed clustered data reads 44.6x more bytes for wide output and 21.3x more for narrow output with upfront fetching.](benchmark-charts/parquet-policy-io.png) + +
+Exact requested bytes, reader calls, and logical ranges + | Data | Progressive bytes | Upfront bytes | Progressive calls / ranges | Upfront calls / ranges | | ----------------------------- | ----------------: | ------------: | -------------------------: | ---------------------: | | random, no indexes, wide | 1,309,357,880 | 1,309,357,880 | 512 / 2048 | 256 / 2048 | @@ -140,6 +163,8 @@ The following counts are from the first run, without prefetch. Byte counts inclu | clustered, indexes, wide | 28,883,141 | 1,286,958,261 | 513 / 4097 | 257 / 2049 | | clustered, indexes, narrow | 13,160,491 | 280,726,486 | 513 / 1025 | 257 / 513 | +
+ For random matches without indexes, upfront reads reduce 512 reader calls to 256 while fetching the same bytes. With indexes, batching also removes the many small selected-page ranges. For indexed clustered matches, progressive fetching reads only selected pages: upfront reads instead fetch **44.6×** as many bytes for wide output and **21.3×** for narrow output. The wide upfront configurations take approximately 2.4–3.0× the progressive time across these runs. To isolate overlap, compare **Upfront** with **Upfront +PF**, which fetch the same chunks. For random wide output without indexes, prefetch reduced the median from 1717.3 to 1551.5 ms (9.7%) in the first run and from 1642.4 to 1542.2 ms (6.1%) in the repeat. This is evidence of potential overlap benefit in this local workload, with the baseline and timing caveats above. 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zR`DSDm*S^9#s1>)_P>5h+2Y%0`qKW5UY%xui0*^+bD8$*S5JV-7|34ycij{9jb>Dy zh<2hBU~fNv0ATaAe*fPI8tz4ao8R^Oub-tPi~jJe{QZyB(t&2l`WYCe6>hkClBsZF z=&-QD$R)xU{N4FvVkzAJ`$Ikdot!0?tHVW}Et!9h;s57dP8k_YzWmp}{eS=ZzhM;r f9}S~e4SB@euV#H+kNICi`ouqf5ib6u Date: Thu, 10 Sep 2026 03:21:19 +0800 Subject: [PATCH 05/13] feat: preserve page pruning with configurable Parquet I/O --- datafusion/common/src/config.rs | 7 + .../common/src/file_options/parquet_writer.rs | 3 + .../core/tests/parquet/filter_pushdown.rs | 201 ++++++++++++++++++ .../datasource-parquet/IO_POLICY_BENCHMARK.md | 187 ++++++++++++++-- .../benchmark-charts/parquet-pages-bytes.png | Bin 0 -> 53348 bytes .../parquet-pages-clickbench.png | Bin 0 -> 97619 bytes .../parquet-pages-timings.png | Bin 0 -> 234901 bytes .../datasource-parquet/examples/io_policy.rs | 7 + .../datasource-parquet/src/file_format.rs | 1 + .../datasource-parquet/src/opener/mod.rs | 86 ++++---- .../datasource-parquet/src/push_decoder.rs | 99 +++++++-- datafusion/datasource-parquet/src/source.rs | 20 +- .../proto/datafusion_common.proto | 3 +- datafusion/proto-common/src/from_proto/mod.rs | 16 ++ .../proto-common/src/generated/pbjson.rs | 18 ++ .../proto-common/src/generated/prost.rs | 3 + datafusion/proto-common/src/to_proto/mod.rs | 1 + datafusion/proto-models/src/from_proto.rs | 1 + .../src/generated/datafusion_proto_common.rs | 3 + datafusion/proto/tests/cases/plans/sources.rs | 1 + .../tests/cases/roundtrip_logical_plan.rs | 2 + .../test_files/information_schema.slt | 2 + docs/source/user-guide/configs.md | 1 + 23 files changed, 569 insertions(+), 93 deletions(-) create mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-pages-bytes.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-pages-clickbench.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-pages-timings.png diff --git a/datafusion/common/src/config.rs b/datafusion/common/src/config.rs index f770cebf88f9..bf0e4f337466 100644 --- a/datafusion/common/src/config.rs +++ b/datafusion/common/src/config.rs @@ -1335,6 +1335,13 @@ config_namespace! { /// reduce the number of rows decoded. This optimization is sometimes called "late materialization". pub pushdown_filters: bool, default = false + /// (reading) Fetch columns progressively as row filtering and decoding need + /// them. If false, fetch output and predicate pages together before evaluating + /// row filters, preserving page-index pruning performed at file open. This + /// can reduce dependent I/O rounds but may fetch pages that row filtering + /// would otherwise avoid. Does not enable filter pushdown or prefetch. + pub progressive_io: bool, default = true + /// (reading) If true, filter expressions evaluated during the parquet decoding operation /// will be reordered heuristically to minimize the cost of evaluation. If false, /// the filters are applied in the same order as written in the query diff --git a/datafusion/common/src/file_options/parquet_writer.rs b/datafusion/common/src/file_options/parquet_writer.rs index 78c14da960b0..b13d3b51bfe0 100644 --- a/datafusion/common/src/file_options/parquet_writer.rs +++ b/datafusion/common/src/file_options/parquet_writer.rs @@ -236,6 +236,7 @@ impl ParquetOptions { skip_metadata: _, metadata_size_hint: _, pushdown_filters: _, + progressive_io: _, reorder_filters: _, force_filter_selections: _, // not used for writer props allow_single_file_parallelism: _, @@ -494,6 +495,7 @@ mod tests { skip_metadata: defaults.skip_metadata, metadata_size_hint: defaults.metadata_size_hint, pushdown_filters: defaults.pushdown_filters, + progressive_io: defaults.progressive_io, reorder_filters: defaults.reorder_filters, force_filter_selections: defaults.force_filter_selections, allow_single_file_parallelism: defaults.allow_single_file_parallelism, @@ -580,6 +582,7 @@ mod tests { TableParquetOptions { global: ParquetOptions { + progressive_io: true, // global options data_pagesize_limit: props.data_page_size_limit(), write_batch_size: props.write_batch_size(), diff --git a/datafusion/core/tests/parquet/filter_pushdown.rs b/datafusion/core/tests/parquet/filter_pushdown.rs index d337979e5fd0..ea7c0d59114c 100644 --- a/datafusion/core/tests/parquet/filter_pushdown.rs +++ b/datafusion/core/tests/parquet/filter_pushdown.rs @@ -809,3 +809,204 @@ async fn pushed_down_predicate_reports_the_original_error() { "expected the original cast error, got {root:?}" ); } + +#[tokio::test] +async fn upfront_io_preserves_page_pruning_from_session_configuration() { + use arrow::array::{Int64Array, StringArray, StructArray}; + use arrow::datatypes::{DataType, Field, Schema}; + use datafusion_common::tree_node::{Transformed, TreeNode}; + use datafusion_datasource::file_scan_config::FileScanConfigBuilder; + use datafusion_datasource::source::DataSourceExec; + use datafusion_datasource_parquet::source::ParquetSource; + use datafusion_execution::memory_pool::GreedyMemoryPool; + use parquet::arrow::ArrowWriter; + use parquet::file::properties::EnabledStatistics; + use std::sync::Arc; + + let nested_fields = vec![Arc::new(Field::new("value", DataType::Int64, false))]; + let schema = Arc::new(Schema::new(vec![ + Field::new("key", DataType::Int64, false), + Field::new("payload", DataType::Utf8, false), + Field::new( + "nested", + DataType::Struct(nested_fields.clone().into()), + false, + ), + ])); + let temp = TempDir::new().unwrap(); + for clustered in [false, true] { + let expected: Vec = (0..3) + .flat_map(|group| { + (0..4096) + .filter(move |row| { + if clustered { + *row < 100 + } else { + row % 128 < 100 + } + }) + .map(move |row| group * 4096 + row) + }) + .collect(); + for indexed in [false, true] { + let path = temp + .path() + .join(format!("pages-{indexed}-{clustered}.parquet")); + let props = WriterProperties::builder() + .set_max_row_group_row_count(Some(4096)) + .set_data_page_row_count_limit(128) + .set_write_batch_size(128) + .set_dictionary_enabled(true) + .set_statistics_enabled(if indexed { + EnabledStatistics::Page + } else { + EnabledStatistics::Chunk + }) + .set_offset_index_disabled(!indexed) + .build(); + let mut writer = ArrowWriter::try_new( + std::fs::File::create(&path).unwrap(), + Arc::clone(&schema), + Some(props), + ) + .unwrap(); + for group in 0..3i64 { + let nested = StructArray::new( + nested_fields.clone().into(), + vec![Arc::new(Int64Array::from_iter_values( + (0..4096).map(|row| group * 4096 + row), + ))], + None, + ); + let batch = RecordBatch::try_new( + Arc::clone(&schema), + vec![ + Arc::new(Int64Array::from_iter_values( + (0..4096).map(|row| if clustered { row } else { row % 128 }), + )), + Arc::new(StringArray::from_iter_values( + (0..4096).map(|row| format!("value-{}", row % 11)), + )), + Arc::new(nested), + ], + ) + .unwrap(); + writer.write(&batch).unwrap(); + writer.flush().unwrap(); + } + let metadata = writer.close().unwrap(); + assert_eq!(metadata.num_row_groups(), 3); + for group in metadata.row_groups() { + assert!(group.column(1).dictionary_page_offset().is_some()); + assert_eq!(group.column(0).offset_index_offset().is_some(), indexed); + } + for predicate_in_output in [false, true] { + let mut expected_batch = None; + let mut baseline_bytes = None; + for (pushdown, progressive) in + [(false, true), (true, true), (true, false)] + { + for budget in [0, 1 << 20] { + let config = SessionConfig::new() + .with_target_partitions(1) + .with_batch_size(127); + let ctx = SessionContext::new_with_config(config); + for (key, value) in [ + ("pushdown_filters", pushdown), + ("progressive_io", progressive), + ] { + ctx.sql(&format!( + "SET datafusion.execution.parquet.{key} = {value}" + )) + .await + .unwrap() + .collect() + .await + .unwrap(); + } + ctx.register_parquet( + "t", + path.to_str().unwrap(), + ParquetReadOptions::default(), + ) + .await + .unwrap(); + let key = if predicate_in_output { "key," } else { "" }; + let plan = ctx.sql(&format!("SELECT {key} payload, nested.value AS value FROM t WHERE key < 100 ORDER BY value")) + .await.unwrap().create_physical_plan().await.unwrap(); + let mut scans = 0; + let plan = plan + .transform_up(|plan| { + if let Some(exec) = plan.downcast_ref::() + && let Some((config, source)) = + exec.downcast_to_file_source::() + { + scans += 1; + assert_eq!( + source + .table_parquet_options() + .global + .progressive_io, + progressive + ); + let source = source.clone().with_row_group_prefetch( + budget, + Arc::new(GreedyMemoryPool::new(1 << 20)), + ); + let config = + FileScanConfigBuilder::from(config.clone()) + .with_source(Arc::new(source)) + .build(); + return Ok(Transformed::yes(Arc::new( + exec.clone().with_data_source(Arc::new(config)), + ))); + } + Ok(Transformed::no(plan)) + }) + .unwrap() + .data; + assert_eq!(scans, 1); + let batches = + collect(Arc::clone(&plan), ctx.task_ctx()).await.unwrap(); + let batch = concat_batches(&plan.schema(), &batches).unwrap(); + let values = batch + .column(batch.num_columns() - 1) + .as_any() + .downcast_ref::() + .unwrap(); + assert_eq!(values.values().as_ref(), expected.as_slice()); + if let Some(expected) = &expected_batch { + assert_eq!(&batch, expected); + } else { + expected_batch = Some(batch); + } + let metrics = TestParquetFile::parquet_metrics(&plan).unwrap(); + let bytes = get_value(&metrics, "bytes_scanned"); + if indexed && clustered { + assert!( + get_pruning_metrics(&metrics, "page_index_rows_pruned").0 + > 0 + ); + } + if !predicate_in_output { + if let Some(expected) = baseline_bytes { + assert_eq!( + bytes, expected, + "clustered={clustered}, indexed={indexed}, pushdown={pushdown}, progressive={progressive}, budget={budget}" + ); + } else { + baseline_bytes = Some(bytes); + } + } + if budget > 0 { + assert!(get_value(&metrics, "prefetch_bytes") > 0); + } + if pushdown { + assert!(get_value(&metrics, "pushdown_rows_pruned") > 0); + } + } + } + } + } + } +} diff --git a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md index 60da19c7114f..9ff49c3d8bb7 100644 --- a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md +++ b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md @@ -20,25 +20,48 @@ # Parquet I/O policy experiment This fork experiment targets DataFusion 55.0.0 so Comet can use the patch through -Cargo git dependencies. Both new options are execution-local `ParquetSource` -builders. Progressive fetching remains the default and prefetch is disabled by -default. - -- `with_progressive_io(false)` fetches the output and predicate column chunks - together when a row group first requests data. Filters still determine which - rows are returned, but can no longer save the bytes already fetched. -- `with_row_group_prefetch(bytes, memory_pool)` fetches at most one next row group - while the current reader produces batches. The budget and memory pool bound - additional compressed bytes; they do not bound current-reader or decoded memory. - The full group is skipped when it cannot fit. Dropping the scan cancels pending - work. A speculative read error is retried through the ordinary demand path. - -The two controls are configured independently. Prefetch includes columns used only -by row filters and always fetches full chunks. Consequently, progressive fetching -with prefetch also changes the I/O policy for groups successfully prefetched. To -isolate overlap while holding fetched chunks constant, compare upfront fetching -with prefetch off/on. Compare progressive and upfront with prefetch off to measure -the dependent-read versus extra-bytes tradeoff. +Cargo git dependencies. Progressive fetching remains the default and next-row-group +prefetch is disabled by default. + +For `SELECT name FROM people WHERE age > 30`, **progressive reads** fetch the `age` +pages, evaluate the filter, then fetch the `name` pages needed for surviving rows. +**Upfront reads** fetch the required `age` and `name` pages together before +running the row filter. Both first use metadata to skip irrelevant row groups +and pages. Upfront reads reduce dependent I/O rounds, but can fetch pages that +the row filter would have ruled out. + +The current implementation preserves the page selection made at file open, +including external row selections and dictionary pages. Adjacent ranges are merged +so a later whole-chunk decoder request can reuse them; gaps left by page pruning +remain unread. Without an offset index, +it fetches complete column chunks. Predicate caching can expand decoder requests +to batch boundaries; those requests remain supported, so this is not a promise +that every scan finishes in exactly one read per row group. + +Use normal DataFusion session configuration, including SQL `SET`, before +registering the table: + +```sql +SET datafusion.execution.parquet.pushdown_filters = true; +SET datafusion.execution.parquet.progressive_io = false; +``` + +`ParquetSource::with_progressive_io(false)` sets the same table option. The setting +survives plan serialization; older serialized plans retain progressive reads. +It does not enable filter pushdown automatically. + +`with_row_group_prefetch(bytes, memory_pool)` separately fetches at most one next +row group's selected output and predicate pages while the current reader produces +batches. Its budget bounds additional compressed bytes, not current-reader or +decoded memory. A group is skipped if its selected bytes cannot fit. Dropping the +scan cancels pending work; speculative read errors retry through demand reads. +This execution-local prefetch option is not serialized. + +Prefetch can fetch pages that later row filtering or dynamic pruning would skip. +Compare progressive and upfront with prefetch **off** to isolate the policy in +[#24393](https://github.com/apache/datafusion/issues/24393). Compare upfront with +prefetch off/on separately to assess overlap. Neither option changes defaults +or establishes that the broader filter-pushdown regressions are resolved. ## Reproduce @@ -76,7 +99,129 @@ excluding reads internal to `get_metadata`; they are not physical disk operation or HTTP request counts. `prefetch_peak` uses DataFusion's existing `PeakRecordingPool` to record peak prefetch reservations. It does not measure current-reader, decoded-buffer, or total process memory. `prefetch_bytes` and `prefetch_row_groups` count completed speculative reads, including bytes subsequently discarded. -## Measurements (2026-09-09) +## Page-selection revision (2026-09-10) + +Apple M4, 10 CPU cores, 24 GiB RAM; Rust 1.97.0, Arrow/Parquet 59.2.0, +`release-nonlto`, two Tokio workers for the synthetic matrix. + +The same 256-group matrix was rerun after preserving page selections. All 192 scans passed the independent row-count/checksum assertions. In all eight workloads and all six policies, requested byte counts now match the pushdown-off control, including prefetch. This equality is specific to this predicate-only fixture; row-filter selectivity, dictionary layout, and predicate-cache expansion can change the comparison in other queries. + +![Current policy timing ratios.](benchmark-charts/parquet-pages-timings.png) + +![Earlier whole-chunk versus current page-selected requested bytes.](benchmark-charts/parquet-pages-bytes.png) + +

+Current medians in milliseconds + +| Workload | Off | Off +PF | Progressive | Progressive +PF | Upfront | Upfront +PF | +| ----------------------------- | -----: | ------: | ----------: | --------------: | ------: | ----------: | +| random, no indexes, wide | 1494.4 | 1385.7 | 1512.0 | 1407.6 | 1516.2 | 1422.4 | +| random, no indexes, narrow | 355.7 | 330.7 | 354.3 | 325.6 | 349.9 | 325.2 | +| random, indexes, wide | 1500.1 | 1390.5 | 1715.8 | 1430.7 | 1534.6 | 1429.2 | +| random, indexes, narrow | 356.8 | 344.4 | 378.9 | 332.8 | 355.3 | 332.4 | +| clustered, no indexes, wide | 1479.3 | 1383.7 | 299.4 | 289.8 | 290.0 | 288.2 | +| clustered, no indexes, narrow | 340.2 | 318.5 | 208.1 | 203.2 | 205.7 | 202.7 | +| clustered, indexes, wide | 44.4 | 37.8 | 47.5 | 36.0 | 42.1 | 36.0 | +| clustered, indexes, narrow | 21.3 | 18.9 | 25.7 | 22.5 | 20.9 | 22.5 | + +
+ +
+Current requested bytes and reader calls (prefetch off) + +| Workload | Bytes (all policies) | Off calls | Progressive calls | Upfront calls | +| ----------------------------- | -------------------: | --------: | ----------------: | ------------: | +| random, no indexes, wide | 1,309,357,880 | 256 | 512 | 256 | +| random, no indexes, narrow | 303,126,105 | 256 | 512 | 256 | +| random, indexes, wide | 1,318,274,373 | 257 | 513 | 257 | +| random, indexes, narrow | 312,042,598 | 257 | 513 | 257 | +| clustered, no indexes, wide | 1,278,042,437 | 256 | 512 | 256 | +| clustered, no indexes, narrow | 271,810,662 | 256 | 512 | 256 | +| clustered, indexes, wide | 28,883,141 | 257 | 513 | 257 | +| clustered, indexes, narrow | 13,160,491 | 257 | 513 | 257 | + +
+ +
+Fresh unmodified DF55 control, prefetch off (milliseconds) + +| Workload | Baseline off | Current off | Baseline progressive | Current progressive | +| ----------------------------- | -----------: | ----------: | -------------------: | ------------------: | +| random, no indexes, wide | 1493.3 | 1494.4 | 1503.0 | 1512.0 | +| random, no indexes, narrow | 354.9 | 355.7 | 356.1 | 354.3 | +| random, indexes, wide | 1494.1 | 1500.1 | 1712.8 | 1715.8 | +| random, indexes, narrow | 359.9 | 356.8 | 378.2 | 378.9 | +| clustered, no indexes, wide | 1497.1 | 1479.3 | 302.2 | 299.4 | +| clustered, no indexes, narrow | 343.7 | 340.2 | 209.5 | 208.1 | +| clustered, indexes, wide | 44.6 | 44.4 | 47.5 | 47.5 | +| clustered, indexes, narrow | 21.1 | 21.3 | 25.4 | 25.7 | + +
+ +The baseline rerun uses the unchanged DF55 executable described below and passed all 64 scan assertions. The current matrix ran before the baseline, with no builds from this task running during either measurement. These are local desktop samples, not evidence that the defaults have zero overhead or that the wider performance epic is resolved. + +### Reported ClickBench query regressions + +The existing `dfbench clickbench` runner executed the unchanged Q10, Q11, Q22, +Q24, Q25, and Q26 query files referenced by the regression work in +[#20324](https://github.com/apache/datafusion/issues/20324). Input was the public +[`hits_0.parquet`](https://datasets.clickhouse.com/hits_compatible/athena_partitioned/hits_0.parquet) +partition: 122,446,530 bytes, 1,000,000 rows, two row groups. This covers one +partition of the 100-file dataset. Target partitions were set to one, filter +reordering was enabled in every case, and next-row-group prefetch was disabled. + +Each case ran four iterations. The table and chart use the median of iterations +1–3, after the first warmup. The second pass reversed the policy order. All 144 +executions succeeded and returned ten rows. The benchmark records row counts; +result values were not independently compared. The SQL reader +regression test separately checks values across all 48 policy/data combinations. + +![ClickBench progressive and upfront timings relative to pushdown off, in two passes.](benchmark-charts/parquet-pages-clickbench.png) + +Q11, Q22, Q24, Q25, and Q26 remained slower with upfront I/O in both passes. +For example, Q25 upfront medians were 6.55 and 7.36 ms, versus 4.86 ms with +pushdown off in each pass. Changing the I/O policy alone does not remove these +observed slowdowns. The small local sample does not identify their cause; +full-dataset measurements and profiling remain necessary before changing defaults. + +
+ClickBench medians in milliseconds + +| Query | Off, first | Progressive, first | Upfront, first | Off, repeat | Progressive, repeat | Upfront, repeat | +| ----- | ---------: | -----------------: | -------------: | ----------: | ------------------: | --------------: | +| Q10 | 5.06 | 5.00 | 5.01 | 4.22 | 4.13 | 4.44 | +| Q11 | 5.03 | 5.83 | 5.90 | 4.76 | 5.26 | 5.08 | +| Q22 | 90.53 | 101.25 | 100.25 | 83.56 | 102.69 | 100.46 | +| Q24 | 6.87 | 8.79 | 8.66 | 7.30 | 8.73 | 9.23 | +| Q25 | 4.86 | 6.77 | 6.55 | 4.86 | 6.69 | 7.36 | +| Q26 | 7.02 | 9.63 | 9.18 | 7.08 | 9.94 | 9.81 | + +
+ +Example command for upfront Q10, using the same build profile as the matrix: + +```sh +cargo build --profile release-nonlto -p datafusion-benchmarks --bin dfbench +curl -LO https://datasets.clickhouse.com/hits_compatible/athena_partitioned/hits_0.parquet +target/release-nonlto/dfbench clickbench --path hits_0.parquet \ + --queries-path benchmarks/queries/clickbench/queries --query 10 \ + --iterations 4 --partitions 1 --pushdown \ + -c datafusion.execution.parquet.reorder_filters=true \ + -c datafusion.execution.parquet.progressive_io=false +``` + +Use `progressive_io=true` for progressive reads. For the off control, omit +`--pushdown` and set `datafusion.execution.parquet.pushdown_filters=false`. +Repeat for query numbers 10, 11, 22, 24, 25, and 26. + +## Earlier measurements: whole-chunk implementation (2026-09-09) + +These historical results describe commit `943ddfc20`, before the page-selection +fix above. The 21–45× byte amplification below is the regression this revision +addresses; it must not be attributed to the current implementation. + +
+Earlier charts, timings, and baseline controls Apple M4, 10 CPU cores, 24 GiB RAM, Rust 1.97.0, `release-nonlto`, two Tokio worker threads. Implementation: [`943ddfc20`](https://github.com/peterxcli/datafusion/commit/943ddfc209147ebd8022853482009c2827d77b59). The four files are approximately 1.29–1.32 GB compressed each. All 384 scans across two complete patched runs and all 64 baseline scans passed the independent row-count and checksum assertions, including warmups. @@ -172,3 +317,5 @@ To isolate overlap, compare **Upfront** with **Upfront +PF**, which fetch the sa All groups fit the 16 MiB prefetch budget in this fixture; 255 future groups were prefetched. Peak compressed prefetch reservations were about 5.1 MB for wide output and 1.1 MB for narrow output. These are exact reservation peaks from `PeakRecordingPool`, not total reader/process memory. Upfront demand allocations are outside that prefetch reservation. The full-chunk prefetch strategy loses much of page pruning's byte savings even when progressive demand reads are selected. Both options therefore remain opt-in. A remote-object-store benchmark with real latency, bandwidth limits, backend request counters, and concurrent scans is still needed before an upstream performance claim. + +
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zu^;m9hP>n`dURz(y+PmfmHdtC2};<>$0VP$*EzjFK&$(cfWNwe`)%4gvB-)BJqne( zqi0fd1c@~s>8%Yj+Lm0yyK>x1DvzZ|0JY+|l4U8Eh`Tw`1pkT8?&>r=+@aNBd$Dgx zur&{qtQllX2HUpF&`n-}JSb7*R|{Q@D11hOfYAQ-!$T=q4WpvSp0G8 z+D*Vpa)FlQ^R6BnLd6via>(EK541b^@IvCa!E9!*=r-{@_#zTfySnAincm`22#!xJd8gOhnH_o4r-l#iN zz$Qx#@84JgdELgN+y|+EZlaChp@fExkQ8Y;|NUxIR0{{?;(vVozb{{AD&>Hiw Tl8@b?v6Aw24fz5&^N0Tp!R-W1 literal 0 HcmV?d00001 diff --git a/datafusion/datasource-parquet/examples/io_policy.rs b/datafusion/datasource-parquet/examples/io_policy.rs index 12360b13db16..e1a2d622adf9 100644 --- a/datafusion/datasource-parquet/examples/io_policy.rs +++ b/datafusion/datasource-parquet/examples/io_policy.rs @@ -217,6 +217,7 @@ async fn main() -> Result<(), Box> { } else { vec![0, 2, 3, 4, 5, 6, 7] }; + let mut expected_bytes = None; for round in 0..4 { // Rotate all six configurations to reduce ordering bias. for offset in 0..6 { @@ -319,6 +320,12 @@ async fn main() -> Result<(), Box> { v => v.as_usize(), }).unwrap_or(0) }; + let bytes = metric("bytes_scanned"); + assert_eq!( + bytes, + *expected_bytes.get_or_insert(bytes), + "I/O policy must preserve this fixture's page pruning without duplicate reads" + ); println!( "{},{},{},{},{},{round},{size},{elapsed:.3},{rows},{checksum},{},{},{},{},{peak},{},{}", if clustered { "clustered" } else { "random" }, diff --git a/datafusion/datasource-parquet/src/file_format.rs b/datafusion/datasource-parquet/src/file_format.rs index 1b25a2c63251..eae79993adc8 100644 --- a/datafusion/datasource-parquet/src/file_format.rs +++ b/datafusion/datasource-parquet/src/file_format.rs @@ -730,6 +730,7 @@ impl From<&ParquetFormatFactory> for protobuf::TableParquetOptions { parquet_options::MetadataSizeHintOpt::MetadataSizeHint(size as u64) }), pushdown_filters: global_options.global.pushdown_filters, + progressive_io: Some(global_options.global.progressive_io), reorder_filters: global_options.global.reorder_filters, force_filter_selections: global_options.global.force_filter_selections, data_pagesize_limit: global_options.global.data_pagesize_limit as u64, diff --git a/datafusion/datasource-parquet/src/opener/mod.rs b/datafusion/datasource-parquet/src/opener/mod.rs index bf6bf3f1719c..0f4b1fa71cfc 100644 --- a/datafusion/datasource-parquet/src/opener/mod.rs +++ b/datafusion/datasource-parquet/src/opener/mod.rs @@ -1483,7 +1483,6 @@ impl RowGroupsPrunedParquetOpen { filter_installed, row_filter_context, } = { - let pushdown_predicate = prepared .pushdown_filters .then_some(prepared.predicate.as_ref()) @@ -1519,6 +1518,21 @@ impl RowGroupsPrunedParquetOpen { decoder_limit: prepared.limit, }; + let mut fetch_selections = + if !prepared.progressive_io || prepared.row_group_prefetch.is_some() { + access_plan + .inner() + .iter() + .map(|access| match access { + crate::RowGroupAccess::Selection(selection) => { + Some(selection.clone()) + } + _ => None, + }) + .collect::>() + } else { + vec![] + }; let prepared_access_plan = prepare_access_plan(access_plan)?; // #24355: a row selection (from page-index pruning, or an externally // supplied `ParquetRowSelection`) is carried by the decoder as one @@ -1545,6 +1559,9 @@ impl RowGroupsPrunedParquetOpen { .zip(prepared_access_plan.fully_matched.iter().copied()) .map(|(rg_index, fully_matched)| RgPlanEntry { rg_index, + row_selection: fetch_selections + .get_mut(rg_index) + .and_then(Option::take), fully_matched, bytes: row_group_bytes(&rg_metadata[rg_index]), }) @@ -1589,7 +1606,6 @@ impl RowGroupsPrunedParquetOpen { builder = builder .with_max_predicate_cache_size(max_predicate_cache_size); } - } } @@ -3373,6 +3389,8 @@ mod test { let props = WriterProperties::builder() .set_max_row_group_row_count(Some(4)) + .set_data_page_row_count_limit(1) + .set_write_batch_size(1) .build(); let data_len = write_parquet_batches( @@ -3407,43 +3425,33 @@ mod test { ) .with_extension(access_plan); - let make_opener = |reverse_scan: bool| { - ParquetMorselizerBuilder::new() - .with_store(Arc::clone(&store)) - .with_schema(Arc::clone(&schema)) - .with_projection_indices(&[0]) - .with_reverse_row_groups(reverse_scan) - .build() - }; - - // Forward scan: RG0(3,4), RG1(5,6,7,8), RG2(9,10) - let opener = make_opener(false); - let stream = open_file(&opener, file.clone()).await.unwrap(); - let forward_values = collect_int32_values(stream).await; - - // Forward scan should produce: RG0(3,4), RG1(5,6,7,8), RG2(9,10) - assert_eq!( - forward_values, - vec![3, 4, 5, 6, 7, 8, 9, 10], - "Forward scan should select correct rows based on RowSelection" - ); - - // Reverse scan - // CORRECT behavior: reverse row groups AND their corresponding selections - // - RG2 is read first, WITH RG2's selection (select 2, skip 2) -> 9, 10 - // - RG1 is read second, WITH RG1's selection (select all) -> 5, 6, 7, 8 - // - RG0 is read third, WITH RG0's selection (skip 2, select 2) -> 3, 4 - let opener = make_opener(true); - let stream = open_file(&opener, file).await.unwrap(); - let reverse_values = collect_int32_values(stream).await; - - // Correct expected result: row groups reversed but each keeps its own selection - // RG2 with its selection (9,10), RG1 with its selection (5,6,7,8), RG0 with its selection (3,4) - assert_eq!( - reverse_values, - vec![9, 10, 5, 6, 7, 8, 3, 4], - "Reverse scan should reverse row group order while maintaining correct RowSelection for each group" - ); + for progressive_io in [false, true] { + for prefetch in [false, true] { + for (reverse, expected) in [ + (false, vec![3, 4, 5, 6, 7, 8, 9, 10]), + (true, vec![9, 10, 5, 6, 7, 8, 3, 4]), + ] { + let mut opener = ParquetMorselizerBuilder::new() + .with_store(Arc::clone(&store)) + .with_schema(Arc::clone(&schema)) + .with_projection_indices(&[0]) + .with_reverse_row_groups(reverse) + .build(); + opener.progressive_io = progressive_io; + opener.row_group_prefetch = + prefetch.then(|| RowGroupPrefetchOptions { + max_bytes: 1 << 20, + memory_pool: Arc::new( + datafusion_execution::memory_pool::GreedyMemoryPool::new( + 1 << 20, + ), + ), + }); + let stream = open_file(&opener, file.clone()).await.unwrap(); + assert_eq!(collect_int32_values(stream).await, expected); + } + } + } } #[tokio::test] diff --git a/datafusion/datasource-parquet/src/push_decoder.rs b/datafusion/datasource-parquet/src/push_decoder.rs index 1bcd675733ed..b5b80f8a8aa7 100644 --- a/datafusion/datasource-parquet/src/push_decoder.rs +++ b/datafusion/datasource-parquet/src/push_decoder.rs @@ -52,7 +52,8 @@ use parquet::DecodeResult; use parquet::arrow::ProjectionMask; use parquet::arrow::arrow_reader::metrics::ArrowReaderMetrics; use parquet::arrow::arrow_reader::{ - ArrowReaderMetadata, ParquetRecordBatchReader, RowFilter, RowSelectionPolicy, + ArrowReaderMetadata, ParquetRecordBatchReader, RowFilter, RowSelection, + RowSelectionPolicy, }; use parquet::arrow::async_reader::AsyncFileReader; use parquet::arrow::push_decoder::{ParquetPushDecoder, ParquetPushDecoderBuilder}; @@ -119,6 +120,8 @@ impl DecoderBuilderConfig<'_> { #[derive(Debug, Clone)] pub(crate) struct RgPlanEntry { pub(crate) rg_index: usize, + /// Selection made before row-filter evaluation, in this row group's coordinates. + pub(crate) row_selection: Option, /// `true` when static pruning proved every row of this RG satisfies the /// predicate, so the per-row `RowFilter` can be skipped as a no-op. pub(crate) fully_matched: bool, @@ -292,23 +295,62 @@ pub(crate) struct RowGroupPrefetchOptions { pub(crate) memory_pool: Arc, } -/// Full chunks shared by upfront demand reads and next-group prefetch. -fn column_chunk_ranges( +/// Preserve the access plan's page pruning when fetching output and predicate +/// columns together. Without a selection or offset index, fetch complete chunks. +fn column_ranges( metadata: &ParquetMetaData, - row_group: usize, + entry: &RgPlanEntry, projection: &ProjectionMask, ) -> Option>> { - metadata - .row_group(row_group) + let offset_index = metadata + .offset_index() + .and_then(|index| index.get(entry.rg_index)); + let mut ranges = Vec::new(); + for (i, column) in metadata + .row_group(entry.rg_index) .columns() .iter() .enumerate() .filter(|(i, _)| projection.leaf_included(*i)) - .map(|(_, column)| { - let (start, len) = column.byte_range(); - start.checked_add(len).map(|end| start..end) - }) - .collect() + { + let (start, len) = column.byte_range(); + let end = start.checked_add(len)?; + if let Some((selection, index)) = entry.row_selection.as_ref().zip(offset_index) { + if !selection.selects_any() { + continue; + } + let pages = &index.get(i)?.page_locations; + // The prefix before the first data page contains the dictionary. + if let Some(first) = pages.first() { + let first_offset = u64::try_from(first.offset).ok()?; + if first_offset < start || first_offset > end { + return None; + } + if first_offset > start { + ranges.push(start..first_offset); + } + } + ranges.extend(selection.scan_ranges(pages)); + } else { + ranges.push(start..end); + } + } + Some(ranges) +} + +/// Make adjacent pages usable for decoder requests that span a whole chunk, +/// without fetching across gaps left by page pruning. +fn merge_ranges(mut ranges: Vec>) -> Vec> { + ranges.sort_unstable_by_key(|range| range.start); + ranges.dedup_by(|next, previous| { + if next.start <= previous.end { + previous.end = previous.end.max(next.end); + true + } else { + false + } + }); + ranges } /// At most one future row group is in flight. The task owns the reservation so @@ -321,14 +363,14 @@ pub(crate) struct PrefetchedRowGroup { impl PrefetchedRowGroup { fn start( - row_group: usize, + entry: &RgPlanEntry, metadata: &ParquetMetaData, projection: &ProjectionMask, options: &RowGroupPrefetchOptions, reader: Arc>>, metrics: &crate::metrics::PrefetchMetrics, ) -> Option { - let ranges = column_chunk_ranges(metadata, row_group, projection)?; + let ranges = merge_ranges(column_ranges(metadata, entry, projection)?); let bytes = ranges.iter().try_fold(0usize, |total, range| { total.checked_add(usize::try_from(range.end - range.start).ok()?) })?; @@ -351,7 +393,7 @@ impl PrefetchedRowGroup { Ok((data, reservation)) }); Some(Self { - row_group, + row_group: entry.rg_index, ranges, task, }) @@ -655,27 +697,33 @@ impl PushDecoderStreamState { }) .map(|entry| entry.rg_index); if let Some(row_group) = row_group { - if let Err(e) = - Self::advance_rg_plan_to(&mut self.rg_plan, row_group) - { + if let Err(e) = Self::advance_rg_plan_to( + &mut self.rg_plan, + row_group, + &mut self.byte_progress, + ) { return Some((Err(e), self)); } if self.upfront_row_group != Some(row_group) { decoder.clear_all_ranges(); self.prefetch_reservation = None; - let Some(chunks) = column_chunk_ranges( + let Some(mut upfront_ranges) = column_ranges( &self.parquet_metadata, - row_group, + self.rg_plan.front().expect("current row group"), &self.fetch_projection, ) else { return Some(( internal_err!( - "Parquet column byte range overflows u64" + "Invalid Parquet column or page byte range" ), self, )); }; - ranges = chunks; + // Predicate caching may expand its selection to + // batch boundaries. Keep the decoder's request + // as well as the page-pruned output ranges. + upfront_ranges.extend(ranges); + ranges = merge_ranges(upfront_ranges); self.upfront_row_group = Some(row_group); } } @@ -724,8 +772,13 @@ impl PushDecoderStreamState { if let Some(options) = &self.row_group_prefetch { match decoder.peek_next_row_group() { Ok(Some(next)) => { + let entry = self + .rg_plan + .iter() + .find(|entry| entry.rg_index == next) + .expect("next row group is in the access plan"); self.pending_prefetch = PrefetchedRowGroup::start( - next, + entry, &self.parquet_metadata, &self.fetch_projection, options, @@ -1549,6 +1602,7 @@ mod tests { .into_iter() .map(|rg_index| RgPlanEntry { rg_index, + row_selection: None, fully_matched: false, bytes: 100 * (rg_index as u64 + 1), }) @@ -1584,6 +1638,7 @@ mod tests { let err = PushDecoderStreamState::advance_rg_plan_to(&mut plan, 5, &mut byte_progress) .expect_err("a target absent from the plan must be an internal error"); + assert!( err.to_string().contains("diverged"), "expected a divergence internal error, got: {err}", diff --git a/datafusion/datasource-parquet/src/source.rs b/datafusion/datasource-parquet/src/source.rs index a129def4a74e..1add32d85c7c 100644 --- a/datafusion/datasource-parquet/src/source.rs +++ b/datafusion/datasource-parquet/src/source.rs @@ -321,7 +321,6 @@ pub struct ParquetSource { /// in the opener. sort_order_for_reorder: Option, row_group_prefetch: Option, - progressive_io: bool, } impl ParquetSource { @@ -349,12 +348,11 @@ impl ParquetSource { reverse_row_groups: false, sort_order_for_reorder: None, row_group_prefetch: None, - progressive_io: true, } } - /// Prefetch one upcoming row group's output and predicate column chunks while decoding - /// the current group. Disabled by default; a zero budget disables it. + /// Prefetch one upcoming row group's output and predicate pages selected at file + /// open while decoding the current group. Disabled by default; a zero budget disables it. /// /// `max_bytes` bounds additional compressed bytes per file stream, not the /// current reader's memory. Prefetch is skipped if a complete fetched row @@ -362,7 +360,7 @@ impl ParquetSource { /// reads continue normally. Use the execution's memory pool to account for /// concurrent scans together. /// - /// This can read extra bytes when page/row filtering or a later dynamic + /// This can read extra bytes when row filtering or a later dynamic /// predicate eliminates prefetched data. Output order is unchanged. Dropping /// the stream cancels its background I/O. This execution-local option is not /// serialized in physical plans; set it on the executing ParquetSource. @@ -378,14 +376,14 @@ impl ParquetSource { self } - /// Fetch column chunks progressively as decoding and row filtering require + /// Fetch pages progressively as decoding and row filtering require /// them (the default). When false, the first demand read for each row group - /// fetches all output and predicate column chunks together. This reduces + /// fetches the output and predicate pages selected at file open together. This reduces /// dependent I/O rounds but can read pages that filtering would skip. - /// Controls demand reads; enabled prefetch always fetches full chunks. - /// This execution-local option is not serialized in physical plans. + /// Controls demand reads independently of next-row-group prefetch. + /// Also configurable as `datafusion.execution.parquet.progressive_io`. pub fn with_progressive_io(mut self, progressive_io: bool) -> Self { - self.progressive_io = progressive_io; + self.table_parquet_options.global.progressive_io = progressive_io; self } @@ -677,7 +675,7 @@ impl FileSource for ParquetSource { Ok(Box::new(ParquetMorselizer { partition_index: partition, row_group_prefetch: self.row_group_prefetch.clone(), - progressive_io: self.progressive_io, + progressive_io: self.table_parquet_options.global.progressive_io, projection: self.projection.clone(), batch_size: self .batch_size diff --git a/datafusion/proto-common/proto/datafusion_common.proto b/datafusion/proto-common/proto/datafusion_common.proto index 712212f6b6ae..f6c3fbd84830 100644 --- a/datafusion/proto-common/proto/datafusion_common.proto +++ b/datafusion/proto-common/proto/datafusion_common.proto @@ -559,6 +559,7 @@ message ParquetOptions { bool pruning = 2; // default = true bool skip_metadata = 3; // default = true bool pushdown_filters = 5; // default = false + optional bool progressive_io = 39; // absent defaults to true for older plans bool reorder_filters = 6; // default = false bool force_filter_selections = 34; // default = false uint64 data_pagesize_limit = 7; // default = 1024 * 1024 @@ -718,4 +719,4 @@ enum MetricCategory { message ExplainAnalyzeCategoriesNode { bool all = 1; repeated MetricCategory only = 2; -} \ No newline at end of file +} diff --git a/datafusion/proto-common/src/from_proto/mod.rs b/datafusion/proto-common/src/from_proto/mod.rs index 0f176ced0d5a..5c04ece48a86 100644 --- a/datafusion/proto-common/src/from_proto/mod.rs +++ b/datafusion/proto-common/src/from_proto/mod.rs @@ -1083,6 +1083,7 @@ impl TryFrom<&protobuf::ParquetOptions> for ParquetOptions { }) .transpose()?, pushdown_filters: value.pushdown_filters, + progressive_io: value.progressive_io.unwrap_or(true), reorder_filters: value.reorder_filters, force_filter_selections: value.force_filter_selections, data_pagesize_limit: to_usize( @@ -1478,6 +1479,21 @@ mod tests { assert_eq!(opts, recovered); } + #[test] + fn test_parquet_progressive_io_round_trip_and_older_plan_default() { + for progressive_io in [false, true] { + let opts = ParquetOptions { + progressive_io, + ..Default::default() + }; + assert_eq!(parquet_options_proto_round_trip(opts.clone()), opts); + let mut proto: crate::protobuf_common::ParquetOptions = + (&opts).try_into().unwrap(); + proto.progressive_io = None; + assert!(ParquetOptions::try_from(&proto).unwrap().progressive_io); + } + } + #[test] fn test_parquet_options_coerce_int96_tz_unset_round_trip() { let opts = ParquetOptions::default(); diff --git a/datafusion/proto-common/src/generated/pbjson.rs b/datafusion/proto-common/src/generated/pbjson.rs index 2e94368bfd01..4e60fc729d56 100644 --- a/datafusion/proto-common/src/generated/pbjson.rs +++ b/datafusion/proto-common/src/generated/pbjson.rs @@ -6381,6 +6381,9 @@ impl serde::Serialize for ParquetOptions { if self.pushdown_filters { len += 1; } + if self.progressive_io.is_some() { + len += 1; + } if self.reorder_filters { len += 1; } @@ -6490,6 +6493,9 @@ impl serde::Serialize for ParquetOptions { if self.pushdown_filters { struct_ser.serialize_field("pushdownFilters", &self.pushdown_filters)?; } + if let Some(v) = self.progressive_io.as_ref() { + struct_ser.serialize_field("progressiveIo", v)?; + } if self.reorder_filters { struct_ser.serialize_field("reorderFilters", &self.reorder_filters)?; } @@ -6683,6 +6689,8 @@ impl<'de> serde::Deserialize<'de> for ParquetOptions { "skipMetadata", "pushdown_filters", "pushdownFilters", + "progressive_io", + "progressiveIo", "reorder_filters", "reorderFilters", "force_filter_selections", @@ -6753,6 +6761,7 @@ impl<'de> serde::Deserialize<'de> for ParquetOptions { Pruning, SkipMetadata, PushdownFilters, + ProgressiveIo, ReorderFilters, ForceFilterSelections, DataPagesizeLimit, @@ -6810,6 +6819,7 @@ impl<'de> serde::Deserialize<'de> for ParquetOptions { "pruning" => Ok(GeneratedField::Pruning), "skipMetadata" | "skip_metadata" => Ok(GeneratedField::SkipMetadata), "pushdownFilters" | "pushdown_filters" => Ok(GeneratedField::PushdownFilters), + "progressiveIo" | "progressive_io" => Ok(GeneratedField::ProgressiveIo), "reorderFilters" | "reorder_filters" => Ok(GeneratedField::ReorderFilters), "forceFilterSelections" | "force_filter_selections" => Ok(GeneratedField::ForceFilterSelections), "dataPagesizeLimit" | "data_pagesize_limit" => Ok(GeneratedField::DataPagesizeLimit), @@ -6865,6 +6875,7 @@ impl<'de> serde::Deserialize<'de> for ParquetOptions { let mut pruning__ = None; let mut skip_metadata__ = None; let mut pushdown_filters__ = None; + let mut progressive_io__ = None; let mut reorder_filters__ = None; let mut force_filter_selections__ = None; let mut data_pagesize_limit__ = None; @@ -6923,6 +6934,12 @@ impl<'de> serde::Deserialize<'de> for ParquetOptions { } pushdown_filters__ = Some(map_.next_value()?); } + GeneratedField::ProgressiveIo => { + if progressive_io__.is_some() { + return Err(serde::de::Error::duplicate_field("progressiveIo")); + } + progressive_io__ = map_.next_value()?; + } GeneratedField::ReorderFilters => { if reorder_filters__.is_some() { return Err(serde::de::Error::duplicate_field("reorderFilters")); @@ -7138,6 +7155,7 @@ impl<'de> serde::Deserialize<'de> for ParquetOptions { pruning: pruning__.unwrap_or_default(), skip_metadata: skip_metadata__.unwrap_or_default(), pushdown_filters: pushdown_filters__.unwrap_or_default(), + progressive_io: progressive_io__, reorder_filters: reorder_filters__.unwrap_or_default(), force_filter_selections: force_filter_selections__.unwrap_or_default(), data_pagesize_limit: data_pagesize_limit__.unwrap_or_default(), diff --git a/datafusion/proto-common/src/generated/prost.rs b/datafusion/proto-common/src/generated/prost.rs index c0e79aec6d87..42c18b3f3512 100644 --- a/datafusion/proto-common/src/generated/prost.rs +++ b/datafusion/proto-common/src/generated/prost.rs @@ -817,6 +817,9 @@ pub struct ParquetOptions { /// default = false #[prost(bool, tag = "5")] pub pushdown_filters: bool, + /// absent defaults to true for older plans + #[prost(bool, optional, tag = "39")] + pub progressive_io: ::core::option::Option, /// default = false #[prost(bool, tag = "6")] pub reorder_filters: bool, diff --git a/datafusion/proto-common/src/to_proto/mod.rs b/datafusion/proto-common/src/to_proto/mod.rs index ae220a53d8d0..834d2f74a771 100644 --- a/datafusion/proto-common/src/to_proto/mod.rs +++ b/datafusion/proto-common/src/to_proto/mod.rs @@ -909,6 +909,7 @@ impl TryFrom<&ParquetOptions> for protobuf::ParquetOptions { skip_metadata: value.skip_metadata, metadata_size_hint_opt: value.metadata_size_hint.map(|v| protobuf::parquet_options::MetadataSizeHintOpt::MetadataSizeHint(v as u64)), pushdown_filters: value.pushdown_filters, + progressive_io: Some(value.progressive_io), reorder_filters: value.reorder_filters, force_filter_selections: value.force_filter_selections, data_pagesize_limit: value.data_pagesize_limit as u64, diff --git a/datafusion/proto-models/src/from_proto.rs b/datafusion/proto-models/src/from_proto.rs index 16903ec388d2..f2b88abfa0ce 100644 --- a/datafusion/proto-models/src/from_proto.rs +++ b/datafusion/proto-models/src/from_proto.rs @@ -372,6 +372,7 @@ impl TryFrom<&ParquetOptionsProto> for ParquetOptions { }) .transpose()?, pushdown_filters: proto.pushdown_filters, + progressive_io: proto.progressive_io.unwrap_or(true), reorder_filters: proto.reorder_filters, force_filter_selections: proto.force_filter_selections, data_pagesize_limit: to_usize( diff --git a/datafusion/proto-models/src/generated/datafusion_proto_common.rs b/datafusion/proto-models/src/generated/datafusion_proto_common.rs index c0e79aec6d87..42c18b3f3512 100644 --- a/datafusion/proto-models/src/generated/datafusion_proto_common.rs +++ b/datafusion/proto-models/src/generated/datafusion_proto_common.rs @@ -817,6 +817,9 @@ pub struct ParquetOptions { /// default = false #[prost(bool, tag = "5")] pub pushdown_filters: bool, + /// absent defaults to true for older plans + #[prost(bool, optional, tag = "39")] + pub progressive_io: ::core::option::Option, /// default = false #[prost(bool, tag = "6")] pub reorder_filters: bool, diff --git a/datafusion/proto/tests/cases/plans/sources.rs b/datafusion/proto/tests/cases/plans/sources.rs index 7aff89eb3fdf..053fb2d0622c 100644 --- a/datafusion/proto/tests/cases/plans/sources.rs +++ b/datafusion/proto/tests/cases/plans/sources.rs @@ -87,6 +87,7 @@ fn roundtrip_parquet_exec_with_pruning_predicate() -> Result<()> { let mut options = TableParquetOptions::new(); options.global.pushdown_filters = true; + options.global.progressive_io = false; let file_source = Arc::new( ParquetSource::new(Arc::clone(&file_schema)) diff --git a/datafusion/proto/tests/cases/roundtrip_logical_plan.rs b/datafusion/proto/tests/cases/roundtrip_logical_plan.rs index 750b20323ad2..e054bc2de1fc 100644 --- a/datafusion/proto/tests/cases/roundtrip_logical_plan.rs +++ b/datafusion/proto/tests/cases/roundtrip_logical_plan.rs @@ -1090,6 +1090,7 @@ async fn roundtrip_logical_plan_copy_to_parquet() -> Result<()> { .clone_from(&key_value_metadata); parquet_format.global.allow_single_file_parallelism = false; + parquet_format.global.progressive_io = false; parquet_format.global.created_by = "test".to_string(); let file_type = format_as_file_type(Arc::new( @@ -1132,6 +1133,7 @@ async fn roundtrip_logical_plan_copy_to_parquet() -> Result<()> { let parquet_config = parquet_factory.options.as_ref().unwrap(); assert_eq!(parquet_config.key_value_metadata, key_value_metadata); assert!(!parquet_config.global.allow_single_file_parallelism); + assert!(!parquet_config.global.progressive_io); assert_eq!(parquet_config.global.created_by, "test".to_string()); } _ => panic!(), diff --git a/datafusion/sqllogictest/test_files/information_schema.slt b/datafusion/sqllogictest/test_files/information_schema.slt index 4551f21f2164..2c79f7ca99e3 100644 --- a/datafusion/sqllogictest/test_files/information_schema.slt +++ b/datafusion/sqllogictest/test_files/information_schema.slt @@ -261,6 +261,7 @@ datafusion.execution.parquet.max_row_group_size 1048576 datafusion.execution.parquet.maximum_buffered_record_batches_per_stream 2 datafusion.execution.parquet.maximum_parallel_row_group_writers 1 datafusion.execution.parquet.metadata_size_hint 524288 +datafusion.execution.parquet.progressive_io true datafusion.execution.parquet.pruning true datafusion.execution.parquet.pushdown_filters false datafusion.execution.parquet.reorder_filters false @@ -422,6 +423,7 @@ datafusion.execution.parquet.max_row_group_size 1048576 (writing) Target maximum datafusion.execution.parquet.maximum_buffered_record_batches_per_stream 2 (writing) By default parallel parquet writer is tuned for minimum memory usage in a streaming execution plan. You may see a performance benefit when writing large parquet files by increasing maximum_parallel_row_group_writers and maximum_buffered_record_batches_per_stream if your system has idle cores and can tolerate additional memory usage. Boosting these values is likely worthwhile when writing out already in-memory data, such as from a cached data frame. datafusion.execution.parquet.maximum_parallel_row_group_writers 1 (writing) By default parallel parquet writer is tuned for minimum memory usage in a streaming execution plan. You may see a performance benefit when writing large parquet files by increasing maximum_parallel_row_group_writers and maximum_buffered_record_batches_per_stream if your system has idle cores and can tolerate additional memory usage. Boosting these values is likely worthwhile when writing out already in-memory data, such as from a cached data frame. datafusion.execution.parquet.metadata_size_hint 524288 (reading) If specified, the parquet reader will try and fetch the last `size_hint` bytes of the parquet file optimistically. If not specified, two reads are required: One read to fetch the 8-byte parquet footer and another to fetch the metadata length encoded in the footer Default setting to 512 KiB, which should be sufficient for most parquet files, it can reduce one I/O operation per parquet file. If the metadata is larger than the hint, two reads will still be performed. +datafusion.execution.parquet.progressive_io true (reading) Fetch columns progressively as row filtering and decoding need them. If false, fetch output and predicate pages together before evaluating row filters, preserving page-index pruning performed at file open. This can reduce dependent I/O rounds but may fetch pages that row filtering would otherwise avoid. Does not enable filter pushdown or prefetch. datafusion.execution.parquet.pruning true (reading) If true, the parquet reader attempts to skip entire row groups based on the predicate in the query and the metadata (min/max values) stored in the parquet file datafusion.execution.parquet.pushdown_filters false (reading) If true, filter expressions are be applied during the parquet decoding operation to reduce the number of rows decoded. This optimization is sometimes called "late materialization". datafusion.execution.parquet.reorder_filters false (reading) If true, filter expressions evaluated during the parquet decoding operation will be reordered heuristically to minimize the cost of evaluation. If false, the filters are applied in the same order as written in the query diff --git a/docs/source/user-guide/configs.md b/docs/source/user-guide/configs.md index fbeb38f1cebe..4190348492b3 100644 --- a/docs/source/user-guide/configs.md +++ b/docs/source/user-guide/configs.md @@ -85,6 +85,7 @@ The following configuration settings are available: | datafusion.execution.parquet.skip_metadata | true | (reading) If true, the parquet reader skip the optional embedded metadata that may be in the file Schema. This setting can help avoid schema conflicts when querying multiple parquet files with schemas containing compatible types but different metadata | | datafusion.execution.parquet.metadata_size_hint | 524288 | (reading) If specified, the parquet reader will try and fetch the last `size_hint` bytes of the parquet file optimistically. If not specified, two reads are required: One read to fetch the 8-byte parquet footer and another to fetch the metadata length encoded in the footer Default setting to 512 KiB, which should be sufficient for most parquet files, it can reduce one I/O operation per parquet file. If the metadata is larger than the hint, two reads will still be performed. | | datafusion.execution.parquet.pushdown_filters | false | (reading) If true, filter expressions are be applied during the parquet decoding operation to reduce the number of rows decoded. This optimization is sometimes called "late materialization". | +| datafusion.execution.parquet.progressive_io | true | (reading) Fetch columns progressively as row filtering and decoding need them. If false, fetch output and predicate pages together before evaluating row filters, preserving page-index pruning performed at file open. This can reduce dependent I/O rounds but may fetch pages that row filtering would otherwise avoid. Does not enable filter pushdown or prefetch. | | datafusion.execution.parquet.reorder_filters | false | (reading) If true, filter expressions evaluated during the parquet decoding operation will be reordered heuristically to minimize the cost of evaluation. If false, the filters are applied in the same order as written in the query | | datafusion.execution.parquet.force_filter_selections | false | (reading) Force the use of RowSelections for filter results, when pushdown_filters is enabled. If false, the reader will automatically choose between a RowSelection and a Bitmap based on the number and pattern of selected rows. | | datafusion.execution.parquet.schema_force_view_types | true | (reading) If true, parquet reader will read columns of `Utf8/Utf8Large` with `Utf8View`, and `Binary/BinaryLarge` with `BinaryView`. | From a16a95f8c6fa2a7191f7d20c03a54e6f460f2bc1 Mon Sep 17 00:00:00 2001 From: peterxcli Date: Thu, 10 Sep 2026 03:35:40 +0800 Subject: [PATCH 06/13] docs: record selected-page prefetch memory measurements --- datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md index 9ff49c3d8bb7..bdad7c9847e1 100644 --- a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md +++ b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md @@ -142,6 +142,13 @@ The same 256-group matrix was rerun after preserving page selections. All 192 sc +All 255 future groups fit the 16 MiB prefetch budget. Peak compressed prefetch +reservations were 4.99–5.12 MB for wide output and 1.06–1.18 MB for narrow output +without effective page pruning. With indexed, clustered matches, the peaks fell +to 78,078 bytes (wide) and 16,587 bytes (narrow). These are exact reservation +peaks from `PeakRecordingPool`; current-reader, decoded-buffer, and process +memory are outside this measurement. + @@ -143,13 +149,15 @@ For narrow output, peak prefetch reservations ranged from 16,587 to 1,184,818 by ## ClickBench -Upfront Q24 measured 8.92 and 8.75 ms, versus main progressive's 10.00 and -10.15 ms. Q25 changed direction between passes: 7.42 versus 6.76 ms in pass 1, -and 6.64 versus 7.09 ms in pass 2. The comparison shows query-specific results -and variation between passes. +Upfront reads were slower than main with pushdown off for **all six queries +in both passes**. Compared with main progressive, Q26 improved in both passes: +**10.73/9.76 ms** versus **11.00/10.09 ms**. Q10 and Q24 changed direction +between passes; Q11 and Q25 were slower in both passes. -Upfront medians exceeded main with pushdown off for Q10, Q22, Q24, Q25, and Q26 -in both passes. +Q22 illustrates the timing variation: main progressive measured 107.18 and +152.95 ms, while patch upfront measured 107.46 and 108.08 ms. The apparent +second-pass gain therefore needs further measurement. Fewer reader calls in +the synthetic fixture do not establish a general query-speed improvement. ![ClickBench elapsed-time ratios for the current patch against main.](benchmark-charts/parquet-main-clickbench.png) @@ -173,29 +181,28 @@ counts; result values were not independently compared. | Query | Main off | Patch off | Main progressive | Patch progressive | Patch upfront | | ----- | -------: | --------: | ---------------: | ----------------: | ------------: | -| Q10 | 5.06 | 5.19 | 5.79 | 5.11 | 5.59 | -| Q11 | 4.97 | 4.89 | 5.81 | 5.76 | 5.74 | -| Q22 | 93.88 | 91.93 | 110.93 | 108.74 | 110.14 | -| Q24 | 8.11 | 7.59 | 10.00 | 9.62 | 8.92 | -| Q25 | 5.11 | 4.87 | 6.76 | 7.05 | 7.42 | -| Q26 | 8.36 | 8.29 | 12.63 | 11.12 | 12.05 | +| Q10 | 4.64 | 4.48 | 5.79 | 5.05 | 5.29 | +| Q11 | 4.93 | 4.95 | 5.51 | 5.61 | 6.29 | +| Q22 | 88.79 | 85.20 | 107.18 | 111.30 | 107.46 | +| Q24 | 7.55 | 9.43 | 10.80 | 9.83 | 11.21 | +| Q25 | 5.16 | 5.12 | 7.33 | 6.89 | 7.57 | +| Q26 | 7.67 | 7.53 | 11.00 | 10.71 | 10.72 | ### Pass 2 | Query | Main off | Patch off | Main progressive | Patch progressive | Patch upfront | | ----- | -------: | --------: | ---------------: | ----------------: | ------------: | -| Q10 | 4.21 | 4.25 | 5.05 | 5.06 | 4.63 | -| Q11 | 5.13 | 5.19 | 5.48 | 5.15 | 5.04 | -| Q22 | 84.72 | 83.33 | 102.40 | 103.86 | 103.36 | -| Q24 | 7.18 | 7.15 | 10.15 | 8.97 | 8.75 | -| Q25 | 4.85 | 4.93 | 7.09 | 6.87 | 6.64 | -| Q26 | 7.42 | 7.37 | 10.19 | 9.93 | 9.63 | +| Q10 | 3.93 | 4.09 | 5.05 | 4.82 | 5.67 | +| Q11 | 4.48 | 4.70 | 5.73 | 5.24 | 5.81 | +| Q22 | 84.01 | 85.92 | 152.95 | 108.44 | 108.08 | +| Q24 | 7.22 | 7.11 | 8.80 | 9.34 | 8.68 | +| Q25 | 4.82 | 5.06 | 6.85 | 7.01 | 6.97 | +| Q26 | 7.25 | 7.45 | 10.09 | 9.85 | 9.76 | These local samples do not establish full-dataset, remote-storage, or Spark -end-to-end performance. Timing differences include all changes between the two -revisions; broader measurements are needed before changing defaults. +end-to-end performance. 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-Exact median elapsed times in milliseconds - -### Pass 1 - -| Workload | Main off | Patch off | Patch off +PF | Main progressive | Patch progressive | Patch progressive +PF | Patch upfront | Patch upfront +PF | -| ----------------------------- | -------: | --------: | ------------: | ---------------: | ----------------: | --------------------: | ------------: | ----------------: | -| random, no indexes, wide | 1521.5 | 1506.8 | 1398.6 | 1543.6 | 1542.5 | 1414.5 | 1524.4 | 1418.9 | -| random, no indexes, narrow | 363.1 | 357.0 | 330.1 | 356.3 | 360.0 | 327.0 | 352.4 | 325.5 | -| random, indexes, wide | 1539.8 | 1512.4 | 1400.2 | 1765.3 | 1825.8 | 1431.5 | 1550.0 | 1547.5 | -| random, indexes, narrow | 366.4 | 361.7 | 335.7 | 402.4 | 380.2 | 334.2 | 361.8 | 336.2 | -| clustered, no indexes, wide | 1551.3 | 1504.2 | 1400.6 | 361.1 | 310.7 | 299.4 | 298.6 | 300.0 | -| clustered, no indexes, narrow | 358.3 | 369.9 | 319.8 | 218.9 | 209.5 | 204.7 | 204.4 | 206.4 | -| clustered, indexes, wide | 45.6 | 43.3 | 35.7 | 48.2 | 46.0 | 34.7 | 41.3 | 34.8 | -| clustered, indexes, narrow | 20.9 | 20.0 | 17.7 | 25.5 | 24.1 | 22.1 | 20.1 | 22.0 | - -### Pass 2 - -| Workload | Main off | Patch off | Patch off +PF | Main progressive | Patch progressive | Patch progressive +PF | Patch upfront | Patch upfront +PF | -| ----------------------------- | -------: | --------: | ------------: | ---------------: | ----------------: | --------------------: | ------------: | ----------------: | -| random, no indexes, wide | 1527.1 | 1499.0 | 1392.5 | 1559.8 | 1520.8 | 1469.7 | 1594.3 | 1408.3 | -| random, no indexes, narrow | 355.4 | 355.1 | 329.0 | 357.1 | 355.3 | 323.4 | 351.5 | 324.4 | -| random, indexes, wide | 1504.0 | 1495.0 | 1400.6 | 1726.7 | 1894.5 | 1424.5 | 1557.1 | 1429.4 | -| random, indexes, narrow | 359.6 | 365.1 | 337.1 | 378.2 | 381.6 | 334.3 | 362.3 | 333.0 | -| clustered, no indexes, wide | 1497.1 | 1502.2 | 1398.0 | 305.8 | 305.9 | 294.3 | 297.1 | 294.1 | -| clustered, no indexes, narrow | 345.4 | 343.4 | 320.5 | 208.1 | 209.7 | 206.2 | 204.0 | 206.4 | -| clustered, indexes, wide | 43.1 | 43.2 | 35.8 | 45.1 | 46.2 | 34.3 | 41.0 | 34.0 | -| clustered, indexes, narrow | 19.7 | 20.3 | 19.1 | 23.7 | 24.2 | 21.9 | 20.1 | 22.7 | +Exact median elapsed times for the synthetic scans + +### Filter rows after decoding + +Upstream reference: filter rows after decoding. All times are milliseconds. + +| Workload | Upstream pass 1 | Upstream pass 2 | Patch, prefetch disabled, pass 1 | Patch, prefetch disabled, pass 2 | Patch, prefetch enabled, pass 1 | Patch, prefetch enabled, pass 2 | +| ----------------------------------------------------------------------- | --------------: | --------------: | -------------------------------: | -------------------------------: | ------------------------------: | ------------------------------: | +| Scattered matching rows; Page index absent · 7 output columns | 1521.53 | 1527.06 | 1506.81 | 1498.96 | 1398.62 | 1392.46 | +| Scattered matching rows; Page index absent · 1 output column | 363.08 | 355.38 | 357.00 | 355.08 | 330.12 | 329.01 | +| Scattered matching rows; Page index present · 7 output columns | 1539.80 | 1503.95 | 1512.38 | 1494.98 | 1400.22 | 1400.60 | +| Scattered matching rows; Page index present · 1 output column | 366.42 | 359.62 | 361.67 | 365.06 | 335.70 | 337.12 | +| Matching rows grouped into pages; Page index absent · 7 output columns | 1551.31 | 1497.14 | 1504.23 | 1502.20 | 1400.58 | 1398.02 | +| Matching rows grouped into pages; Page index absent · 1 output column | 358.32 | 345.43 | 369.85 | 343.39 | 319.80 | 320.48 | +| Matching rows grouped into pages; Page index present · 7 output columns | 45.63 | 43.08 | 43.29 | 43.25 | 35.70 | 35.78 | +| Matching rows grouped into pages; Page index present · 1 output column | 20.95 | 19.71 | 20.04 | 20.32 | 17.70 | 19.13 | + +### Read filter columns first + +Upstream reference: read filter columns first. All times are milliseconds. + +| Workload | Upstream pass 1 | Upstream pass 2 | Patch, prefetch disabled, pass 1 | Patch, prefetch disabled, pass 2 | Patch, prefetch enabled, pass 1 | Patch, prefetch enabled, pass 2 | +| ----------------------------------------------------------------------- | --------------: | --------------: | -------------------------------: | -------------------------------: | ------------------------------: | ------------------------------: | +| Scattered matching rows; Page index absent · 7 output columns | 1543.61 | 1559.83 | 1542.49 | 1520.79 | 1414.53 | 1469.71 | +| Scattered matching rows; Page index absent · 1 output column | 356.33 | 357.13 | 360.01 | 355.26 | 327.01 | 323.43 | +| Scattered matching rows; Page index present · 7 output columns | 1765.25 | 1726.66 | 1825.76 | 1894.46 | 1431.53 | 1424.55 | +| Scattered matching rows; Page index present · 1 output column | 402.42 | 378.23 | 380.15 | 381.61 | 334.23 | 334.29 | +| Matching rows grouped into pages; Page index absent · 7 output columns | 361.10 | 305.83 | 310.71 | 305.89 | 299.42 | 294.27 | +| Matching rows grouped into pages; Page index absent · 1 output column | 218.94 | 208.05 | 209.51 | 209.67 | 204.71 | 206.19 | +| Matching rows grouped into pages; Page index present · 7 output columns | 48.21 | 45.14 | 46.03 | 46.18 | 34.67 | 34.31 | +| Matching rows grouped into pages; Page index present · 1 output column | 25.51 | 23.69 | 24.07 | 24.20 | 22.06 | 21.89 | + +### Read filter and output columns together + +Upstream reference: read filter columns first. All times are milliseconds. + +| Workload | Upstream pass 1 | Upstream pass 2 | Patch, prefetch disabled, pass 1 | Patch, prefetch disabled, pass 2 | Patch, prefetch enabled, pass 1 | Patch, prefetch enabled, pass 2 | +| ----------------------------------------------------------------------- | --------------: | --------------: | -------------------------------: | -------------------------------: | ------------------------------: | ------------------------------: | +| Scattered matching rows; Page index absent · 7 output columns | 1543.61 | 1559.83 | 1524.44 | 1594.31 | 1418.95 | 1408.30 | +| Scattered matching rows; Page index absent · 1 output column | 356.33 | 357.13 | 352.35 | 351.53 | 325.45 | 324.37 | +| Scattered matching rows; Page index present · 7 output columns | 1765.25 | 1726.66 | 1550.04 | 1557.13 | 1547.51 | 1429.41 | +| Scattered matching rows; Page index present · 1 output column | 402.42 | 378.23 | 361.80 | 362.27 | 336.21 | 333.01 | +| Matching rows grouped into pages; Page index absent · 7 output columns | 361.10 | 305.83 | 298.56 | 297.11 | 300.00 | 294.13 | +| Matching rows grouped into pages; Page index absent · 1 output column | 218.94 | 208.05 | 204.40 | 204.03 | 206.42 | 206.41 | +| Matching rows grouped into pages; Page index present · 7 output columns | 48.21 | 45.14 | 41.34 | 41.04 | 34.84 | 34.00 | +| Matching rows grouped into pages; Page index present · 1 output column | 25.51 | 23.69 | 20.09 | 20.14 | 22.03 | 22.66 |
@@ -121,83 +154,106 @@ artificial I/O or downstream delay. Other desktop activity was uncontrolled. `bytes_scanned` includes metadata and speculative reads. Reader calls count invocations observed by the reader wrapper, excluding footer reads inside `get_metadata`. They do not count physical disk operations or HTTP requests. -The following counts use prefetch off and were identical across the two passes. +The following counts use prefetch disabled and were identical across the two passes.
Exact requested bytes and reader calls -| Workload | Main progressive bytes | Patch upfront bytes | Main progressive calls | Patch progressive calls | Patch upfront calls | -| ----------------------------- | ---------------------: | ------------------: | ---------------------: | ----------------------: | ------------------: | -| random, no indexes, wide | 1,309,357,880 | 1,309,357,880 | 512 | 512 | 256 | -| random, no indexes, narrow | 303,126,105 | 303,126,105 | 512 | 512 | 256 | -| random, indexes, wide | 1,318,274,373 | 1,318,274,373 | 513 | 513 | 257 | -| random, indexes, narrow | 312,042,598 | 312,042,598 | 513 | 513 | 257 | -| clustered, no indexes, wide | 1,278,042,437 | 1,278,042,437 | 512 | 512 | 256 | -| clustered, no indexes, narrow | 271,810,662 | 271,810,662 | 512 | 512 | 256 | -| clustered, indexes, wide | 28,883,141 | 28,883,141 | 513 | 513 | 257 | -| clustered, indexes, narrow | 13,160,491 | 13,160,491 | 513 | 513 | 257 | +| Workload | Upstream, filter columns first bytes | Patch, columns together bytes | Upstream, filter columns first calls | Patch, filter columns first calls | Patch, columns together calls | +| ----------------------------------------------------------------------- | -----------------------------------: | ----------------------------: | -----------------------------------: | --------------------------------: | ----------------------------: | +| Scattered matching rows; Page index absent · 7 output columns | 1,309,357,880 | 1,309,357,880 | 512 | 512 | 256 | +| Scattered matching rows; Page index absent · 1 output column | 303,126,105 | 303,126,105 | 512 | 512 | 256 | +| Scattered matching rows; Page index present · 7 output columns | 1,318,274,373 | 1,318,274,373 | 513 | 513 | 257 | +| Scattered matching rows; Page index present · 1 output column | 312,042,598 | 312,042,598 | 513 | 513 | 257 | +| Matching rows grouped into pages; Page index absent · 7 output columns | 1,278,042,437 | 1,278,042,437 | 512 | 512 | 256 | +| Matching rows grouped into pages; Page index absent · 1 output column | 271,810,662 | 271,810,662 | 512 | 512 | 256 | +| Matching rows grouped into pages; Page index present · 7 output columns | 28,883,141 | 28,883,141 | 513 | 513 | 257 | +| Matching rows grouped into pages; Page index present · 1 output column | 13,160,491 | 13,160,491 | 513 | 513 | 257 |
-Prefetch memory is measured with `PeakRecordingPool`. Main has no prefetch +Prefetch memory is measured with `PeakRecordingPool`. Upstream has no prefetch reservation. The patch's figures cover additional compressed reservations; current-reader, decoded-buffer, and total process memory are outside this metric. -For wide output, peak prefetch reservations ranged from 78,078 to 5,115,597 bytes. +For 7 output columns, peak prefetch reservations ranged from 78,078 to 5,115,597 bytes. -For narrow output, peak prefetch reservations ranged from 16,587 to 1,184,818 bytes. +For 1 output column, peak prefetch reservations ranged from 16,587 to 1,184,818 bytes. ## ClickBench -Upfront reads were slower than main with pushdown off for **all six queries -in both passes**. Compared with main progressive, Q26 improved in both passes: -**10.73/9.76 ms** versus **11.00/10.09 ms**. Q10 and Q24 changed direction -between passes; Q11 and Q25 were slower in both passes. +The runner now accepts `--prefetch-bytes`, so this comparison includes both +prefetch settings for all three reading modes. All 384 executions returned ten +rows. Every execution with prefetch enabled completed one row-group prefetch, +verified through the reader's `prefetch_row_groups` metric. -Q22 illustrates the timing variation: main progressive measured 107.18 and -152.95 ms, while patch upfront measured 107.46 and 108.08 ms. The apparent -second-pass gain therefore needs further measurement. Fewer reader calls in -the synthetic fixture do not establish a general query-speed improvement. +Prefetch had a mixed effect on this input. With row filtering after decoding, +Q22 took **77.21/76.88 ms with prefetch** versus **82.25/79.58 ms without** +across the two passes (3–6% less time). Reading filter and output columns +together, Q11 took **4.87/5.00 ms with prefetch** versus **5.54/5.41 ms without** +(7–12% less). -![ClickBench elapsed-time ratios for the current patch against main.](benchmark-charts/parquet-main-clickbench.png) +Other cases changed direction. Reading columns together, Q25 took +6.17 versus 6.51 ms with/without prefetch in pass 1, but 6.89 versus 6.38 ms +in pass 2. Both modes that filter during decoding remained slower than +upstream filtering after decoding for every query, even with prefetch enabled. + +![ClickBench reading modes with prefetch enabled and disabled, explicit upstream references, and descriptive query labels.](benchmark-charts/parquet-main-clickbench.png) The existing `dfbench clickbench` runner executes unchanged Q10, Q11, Q22, Q24, Q25, and Q26 SQL files against the same public [`hits_0.parquet`](https://datasets.clickhouse.com/hits_compatible/athena_partitioned/hits_0.parquet) file: 122,446,530 bytes, 1,000,000 rows, and two row groups. This is one partition of the 100-file dataset. Both revisions use one target partition and enable -filter reordering. Prefetch is disabled. +filter reordering. Prefetch uses a 64 MiB budget per file stream and the query's +memory pool. The file provides only one next-row-group prefetch per scan. Each case has one warmup and three measured iterations. The second pass reverses -the order of the five revision/configuration combinations. Patch off is compared -with main off; patch progressive and upfront are compared with main progressive. -All 240 executions succeeded and returned ten rows. The benchmark records row -counts; result values were not independently compared. +the order of all eight revision/configuration combinations. Each chart panel +names its upstream reference. Query result values were not independently +compared; validation checks row counts and successful prefetch activity.
-Exact median ClickBench times in milliseconds - -### Pass 1 - -| Query | Main off | Patch off | Main progressive | Patch progressive | Patch upfront | -| ----- | -------: | --------: | ---------------: | ----------------: | ------------: | -| Q10 | 4.64 | 4.48 | 5.79 | 5.05 | 5.29 | -| Q11 | 4.93 | 4.95 | 5.51 | 5.61 | 6.29 | -| Q22 | 88.79 | 85.20 | 107.18 | 111.30 | 107.46 | -| Q24 | 7.55 | 9.43 | 10.80 | 9.83 | 11.21 | -| Q25 | 5.16 | 5.12 | 7.33 | 6.89 | 7.57 | -| Q26 | 7.67 | 7.53 | 11.00 | 10.71 | 10.72 | - -### Pass 2 - -| Query | Main off | Patch off | Main progressive | Patch progressive | Patch upfront | -| ----- | -------: | --------: | ---------------: | ----------------: | ------------: | -| Q10 | 3.93 | 4.09 | 5.05 | 4.82 | 5.67 | -| Q11 | 4.48 | 4.70 | 5.73 | 5.24 | 5.81 | -| Q22 | 84.01 | 85.92 | 152.95 | 108.44 | 108.08 | -| Q24 | 7.22 | 7.11 | 8.80 | 9.34 | 8.68 | -| Q25 | 4.82 | 5.06 | 6.85 | 7.01 | 6.97 | -| Q26 | 7.25 | 7.45 | 10.09 | 9.85 | 9.76 | +Exact median ClickBench times + +### Filter rows after decoding + +Upstream reference: filter rows after decoding. All times are milliseconds. + +| Workload | Upstream pass 1 | Upstream pass 2 | Patch, prefetch disabled, pass 1 | Patch, prefetch disabled, pass 2 | Patch, prefetch enabled, pass 1 | Patch, prefetch enabled, pass 2 | +| ---------------------------------------------------- | --------------: | --------------: | -------------------------------: | -------------------------------: | ------------------------------: | ------------------------------: | +| Q10: distinct users by phone model | 3.98 | 3.77 | 3.70 | 3.85 | 3.74 | 3.81 | +| Q11: distinct users by phone and model | 4.07 | 4.20 | 4.35 | 4.30 | 4.13 | 4.11 | +| Q22: group search phrases with URL and title filters | 80.18 | 79.12 | 82.25 | 79.58 | 77.21 | 76.88 | +| Q24: first search phrases by event time | 6.39 | 6.82 | 6.64 | 6.79 | 6.66 | 6.58 | +| Q25: first search phrases alphabetically | 4.32 | 4.39 | 4.45 | 4.41 | 4.41 | 4.50 | +| Q26: first search phrases by event time and phrase | 6.86 | 6.72 | 6.85 | 6.98 | 7.07 | 6.72 | + +### Read filter columns first + +Upstream reference: read filter columns first. All times are milliseconds. + +| Workload | Upstream pass 1 | Upstream pass 2 | Patch, prefetch disabled, pass 1 | Patch, prefetch disabled, pass 2 | Patch, prefetch enabled, pass 1 | Patch, prefetch enabled, pass 2 | +| ---------------------------------------------------- | --------------: | --------------: | -------------------------------: | -------------------------------: | ------------------------------: | ------------------------------: | +| Q10: distinct users by phone model | 4.85 | 4.25 | 4.24 | 4.48 | 4.69 | 4.42 | +| Q11: distinct users by phone and model | 5.01 | 4.99 | 4.49 | 5.36 | 4.72 | 5.30 | +| Q22: group search phrases with URL and title filters | 94.45 | 96.76 | 94.14 | 96.46 | 94.53 | 94.01 | +| Q24: first search phrases by event time | 8.24 | 8.45 | 8.48 | 8.64 | 8.55 | 8.24 | +| Q25: first search phrases alphabetically | 7.15 | 6.19 | 8.40 | 6.45 | 6.90 | 6.28 | +| Q26: first search phrases by event time and phrase | 8.83 | 9.48 | 9.38 | 9.90 | 9.42 | 9.41 | + +### Read filter and output columns together + +Upstream reference: read filter columns first. All times are milliseconds. + +| Workload | Upstream pass 1 | Upstream pass 2 | Patch, prefetch disabled, pass 1 | Patch, prefetch disabled, pass 2 | Patch, prefetch enabled, pass 1 | Patch, prefetch enabled, pass 2 | +| ---------------------------------------------------- | --------------: | --------------: | -------------------------------: | -------------------------------: | ------------------------------: | ------------------------------: | +| Q10: distinct users by phone model | 4.85 | 4.25 | 4.73 | 4.61 | 4.34 | 4.48 | +| Q11: distinct users by phone and model | 5.01 | 4.99 | 5.54 | 5.41 | 4.87 | 5.00 | +| Q22: group search phrases with URL and title filters | 94.45 | 96.76 | 96.22 | 94.75 | 92.33 | 96.46 | +| Q24: first search phrases by event time | 8.24 | 8.45 | 8.25 | 8.51 | 7.69 | 8.29 | +| Q25: first search phrases alphabetically | 7.15 | 6.19 | 6.51 | 6.37 | 6.17 | 6.89 | +| Q26: first search phrases by event time and phrase | 8.83 | 9.48 | 9.00 | 9.49 | 8.76 | 8.98 |
@@ -216,7 +272,8 @@ cargo build --locked --profile release-nonlto \ For main, copy the benchmark example from the patch, remove the `with_row_group_prefetch` and `with_progressive_io` builder calls, and restrict -the configuration loop to off/progressive without prefetch. Enable Tokio's +the configuration loop to filtering after decoding and reading filter columns +first, both without prefetch. Enable Tokio's `rt-multi-thread` feature in the Parquet crate's dev dependencies. Run the patch first to generate the shared input directory, then use that same @@ -235,7 +292,13 @@ target/release-nonlto/dfbench clickbench --path hits_0.parquet \ -c datafusion.execution.parquet.reorder_filters=true ``` -On the patch, add `-c datafusion.execution.parquet.progressive_io=false` for -upfront reads. For the off control on either revision, omit `--pushdown` and add -`-c datafusion.execution.parquet.pushdown_filters=false`. Repeat for query numbers -10, 11, 22, 24, 25, and 26, then reverse the configuration order. +On the patch, add `-c datafusion.execution.parquet.progressive_io=false` to +read filter and output columns together. Add `--prefetch-bytes 67108864` to +enable prefetch, or `--prefetch-bytes 0` to disable it. The runner prints the +number of successfully prefetched row groups after each timed execution. + +To filter after decoding, omit `--pushdown` and add +`-c datafusion.execution.parquet.pushdown_filters=false`. Run each of the three +patch reading modes with both prefetch budgets. Upstream runs filtering after +decoding and reading filter columns first. 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Mon Sep 17 00:00:00 2001 From: peterxcli Date: Sat, 12 Sep 2026 09:32:21 +0800 Subject: [PATCH 11/13] fix: start Parquet prefetch before continuing query CPU work --- benchmarks/examples/io_overlap.rs | 317 ++++++++++++++ .../IO_OVERLAP_PROFILING.md | 235 +++++++++++ .../datasource-parquet/IO_POLICY_BENCHMARK.md | 8 +- .../benchmark-charts/io-overlap-native.png | Bin 0 -> 175944 bytes .../benchmark-charts/io-overlap-queries.csv | 385 ++++++++++++++++++ .../benchmark-charts/io-overlap-runtime.png | Bin 0 -> 170686 bytes .../benchmark-charts/io-overlap-timeline.json | 1 + .../benchmark-charts/plot_io_overlap.py | 91 +++++ .../datasource-parquet/src/push_decoder.rs | 44 ++ 9 files changed, 1079 insertions(+), 2 deletions(-) create mode 100644 benchmarks/examples/io_overlap.rs create mode 100644 datafusion/datasource-parquet/IO_OVERLAP_PROFILING.md create mode 100644 datafusion/datasource-parquet/benchmark-charts/io-overlap-native.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/io-overlap-queries.csv create mode 100644 datafusion/datasource-parquet/benchmark-charts/io-overlap-runtime.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/io-overlap-timeline.json create mode 100644 datafusion/datasource-parquet/benchmark-charts/plot_io_overlap.py diff --git a/benchmarks/examples/io_overlap.rs b/benchmarks/examples/io_overlap.rs new file mode 100644 index 000000000000..3c83d6be11ad --- /dev/null +++ b/benchmarks/examples/io_overlap.rs @@ -0,0 +1,317 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +//! Profile a complete filtered aggregate over an existing io_policy fixture. +//! Compare the same input and configuration with prefetch disabled and enabled. +//! The query runs on a Tokio worker so synchronous decoding exercises the same +//! scheduler as a query task. Optional events record runtime parking and reader +//! future lifetimes, which include scheduling delay and are not disk I/O times. +use std::ops::Range; +use std::sync::atomic::{AtomicUsize, Ordering}; +use std::sync::{Arc, Mutex}; +use std::time::{SystemTime, UNIX_EPOCH}; + +use arrow::array::Int64Array; +use bytes::Bytes; +use clap::Parser; +use datafusion::datasource::listing::PartitionedFile; +use datafusion::datasource::physical_plan::parquet::{ + DefaultParquetFileReaderFactory, ParquetFileReaderFactory, +}; +use datafusion::datasource::physical_plan::{FileScanConfigBuilder, ParquetSource}; +use datafusion::datasource::source::DataSourceExec; +use datafusion::physical_plan::metrics::ExecutionPlanMetricsSet; +use datafusion::physical_plan::{collect, displayable}; +use datafusion::prelude::*; +use datafusion_common::instant::Instant; +use datafusion_common::tree_node::{Transformed, TreeNode, TreeNodeRecursion}; +use datafusion_common_runtime::SpawnedTask; +use futures::future::BoxFuture; +use parquet::arrow::{arrow_reader::ArrowReaderOptions, async_reader::AsyncFileReader}; +use parquet::file::metadata::ParquetMetaData; + +#[cfg(feature = "mimalloc")] +#[global_allocator] +static ALLOC: mimalloc::MiMalloc = mimalloc::MiMalloc; + +#[derive(Debug, Parser)] +struct Options { + #[arg(long)] + path: String, + #[arg(long, value_parser = ["off", "progressive", "upfront"])] + mode: String, + #[arg(long, default_value_t = 0)] + prefetch_bytes: usize, + #[arg(long, default_value_t = 8)] + partitions: usize, + #[arg(long, default_value_t = 8)] + iterations: usize, + #[arg(long, default_value_t = 0)] + start_delay_ms: u64, + #[arg(long)] + trace: Option, + #[arg(long)] + narrow: bool, + #[arg(long)] + expected_rows: i64, + #[arg(long)] + expected_checksum: i64, +} + +fn timestamp() -> u128 { + SystemTime::now() + .duration_since(UNIX_EPOCH) + .unwrap() + .as_nanos() +} + +#[derive(Debug, Default)] +struct Trace(Mutex>); +impl Trace { + fn record(&self, name: &str, start: u128, duration: u128, partition: Option) { + self.0.lock().unwrap().push(serde_json::json!({ + "name": name, "start_ns": start, "duration_ns": duration, + "thread": std::thread::current().name().unwrap_or("main"), "partition": partition, + })); + } +} + +struct Reader { + inner: Box, + trace: Arc, + partition: usize, +} +impl AsyncFileReader for Reader { + fn get_bytes( + &mut self, + range: Range, + ) -> BoxFuture<'_, parquet::errors::Result> { + Box::pin(async move { + let start = timestamp(); + let result = self.inner.get_bytes(range).await; + self.trace + .record("read", start, timestamp() - start, Some(self.partition)); + result + }) + } + fn get_byte_ranges( + &mut self, + ranges: Vec>, + ) -> BoxFuture<'_, parquet::errors::Result>> { + Box::pin(async move { + let start = timestamp(); + let result = self.inner.get_byte_ranges(ranges).await; + self.trace + .record("read", start, timestamp() - start, Some(self.partition)); + result + }) + } + fn get_metadata<'a>( + &'a mut self, + options: Option<&'a ArrowReaderOptions>, + ) -> BoxFuture<'a, parquet::errors::Result>> { + Box::pin(async move { + let start = timestamp(); + let result = self.inner.get_metadata(options).await; + self.trace.record( + "metadata", + start, + timestamp() - start, + Some(self.partition), + ); + result + }) + } +} + +#[derive(Debug)] +struct Factory { + inner: DefaultParquetFileReaderFactory, + trace: Arc, +} +impl ParquetFileReaderFactory for Factory { + fn create_reader( + &self, + partition: usize, + file: PartitionedFile, + hint: Option, + metrics: &ExecutionPlanMetricsSet, + ) -> datafusion::error::Result> { + Ok(Box::new(Reader { + inner: self.inner.create_reader(partition, file, hint, metrics)?, + trace: Arc::clone(&self.trace), + partition, + })) + } +} + +fn main() -> datafusion::error::Result<()> { + let opt = Options::parse(); + std::thread::sleep(std::time::Duration::from_millis(opt.start_delay_ms)); + let trace = opt.trace.as_ref().map(|_| Arc::new(Trace::default())); + let path = opt.trace.clone(); + let thread_number = AtomicUsize::new(0); + let mut runtime = tokio::runtime::Builder::new_multi_thread(); + runtime + .worker_threads(8) + .thread_name_fn(move || { + format!( + "df-thread-{}", + thread_number.fetch_add(1, Ordering::Relaxed) + ) + }) + .enable_all(); + if let Some(trace) = &trace { + let parked = Arc::clone(trace); + let active = Arc::clone(trace); + runtime.on_thread_park(move || parked.record("park", timestamp(), 0, None)); + runtime.on_thread_unpark(move || active.record("unpark", timestamp(), 0, None)); + } + let runtime = runtime.build()?; + let task_trace = trace.clone(); + let result = runtime.block_on(async { + SpawnedTask::spawn(run(opt, task_trace)) + .join_unwind() + .await + .unwrap() + }); + drop(runtime); + if let (Some(trace), Some(path)) = (trace, path) { + std::fs::write(path, serde_json::to_vec(&*trace.0.lock().unwrap()).unwrap())?; + } + result +} + +async fn run(opt: Options, trace: Option>) -> datafusion::error::Result<()> { + assert!(opt.partitions > 0 && opt.iterations > 0); + let mut config = SessionConfig::new() + .with_target_partitions(opt.partitions) + .with_batch_size(8192); + config.options_mut().execution.parquet.pushdown_filters = opt.mode != "off"; + config.options_mut().execution.parquet.progressive_io = opt.mode != "upfront"; + let ctx = SessionContext::new_with_config(config); + ctx.register_parquet("input", &opt.path, ParquetReadOptions::default()) + .await?; + let runtime_metrics = tokio::runtime::Handle::current().metrics(); + let busy = || { + (0..runtime_metrics.num_workers()) + .map(|i| runtime_metrics.worker_total_busy_duration(i)) + .collect::>() + }; + for iteration in 0..opt.iterations { + let busy_before = busy(); + let start_ns = timestamp(); + let start = Instant::now(); + let projection = if opt.narrow { + "c0" + } else { + "c0 + c2 + c3 + c4 + c5 + c6 + c7" + }; + let dataframe = ctx.sql(&format!( + "SELECT COUNT(*) AS rows, SUM({projection}) AS checksum FROM input WHERE c1 < 10000" + )).await?; + let task = Arc::new(dataframe.task_ctx()); + let mut plan = dataframe.create_physical_plan().await?; + if opt.prefetch_bytes > 0 || trace.is_some() { + plan = plan + .transform_up(|plan| { + if let Some(exec) = plan.downcast_ref::() + && let Some((config, source)) = + exec.downcast_to_file_source::() + { + let mut source = source.clone().with_row_group_prefetch( + opt.prefetch_bytes, + Arc::clone(&ctx.runtime_env().memory_pool), + ); + if let Some(trace) = &trace { + source = source.with_parquet_file_reader_factory(Arc::new( + Factory { + inner: DefaultParquetFileReaderFactory::new( + Arc::new( + object_store::local::LocalFileSystem::new(), + ), + ), + trace: Arc::clone(trace), + }, + )); + } + let config = FileScanConfigBuilder::from(config.clone()) + .with_source(Arc::new(source)) + .build(); + return Ok(Transformed::yes(Arc::new( + exec.clone().with_data_source(Arc::new(config)), + ))); + } + Ok(Transformed::no(plan)) + })? + .data; + } + let batches = collect(Arc::clone(&plan), task).await?; + let elapsed_ms = start.elapsed().as_secs_f64() * 1000.0; + let worker_busy_ms = busy() + .iter() + .zip(&busy_before) + .map(|(after, before)| { + after.checked_sub(*before).unwrap().as_secs_f64() * 1000.0 + }) + .collect::>(); + assert_eq!(batches.len(), 1); + assert_eq!(batches[0].num_rows(), 1); + let value = |i| { + batches[0] + .column(i) + .as_any() + .downcast_ref::() + .unwrap() + .value(0) + }; + assert_eq!( + (value(0), value(1)), + (opt.expected_rows, opt.expected_checksum) + ); + let mut counters = serde_json::Map::new(); + for name in [ + "bytes_scanned", + "prefetch_bytes", + "prefetch_row_groups", + "prefetch_budget_skips", + "prefetch_wait_time", + ] { + let mut total = 0; + plan.apply(|plan| { + if let Some(metric) = + plan.metrics().and_then(|metrics| metrics.sum_by_name(name)) + { + total += metric.as_usize(); + } + Ok(TreeNodeRecursion::Continue) + })?; + counters.insert(name.into(), total.into()); + } + println!( + "{}", + serde_json::json!({ + "pid": std::process::id(), "iteration": iteration, "start_ns": start_ns, "elapsed_ms": elapsed_ms, + "rows": value(0), "checksum": value(1), "counters": counters, "worker_busy_ms": worker_busy_ms, + }) + ); + if iteration == 0 { + eprintln!("{}", displayable(plan.as_ref()).indent(true)); + } + } + Ok(()) +} diff --git a/datafusion/datasource-parquet/IO_OVERLAP_PROFILING.md b/datafusion/datasource-parquet/IO_OVERLAP_PROFILING.md new file mode 100644 index 000000000000..fbdfe56a4539 --- /dev/null +++ b/datafusion/datasource-parquet/IO_OVERLAP_PROFILING.md @@ -0,0 +1,235 @@ + + +# Whole-query CPU and I/O overlap + +The September 12 profile found a scheduling gap in the existing prefetch patch: +a spawned read could stay queued while the query task continued synchronous +decoding on the same Tokio worker. Yielding once after successfully scheduling +the next row group removes most of that wait. This is a five-line production +change; prefetch depth and memory limits stay the same. + +## What was measured + +The baseline is [PR head `a8f744db6`](https://github.com/peterxcli/datafusion/commit/a8f744db6bf6f147e2ea88fcf03e0f0b977f4683), +which contains reader implementation `801cb0e52`. The after binary adds the yield +in `PushDecoderStreamState::next`. Both use DataFusion 55, Parquet 59.3.0, +Tokio 1.53.1, Rust 1.95.0, the same lockfile, and `release-nonlto` on an Apple M4 +with 10 cores and 24 GiB RAM. This isolates the change to the current patch; +the [earlier report](IO_POLICY_BENCHMARK.md), built with Rust 1.97.0, compares the original patch against +upstream `408696966`. + +The [runner](../../benchmarks/examples/io_overlap.rs) executes a complete SQL +filtered aggregate in a spawned Tokio task, with eight runtime workers and +either one or eight scan partitions. The earlier reader harness consumed its +stream from the runtime's top-level `block_on`, which does not exercise the +same worker-local scheduling path. + +```sql +SELECT COUNT(*) AS rows, SUM(c0 + c2 + c3 + c4 + c5 + c6 + c7) AS checksum +FROM input +WHERE c1 < 10000; +``` + +The narrow query uses `SUM(c0)`. Both reuse the existing 256-row-group fixtures: +33,554,432 rows, eight Int64 columns, Snappy, and 2 GiB of logical values per +file. Random matching rows have no page index; clustered matching rows have +page indexes. These two workloads vary both distribution and index availability, +so they are not a controlled comparison of either variable alone. + +All 384 before/after query executions checked row counts and checksums against +values independently calculated by the fixture generator. Every paired run also +requested identical bytes, prefetched the same row groups, and had identical +budget-skip counts. The prefetch budget was 16 MiB per stream. + +Timing runs had no tracing, artificial latency, or concurrent builds. Each +process ran one warmup and three measured iterations. Pass 1 ran before/after +pairs; pass 2 reversed both pair order and case order. These are warm local-file +measurements on an active desktop. The two passes expose variation rather than +provide confidence intervals. + +## Does the read overlap CPU work? + +![Whole query and an 80 ms detail of worker activity and pending reads.](benchmark-charts/io-overlap-runtime.png) + +With one partition, the runtime event trace changed as follows (medians of three +measured wide queries with row filtering after decoding and prefetch enabled): + +| Metric | Before | After | +| ---------------------------------------------------------------- | -------: | ------: | +| Read-pending time overlapping at least one active runtime worker | 2.2% | 99.7% | +| Wall time with all query workers parked | 156.6 ms | 0.69 ms | +| Wall time with all query workers parked while a read was pending | 156.5 ms | 0.52 ms | + +Here, active means not parked by Tokio; it can include time when the OS +preempts that thread. A reader interval is the lifetime of its asynchronous +read future, including submission, local-file work, scheduling, and wakeup. +It is not a measurement of physical disk latency. Tracing runs install a reader +wrapper and are separate from the uninstrumented timing comparison. + +One scan partition only supplies roughly one worker's worth of CPU work. +Seven otherwise idle runtime workers are therefore expected. The useful change +is removing periods when the query has no worker doing work while awaiting I/O, +not making all eight workers busy on a serial scan. + +The native System Trace profile independently records actual OS thread states +for eight-partition queries. Worker identities come from thread names, or the +Tokio worker entry point in sampled call stacks when Instruments omits names. +The parser checks that all eight workers are identified and their states cover +the entire query window. Each figure shows the measured query nearest its +recording's median duration, with identical horizontal scales. + +![Eight query workers and summed local-file pool CPU, before and after the yield.](benchmark-charts/io-overlap-native.png) + +| Native metric, median of 15 measured queries | Before | After | +| -------------------------------------------- | ---------: | ---------: | +| Summed worker blocked time | 409.6 ms | 174.8 ms | +| Summed worker running time | 1,906.2 ms | 1,953.5 ms | +| Summed worker preempted time | 237.5 ms | 483.8 ms | +| Summed worker runnable time | 38.7 ms | 22.4 ms | +| Profiled query wall time | 324.4 ms | 329.1 ms | + +Blocked time fell 57%, but this native recording does **not** show a wall-time +improvement: OS preemption rose in the second recording. The red portions are +runnable/preempted work, not idle CPU. The separate, uninstrumented comparisons +below establish the latency effect. Summed thread times can exceed query wall +time because eight workers run concurrently; medians of individual states do +not have to sum to the median total. + +The [plot input](benchmark-charts/io-overlap-timeline.json) contains only this +benchmark's representative thread intervals and per-query summaries. Plot +coordinates are rounded to microseconds; summaries retain full precision. +[`plot_io_overlap.py`](benchmark-charts/plot_io_overlap.py) regenerates both +figures using matplotlib. Native recordings and runtime traces are separate +from the uninstrumented timing measurements. + +## Query latency + +For random rows without page indexes, the single-partition queries used +7–11% less elapsed time across both widths and all three reading modes. +Eight-partition wide queries with pushdown used 5–9% less time in both passes. +The eight-partition queries already overlap I/O across partitions, so this +change has less idle time available to remove. + +Small indexed queries remain mixed. For example, the eight-partition narrow +progressive query used 1.5%/2.7% more time (about 0.06/0.10 ms), while the indexed +wide upfront query changed from 25.6% faster to 14.0% slower across passes. +The random wide query without pushdown also drifted substantially in pass 2. +These results do not justify a universal speedup claim or a new default. + +Modes: `off` filters after decoding; `progressive` pushes filters down and reads +filter columns before output columns; `upfront` pushes filters down and reads +both sets of selected pages together. Prefetch is enabled in every row below. +Values are median milliseconds; a negative change means less query time. + +| Distribution / index | Output columns | Partitions | Mode | Before 1 | After 1 | Change 1 | Before 2 | After 2 | Change 2 | +| -------------------- | -------------: | ---------: | ----------- | -------: | ------: | -------: | -------: | ------: | -------: | +| random | 7 | 1 | off | 1503.04 | 1389.32 | -7.6% | 1527.62 | 1385.18 | -9.3% | +| random | 7 | 1 | progressive | 1540.56 | 1421.13 | -7.8% | 1543.21 | 1415.57 | -8.3% | +| random | 7 | 1 | upfront | 1574.20 | 1408.12 | -10.5% | 1542.94 | 1404.79 | -9.0% | +| random | 7 | 8 | off | 301.52 | 293.11 | -2.8% | 378.27 | 312.95 | -17.3% | +| random | 7 | 8 | progressive | 288.37 | 270.91 | -6.1% | 295.61 | 270.31 | -8.6% | +| random | 7 | 8 | upfront | 300.03 | 284.30 | -5.2% | 294.91 | 275.65 | -6.5% | +| random | 1 | 1 | off | 350.97 | 322.95 | -8.0% | 352.61 | 323.98 | -8.1% | +| random | 1 | 1 | progressive | 349.38 | 319.09 | -8.7% | 349.64 | 323.98 | -7.3% | +| random | 1 | 1 | upfront | 361.43 | 323.54 | -10.5% | 352.47 | 318.99 | -9.5% | +| random | 1 | 8 | off | 75.25 | 66.84 | -11.2% | 70.31 | 67.30 | -4.3% | +| random | 1 | 8 | progressive | 69.24 | 66.04 | -4.6% | 67.44 | 68.87 | +2.1% | +| random | 1 | 8 | upfront | 70.33 | 66.41 | -5.6% | 70.42 | 70.34 | -0.1% | +| clustered | 7 | 1 | off | 36.60 | 29.83 | -18.5% | 36.77 | 29.83 | -18.9% | +| clustered | 7 | 1 | progressive | 35.13 | 27.91 | -20.6% | 35.35 | 28.31 | -19.9% | +| clustered | 7 | 1 | upfront | 34.80 | 28.08 | -19.3% | 34.60 | 31.84 | -8.0% | +| clustered | 7 | 8 | off | 9.64 | 9.25 | -4.1% | 9.08 | 9.33 | +2.8% | +| clustered | 7 | 8 | progressive | 8.18 | 7.43 | -9.1% | 8.30 | 7.46 | -10.1% | +| clustered | 7 | 8 | upfront | 10.03 | 7.46 | -25.6% | 8.64 | 9.86 | +14.0% | +| clustered | 1 | 1 | off | 14.11 | 10.02 | -29.0% | 13.03 | 9.78 | -24.9% | +| clustered | 1 | 1 | progressive | 14.08 | 12.68 | -9.9% | 15.60 | 12.59 | -19.3% | +| clustered | 1 | 1 | upfront | 14.42 | 12.56 | -12.9% | 13.73 | 12.38 | -9.9% | +| clustered | 1 | 8 | off | 3.94 | 3.79 | -3.7% | 4.02 | 3.85 | -4.3% | +| clustered | 1 | 8 | progressive | 3.70 | 3.76 | +1.5% | 3.73 | 3.83 | +2.7% | +| clustered | 1 | 8 | upfront | 3.92 | 3.64 | -7.0% | 3.94 | 3.73 | -5.3% | + +[All query measurements, including warmups](benchmark-charts/io-overlap-queries.csv) +are available as CSV. Iteration zero is excluded from the medians. +`prefetch_wait_time` is summed across partitions and measured in nanoseconds. + +## Why the yield is needed + +On this Tokio version, spawning from a worker puts the new task into its local +LIFO slot. Another worker cannot steal that slot. The scan can keep decoding +and its consumer can keep processing ready batches without returning to the +scheduler, so the queued read starts only when the consumer finally awaits it. + +The decoder now yields immediately after a prefetch task is successfully +created, giving that task a chance to issue its read before CPU work continues. +It does not yield when prefetch is disabled, the budget cannot be reserved, or +there is no next row group. + +`prefetch_starts_while_consumer_uses_worker` reproduces the issue inside a spawned +query task: after receiving a batch, the consumer does CPU work without an +await, while another worker can start the next read. It fails against the +baseline (one read started instead of two) and passes with the yield. The +existing decoder tests cover cancellation, budget pressure, speculative errors, +pruning, and ordering. + +## Reproduce + +Generate or reuse fixtures and independent expected values with the existing +`datafusion-datasource-parquet` `io_policy` example (arguments `256 /tmp/io-data`). +Build the query runner on the baseline and on this patch, keeping separate +copies of the executable. The timed binaries used Rust 1.95.0; repository +validation uses its pinned Rust 1.97.0: + +```bash +cargo +1.95.0 build --locked --profile release-nonlto \ + -p datafusion-benchmarks --example io_overlap + +target/release-nonlto/examples/io_overlap \ + --path /tmp/io-data/scan-false-false-256.parquet \ + --mode off --partitions 1 --prefetch-bytes 16777216 --iterations 4 \ + --expected-rows 336704 --expected-checksum 6662726950778 +``` + +Use `--partitions 8`, `--mode progressive`, `--mode upfront`, and `--narrow` for +the other cases, with the appropriate generator checksum for each projection. +`--prefetch-bytes 0` supplies the disabled control. `--trace events.json` records +runtime park/unpark events and read-future intervals. On macOS, Instruments +System Trace records actual thread running/blocked states; `--start-delay-ms 3000` allows tracing to start before the runtime creates its threads. Keep native +profiles separate from timing runs and exclude the first iteration. + +## Remaining scope + +The within-row-group I/O policy requested by +[DataFusion #24393](https://github.com/apache/datafusion/issues/24393) is separate +from next-row-group prefetch. `progressive_io=false` provides the former; this +follow-up repairs scheduling of the latter. The issue's proposed defaults still +need an upstream decision. Physical object-store request counts, remote latency, +and the Spark/Comet execution path remain unmeasured by this profile. The Comet +POC is still pinned to its earlier DataFusion implementation. + +## Validation + +The new scheduler regression failed on the baseline and passed after the change. +All 259 Parquet library tests passed (one manual benchmark ignored), including +cancellation, ordering, pruning, budget exhaustion, and speculative I/O failure. +The timing comparison independently validated all 384 query results and equal +requested-byte/prefetch counters between revisions. + +The SQL page-pruning regression, workspace Clippy, formatting, and the standard +`dev/rust_lint.sh` suite also passed using the pinned Rust 1.97.0. diff --git a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md index fddc08dcc282..baddc4fc5a7d 100644 --- a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md +++ b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md @@ -17,9 +17,9 @@ under the License. --> -# Parquet I/O benchmarks: current patch versus upstream main +# Parquet I/O benchmarks: September 10 patch versus upstream main -These measurements compare the current patch with upstream `main` fetched on +These measurements compare reader implementation `801cb0e52` with upstream `main` fetched on 2026-09-10. Both use Rust 1.97.0 and the `release-nonlto` profile on an Apple M4 with 10 CPU cores and 24 GiB RAM. @@ -28,6 +28,10 @@ with 10 CPU cores and 24 GiB RAM. | [Upstream main `408696966`](https://github.com/apache/datafusion/commit/4086969669f96c0d5de03f442f2dba1869d2ee80) | 55.0.0 | 59.3.0 | | [Current reader implementation `801cb0e52`](https://github.com/peterxcli/datafusion/commit/801cb0e52) | 55.0.0 | 59.3.0 | +The later [whole-query CPU/I/O profile](IO_OVERLAP_PROFILING.md) found and fixed a +Tokio scheduling delay that this reader-only harness did not expose. The timings +below precede that fix. + The patch is rebased onto the measured upstream commit. 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diff --git a/datafusion/datasource-parquet/benchmark-charts/plot_io_overlap.py b/datafusion/datasource-parquet/benchmark-charts/plot_io_overlap.py new file mode 100644 index 000000000000..4d8585fbd7f3 --- /dev/null +++ b/datafusion/datasource-parquet/benchmark-charts/plot_io_overlap.py @@ -0,0 +1,91 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +"""Render the captured, anonymized benchmark timelines with matplotlib.""" +import json +from pathlib import Path + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.patches import Patch + +ROOT = Path(__file__).resolve().parent +DATA = json.loads((ROOT / "io-overlap-timeline.json").read_text()) +GOLD, BLUE, RED, GRAY = "#e7b928", "#46a9ee", "#d87770", "#343944" +plt.rcParams.update({"font.size": 11, "figure.facecolor": "#f8fafc"}) + + +def style(ax): + ax.set_facecolor("#20242b") + ax.grid(axis="x", alpha=0.15) + ax.set_axisbelow(True) + for spine in ax.spines.values(): + spine.set_visible(False) + + +fig, axes = plt.subplots(2, 2, figsize=(15, 7), height_ratios=[4, 1], sharex="col") +limit = max(d["elapsed_ms"] for d in DATA["native"].values()) * 1.04 +for col, label in enumerate(["current", "yield"]): + d = DATA["native"][label] + assert len(d["workers"]) == 8 + ax = axes[0, col] + style(ax) + for i, spans in enumerate(d["workers"]): + for state, color in [("Blocked", GRAY), ("Preempted", RED), ("Runnable", RED), ("Running", GOLD)]: + ax.broken_barh([(a, b-a) for a, b, s in spans if s == state], (i-0.35, 0.7), facecolors=color) + ax.set(yticks=range(8), yticklabels=[f"Worker {i+1}" for i in range(8)], ylim=(7.6, -0.6)) + ax.set_title(f'{"Before" if col == 0 else "After yield"} · {d["elapsed_ms"]:.1f} ms', loc="left", fontweight="bold") + io = axes[1, col] + style(io) + edges = np.arange(0, limit+0.5, 0.5) + cpu = np.zeros(len(edges)-1) + for spans in d["io"]: + for a, b, state in spans: + if state == "Running": + cpu += np.maximum(0, np.minimum(edges[1:], b)-np.maximum(edges[:-1], a))/0.5 + io.stairs(cpu, edges, fill=True, color=BLUE) + io.set(xlabel="Time since query start (ms)", ylabel="I/O pool\nCPU cores", xlim=(0, limit), ylim=(0, 8), yticks=[0, 4, 8]) + for a in [ax, io]: + a.axvline(d["elapsed_ms"], color="#a7b3c4", lw=1, ls="--") +fig.suptitle("Eight scan partitions: actual CPU execution and waiting", x=0.06, ha="left", fontsize=18, fontweight="bold") +fig.legend(handles=[Patch(color=GOLD, label="Running on CPU"), Patch(color=RED, label="Runnable / preempted"), Patch(color=GRAY, label="Blocked"), Patch(color=BLUE, label="Local-file pool CPU (sum)")], loc="lower center", ncol=4, frameon=False) +fig.subplots_adjust(left=0.08, right=0.98, top=0.88, bottom=0.14, hspace=0.1, wspace=0.18) +fig.savefig(ROOT / "io-overlap-native.png", dpi=180) +plt.close(fig) + +fig, axes = plt.subplots(2, 2, figsize=(15, 6.4)) +limit = max(d["elapsed_ms"] for d in DATA["runtime"].values()) * 1.02 +for col, label in enumerate(["current", "yield"]): + d = DATA["runtime"][label] + for row in range(2): + ax = axes[row, col] + style(ax) + ax.broken_barh([(a, b-a) for a, b in d["active"]], (0.65, 0.6), facecolors=GOLD) + ax.broken_barh([(a, b-a) for a, b in d["reads"]], (-0.25, 0.6), facecolors=BLUE) + ax.set(yticks=[0.05, 0.95], yticklabels=["Read pending", "Worker active"], ylim=(-0.6, 1.6), xlabel="Time since query start (ms)", xlim=(0, limit) if row == 0 else (30, 110)) + if row == 0: + ax.axvline(d["elapsed_ms"], color="#a7b3c4", ls="--", lw=1) + ax.set_title(f'{"Before" if col == 0 else "After yield"} · whole query, {d["elapsed_ms"]:.1f} ms', loc="left", fontweight="bold") + else: + ax.set_title("Same 80 ms window, enlarged", loc="left") +fig.suptitle("One scan partition: queued reads become overlapping reads", x=0.08, ha="left", fontsize=18, fontweight="bold") +fig.text(0.08, 0.025, "Worker active = at least one Tokio worker unparked; read pending = reader future lifetime. These are not OS CPU or disk-time measurements.", fontsize=10) +fig.subplots_adjust(left=0.10, right=0.98, top=0.87, bottom=0.13, hspace=0.52, wspace=0.22) +fig.savefig(ROOT / "io-overlap-runtime.png", dpi=180) +plt.close(fig) diff --git a/datafusion/datasource-parquet/src/push_decoder.rs b/datafusion/datasource-parquet/src/push_decoder.rs index 89a61885679c..d242695af143 100644 --- a/datafusion/datasource-parquet/src/push_decoder.rs +++ b/datafusion/datasource-parquet/src/push_decoder.rs @@ -795,6 +795,11 @@ impl PushDecoderStreamState { Arc::clone(&self.reader), &self.prefetch_metrics, ); + if self.pending_prefetch.is_some() { + // Let the I/O task start before this worker + // continues decoding or consuming batches. + tokio::task::yield_now().await; + } } Ok(None) => {} Err(e) => return Some((Err(e.into()), self)), @@ -1241,6 +1246,45 @@ mod tests { assert_pool_released(&pool).await; } + #[tokio::test(flavor = "multi_thread", worker_threads = 2)] + async fn prefetch_starts_while_consumer_uses_worker() { + use datafusion_common::instant::Instant; + use datafusion_execution::memory_pool::GreedyMemoryPool; + use std::sync::atomic::Ordering; + + SpawnedTask::spawn(async { + let pool: Arc = Arc::new(GreedyMemoryPool::new(1 << 20)); + let control = Arc::new(ReadControl { + block_second: true, + ..Default::default() + }); + let mut stream = prefetch_test_stream( + 1 << 20, + Arc::clone(&pool), + Arc::clone(&control), + None, + None, + ); + stream.next().await.unwrap().unwrap(); + // A consumer can keep doing CPU work after receiving a batch. + // Waiting on a Notify here would yield and hide a delayed I/O start. + let deadline = Instant::now() + std::time::Duration::from_millis(100); + while control.calls.load(Ordering::SeqCst) < 2 && Instant::now() < deadline { + std::hint::spin_loop(); + } + assert_eq!( + control.calls.load(Ordering::SeqCst), + 2, + "prefetch must start while the consumer is using a runtime worker" + ); + drop(stream); + assert_pool_released(&pool).await; + }) + .join_unwind() + .await + .unwrap(); + } + #[tokio::test] async fn prefetch_budget_pool_pressure_limit_and_cancellation() { use datafusion_execution::memory_pool::GreedyMemoryPool; From 1409829d8345540e9790269543a233dc501f4a45 Mon Sep 17 00:00:00 2001 From: peterxcli Date: Sat, 12 Sep 2026 10:53:31 +0800 Subject: [PATCH 12/13] docs: profile Parquet CPU and I/O overlap on Linux --- .../datasource-parquet/IO_OVERLAP_AWS.md | 219 ++++++++++ .../IO_OVERLAP_PROFILING.md | 3 + .../aws-20260912/aws-queries.csv | 385 ++++++++++++++++++ .../aws-20260912/aws-query-timings.png | Bin 0 -> 260678 bytes .../aws-20260912/io-overlap-native.png | Bin 0 -> 185940 bytes .../aws-20260912/io-overlap-runtime.png | Bin 0 -> 185701 bytes .../aws-20260912/io-overlap-timeline.json | 1 + 7 files changed, 608 insertions(+) create mode 100644 datafusion/datasource-parquet/IO_OVERLAP_AWS.md create mode 100644 datafusion/datasource-parquet/benchmark-charts/aws-20260912/aws-queries.csv create mode 100644 datafusion/datasource-parquet/benchmark-charts/aws-20260912/aws-query-timings.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/aws-20260912/io-overlap-native.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/aws-20260912/io-overlap-runtime.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/aws-20260912/io-overlap-timeline.json diff --git a/datafusion/datasource-parquet/IO_OVERLAP_AWS.md b/datafusion/datasource-parquet/IO_OVERLAP_AWS.md new file mode 100644 index 000000000000..d96f1d2e9875 --- /dev/null +++ b/datafusion/datasource-parquet/IO_OVERLAP_AWS.md @@ -0,0 +1,219 @@ + + +# CPU and I/O overlap on chia-ping-aws1 + +The September 12 run reproduces the scheduling improvement on Linux. With one +scan partition, the old patch spends about 426 ms with every query worker +parked; the yield reduces this to 1.9 ms. Linux scheduler events independently +show local-file I/O CPU work moving from almost no overlap with query workers +to 99.6% overlap. Query-worker CPU time is essentially unchanged. The work is +happening concurrently, and the query finishes earlier. + +## Comparison and controls + +Both binaries ran on `chia-ping-aws1` (`peterxcli-ubuntu`): an x86_64 KVM guest +with 16 vCPUs exposed as AMD EPYC 7282, 31 GiB RAM, and Linux 6.8.0-134-generic. +Both used pinned Rust 1.97.0, `release-nonlto`, the same Cargo.lock, and the exact +same [whole-query harness](../../benchmarks/examples/io_overlap.rs). + +The baseline is the existing patch at +[`a8f744db6`](https://github.com/peterxcli/datafusion/commit/a8f744db6bf6f147e2ea88fcf03e0f0b977f4683). +The after binary is +[`3eaddb5f3`](https://github.com/peterxcli/datafusion/commit/3eaddb5f3f486442b520dcfd65e7a28a39c8707f), +which adds the five-line scheduling yield. This compares the current patch +before and after that fix; the [earlier baseline report](IO_POLICY_BENCHMARK.md) +compares the original patch with upstream main. + +The [previous profiling report](IO_OVERLAP_PROFILING.md) describes the SQL and +fixture generator. Each fixture contains 33,554,432 rows in 256 row groups, +eight Int64 columns, Snappy compression, and 2 GiB of logical values. The exact +same files were copied from the local experiment and verified by SHA-256: + +```text +scan-false-false-256.parquet 78cd3fbe2728050055171ff4affc0c50e313464222b5dda7509aa12c67cd0a8d +scan-true-true-256.parquet 75b53d123f54bd2d17832f1c8992c9a71953fc7f56d162ebaff7ca4cee0c9199 +``` + +There are 24 cases: scattered/no-index or clustered/indexed rows, one or seven +output columns, one or eight partitions, and three reading modes. All use eight +Tokio workers and 16 MiB prefetch per stream. Each process runs one warmup and +three measured queries. Pass 2 reverses both case order and before/after order. +All **384 executions** checked independently expected counts and checksums; +paired runs also had identical requested bytes, prefetched bytes, prefetched row +groups, and budget skips. There were 288 measured executions after warmups. + +These are warm local-file reads without artificial latency. Timing runs had no +tracing or concurrent builds. The VM's aggregate CPU steal during benchmark +processes had a median of 8.0% and a maximum of 18.6%. The two passes expose +variation; they do not establish confidence intervals or performance on S3. + +## CPU and I/O timelines + +![One partition: whole query and enlarged worker/read intervals.](benchmark-charts/aws-20260912/io-overlap-runtime.png) + +The runtime trace for scattered rows, seven output columns, one partition, +and filtering after decode gives these medians over three measured queries: + +| Runtime metric | Before | After | +| ---------------------------------------------- | ---------: | ---------: | +| Read-pending time overlapping an active worker | 4.20% | 99.65% | +| Wall time with all eight workers parked | 426.01 ms | 1.89 ms | +| All workers parked while a read was pending | 425.58 ms | 1.56 ms | +| Summed reader-future lifetime | 446.23 ms | 448.09 ms | +| Profiled query wall time | 2870.42 ms | 2383.12 ms | + +An active worker is unparked by Tokio; it may still be preempted by the OS. +A pending read is a reader future's lifetime, including scheduling and wakeup. +The enlarged window shows reads overlapping query activity after the fix. +With prefetch disabled, the same control still has 418/434 ms of all-worker +parked time before/after. The improvement depends on the prefetch path. + +A separate Linux `perf` recording confirms actual guest scheduler overlap for +the same single-partition query: + +| Native metric, median of three measured queries | Before | After | +| -------------------------------------------------------- | ---------: | ---------: | +| I/O-pool running time overlapping a running query worker | 0.026% | 99.643% | +| Summed query-worker running time | 2485.72 ms | 2482.72 ms | +| Summed I/O-pool running time | 437.39 ms | 457.05 ms | +| Profiled query wall time | 2908.32 ms | 2446.64 ms | + +The I/O CPU overlap percentage is weighted by each I/O thread's running time. +It measures local-file pool work, not physical disk latency. CPU steal was +0.33%/0.68% during these recordings. One partition supplies roughly one worker's +worth of query work; the other seven workers need not become busy. + +![Eight partitions: guest worker states and summed I/O-pool CPU.](benchmark-charts/aws-20260912/io-overlap-native.png) + +For eight partitions, medians over 15 measured queries are: + +| Native metric | Before | After | +| --------------------------- | ---------: | ---------: | +| Summed worker blocked time | 749.33 ms | 283.60 ms | +| Summed worker running time | 2951.73 ms | 2822.82 ms | +| Summed worker runnable time | 5.82 ms | 1.68 ms | +| Profiled query wall time | 460.43 ms | 383.71 ms | + +Blocked time falls **62%**. Both revisions already overlap almost all I/O-pool +CPU time with at least one worker (99.5%/98.7%); the change reduces waiting +within individual scan streams. Each plot uses the query nearest its recording's +median elapsed time and the same horizontal scale. Medians of individual +metrics need not add up to the median total. CPU steal was 7.01%/5.92% during +these recordings, so their 16.7% latency reduction is not an isolated estimate +of the fix's effect. + +Worker roles come from actual Tokio park/unpark callbacks matched to process +and thread IDs, rather than thread creation order. The parser checks all eight +workers, full query-window coverage, and absence of lost perf records; its +regression check includes out-of-order worker names. Scheduler and query clocks +are aligned through `CLOCK_MONOTONIC`/realtime calibration before and after +each recording, with less than 20 ns offset drift. Guest `Running` can include +hypervisor steal time, so these are guest scheduler states, not physical-host +CPU utilization measurements. + +[Filtered timeline data](benchmark-charts/aws-20260912/io-overlap-timeline.json) +contains benchmark intervals and per-query summaries. Raw system-wide scheduler +records stay on the benchmark host. Native profiles and runtime traces are +separate from the untraced timing runs. + +## Untraced query times + +![Before/after elapsed-time changes for both passes.](benchmark-charts/aws-20260912/aws-query-timings.png) + +Scattered-row wide queries used **9–16% less elapsed time with one partition** +across reading modes and both passes. Eight-partition wide progressive queries +used 6.1%/4.7% less time; upfront queries used 23.5%/9.6% less. For the wide +single-partition query without pushdown, prefetch wait fell from 463.5/456.0 ms +to 1.60/1.45 ms across the two passes. + +Short indexed queries remain mixed. The narrow eight-partition query without +pushdown changed from 34.7% slower to 9.9% faster between passes; narrow upfront +changed from 10.7% faster to 21.3% slower. These results support the scheduling +fix for the measured long scans, but do not justify a universal speedup or a +change to the default prefetch setting. + +`off` filters after decode; `progressive` reads filter columns before output +columns; `upfront` reads both sets of selected pages together. Prefetch is on +in every row. Values below are medians excluding iteration zero; negative +changes mean less elapsed time. Distribution and page-index availability vary +together and cannot be evaluated independently with these two fixtures. + +| Distribution / index | Output columns | Partitions | Mode | Before 1 (ms) | After 1 (ms) | Change 1 | Before 2 (ms) | After 2 (ms) | Change 2 | +| -------------------- | -------------: | ---------: | ----------- | ------------: | -----------: | -------: | ------------: | -----------: | -------: | +| random | 7 | 1 | off | 2953.13 | 2525.91 | -14.5% | 2842.66 | 2390.69 | -15.9% | +| random | 7 | 1 | progressive | 2816.53 | 2572.89 | -8.7% | 2927.42 | 2448.01 | -16.4% | +| random | 7 | 1 | upfront | 2818.31 | 2552.61 | -9.4% | 2899.45 | 2510.80 | -13.4% | +| random | 7 | 8 | off | 626.42 | 429.55 | -31.4% | 452.00 | 421.77 | -6.7% | +| random | 7 | 8 | progressive | 462.83 | 434.51 | -6.1% | 443.83 | 422.86 | -4.7% | +| random | 7 | 8 | upfront | 540.24 | 413.05 | -23.5% | 440.30 | 397.86 | -9.6% | +| random | 1 | 1 | off | 804.82 | 727.86 | -9.6% | 826.51 | 681.61 | -17.5% | +| random | 1 | 1 | progressive | 763.81 | 747.69 | -2.1% | 885.58 | 792.44 | -10.5% | +| random | 1 | 1 | upfront | 860.02 | 741.11 | -13.8% | 809.91 | 714.73 | -11.8% | +| random | 1 | 8 | off | 146.15 | 133.81 | -8.4% | 135.88 | 122.71 | -9.7% | +| random | 1 | 8 | progressive | 150.30 | 124.84 | -16.9% | 139.08 | 131.93 | -5.1% | +| random | 1 | 8 | upfront | 162.35 | 119.61 | -26.3% | 165.34 | 122.73 | -25.8% | +| clustered | 7 | 1 | off | 102.79 | 75.90 | -26.2% | 137.55 | 96.66 | -29.7% | +| clustered | 7 | 1 | progressive | 116.31 | 85.64 | -26.4% | 107.40 | 85.11 | -20.8% | +| clustered | 7 | 1 | upfront | 133.42 | 91.29 | -31.6% | 105.39 | 75.86 | -28.0% | +| clustered | 7 | 8 | off | 32.39 | 25.51 | -21.2% | 34.94 | 27.52 | -21.2% | +| clustered | 7 | 8 | progressive | 27.94 | 25.18 | -9.9% | 25.27 | 19.80 | -21.6% | +| clustered | 7 | 8 | upfront | 34.66 | 29.33 | -15.4% | 26.32 | 22.67 | -13.9% | +| clustered | 1 | 1 | off | 50.84 | 34.11 | -32.9% | 53.03 | 40.14 | -24.3% | +| clustered | 1 | 1 | progressive | 44.46 | 37.15 | -16.4% | 45.96 | 42.67 | -7.2% | +| clustered | 1 | 1 | upfront | 52.42 | 55.71 | +6.3% | 49.07 | 31.14 | -36.5% | +| clustered | 1 | 8 | off | 11.44 | 15.41 | +34.7% | 12.07 | 10.87 | -9.9% | +| clustered | 1 | 8 | progressive | 14.91 | 18.58 | +24.6% | 11.32 | 10.70 | -5.5% | +| clustered | 1 | 8 | upfront | 11.78 | 10.53 | -10.7% | 11.33 | 13.75 | +21.3% | + +[All query measurements](benchmark-charts/aws-20260912/aws-queries.csv) include +warmups, result checks, byte counters, and process-level host CPU steal. +`prefetch_wait_time` is in nanoseconds and is summed across scan partitions. + +## Reproduce on the benchmark host + +The isolated checkout is `/home/ubuntu/oss/datafusion-io-overlap-20260912`. +Binaries, inputs, scripts, raw profiles, and all three plots are retained in +`/home/ubuntu/oss/datafusion-io-overlap-results-20260912`. Existing dirty +DataFusion checkouts were preserved. Benchmark execution, analysis, and PNG +rendering all ran on `chia-ping-aws1`. + +```bash +ssh chia-ping-aws1 +cd /home/ubuntu/oss/datafusion-io-overlap-results-20260912 + +# Repeat the untraced matrix using the retained before/after binaries. +python3 compare_fix.py + +# Validate and re-analyze the captured Linux scheduler records. +python3 check_native_parser.py +python3 analyze_native.py before-native-p8 after-native-p8 before-native-p1 after-native-p1 + +# Regenerate the CSV, filtered timeline JSON, and three PNG figures. +venv/bin/python prepare_plots.py +``` + +`record_events.py` and `record_native.py` retain the capture commands; +`toolchain.txt`, `binary-sha256.txt`, per-process host snapshots, and clock +calibration files record the execution environment. Repeated commands replace +their corresponding results, so copy the result directory before another run. + +This experiment reaches the requested overlap improvement on these warm local +scans. Physical object-store requests, remote-storage latency, and execution +through Spark/Comet remain unmeasured. diff --git a/datafusion/datasource-parquet/IO_OVERLAP_PROFILING.md b/datafusion/datasource-parquet/IO_OVERLAP_PROFILING.md index fbdfe56a4539..a333e6320b98 100644 --- a/datafusion/datasource-parquet/IO_OVERLAP_PROFILING.md +++ b/datafusion/datasource-parquet/IO_OVERLAP_PROFILING.md @@ -25,6 +25,9 @@ decoding on the same Tokio worker. Yielding once after successfully scheduling the next row group removes most of that wait. This is a five-line production change; prefetch depth and memory limits stay the same. +The [Linux follow-up on chia-ping-aws1](IO_OVERLAP_AWS.md) repeats this comparison +with Rust 1.97.0 and includes Linux scheduler timelines and all query results. + ## What was measured The baseline is [PR head `a8f744db6`](https://github.com/peterxcli/datafusion/commit/a8f744db6bf6f147e2ea88fcf03e0f0b977f4683), diff --git a/datafusion/datasource-parquet/benchmark-charts/aws-20260912/aws-queries.csv b/datafusion/datasource-parquet/benchmark-charts/aws-20260912/aws-queries.csv new file mode 100644 index 000000000000..b2d6deadee35 --- /dev/null +++ b/datafusion/datasource-parquet/benchmark-charts/aws-20260912/aws-queries.csv @@ -0,0 +1,385 @@ +revision,distribution,page_index,output_columns,partitions,mode,pass_number,iteration,elapsed_ms,rows,checksum,host_steal_percent,bytes_scanned,prefetch_bytes,prefetch_row_groups,prefetch_budget_skips,prefetch_wait_time 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1.97.0","before_revision":"a8f744db6bf6f147e2ea88fcf03e0f0b977f4683","after_revision":"3eaddb5f3f486442b520dcfd65e7a28a39c8707f"} From 725c92a0b24c40dcd8a6f3eb63bdb3959af8b149 Mon Sep 17 00:00:00 2001 From: peterxcli Date: Sat, 12 Sep 2026 12:09:35 +0800 Subject: [PATCH 13/13] docs: profile every query on full partitioned ClickBench --- .../CLICKBENCH_FULL_PROFILING.md | 250 + .../datasource-parquet/IO_OVERLAP_AWS.md | 3 + .../binary-harness-sha256.txt | 5 + .../dataset-manifest.json | 702 + .../clickbench-full-20260912/index.html | 20 + .../clickbench-full-20260912/overview.png | Bin 0 -> 141630 bytes .../q00-timeline.json | 1 + .../clickbench-full-20260912/q00.png | Bin 0 -> 196834 bytes .../q01-timeline.json | 1 + .../clickbench-full-20260912/q01.png | Bin 0 -> 215296 bytes .../q02-timeline.json | 1 + .../clickbench-full-20260912/q02.png | Bin 0 -> 225520 bytes .../q03-timeline.json | 1 + .../clickbench-full-20260912/q03.png | Bin 0 -> 230702 bytes .../q04-timeline.json | 1 + 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datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/q39-timeline.json create mode 100644 datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/q39.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/q40-timeline.json create mode 100644 datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/q40.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/q41-timeline.json create mode 100644 datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/q41.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/q42-timeline.json create mode 100644 datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/q42.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/queries-manifest.json create mode 100644 datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/queries.csv create mode 100644 datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/result-validation.json create mode 100644 datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/summary.json create mode 100644 datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/toolchain.txt diff --git a/datafusion/datasource-parquet/CLICKBENCH_FULL_PROFILING.md b/datafusion/datasource-parquet/CLICKBENCH_FULL_PROFILING.md new file mode 100644 index 000000000000..82fd2efb1192 --- /dev/null +++ b/datafusion/datasource-parquet/CLICKBENCH_FULL_PROFILING.md @@ -0,0 +1,250 @@ + + +# Full partitioned ClickBench on chia-ping-aws1 + +All 43 queries completed against all 100 partition files: **99,997,497 rows, +325 row groups, and 14,737,666,736 compressed bytes**. Benchmark execution and +all final chart rendering ran on chia-ping-aws1 on September 12, 2026. + +The scheduling fix produces a modest, mixed improvement on this workload. +The sum of per-query timing medians falls **0.06% in pass 1 and 2.04% in pass 2**. +Eleven queries are faster in both passes, seven are slower in both, and 25 +change direction. These results do not reproduce the broad speedup seen in +the earlier synthetic wide-scan experiment. + +[43-query chart gallery](benchmark-charts/clickbench-full-20260912/index.html) · +[all timing measurements](benchmark-charts/clickbench-full-20260912/queries.csv) · +[summary JSON](benchmark-charts/clickbench-full-20260912/summary.json) + +The HTML gallery can be opened locally after downloading its directory. +The table below also links directly to every chart on GitHub. Each chart +contains both timing passes and separate before/after CPU and I/O timelines. +Query labels **Q01–Q43** map to the runner's **q0.sql–q42.sql**. + +## What is compared + +Before is the existing patch at +[`a8f744db6`](https://github.com/peterxcli/datafusion/commit/a8f744db6bf6f147e2ea88fcf03e0f0b977f4683). +After is the scheduling fix at +[`3eaddb5f3`](https://github.com/peterxcli/datafusion/commit/3eaddb5f3f486442b520dcfd65e7a28a39c8707f), +which yields after spawning next-row-group prefetch so the queued read can run. +Both revisions already include the upfront I/O policy and optional prefetch. +This experiment isolates that scheduling change; the +[earlier report](IO_POLICY_BENCHMARK.md) compares the original patch with upstream main. + +Both binaries use Rust 1.97.0, the same Cargo.lock, `release-nonlto`, mimalloc, +eight Tokio workers, eight target partitions, a **12G fair memory pool**, +filter pushdown with predicate reordering, `progressive_io=false`, and +**64 MiB prefetch per stream**. Both compile the same benchmark harness. +The host is a KVM Linux 6.8.0-134-generic guest exposing 16 AMD EPYC 7282 vCPUs +and 31 GiB RAM. These are warm local-file reads, without artificial latency +or an OS cache flush. Warmup does not guarantee the entire dataset remains +resident after memory-intensive queries. + +The dataset URLs follow the official +[ClickBench DataFusion setup](https://github.com/ClickHouse/ClickBench/tree/main/datafusion). +[File sizes, SHA-256 checksums, and Parquet footer counts](benchmark-charts/clickbench-full-20260912/dataset-manifest.json) +verify all 100 files. Q01 independently returns the full row count on both revisions. +[Query SQL and checksums](benchmark-charts/clickbench-full-20260912/queries-manifest.json) preserve the 43 queries +from the pinned DataFusion ClickBench runner, including its normal hits view +and date/string registration behavior. + +## Timing results + +Each query/revision process runs one excluded warmup and three measured +iterations. Pass 1 runs before then after in query order; pass 2 reverses both +query and revision order. This gives **688 timing executions, 516 measured**. +Queries run one at a time, without concurrent compilation or scheduler recording. +The timer covers planning and result collection; result validation and metric +capture happen afterward. + +| Sum of 43 per-query medians | Before | After | Change | +| --------------------------- | -------: | -------: | -----: | +| Pass 1 | 81.947 s | 81.900 s | -0.06% | +| Pass 2 | 83.694 s | 81.990 s | -2.04% | + +These sums are a workload summary, not an official ClickBench score or one +continuous query. Three measured runs per case and two orderings do not +establish confidence intervals. Host-wide CPU steal during timing processes +has a **4.45% median and 9.79% maximum**. + +Q33, Q34, and Q35 spill approximately **3.6–4.2 GiB** under the 12G memory cap. +Their charts therefore include aggregation/sort spill work as well as scan I/O. +Q35 is slower in both passes (+1.8% and +10.9%); that regression remains visible +in the aggregate instead of being excluded. + +![All-query timing changes; negative is faster.](benchmark-charts/clickbench-full-20260912/overview.png) + +## Does CPU and I/O overlap improve? + +Separate Linux scheduler recordings cover every query/revision, again with one +warmup and three measured iterations: **344 profiled executions, 258 measured**. +Together with the timings, all **1,032 executions** completed successfully. + +Summing the per-query medians from those recordings gives: + +| Guest worker metric | Before | After | +| ------------------------------------ | --------: | --------: | +| Blocked time, summed across workers | 50.513 s | 47.514 s | +| Running time, summed across workers | 642.669 s | 638.352 s | +| Runnable time, summed across workers | 1.012 s | 0.932 s | +| Query wall time | 86.807 s | 86.393 s | + +Summed worker-blocked time is **5.94% lower**, so the profiles show some progress +toward keeping query workers busy. The timing runs show that this does not +translate into a uniform query speedup. These are sums of medians across +43 separate profiles; independent state medians need not add to the median +total. They do not measure physical-host idle CPU time. + +For Q05, `COUNT(DISTINCT UserID)`, median summed prefetch wait falls from +173.59 ms to 5.01 ms across untraced measurements, with 218 prefetched row groups +in both revisions. Profiled worker-blocked time falls from 619.4 ms to 381.8 ms. +However, the untraced timing improvement changes from 6.8% faster in pass 1 +to 0.6% slower in pass 2. + +![Q05 before/after timing and scheduler states.](benchmark-charts/clickbench-full-20260912/q04.png) + +Q29 also reduces first-pass summed prefetch wait from 827.69 ms to 5.92 ms, +while reading the same 2,230,272,811 Parquet bytes and prefetching 218 row groups. +Its timing improves only 2.4% and 0.3%. Q21 retains substantial prefetch wait +(827.23 ms to 765.15 ms in pass 1), and its timing changes direction between +passes. This identifies remaining work without treating every waiting interval +as something the scheduling yield can remove. + +With eight partitions, I/O-pool CPU already overlaps at least one running query +worker for almost all its duration in most queries before the fix. That metric +alone hides waiting within individual streams. The per-worker timelines and +summed blocked times provide the more useful comparison here. + +Each timeline shows the measured iteration nearest its recording's median wall +time, using equal before/after time and I/O scales. Timing bars come from +separate untraced runs. Gold means scheduled on a guest CPU, red means runnable, +and gray means blocked. Blue sums CPU execution in Tokio's blocking pool, +including local reads and spill I/O. It is not physical disk latency or a count +of Parquet requests. Guest scheduled time may include hypervisor steal. + +Workers are identified by actual Tokio park/unpark callbacks matched to process +and thread IDs. The parser checks eight workers, complete query-window coverage, +and no lost perf records. Query and scheduler clocks use before/after +realtime-to-`CLOCK_MONOTONIC` calibration; maximum observed offset drift is 25 ns. +Filtered per-query timeline JSON is linked beside each chart. Raw system-wide +perf records remain on the benchmark host. + +## Result checks and limitations + +Every query returns the same row count and column types across all 24 of its +executions. **35 queries return matching result multisets**, allowing 1e-12 +relative/absolute tolerance for floating-point values. Eight queries return +different rows where their SQL leaves result membership underdetermined: + +- Q18 has `LIMIT 10` without `ORDER BY`; Q25 orders by an unprojected EventTime + without a tie breaker. Only row count and schema agreement are established + for these two queries; this run does not prove full value equivalence. +- Q32, Q33, Q39, Q40, Q41, and Q42 select different members tied at the ordering + boundary. Their returned ordering-count sequences match in every execution. + +The [validation record](benchmark-charts/clickbench-full-20260912/result-validation.json) retains the reference +rows and all differences. This is comparison between revisions, not an +independent correctness oracle for every query. Dynamic filtering, TopK pruning, +and spills can also change scan or prefetch counters while final results agree. + +This run does not establish cold-cache, S3, or Spark performance. It also does +not settle [issue #24393](https://github.com/apache/datafusion/issues/24393): +that issue asks for filter evaluation and the within-row-group I/O policy to +be independent, including a default-policy decision. Both sides here already +use upfront I/O. A full comparison of pushdown/progressive/prefetch combinations +against upstream and storage-level request measurements remains needed to +support that broader claim. + +## Every query + +All elapsed times are milliseconds. Each cell shows before → after medians; +negative change means faster. Follow a query link for its timing and CPU/I/O chart. + +| Query / source file | Pass 1 (ms) | Change | Pass 2 (ms) | Change | +| ------------------------------------------------------------------ | ------------------: | ------: | ------------------: | ------: | +| [Q01 / q0.sql](benchmark-charts/clickbench-full-20260912/q00.png) | 3.04 → 3.56 | +16.98% | 3.02 → 3.42 | +13.08% | +| [Q02 / q1.sql](benchmark-charts/clickbench-full-20260912/q01.png) | 42.39 → 46.80 | +10.39% | 42.09 → 43.01 | +2.17% | +| [Q03 / q2.sql](benchmark-charts/clickbench-full-20260912/q02.png) | 182.11 → 123.04 | -32.44% | 126.04 → 111.59 | -11.46% | +| [Q04 / q3.sql](benchmark-charts/clickbench-full-20260912/q03.png) | 147.76 → 128.44 | -13.07% | 127.53 → 129.83 | +1.80% | +| [Q05 / q4.sql](benchmark-charts/clickbench-full-20260912/q04.png) | 1389.91 → 1295.94 | -6.76% | 1195.95 → 1203.32 | +0.62% | +| [Q06 / q5.sql](benchmark-charts/clickbench-full-20260912/q05.png) | 1172.97 → 1040.70 | -11.28% | 1012.72 → 1079.33 | +6.58% | +| [Q07 / q6.sql](benchmark-charts/clickbench-full-20260912/q06.png) | 4.41 → 3.09 | -29.95% | 3.17 → 2.94 | -7.38% | +| [Q08 / q7.sql](benchmark-charts/clickbench-full-20260912/q07.png) | 52.88 → 56.80 | +7.42% | 53.27 → 53.39 | +0.22% | +| [Q09 / q8.sql](benchmark-charts/clickbench-full-20260912/q08.png) | 1471.84 → 1575.23 | +7.02% | 1537.28 → 1450.34 | -5.66% | +| [Q10 / q9.sql](benchmark-charts/clickbench-full-20260912/q09.png) | 1727.87 → 1729.79 | +0.11% | 1694.63 → 1556.50 | -8.15% | +| [Q11 / q10.sql](benchmark-charts/clickbench-full-20260912/q10.png) | 389.09 → 384.78 | -1.11% | 426.28 → 373.84 | -12.30% | +| [Q12 / q11.sql](benchmark-charts/clickbench-full-20260912/q11.png) | 445.82 → 432.27 | -3.04% | 435.77 → 401.37 | -7.89% | +| [Q13 / q12.sql](benchmark-charts/clickbench-full-20260912/q12.png) | 1208.87 → 1188.41 | -1.69% | 1145.58 → 1157.32 | +1.02% | +| [Q14 / q13.sql](benchmark-charts/clickbench-full-20260912/q13.png) | 1659.16 → 1729.72 | +4.25% | 1667.80 → 1643.95 | -1.43% | +| [Q15 / q14.sql](benchmark-charts/clickbench-full-20260912/q14.png) | 1166.88 → 1191.48 | +2.11% | 1400.49 → 1199.67 | -14.34% | +| [Q16 / q15.sql](benchmark-charts/clickbench-full-20260912/q15.png) | 1386.00 → 1343.90 | -3.04% | 1354.63 → 1387.31 | +2.41% | +| [Q17 / q16.sql](benchmark-charts/clickbench-full-20260912/q16.png) | 2515.70 → 2404.66 | -4.41% | 2659.07 → 2660.29 | +0.05% | +| [Q18 / q17.sql](benchmark-charts/clickbench-full-20260912/q17.png) | 2532.34 → 2537.73 | +0.21% | 2581.94 → 2512.76 | -2.68% | +| [Q19 / q18.sql](benchmark-charts/clickbench-full-20260912/q18.png) | 5310.45 → 4960.40 | -6.59% | 5457.76 → 5173.37 | -5.21% | +| [Q20 / q19.sql](benchmark-charts/clickbench-full-20260912/q19.png) | 134.55 → 136.18 | +1.21% | 135.43 → 123.29 | -8.97% | +| [Q21 / q20.sql](benchmark-charts/clickbench-full-20260912/q20.png) | 1470.94 → 1535.67 | +4.40% | 1505.51 → 1461.89 | -2.90% | +| [Q22 / q21.sql](benchmark-charts/clickbench-full-20260912/q21.png) | 1692.32 → 1697.08 | +0.28% | 1813.83 → 1813.40 | -0.02% | +| [Q23 / q22.sql](benchmark-charts/clickbench-full-20260912/q22.png) | 2900.47 → 2918.16 | +0.61% | 2788.60 → 2721.57 | -2.40% | +| [Q24 / q23.sql](benchmark-charts/clickbench-full-20260912/q23.png) | 363.22 → 340.59 | -6.23% | 325.94 → 386.40 | +18.55% | +| [Q25 / q24.sql](benchmark-charts/clickbench-full-20260912/q24.png) | 95.91 → 95.29 | -0.64% | 90.49 → 93.24 | +3.04% | +| [Q26 / q25.sql](benchmark-charts/clickbench-full-20260912/q25.png) | 497.06 → 479.66 | -3.50% | 457.09 → 489.36 | +7.06% | +| [Q27 / q26.sql](benchmark-charts/clickbench-full-20260912/q26.png) | 132.41 → 142.00 | +7.25% | 137.38 → 136.26 | -0.82% | +| [Q28 / q27.sql](benchmark-charts/clickbench-full-20260912/q27.png) | 1604.46 → 1561.59 | -2.67% | 1580.22 → 1442.62 | -8.71% | +| [Q29 / q28.sql](benchmark-charts/clickbench-full-20260912/q28.png) | 7950.48 → 7757.44 | -2.43% | 7655.07 → 7630.49 | -0.32% | +| [Q30 / q29.sql](benchmark-charts/clickbench-full-20260912/q29.png) | 120.04 → 119.05 | -0.82% | 214.13 → 199.92 | -6.63% | +| [Q31 / q30.sql](benchmark-charts/clickbench-full-20260912/q30.png) | 1050.97 → 1014.89 | -3.43% | 1184.28 → 1180.41 | -0.33% | +| [Q32 / q31.sql](benchmark-charts/clickbench-full-20260912/q31.png) | 1043.93 → 1027.22 | -1.60% | 1026.01 → 1065.99 | +3.90% | +| [Q33 / q32.sql](benchmark-charts/clickbench-full-20260912/q32.png) | 15698.66 → 15983.75 | +1.82% | 17130.18 → 15586.50 | -9.01% | +| [Q34 / q33.sql](benchmark-charts/clickbench-full-20260912/q33.png) | 11392.31 → 11734.83 | +3.01% | 11752.93 → 11349.41 | -3.43% | +| [Q35 / q34.sql](benchmark-charts/clickbench-full-20260912/q34.png) | 11264.82 → 11469.09 | +1.81% | 11136.95 → 12349.38 | +10.89% | +| [Q36 / q35.sql](benchmark-charts/clickbench-full-20260912/q35.png) | 1166.78 → 1137.95 | -2.47% | 1282.73 → 1230.38 | -4.08% | +| [Q37 / q36.sql](benchmark-charts/clickbench-full-20260912/q36.png) | 117.98 → 118.65 | +0.56% | 116.49 → 131.44 | +12.83% | +| [Q38 / q37.sql](benchmark-charts/clickbench-full-20260912/q37.png) | 62.15 → 64.28 | +3.42% | 60.70 → 64.87 | +6.86% | +| [Q39 / q38.sql](benchmark-charts/clickbench-full-20260912/q38.png) | 60.56 → 61.01 | +0.75% | 60.78 → 59.56 | -2.01% | +| [Q40 / q39.sql](benchmark-charts/clickbench-full-20260912/q39.png) | 217.12 → 231.55 | +6.65% | 216.76 → 232.89 | +7.45% | +| [Q41 / q40.sql](benchmark-charts/clickbench-full-20260912/q40.png) | 38.09 → 34.34 | -9.84% | 33.71 → 35.00 | +3.81% | +| [Q42 / q41.sql](benchmark-charts/clickbench-full-20260912/q41.png) | 33.65 → 31.48 | -6.44% | 32.65 → 32.12 | -1.61% | +| [Q43 / q42.sql](benchmark-charts/clickbench-full-20260912/q42.png) | 28.53 → 31.07 | +8.89% | 31.33 → 30.28 | -3.34% | + +## Reproduction and artifacts + +The complete run is retained on chia-ping-aws1 at +`/home/ubuntu/oss/datafusion-clickbench-full-20260912`: inputs, paired binaries, +harness sources, timing records, native records, validation, and chart scripts. +The isolated source checkout is +`/home/ubuntu/oss/datafusion-io-overlap-20260912`. + +```bash +cd /home/ubuntu/oss/datafusion-clickbench-full-20260912 +# Completed cases are reused; change script results paths for a fresh run. +bash run_all.sh +/home/ubuntu/oss/datafusion-io-overlap-results-20260912/venv/bin/python plot_queries.py +``` + +`build.sh` builds both pinned revisions with the same `clickbench.rs` and +`clickbench_overlap.rs` harness. `download.sh` and `verify_data.py` obtain and +check the full dataset. `run_queries.py` performs the two timing passes; +`record_native.py` records each query/revision separately. `check_results.py` +compares returned values, and `check_native_parser.py` checks scheduler parsing, +including worker names assigned out of creation order. `plot_queries.py` +generates all 43 charts, the overview, CSV, and HTML gallery from saved records. +The downloadable results archive contains these scripts and benchmark records; +large inputs, binaries, and system-wide traces remain on the host. diff --git a/datafusion/datasource-parquet/IO_OVERLAP_AWS.md b/datafusion/datasource-parquet/IO_OVERLAP_AWS.md index d96f1d2e9875..bb89bba6790b 100644 --- a/datafusion/datasource-parquet/IO_OVERLAP_AWS.md +++ b/datafusion/datasource-parquet/IO_OVERLAP_AWS.md @@ -26,6 +26,9 @@ show local-file I/O CPU work moving from almost no overlap with query workers to 99.6% overlap. Query-worker CPU time is essentially unchanged. The work is happening concurrently, and the query finishes earlier. +The subsequent [full partitioned ClickBench run](CLICKBENCH_FULL_PROFILING.md) +contains 43 before/after query charts and shows a smaller, mixed timing benefit. + ## Comparison and controls Both binaries ran on `chia-ping-aws1` (`peterxcli-ubuntu`): an x86_64 KVM guest diff --git a/datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/binary-harness-sha256.txt b/datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/binary-harness-sha256.txt new file mode 100644 index 000000000000..dcc0cbdb11bc --- /dev/null +++ b/datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/binary-harness-sha256.txt @@ -0,0 +1,5 @@ +72c9059841e4433b54504260c16e5177ba48e037ef8c3872c0e9625d869a8da6 /home/ubuntu/oss/datafusion-clickbench-full-20260912/bin/after +be9a785385f40b732972aec74c783c912a74807e5c6cbb9e5f198e245d7b7587 /home/ubuntu/oss/datafusion-clickbench-full-20260912/bin/before 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"e283874f46a7894d76ab3cf7c3918628c2603c4de461cc57f44d19ffd626bea2" + }, + { + "file": "hits_95.parquet", + "bytes": 126081196, + "rows": 1000000, + "row_groups": 4, + "sha256": "7ff1ba6ee12860bf9d8df5c62f4e63d82c14b4d1ebfa7818704cbd41e075b92d" + }, + { + "file": "hits_96.parquet", + "bytes": 184175027, + "rows": 1000000, + "row_groups": 4, + "sha256": "169d2945e81533f76f37d19c27d218432dc297c3e04cfc3af3a82c3aa5cf3901" + }, + { + "file": "hits_97.parquet", + "bytes": 141586514, + "rows": 1000000, + "row_groups": 3, + "sha256": "3338683fb7937640d977d6adfdd662896828cb422a31dca2d6e38c10dfa81814" + }, + { + "file": "hits_98.parquet", + "bytes": 151992636, + "rows": 1000000, + "row_groups": 4, + "sha256": "5a077f3ca0a59f61082f52338f22572e50d88d2506e416062a3eead07a63db73" + }, + { + "file": "hits_99.parquet", + "bytes": 160289383, + "rows": 997497, + "row_groups": 3, + "sha256": "9f2d7b7a75592fcbf97a9b6b8f86d7ece2dd68c71bf14c485ecfe7aad27fe31c" + } +] diff --git a/datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/index.html b/datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/index.html new file mode 100644 index 000000000000..ec5cf261ab80 --- /dev/null +++ b/datafusion/datasource-parquet/benchmark-charts/clickbench-full-20260912/index.html @@ -0,0 +1,20 @@ + + +Full ClickBench CPU/I/O comparison

Full partitioned ClickBench · chia-ping-aws1

99,997,497 rows · 100 files · previous patch a8f744db6 → scheduling fix 3eaddb5f3. Eight workers, upfront reads with filter pushdown, 64 MiB prefetch per stream, 12G memory pool. Each timing pass has one excluded warmup and three measured runs; pass 2 reverses case and revision order. Native traces have separate warmup and three measured runs. CPU colors show guest scheduling, which can include hypervisor steal.

Result: sums of query medians improved 0.06% and 2.04% in the two timing passes; 11 queries improved in both, 7 slowed in both, and 25 changed direction. Summed per-query median worker-blocked time fell 5.94% in separate profiles. Timing-process CPU steal: median 4.45%, maximum 9.79%. Q33–Q35 spilled about 3.6–4.2 GiB under the 12G pool limit.

All timing measurements · Summary data

All-query overview

Q01 · q0.sql

Query SQL
SELECT COUNT(*) FROM hits;
Q1 timing and CPU/I/O comparison

Filtered timeline data

Q02 · q1.sql

Query SQL
SELECT COUNT(*) FROM hits WHERE "AdvEngineID" <> 0;
Q2 timing and CPU/I/O comparison

Filtered timeline data

Q03 · q2.sql

Query SQL
SELECT SUM("AdvEngineID"), COUNT(*), AVG("ResolutionWidth") FROM hits;
Q3 timing and CPU/I/O comparison

Filtered timeline data

Q04 · q3.sql

Query SQL
SELECT AVG("UserID") FROM hits;
Q4 timing and CPU/I/O comparison

Filtered timeline data

Q05 · q4.sql

Query SQL
SELECT COUNT(DISTINCT "UserID") FROM hits;
Q5 timing and CPU/I/O comparison

Filtered timeline data

Q06 · q5.sql

Query SQL
SELECT COUNT(DISTINCT "SearchPhrase") FROM hits;
Q6 timing and CPU/I/O comparison

Filtered timeline data

Q07 · q6.sql

Query SQL
SELECT MIN("EventDate"), MAX("EventDate") FROM hits;
Q7 timing and CPU/I/O comparison

Filtered timeline data

Q08 · q7.sql

Query SQL
SELECT "AdvEngineID", COUNT(*) FROM hits WHERE "AdvEngineID" <> 0 GROUP BY "AdvEngineID" ORDER BY COUNT(*) DESC;
Q8 timing and CPU/I/O comparison

Filtered timeline data

Q09 · q8.sql

Query SQL
SELECT "RegionID", COUNT(DISTINCT "UserID") AS u FROM hits GROUP BY "RegionID" ORDER BY u DESC LIMIT 10;
Q9 timing and CPU/I/O comparison

Filtered timeline data

Q10 · q9.sql

Query SQL
SELECT "RegionID", SUM("AdvEngineID"), COUNT(*) AS c, AVG("ResolutionWidth"), COUNT(DISTINCT "UserID") FROM hits GROUP BY "RegionID" ORDER BY c DESC LIMIT 10;
Q10 timing and CPU/I/O comparison

Filtered timeline data

Q11 · q10.sql

Query SQL
SELECT "MobilePhoneModel", COUNT(DISTINCT "UserID") AS u FROM hits WHERE "MobilePhoneModel" <> '' GROUP BY "MobilePhoneModel" ORDER BY u DESC LIMIT 10;
Q11 timing and CPU/I/O comparison

Filtered timeline data

Q12 · q11.sql

Query SQL
SELECT "MobilePhone", "MobilePhoneModel", COUNT(DISTINCT "UserID") AS u FROM hits WHERE "MobilePhoneModel" <> '' GROUP BY "MobilePhone", "MobilePhoneModel" ORDER BY u DESC LIMIT 10;
Q12 timing and CPU/I/O comparison

Filtered timeline data

Q13 · q12.sql

Query SQL
SELECT "SearchPhrase", COUNT(*) AS c FROM hits WHERE "SearchPhrase" <> '' GROUP BY "SearchPhrase" ORDER BY c DESC LIMIT 10;
Q13 timing and CPU/I/O comparison

Filtered timeline data

Q14 · q13.sql

Query SQL
SELECT "SearchPhrase", COUNT(DISTINCT "UserID") AS u FROM hits WHERE "SearchPhrase" <> '' GROUP BY "SearchPhrase" ORDER BY u DESC LIMIT 10;
Q14 timing and CPU/I/O comparison

Filtered timeline data

Q15 · q14.sql

Query SQL
SELECT "SearchEngineID", "SearchPhrase", COUNT(*) AS c FROM hits WHERE "SearchPhrase" <> '' GROUP BY "SearchEngineID", "SearchPhrase" ORDER BY c DESC LIMIT 10;
Q15 timing and CPU/I/O comparison

Filtered timeline data

Q16 · q15.sql

Query SQL
SELECT "UserID", COUNT(*) FROM hits GROUP BY "UserID" ORDER BY COUNT(*) DESC LIMIT 10;
Q16 timing and CPU/I/O comparison

Filtered timeline data

Q17 · q16.sql

Query SQL
SELECT "UserID", "SearchPhrase", COUNT(*) FROM hits GROUP BY "UserID", "SearchPhrase" ORDER BY COUNT(*) DESC LIMIT 10;
Q17 timing and CPU/I/O comparison

Filtered timeline data

Q18 · q17.sql

Query SQL
SELECT "UserID", "SearchPhrase", COUNT(*) FROM hits GROUP BY "UserID", "SearchPhrase" LIMIT 10;
Q18 timing and CPU/I/O comparison

Filtered timeline data

Q19 · q18.sql

Query SQL
SELECT "UserID", extract(minute FROM to_timestamp_seconds("EventTime")) AS m, "SearchPhrase", COUNT(*) FROM hits GROUP BY "UserID", m, "SearchPhrase" ORDER BY COUNT(*) DESC LIMIT 10;
Q19 timing and CPU/I/O comparison

Filtered timeline data

Q20 · q19.sql

Query SQL
SELECT "UserID" FROM hits WHERE "UserID" = 435090932899640449;
Q20 timing and CPU/I/O comparison

Filtered timeline data

Q21 · q20.sql

Query SQL
SELECT COUNT(*) FROM hits WHERE "URL" LIKE '%google%';
Q21 timing and CPU/I/O comparison

Filtered timeline data

Q22 · q21.sql

Query SQL
SELECT "SearchPhrase", MIN("URL"), COUNT(*) AS c FROM hits WHERE "URL" LIKE '%google%' AND "SearchPhrase" <> '' GROUP BY "SearchPhrase" ORDER BY c DESC LIMIT 10;
Q22 timing and CPU/I/O comparison

Filtered timeline data

Q23 · q22.sql

Query SQL
SELECT "SearchPhrase", MIN("URL"), MIN("Title"), COUNT(*) AS c, COUNT(DISTINCT "UserID") FROM hits WHERE "Title" LIKE '%Google%' AND "URL" NOT LIKE '%.google.%' AND "SearchPhrase" <> '' GROUP BY "SearchPhrase" ORDER BY c DESC LIMIT 10;
Q23 timing and CPU/I/O comparison

Filtered timeline data

Q24 · q23.sql

Query SQL
SELECT * FROM hits WHERE "URL" LIKE '%google%' ORDER BY "EventTime" LIMIT 10;
Q24 timing and CPU/I/O comparison

Filtered timeline data

Q25 · q24.sql

Query SQL
SELECT "SearchPhrase" FROM hits WHERE "SearchPhrase" <> '' ORDER BY "EventTime" LIMIT 10;
Q25 timing and CPU/I/O comparison

Filtered timeline data

Q26 · q25.sql

Query SQL
SELECT "SearchPhrase" FROM hits WHERE "SearchPhrase" <> '' ORDER BY "SearchPhrase" LIMIT 10;
Q26 timing and CPU/I/O comparison

Filtered timeline data

Q27 · q26.sql

Query SQL
SELECT "SearchPhrase" FROM hits WHERE "SearchPhrase" <> '' ORDER BY "EventTime", "SearchPhrase" LIMIT 10;
Q27 timing and CPU/I/O comparison

Filtered timeline data

Q28 · q27.sql

Query SQL
SELECT "CounterID", AVG(octet_length("URL")) AS l, COUNT(*) AS c FROM hits WHERE "URL" <> '' GROUP BY "CounterID" HAVING COUNT(*) > 100000 ORDER BY l DESC LIMIT 25;
Q28 timing and CPU/I/O comparison

Filtered timeline data

Q29 · q28.sql

Query SQL
SELECT REGEXP_REPLACE("Referer", '^https?://(?:www\.)?([^/]+)/.*$', '\1') AS k, AVG(octet_length("Referer")) AS l, COUNT(*) AS c, MIN("Referer") FROM hits WHERE "Referer" <> '' GROUP BY k HAVING COUNT(*) > 100000 ORDER BY l DESC LIMIT 25;
Q29 timing and CPU/I/O comparison

Filtered timeline data

Q30 · q29.sql

Query SQL
SELECT SUM("ResolutionWidth"), SUM("ResolutionWidth" + 1), SUM("ResolutionWidth" + 2), SUM("ResolutionWidth" + 3), SUM("ResolutionWidth" + 4), SUM("ResolutionWidth" + 5), SUM("ResolutionWidth" + 6), SUM("ResolutionWidth" + 7), SUM("ResolutionWidth" + 8), SUM("ResolutionWidth" + 9), SUM("ResolutionWidth" + 10), SUM("ResolutionWidth" + 11), SUM("ResolutionWidth" + 12), SUM("ResolutionWidth" + 13), SUM("ResolutionWidth" + 14), SUM("ResolutionWidth" + 15), SUM("ResolutionWidth" + 16), SUM("ResolutionWidth" + 17), SUM("ResolutionWidth" + 18), SUM("ResolutionWidth" + 19), SUM("ResolutionWidth" + 20), SUM("ResolutionWidth" + 21), SUM("ResolutionWidth" + 22), SUM("ResolutionWidth" + 23), SUM("ResolutionWidth" + 24), SUM("ResolutionWidth" + 25), SUM("ResolutionWidth" + 26), SUM("ResolutionWidth" + 27), SUM("ResolutionWidth" + 28), SUM("ResolutionWidth" + 29), SUM("ResolutionWidth" + 30), SUM("ResolutionWidth" + 31), SUM("ResolutionWidth" + 32), SUM("ResolutionWidth" + 33), SUM("ResolutionWidth" + 34), SUM("ResolutionWidth" + 35), SUM("ResolutionWidth" + 36), SUM("ResolutionWidth" + 37), SUM("ResolutionWidth" + 38), SUM("ResolutionWidth" + 39), SUM("ResolutionWidth" + 40), SUM("ResolutionWidth" + 41), SUM("ResolutionWidth" + 42), SUM("ResolutionWidth" + 43), SUM("ResolutionWidth" + 44), SUM("ResolutionWidth" + 45), SUM("ResolutionWidth" + 46), SUM("ResolutionWidth" + 47), SUM("ResolutionWidth" + 48), SUM("ResolutionWidth" + 49), SUM("ResolutionWidth" + 50), SUM("ResolutionWidth" + 51), SUM("ResolutionWidth" + 52), SUM("ResolutionWidth" + 53), SUM("ResolutionWidth" + 54), SUM("ResolutionWidth" + 55), SUM("ResolutionWidth" + 56), SUM("ResolutionWidth" + 57), SUM("ResolutionWidth" + 58), SUM("ResolutionWidth" + 59), SUM("ResolutionWidth" + 60), SUM("ResolutionWidth" + 61), SUM("ResolutionWidth" + 62), SUM("ResolutionWidth" + 63), SUM("ResolutionWidth" + 64), SUM("ResolutionWidth" + 65), SUM("ResolutionWidth" + 66), SUM("ResolutionWidth" + 67), SUM("ResolutionWidth" + 68), SUM("ResolutionWidth" + 69), SUM("ResolutionWidth" + 70), SUM("ResolutionWidth" + 71), SUM("ResolutionWidth" + 72), SUM("ResolutionWidth" + 73), SUM("ResolutionWidth" + 74), SUM("ResolutionWidth" + 75), SUM("ResolutionWidth" + 76), SUM("ResolutionWidth" + 77), SUM("ResolutionWidth" + 78), SUM("ResolutionWidth" + 79), SUM("ResolutionWidth" + 80), SUM("ResolutionWidth" + 81), SUM("ResolutionWidth" + 82), SUM("ResolutionWidth" + 83), SUM("ResolutionWidth" + 84), SUM("ResolutionWidth" + 85), SUM("ResolutionWidth" + 86), SUM("ResolutionWidth" + 87), SUM("ResolutionWidth" + 88), SUM("ResolutionWidth" + 89) FROM hits;
Q30 timing and CPU/I/O comparison

Filtered timeline data

Q31 · q30.sql

Query SQL
SELECT "SearchEngineID", "ClientIP", COUNT(*) AS c, SUM("IsRefresh"), AVG("ResolutionWidth") FROM hits WHERE "SearchPhrase" <> '' GROUP BY "SearchEngineID", "ClientIP" ORDER BY c DESC LIMIT 10;
Q31 timing and CPU/I/O comparison

Filtered timeline data

Q32 · q31.sql

Query SQL
SELECT "WatchID", "ClientIP", COUNT(*) AS c, SUM("IsRefresh"), AVG("ResolutionWidth") FROM hits WHERE "SearchPhrase" <> '' GROUP BY "WatchID", "ClientIP" ORDER BY c DESC LIMIT 10;
Q32 timing and CPU/I/O comparison

Filtered timeline data

Q33 · q32.sql

Query SQL
SELECT "WatchID", "ClientIP", COUNT(*) AS c, SUM("IsRefresh"), AVG("ResolutionWidth") FROM hits GROUP BY "WatchID", "ClientIP" ORDER BY c DESC LIMIT 10;
Q33 timing and CPU/I/O comparison

Filtered timeline data

Q34 · q33.sql

Query SQL
SELECT "URL", COUNT(*) AS c FROM hits GROUP BY "URL" ORDER BY c DESC LIMIT 10;
Q34 timing and CPU/I/O comparison

Filtered timeline data

Q35 · q34.sql

Query SQL
SELECT 1, "URL", COUNT(*) AS c FROM hits GROUP BY 1, "URL" ORDER BY c DESC LIMIT 10;
Q35 timing and CPU/I/O comparison

Filtered timeline data

Q36 · q35.sql

Query SQL
SELECT "ClientIP", "ClientIP" - 1, "ClientIP" - 2, "ClientIP" - 3, COUNT(*) AS c FROM hits GROUP BY "ClientIP", "ClientIP" - 1, "ClientIP" - 2, "ClientIP" - 3 ORDER BY c DESC LIMIT 10;
Q36 timing and CPU/I/O comparison

Filtered timeline data

Q37 · q36.sql

Query SQL
SELECT "URL", COUNT(*) AS PageViews FROM hits WHERE "CounterID" = 62 AND "EventDate" >= '2013-07-01' AND "EventDate" <= '2013-07-31' AND "DontCountHits" = 0 AND "IsRefresh" = 0 AND "URL" <> '' GROUP BY "URL" ORDER BY PageViews DESC LIMIT 10;
Q37 timing and CPU/I/O comparison

Filtered timeline data

Q38 · q37.sql

Query SQL
SELECT "Title", COUNT(*) AS PageViews FROM hits WHERE "CounterID" = 62 AND "EventDate" >= '2013-07-01' AND "EventDate" <= '2013-07-31' AND "DontCountHits" = 0 AND "IsRefresh" = 0 AND "Title" <> '' GROUP BY "Title" ORDER BY PageViews DESC LIMIT 10;
Q38 timing and CPU/I/O comparison

Filtered timeline data

Q39 · q38.sql

Query SQL
SELECT "URL", COUNT(*) AS PageViews FROM hits WHERE "CounterID" = 62 AND "EventDate" >= '2013-07-01' AND "EventDate" <= '2013-07-31' AND "IsRefresh" = 0 AND "IsLink" <> 0 AND "IsDownload" = 0 GROUP BY "URL" ORDER BY PageViews DESC LIMIT 10 OFFSET 1000;
Q39 timing and CPU/I/O comparison

Filtered timeline data

Q40 · q39.sql

Query SQL
SELECT "TraficSourceID", "SearchEngineID", "AdvEngineID", CASE WHEN ("SearchEngineID" = 0 AND "AdvEngineID" = 0) THEN "Referer" ELSE '' END AS Src, "URL" AS Dst, COUNT(*) AS PageViews FROM hits WHERE "CounterID" = 62 AND "EventDate" >= '2013-07-01' AND "EventDate" <= '2013-07-31' AND "IsRefresh" = 0 GROUP BY "TraficSourceID", "SearchEngineID", "AdvEngineID", Src, Dst ORDER BY PageViews DESC LIMIT 10 OFFSET 1000;
Q40 timing and CPU/I/O comparison

Filtered timeline data

Q41 · q40.sql

Query SQL
SELECT "URLHash", "EventDate", COUNT(*) AS PageViews FROM hits WHERE "CounterID" = 62 AND "EventDate" >= '2013-07-01' AND "EventDate" <= '2013-07-31' AND "IsRefresh" = 0 AND "TraficSourceID" IN (-1, 6) AND "RefererHash" = 3594120000172545465 GROUP BY "URLHash", "EventDate" ORDER BY PageViews DESC LIMIT 10 OFFSET 100;
Q41 timing and CPU/I/O comparison

Filtered timeline data

Q42 · q41.sql

Query SQL
SELECT "WindowClientWidth", "WindowClientHeight", COUNT(*) AS PageViews FROM hits WHERE "CounterID" = 62 AND "EventDate" >= '2013-07-01' AND "EventDate" <= '2013-07-31' AND "IsRefresh" = 0 AND "DontCountHits" = 0 AND "URLHash" = 2868770270353813622 GROUP BY "WindowClientWidth", "WindowClientHeight" ORDER BY PageViews DESC LIMIT 10 OFFSET 10000;
Q42 timing and CPU/I/O comparison

Filtered timeline data

Q43 · q42.sql

Query SQL
SELECT DATE_TRUNC('minute', to_timestamp_seconds("EventTime")) AS M, COUNT(*) AS PageViews FROM hits WHERE "CounterID" = 62 AND "EventDate" >= '2013-07-14' AND "EventDate" <= '2013-07-15' AND "IsRefresh" = 0 AND "DontCountHits" = 0 GROUP BY DATE_TRUNC('minute', to_timestamp_seconds("EventTime")) ORDER BY DATE_TRUNC('minute', M) LIMIT 10 OFFSET 1000;
Q43 timing and CPU/I/O comparison

Filtered timeline data

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z?Aiob;%$>3MovDLoj>3|8F2P!_*+>X?ciHG8|h)@dfGAo859YWr&5}WWD9nPaT=M| zh0K)$59lF3;*zv6VhQksTjTFrOB>Y8B%5E3xj9({ z=M?66S?VojAUGVXqD*RQ?lC)I3BdH4$XUX%xCQkO6o|)y&)iyp9lZ9sx=t+QO$aV zQyh*|OxB9W+CDo+!MW~*07bB!+;|e+FBR?|9*7$|svGvF@;8jy>FjvIk!#E7(Gk#E zF<&9nRd8A$J|xImXhZg`_CJ@*XMf-Q=fB)-WQYFryMMo?{qDg3_{6K^ zwEywqJ7yFdO8@;?X^oV!q5tvi7feq${^PTb36lMv`@v}+_Q`86m(`R->A4}ZP-S_w Kn|asoJ^L?`_+#q; From a8f744db6bf6f147e2ea88fcf03e0f0b977f4683 Mon Sep 17 00:00:00 2001 From: peterxcli Date: Thu, 10 Sep 2026 14:55:39 +0800 Subject: [PATCH 10/13] bench: measure ClickBench prefetch and clarify Parquet charts --- benchmarks/src/clickbench.rs | 50 ++- .../datasource-parquet/IO_POLICY_BENCHMARK.md | 301 +++++++++++------- .../parquet-main-clickbench.png | Bin 103188 -> 278004 bytes .../benchmark-charts/parquet-main-timings.png | Bin 181067 -> 421800 bytes 4 files changed, 231 insertions(+), 120 deletions(-) diff --git a/benchmarks/src/clickbench.rs b/benchmarks/src/clickbench.rs index b6d118129a48..4d6b5810a14f 100644 --- a/benchmarks/src/clickbench.rs +++ b/benchmarks/src/clickbench.rs @@ -18,16 +18,21 @@ use std::fs; use std::io::ErrorKind; use std::path::{Path, PathBuf}; +use std::sync::Arc; use crate::util::{BenchmarkRun, CommonOpt, QueryResult, print_memory_stats}; use clap::Args; +use datafusion::datasource::physical_plan::{FileScanConfigBuilder, ParquetSource}; +use datafusion::datasource::source::DataSourceExec; use datafusion::logical_expr::{ExplainFormat, ExplainOption}; +use datafusion::physical_plan::collect; use datafusion::{ error::{DataFusionError, Result}, prelude::SessionContext, }; use datafusion_common::exec_datafusion_err; use datafusion_common::instant::Instant; +use datafusion_common::tree_node::{Transformed, TreeNode, TreeNodeRecursion}; /// SQL to create the hits view with proper EventDate casting. /// @@ -62,6 +67,11 @@ pub struct RunOpt { #[arg(long = "pushdown")] pushdown: bool, + /// Maximum compressed bytes to prefetch for the next Parquet row group. + /// Zero disables prefetch. Uses the query's memory pool. + #[arg(long, default_value_t = 0)] + prefetch_bytes: usize, + /// Common options #[command(flatten)] common: CommonOpt, @@ -254,8 +264,46 @@ impl RunOpt { let mut query_results = vec![]; for i in 0..self.iterations() { let start = Instant::now(); - let results = ctx.sql(sql).await?.collect().await?; + let dataframe = ctx.sql(sql).await?; + let task_ctx = Arc::new(dataframe.task_ctx()); + let mut plan = dataframe.create_physical_plan().await?; + if self.prefetch_bytes > 0 { + plan = plan + .transform_up(|plan| { + if let Some(exec) = plan.downcast_ref::() + && let Some((config, source)) = + exec.downcast_to_file_source::() + { + let source = source.clone().with_row_group_prefetch( + self.prefetch_bytes, + Arc::clone(&ctx.runtime_env().memory_pool), + ); + let config = FileScanConfigBuilder::from(config.clone()) + .with_source(Arc::new(source)) + .build(); + return Ok(Transformed::yes(Arc::new( + exec.clone().with_data_source(Arc::new(config)), + ))); + } + Ok(Transformed::no(plan)) + })? + .data; + } + let results = collect(Arc::clone(&plan), task_ctx).await?; let elapsed = start.elapsed(); + if self.prefetch_bytes > 0 { + let mut prefetched = 0; + plan.apply(|plan| { + if let Some(value) = plan + .metrics() + .and_then(|metrics| metrics.sum_by_name("prefetch_row_groups")) + { + prefetched += value.as_usize(); + } + Ok(TreeNodeRecursion::Continue) + })?; + println!("Prefetched row groups: {prefetched}"); + } let ms = elapsed.as_secs_f64() * 1000.0; millis.push(ms); let row_count: usize = results.iter().map(|b| b.num_rows()).sum(); diff --git a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md index d0150d60f9d4..fddc08dcc282 100644 --- a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md +++ b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md @@ -26,39 +26,55 @@ with 10 CPU cores and 24 GiB RAM. | Revision | DataFusion | Arrow/Parquet | | ----------------------------------------------------------------------------------------------------------------- | ---------- | ------------- | | [Upstream main `408696966`](https://github.com/apache/datafusion/commit/4086969669f96c0d5de03f442f2dba1869d2ee80) | 55.0.0 | 59.3.0 | -| [Current patch implementation `801cb0e52`](https://github.com/peterxcli/datafusion/commit/801cb0e52) | 55.0.0 | 59.3.0 | +| [Current reader implementation `801cb0e52`](https://github.com/peterxcli/datafusion/commit/801cb0e52) | 55.0.0 | 59.3.0 | The patch is rebased onto the measured upstream commit. Both revisions use the same dependency lockfile, with Arrow/Parquet 59.3.0. +## Reading modes shown in the charts + +| Chart label | What happens | `pushdown_filters` | `progressive_io` | +| --------------------------------------- | --------------------------------------------------------------------------------------------- | ------------------ | ---------------- | +| Filter rows after decoding | Decode the scan output, then apply the row filter. | `false` | `true` | +| Read filter columns first | Read columns used by the filter, evaluate it, then read output pages needed by matching rows. | `true` | `true` | +| Read filter and output columns together | Fetch both sets of required pages for the current row group before evaluating the row filter. | `true` | `false` | + +Prefetch means reading the next row group while decoding the current one. Each +chart shows all three reading modes with prefetch enabled and disabled. The +synthetic scans use a 16 MiB prefetch budget per file stream. ClickBench uses +64 MiB so that its larger projected row groups, including Q22's string columns, +fit within the budget. + +Each panel names the upstream behavior used as its reference. Points show the +percentage change in elapsed time: −10% means 10% less time, and 0% means the +same time. The legend identifies both the prefetch setting and the measurement +pass. Pass 2 reverses execution order. + ## Synthetic Parquet scans -![Elapsed-time ratios for the current patch against main, with pushdown off and on.](benchmark-charts/parquet-main-timings.png) - -The left panel compares patch configurations with main's pushdown-off scan. -The right panel compares them with main's progressive scan with pushdown enabled. -Values below 1× are faster; values above 1× are slower. Each point is the median -of three measured scans after one warmup. Circles and diamonds show two passes -with the revision order reversed. - -Upfront I/O reduced reader calls from **512 to 256** without indexes and from -**513 to 257** with indexes. Requested bytes matched main in every workload, -including the indexed, clustered cases: 28.9 MB for wide output and 13.2 MB for -narrow output. All patch configurations, including prefetch, requested the same -bytes in this fixture. - -With prefetch off, indexed clustered wide output took **41.3/41.0 ms** with -upfront reads versus **48.2/45.1 ms** on main progressive across the two passes -(9–14% less elapsed time). Narrow output took **20.1/20.1 ms** versus -**25.5/23.7 ms** (15–21% less). - -Gains depended on the workload. Random wide output without indexes changed -direction: 1.2% faster in pass 1 and 2.2% slower in pass 2. Main's clustered -wide scan without indexes also moved from 361.1 to 305.8 ms between passes, -while patch upfront measured 298.6 and 297.1 ms. These warm desktop timings -retain variation that two reversed passes cannot eliminate. Patch progressive -was also 3.4%/9.7% slower than main progressive on indexed random wide output; -this control warrants profiling. +![Three reading modes with prefetch enabled and disabled, compared with the named upstream reference.](benchmark-charts/parquet-main-timings.png) + +Reading filter and output columns together reduced reader calls from **512 to +256** without page indexes and from **513 to 257** with page indexes. All +configurations requested the same bytes. With page indexes and matching rows +grouped into pages, scans read 28.9 MB for 7 output columns and 13.2 MB for +1 output column. + +For that workload, reading columns together with prefetch disabled took +**41.3/41.0 ms** for 7 output columns across the two passes. Upstream reading +filter columns first took **48.2/45.1 ms**, so the patch used 9–14% less time. +Returning 1 column took **20.1/20.1 ms** versus **25.5/23.7 ms** (15–21% less). + +Results varied by workload. With scattered matching rows, 7 output columns, +and no page index, the patch used 1.2% less time in pass 1 and 2.2% more in +pass 2. With matching rows grouped into pages, 7 output columns, and no page +index, upstream moved from 361.1 to 305.8 ms between passes; the patch reading +columns together measured 298.6 and 297.1 ms. These desktop measurements retain +variation that two reversed passes cannot eliminate. + +The patch reading filter columns first was also 3.4%/9.7% slower than upstream +using the same mode when matching rows were scattered, page indexes were +present, and the output had 7 columns. This control warrants profiling. The [benchmark example](examples/io_policy.rs) uses four Snappy Parquet files, each containing 256 row groups of 131,072 rows and eight Int64 columns: @@ -66,17 +82,15 @@ each containing 256 row groups of 131,072 rows and eight Int64 columns: files, generated by the patch runner. File generation and expected-result calculation are outside the timed region. -The predicate is `c1 < 10000`. Wide output returns seven columns; narrow output -returns one. Both omit the predicate column. The data has scattered or -page-clustered matches, with or without column and offset indexes. Every scan -checks its row count and checksum against values calculated during generation. +The predicate is `c1 < 10000`. The output contains either 7 columns or 1 column, +always omitting the predicate column. Matching rows are either scattered or +grouped into pages. Each distribution is tested with and without column and +offset indexes. Every scan checks its row count and checksum against values +calculated during generation. All 512 full-size scans passed, as did 256 smoke scans on one-row-group files. -Main runs pushdown off and progressive reads. The patch runs those configurations -plus upfront reads, each with prefetch off/on. Upfront reads fetch selected -predicate and output pages together before evaluating the row filter. Prefetch -uses a 16 MiB budget for one future row group. Statistics and page-index pruning -remain enabled in every configuration. Pushdown-off plans apply `FilterExec` +Statistics and page-index pruning remain enabled in every configuration. +When row filtering follows decoding, `FilterExec` applies the same predicate before projecting the output columns. The runtime has two Tokio workers and an 8,192-row batch size. Each pass rotates @@ -86,33 +100,52 @@ this task ran during measurement. These are warm local-file timings, with no artificial I/O or downstream delay. Other desktop activity was uncontrolled.

    Fresh unmodified DF55 control, prefetch off (milliseconds) From 932f9d4f4112d9df9096d07274c199dde0ecbc3d Mon Sep 17 00:00:00 2001 From: peterxcli Date: Thu, 10 Sep 2026 10:00:48 +0800 Subject: [PATCH 07/13] docs: compare Parquet I/O benchmarks with upstream main --- .../datasource-parquet/IO_POLICY_BENCHMARK.md | 456 +++++++----------- .../parquet-main-clickbench.png | Bin 0 -> 95539 bytes .../benchmark-charts/parquet-main-timings.png | Bin 0 -> 177634 bytes .../benchmark-charts/parquet-pages-bytes.png | Bin 53348 -> 0 bytes .../parquet-pages-clickbench.png | Bin 97619 -> 0 bytes .../parquet-pages-timings.png | Bin 234901 -> 0 bytes .../parquet-policy-baseline.png | Bin 158534 -> 0 bytes .../benchmark-charts/parquet-policy-io.png | Bin 143641 -> 0 bytes .../parquet-policy-timings.png | Bin 399568 -> 0 bytes .../datasource-parquet/examples/io_policy.rs | 51 +- 10 files changed, 217 insertions(+), 290 deletions(-) create mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-main-clickbench.png create mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-main-timings.png delete mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-pages-bytes.png delete mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-pages-clickbench.png delete mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-pages-timings.png delete mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-policy-baseline.png delete mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-policy-io.png delete mode 100644 datafusion/datasource-parquet/benchmark-charts/parquet-policy-timings.png diff --git a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md index bdad7c9847e1..62dca8737882 100644 --- a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md +++ b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md @@ -17,312 +17,218 @@ under the License. --> -# Parquet I/O policy experiment - -This fork experiment targets DataFusion 55.0.0 so Comet can use the patch through -Cargo git dependencies. Progressive fetching remains the default and next-row-group -prefetch is disabled by default. - -For `SELECT name FROM people WHERE age > 30`, **progressive reads** fetch the `age` -pages, evaluate the filter, then fetch the `name` pages needed for surviving rows. -**Upfront reads** fetch the required `age` and `name` pages together before -running the row filter. Both first use metadata to skip irrelevant row groups -and pages. Upfront reads reduce dependent I/O rounds, but can fetch pages that -the row filter would have ruled out. - -The current implementation preserves the page selection made at file open, -including external row selections and dictionary pages. Adjacent ranges are merged -so a later whole-chunk decoder request can reuse them; gaps left by page pruning -remain unread. Without an offset index, -it fetches complete column chunks. Predicate caching can expand decoder requests -to batch boundaries; those requests remain supported, so this is not a promise -that every scan finishes in exactly one read per row group. - -Use normal DataFusion session configuration, including SQL `SET`, before -registering the table: - -```sql -SET datafusion.execution.parquet.pushdown_filters = true; -SET datafusion.execution.parquet.progressive_io = false; -``` - -`ParquetSource::with_progressive_io(false)` sets the same table option. The setting -survives plan serialization; older serialized plans retain progressive reads. -It does not enable filter pushdown automatically. - -`with_row_group_prefetch(bytes, memory_pool)` separately fetches at most one next -row group's selected output and predicate pages while the current reader produces -batches. Its budget bounds additional compressed bytes, not current-reader or -decoded memory. A group is skipped if its selected bytes cannot fit. Dropping the -scan cancels pending work; speculative read errors retry through demand reads. -This execution-local prefetch option is not serialized. - -Prefetch can fetch pages that later row filtering or dynamic pruning would skip. -Compare progressive and upfront with prefetch **off** to isolate the policy in -[#24393](https://github.com/apache/datafusion/issues/24393). Compare upfront with -prefetch off/on separately to assess overlap. Neither option changes defaults -or establishes that the broader filter-pushdown regressions are resolved. - -## Reproduce - -```sh -cargo run --profile release-nonlto -p datafusion-datasource-parquet \ - --example io_policy -- 256 > io-policy.csv -``` +# Parquet I/O benchmarks: current patch versus upstream main + +These measurements compare the current patch with upstream `main` fetched on +2026-09-10. Both use Rust 1.97.0 and the `release-nonlto` profile on an Apple M4 +with 10 CPU cores and 24 GiB RAM. + +| Revision | DataFusion | Arrow/Parquet | +| ----------------------------------------------------------------------------------------------------------------- | ---------- | ------------- | +| [Upstream main `40488988a`](https://github.com/apache/datafusion/commit/40488988ad596c9b093ad60e1453430d803ce33c) | 55.0.0 | 59.3.0 | +| [Current patch implementation `8411b35ba`](https://github.com/peterxcli/datafusion/commit/8411b35ba) | 55.0.0 | 59.2.0 | + +Each revision uses its checked-in dependency lockfile. The comparison includes +changes elsewhere in main and differences in dependency versions. + +## Synthetic Parquet scans + +![Elapsed-time ratios for the current patch against main, with pushdown off and on.](benchmark-charts/parquet-main-timings.png) + +The left panel compares patch configurations with main's pushdown-off scan. +The right panel compares them with main's progressive scan with pushdown enabled. +Values below 1× are faster; values above 1× are slower. Each point is the median +of three measured scans after one warmup. Circles and diamonds show two passes +with the revision order reversed. + +Upfront I/O reduced reader calls from **512 to 256** without indexes and from +**513 to 257** with indexes. Requested bytes matched main in every workload, +including the indexed, clustered cases: 28.9 MB for wide output and 13.2 MB for +narrow output. All patch configurations, including prefetch, requested the same +bytes in this fixture. + +**Elapsed-time comparisons were unstable across passes.** For example, main's +progressive scan of indexed, clustered narrow output measured 273.3 ms in pass 1 +and 26.4 ms in pass 2. Patch progressive measured 27.8 and 26.5 ms. The +pass-1 timing gap therefore does not establish a stable patch speedup. Desktop +processes were active during the run; this measurement cannot isolate their +contribution to the variation. + +The [benchmark example](examples/io_policy.rs) uses four Snappy Parquet files, +each containing 256 row groups of 131,072 rows and eight Int64 columns: +33,554,432 rows and 2 GiB of logical values per file. Both binaries read the same +files, generated by the patch runner. File generation and expected-result +calculation are outside the timed region. + +The predicate is `c1 < 10000`. Wide output returns seven columns; narrow output +returns one. Both omit the predicate column. The data has scattered or +page-clustered matches, with or without column and offset indexes. Every scan +checks its row count and checksum against values calculated during generation. +All 512 full-size scans passed, as did 256 smoke scans on one-row-group files. + +Main runs pushdown off and progressive reads. The patch runs those configurations +plus upfront reads, each with prefetch off/on. Upfront reads fetch selected +predicate and output pages together before evaluating the row filter. Prefetch +uses a 16 MiB budget for one future row group. Statistics and page-index pruning +remain enabled in every configuration. Pushdown-off plans apply `FilterExec` +before projecting the output columns. + +The runtime has two Tokio workers and an 8,192-row batch size. Each pass rotates +configuration order across one warmup and three measured rounds. Revision order +is patch/main for the first pass and main/patch for the second. No builds from +this task ran during measurement. These are warm local-file timings, with no +artificial I/O or downstream delay. Other desktop activity was uncontrolled. -The example creates four local Snappy Parquet files in a temporary directory, -using 256 row groups of 131,072 rows and eight Int64 columns per file. That is -33,554,432 rows and 2 GiB of logical values per file. Files are generated and -measured one at a time; generation is outside the timed region. +
    +Exact median elapsed times in milliseconds + +### Pass 1 + +| Workload | Main off | Patch off | Patch off +PF | Main progressive | Patch progressive | Patch progressive +PF | Patch upfront | Patch upfront +PF | +| ----------------------------- | -------: | --------: | ------------: | ---------------: | ----------------: | --------------------: | ------------: | ----------------: | +| random, no indexes, wide | 1607.1 | 2496.6 | 2148.2 | 1625.1 | 2684.2 | 2258.6 | 2511.7 | 2082.2 | +| random, no indexes, narrow | 379.0 | 388.4 | 352.2 | 384.2 | 386.5 | 346.6 | 380.9 | 348.0 | +| random, indexes, wide | 1928.1 | 1478.8 | 1384.9 | 3096.5 | 1728.4 | 1432.1 | 1532.2 | 1427.5 | +| random, indexes, narrow | 755.0 | 357.2 | 336.9 | 946.1 | 376.8 | 334.8 | 357.3 | 332.6 | +| clustered, no indexes, wide | 2453.4 | 1566.2 | 1489.5 | 832.4 | 305.1 | 293.1 | 295.7 | 317.6 | +| clustered, no indexes, narrow | 781.1 | 378.6 | 350.2 | 687.1 | 226.0 | 222.6 | 224.7 | 225.8 | +| clustered, indexes, wide | 58.7 | 54.8 | 44.9 | 64.9 | 55.2 | 40.6 | 49.2 | 40.3 | +| clustered, indexes, narrow | 154.4 | 22.7 | 21.4 | 273.3 | 27.8 | 25.7 | 22.6 | 25.7 | + +### Pass 2 + +| Workload | Main off | Patch off | Patch off +PF | Main progressive | Patch progressive | Patch progressive +PF | Patch upfront | Patch upfront +PF | +| ----------------------------- | -------: | --------: | ------------: | ---------------: | ----------------: | --------------------: | ------------: | ----------------: | +| random, no indexes, wide | 1629.0 | 1574.1 | 1429.3 | 1623.3 | 1550.0 | 1553.2 | 1639.8 | 1457.3 | +| random, no indexes, narrow | 380.9 | 380.2 | 340.2 | 379.3 | 373.8 | 325.0 | 366.7 | 343.3 | +| random, indexes, wide | 1543.6 | 1568.6 | 1448.3 | 1754.4 | 1813.3 | 1495.8 | 1617.8 | 1478.8 | +| random, indexes, narrow | 441.4 | 378.3 | 346.0 | 404.6 | 386.7 | 349.8 | 382.4 | 343.9 | +| clustered, no indexes, wide | 1696.9 | 1555.7 | 1494.5 | 350.0 | 320.6 | 314.6 | 312.3 | 308.0 | +| clustered, no indexes, narrow | 371.7 | 384.2 | 349.8 | 220.4 | 219.3 | 224.5 | 221.3 | 220.6 | +| clustered, indexes, wide | 48.5 | 45.8 | 38.2 | 49.7 | 49.5 | 38.4 | 46.3 | 38.1 | +| clustered, indexes, narrow | 21.1 | 22.1 | 19.1 | 26.4 | 26.5 | 24.1 | 22.2 | 24.0 | -The six configurations are pushdown off, pushdown with progressive fetching, and -pushdown with upfront fetching, each with prefetch off/on. The prefetch budget is -16 MiB. Every configuration evaluates the same predicate (`c1 < 10000`) and returns -the same columns. Pushdown-off plans retain a physical `FilterExec` before the -output projection. All configurations retain statistics and page-index pruning. +
    -Each configuration runs against both scattered and page-clustered matches, with -and without column/offset indexes, and with either one or seven output columns. -The filter column is omitted from the output in every case. Writer metadata -assertions verify index presence/absence. Expected row counts and checksums are -calculated during generation and checked on every scan. +### Requested bytes, reader calls, and memory -Round zero warms every configuration. The report uses medians of rounds 1–3, -rotating the order of all six configurations each round. The measured region -includes plan execution, decoding, filtering, and summing every output value. -There is no artificial I/O or downstream delay. These are warm local-file results; -they do not establish Spark end-to-end or remote-storage speedups. +`bytes_scanned` includes metadata and speculative reads. Reader calls count +invocations observed by the reader wrapper, excluding footer reads inside +`get_metadata`. They do not count physical disk operations or HTTP requests. +The following counts use prefetch off and were identical across the two passes. -`bytes_scanned` counts requested bytes, including metadata and speculative reads. -`reader_calls` and `reader_ranges` count calls/ranges observed by the reader wrapper, -excluding reads internal to `get_metadata`; they are not physical disk operations -or HTTP request counts. `prefetch_peak` uses DataFusion's existing `PeakRecordingPool` to record peak prefetch reservations. It does not measure current-reader, decoded-buffer, or total process memory. `prefetch_bytes` and `prefetch_row_groups` count completed -speculative reads, including bytes subsequently discarded. +
    +Exact requested bytes and reader calls + +| Workload | Main progressive bytes | Patch upfront bytes | Main progressive calls | Patch progressive calls | Patch upfront calls | +| ----------------------------- | ---------------------: | ------------------: | ---------------------: | ----------------------: | ------------------: | +| random, no indexes, wide | 1,309,357,880 | 1,309,357,880 | 512 | 512 | 256 | +| random, no indexes, narrow | 303,126,105 | 303,126,105 | 512 | 512 | 256 | +| random, indexes, wide | 1,318,274,373 | 1,318,274,373 | 513 | 513 | 257 | +| random, indexes, narrow | 312,042,598 | 312,042,598 | 513 | 513 | 257 | +| clustered, no indexes, wide | 1,278,042,437 | 1,278,042,437 | 512 | 512 | 256 | +| clustered, no indexes, narrow | 271,810,662 | 271,810,662 | 512 | 512 | 256 | +| clustered, indexes, wide | 28,883,141 | 28,883,141 | 513 | 513 | 257 | +| clustered, indexes, narrow | 13,160,491 | 13,160,491 | 513 | 513 | 257 | -## Page-selection revision (2026-09-10) +
    -Apple M4, 10 CPU cores, 24 GiB RAM; Rust 1.97.0, Arrow/Parquet 59.2.0, -`release-nonlto`, two Tokio workers for the synthetic matrix. +Prefetch memory is measured with `PeakRecordingPool`. Main has no prefetch +reservation. The patch's figures cover additional compressed reservations; +current-reader, decoded-buffer, and total process memory are outside this metric. -The same 256-group matrix was rerun after preserving page selections. All 192 scans passed the independent row-count/checksum assertions. In all eight workloads and all six policies, requested byte counts now match the pushdown-off control, including prefetch. This equality is specific to this predicate-only fixture; row-filter selectivity, dictionary layout, and predicate-cache expansion can change the comparison in other queries. +For wide output, peak prefetch reservations ranged from 78,078 to 5,115,597 bytes. -![Current policy timing ratios.](benchmark-charts/parquet-pages-timings.png) +For narrow output, peak prefetch reservations ranged from 16,587 to 1,184,818 bytes. -![Earlier whole-chunk versus current page-selected requested bytes.](benchmark-charts/parquet-pages-bytes.png) +## ClickBench -
    -Current medians in milliseconds - -| Workload | Off | Off +PF | Progressive | Progressive +PF | Upfront | Upfront +PF | -| ----------------------------- | -----: | ------: | ----------: | --------------: | ------: | ----------: | -| random, no indexes, wide | 1494.4 | 1385.7 | 1512.0 | 1407.6 | 1516.2 | 1422.4 | -| random, no indexes, narrow | 355.7 | 330.7 | 354.3 | 325.6 | 349.9 | 325.2 | -| random, indexes, wide | 1500.1 | 1390.5 | 1715.8 | 1430.7 | 1534.6 | 1429.2 | -| random, indexes, narrow | 356.8 | 344.4 | 378.9 | 332.8 | 355.3 | 332.4 | -| clustered, no indexes, wide | 1479.3 | 1383.7 | 299.4 | 289.8 | 290.0 | 288.2 | -| clustered, no indexes, narrow | 340.2 | 318.5 | 208.1 | 203.2 | 205.7 | 202.7 | -| clustered, indexes, wide | 44.4 | 37.8 | 47.5 | 36.0 | 42.1 | 36.0 | -| clustered, indexes, narrow | 21.3 | 18.9 | 25.7 | 22.5 | 20.9 | 22.5 | +Upfront Q24 measured 8.92 and 8.75 ms, versus main progressive's 10.00 and +10.15 ms. Q25 changed direction between passes: 7.42 versus 6.76 ms in pass 1, +and 6.64 versus 7.09 ms in pass 2. The comparison shows query-specific results +and variation between passes. -
    +Upfront medians exceeded main with pushdown off for Q10, Q22, Q24, Q25, and Q26 +in both passes. -
    -Current requested bytes and reader calls (prefetch off) - -| Workload | Bytes (all policies) | Off calls | Progressive calls | Upfront calls | -| ----------------------------- | -------------------: | --------: | ----------------: | ------------: | -| random, no indexes, wide | 1,309,357,880 | 256 | 512 | 256 | -| random, no indexes, narrow | 303,126,105 | 256 | 512 | 256 | -| random, indexes, wide | 1,318,274,373 | 257 | 513 | 257 | -| random, indexes, narrow | 312,042,598 | 257 | 513 | 257 | -| clustered, no indexes, wide | 1,278,042,437 | 256 | 512 | 256 | -| clustered, no indexes, narrow | 271,810,662 | 256 | 512 | 256 | -| clustered, indexes, wide | 28,883,141 | 257 | 513 | 257 | -| clustered, indexes, narrow | 13,160,491 | 257 | 513 | 257 | +![ClickBench elapsed-time ratios for the current patch against main.](benchmark-charts/parquet-main-clickbench.png) -
    +The existing `dfbench clickbench` runner executes unchanged Q10, Q11, Q22, Q24, +Q25, and Q26 SQL files against the same public +[`hits_0.parquet`](https://datasets.clickhouse.com/hits_compatible/athena_partitioned/hits_0.parquet) +file: 122,446,530 bytes, 1,000,000 rows, and two row groups. This is one partition +of the 100-file dataset. Both revisions use one target partition and enable +filter reordering. Prefetch is disabled. -All 255 future groups fit the 16 MiB prefetch budget. Peak compressed prefetch -reservations were 4.99–5.12 MB for wide output and 1.06–1.18 MB for narrow output -without effective page pruning. With indexed, clustered matches, the peaks fell -to 78,078 bytes (wide) and 16,587 bytes (narrow). These are exact reservation -peaks from `PeakRecordingPool`; current-reader, decoded-buffer, and process -memory are outside this measurement. +Each case has one warmup and three measured iterations. The second pass reverses +the order of the five revision/configuration combinations. Patch off is compared +with main off; patch progressive and upfront are compared with main progressive. +All 240 executions succeeded and returned ten rows. The benchmark records row +counts; result values were not independently compared.
    -Fresh unmodified DF55 control, prefetch off (milliseconds) - -| Workload | Baseline off | Current off | Baseline progressive | Current progressive | -| ----------------------------- | -----------: | ----------: | -------------------: | ------------------: | -| random, no indexes, wide | 1493.3 | 1494.4 | 1503.0 | 1512.0 | -| random, no indexes, narrow | 354.9 | 355.7 | 356.1 | 354.3 | -| random, indexes, wide | 1494.1 | 1500.1 | 1712.8 | 1715.8 | -| random, indexes, narrow | 359.9 | 356.8 | 378.2 | 378.9 | -| clustered, no indexes, wide | 1497.1 | 1479.3 | 302.2 | 299.4 | -| clustered, no indexes, narrow | 343.7 | 340.2 | 209.5 | 208.1 | -| clustered, indexes, wide | 44.6 | 44.4 | 47.5 | 47.5 | -| clustered, indexes, narrow | 21.1 | 21.3 | 25.4 | 25.7 | +Exact median ClickBench times in milliseconds + +### Pass 1 + +| Query | Main off | Patch off | Main progressive | Patch progressive | Patch upfront | +| ----- | -------: | --------: | ---------------: | ----------------: | ------------: | +| Q10 | 5.06 | 5.19 | 5.79 | 5.11 | 5.59 | +| Q11 | 4.97 | 4.89 | 5.81 | 5.76 | 5.74 | +| Q22 | 93.88 | 91.93 | 110.93 | 108.74 | 110.14 | +| Q24 | 8.11 | 7.59 | 10.00 | 9.62 | 8.92 | +| Q25 | 5.11 | 4.87 | 6.76 | 7.05 | 7.42 | +| Q26 | 8.36 | 8.29 | 12.63 | 11.12 | 12.05 | + +### Pass 2 + +| Query | Main off | Patch off | Main progressive | Patch progressive | Patch upfront | +| ----- | -------: | --------: | ---------------: | ----------------: | ------------: | +| Q10 | 4.21 | 4.25 | 5.05 | 5.06 | 4.63 | +| Q11 | 5.13 | 5.19 | 5.48 | 5.15 | 5.04 | +| Q22 | 84.72 | 83.33 | 102.40 | 103.86 | 103.36 | +| Q24 | 7.18 | 7.15 | 10.15 | 8.97 | 8.75 | +| Q25 | 4.85 | 4.93 | 7.09 | 6.87 | 6.64 | +| Q26 | 7.42 | 7.37 | 10.19 | 9.93 | 9.63 |
    -The baseline rerun uses the unchanged DF55 executable described below and passed all 64 scan assertions. The current matrix ran before the baseline, with no builds from this task running during either measurement. These are local desktop samples, not evidence that the defaults have zero overhead or that the wider performance epic is resolved. +These local samples do not establish full-dataset, remote-storage, or Spark +end-to-end performance. Timing differences include all changes between the two +revisions; broader measurements are needed before changing defaults. -### Reported ClickBench query regressions - -The existing `dfbench clickbench` runner executed the unchanged Q10, Q11, Q22, -Q24, Q25, and Q26 query files referenced by the regression work in -[#20324](https://github.com/apache/datafusion/issues/20324). Input was the public -[`hits_0.parquet`](https://datasets.clickhouse.com/hits_compatible/athena_partitioned/hits_0.parquet) -partition: 122,446,530 bytes, 1,000,000 rows, two row groups. This covers one -partition of the 100-file dataset. Target partitions were set to one, filter -reordering was enabled in every case, and next-row-group prefetch was disabled. +## Reproduce -Each case ran four iterations. The table and chart use the median of iterations -1–3, after the first warmup. The second pass reversed the policy order. All 144 -executions succeeded and returned ten rows. The benchmark records row counts; -result values were not independently compared. The SQL reader -regression test separately checks values across all 48 policy/data combinations. +Build both runners using the same Rust toolchain and profile: -![ClickBench progressive and upfront timings relative to pushdown off, in two passes.](benchmark-charts/parquet-pages-clickbench.png) +```sh +cargo build --locked --profile release-nonlto \ + -p datafusion-datasource-parquet --example io_policy \ + -p datafusion-benchmarks --bin dfbench +``` -Q11, Q22, Q24, Q25, and Q26 remained slower with upfront I/O in both passes. -For example, Q25 upfront medians were 6.55 and 7.36 ms, versus 4.86 ms with -pushdown off in each pass. Changing the I/O policy alone does not remove these -observed slowdowns. The small local sample does not identify their cause; -full-dataset measurements and profiling remain necessary before changing defaults. +For main, copy the benchmark example from the patch, remove the +`with_row_group_prefetch` and `with_progressive_io` builder calls, and restrict +the configuration loop to off/progressive without prefetch. Enable Tokio's +`rt-multi-thread` feature in the Parquet crate's dev dependencies. -
    -ClickBench medians in milliseconds +Run the patch first to generate the shared input directory, then use that same +absolute directory for both binaries. Repeat in the opposite revision order. -| Query | Off, first | Progressive, first | Upfront, first | Off, repeat | Progressive, repeat | Upfront, repeat | -| ----- | ---------: | -----------------: | -------------: | ----------: | ------------------: | --------------: | -| Q10 | 5.06 | 5.00 | 5.01 | 4.22 | 4.13 | 4.44 | -| Q11 | 5.03 | 5.83 | 5.90 | 4.76 | 5.26 | 5.08 | -| Q22 | 90.53 | 101.25 | 100.25 | 83.56 | 102.69 | 100.46 | -| Q24 | 6.87 | 8.79 | 8.66 | 7.30 | 8.73 | 9.23 | -| Q25 | 4.86 | 6.77 | 6.55 | 4.86 | 6.69 | 7.36 | -| Q26 | 7.02 | 9.63 | 9.18 | 7.08 | 9.94 | 9.81 | - -
    +```sh +target/release-nonlto/examples/io_policy 256 /tmp/io-policy-data > io-policy.csv +``` -Example command for upfront Q10, using the same build profile as the matrix: +For ClickBench, use the same query directory and input file in both checkouts: ```sh -cargo build --profile release-nonlto -p datafusion-benchmarks --bin dfbench -curl -LO https://datasets.clickhouse.com/hits_compatible/athena_partitioned/hits_0.parquet target/release-nonlto/dfbench clickbench --path hits_0.parquet \ --queries-path benchmarks/queries/clickbench/queries --query 10 \ --iterations 4 --partitions 1 --pushdown \ - -c datafusion.execution.parquet.reorder_filters=true \ - -c datafusion.execution.parquet.progressive_io=false + -c datafusion.execution.parquet.reorder_filters=true ``` -Use `progressive_io=true` for progressive reads. For the off control, omit -`--pushdown` and set `datafusion.execution.parquet.pushdown_filters=false`. -Repeat for query numbers 10, 11, 22, 24, 25, and 26. - -## Earlier measurements: whole-chunk implementation (2026-09-09) - -These historical results describe commit `943ddfc20`, before the page-selection -fix above. The 21–45× byte amplification below is the regression this revision -addresses; it must not be attributed to the current implementation. - -
    -Earlier charts, timings, and baseline controls - -Apple M4, 10 CPU cores, 24 GiB RAM, Rust 1.97.0, `release-nonlto`, two Tokio worker threads. Implementation: [`943ddfc20`](https://github.com/peterxcli/datafusion/commit/943ddfc209147ebd8022853482009c2827d77b59). The four files are approximately 1.29–1.32 GB compressed each. All 384 scans across two complete patched runs and all 64 baseline scans passed the independent row-count and checksum assertions, including warmups. - -Each plotted value is a median of three measured scans. `+PF` means a 16 MiB prefetch budget. Both runs are shown because timings moved substantially even though no other build from this experiment ran during measurement. Other desktop activity was uncontrolled. These measurements support the I/O tradeoff; they do not establish a stable overall speedup or zero overhead with the options disabled. - -### Timing comparison - -Each panel uses the same logarithmic scale. **1× is progressive fetching without prefetch in that workload and run**; points to the left are faster, points to the right are slower. Normalizing each run makes policy differences visible across workloads, but hides absolute timing drift; the unmodified DF55 comparison below shows that separately. - -![Six-policy elapsed-time comparison across eight workloads. Blue circles show the first run and orange triangles the repeat, relative to progressive fetching without prefetch.](benchmark-charts/parquet-policy-timings.png) - -
    -Exact timing medians in milliseconds: first and repeat runs - -### First run - -| Data | Off | Off +PF | Progressive | Progressive +PF | Upfront | Upfront +PF | -| ----------------------------- | -----: | ------: | ----------: | --------------: | ------: | ----------: | -| random, no indexes, wide | 1856.0 | 1693.7 | 1845.9 | 1646.1 | 1717.3 | 1551.5 | -| random, no indexes, narrow | 365.9 | 337.5 | 379.3 | 334.8 | 360.2 | 331.5 | -| random, indexes, wide | 1654.3 | 1548.9 | 1772.4 | 1459.7 | 1586.5 | 1460.5 | -| random, indexes, narrow | 436.3 | 366.2 | 489.2 | 362.6 | 399.5 | 415.8 | -| clustered, no indexes, wide | 1807.7 | 1627.7 | 375.3 | 353.2 | 357.0 | 315.4 | -| clustered, no indexes, narrow | 351.3 | 327.5 | 217.3 | 213.2 | 210.5 | 210.8 | -| clustered, indexes, wide | 46.1 | 117.8 | 48.5 | 118.9 | 132.8 | 116.0 | -| clustered, indexes, narrow | 21.3 | 35.8 | 25.8 | 39.9 | 38.9 | 39.8 | - -### Repeat run - -| Data | Off | Off +PF | Progressive | Progressive +PF | Upfront | Upfront +PF | -| ----------------------------- | -----: | ------: | ----------: | --------------: | ------: | ----------: | -| random, no indexes, wide | 1729.8 | 1499.5 | 1655.6 | 1498.1 | 1642.4 | 1542.2 | -| random, no indexes, narrow | 416.2 | 371.6 | 404.0 | 347.8 | 411.2 | 371.0 | -| random, indexes, wide | 1841.6 | 1698.7 | 2374.9 | 1620.4 | 1798.1 | 1638.5 | -| random, indexes, narrow | 528.4 | 473.3 | 556.2 | 483.0 | 508.3 | 480.6 | -| clustered, no indexes, wide | 2099.7 | 1916.2 | 450.0 | 466.4 | 445.9 | 462.4 | -| clustered, no indexes, narrow | 369.0 | 353.3 | 298.8 | 275.6 | 245.6 | 255.0 | -| clustered, indexes, wide | 48.0 | 131.5 | 50.6 | 131.8 | 152.2 | 133.7 | -| clustered, indexes, narrow | 22.5 | 40.3 | 26.3 | 44.1 | 41.2 | 43.2 | - -
    - -### Unmodified DataFusion 55 control - -The baseline is [`d55523420`](https://github.com/peterxcli/datafusion/commit/d55523420), the fork's `codex/df55-base`. It uses the same benchmark harness, compiler/profile, dependency lock versions, and two-worker runtime, with only the new builder calls removed and the configurations restricted to off/progressive without prefetch. It ran after the first patched matrix and before the repeat. The table compares the progressive configuration without prefetch in all three runs. - -![Patched progressive reads without prefetch compared with unmodified DataFusion 55. Most points exceed 1x, and first-run and repeat ratios differ.](benchmark-charts/parquet-policy-baseline.png) - -
    -Exact baseline timing medians in milliseconds - -| Data | DF 55 | Patched first | Patched repeat | -| ----------------------------- | -----: | ------------: | -------------: | -| random, no indexes, wide | 1537.6 | 1845.9 | 1655.6 | -| random, no indexes, narrow | 358.5 | 379.3 | 404.0 | -| random, indexes, wide | 1763.0 | 1772.4 | 2374.9 | -| random, indexes, narrow | 380.9 | 489.2 | 556.2 | -| clustered, no indexes, wide | 308.7 | 375.3 | 450.0 | -| clustered, no indexes, narrow | 210.8 | 217.3 | 298.8 | -| clustered, indexes, wide | 50.4 | 48.5 | 50.6 | -| clustered, indexes, narrow | 25.6 | 25.8 | 26.3 | - -
    - -Several disabled-option controls were slower than unmodified DF 55, and the repeat also moved substantially. This experiment cannot distinguish code overhead from machine/load drift. A stable machine with interleaved baseline/patch trials is required before claiming an end-to-end improvement or promoting defaults. - -### Reader I/O and memory - -The following counts are from the first run, without prefetch. Byte counts include metadata. Calls and ranges have the wrapper scope described above. - -![Progressive and upfront requested bytes for all eight workloads. Indexed clustered data reads 44.6x more bytes for wide output and 21.3x more for narrow output with upfront fetching.](benchmark-charts/parquet-policy-io.png) - -
    -Exact requested bytes, reader calls, and logical ranges - -| Data | Progressive bytes | Upfront bytes | Progressive calls / ranges | Upfront calls / ranges | -| ----------------------------- | ----------------: | ------------: | -------------------------: | ---------------------: | -| random, no indexes, wide | 1,309,357,880 | 1,309,357,880 | 512 / 2048 | 256 / 2048 | -| random, no indexes, narrow | 303,126,105 | 303,126,105 | 512 / 512 | 256 / 512 | -| random, indexes, wide | 1,318,274,373 | 1,318,274,373 | 513 / 229633 | 257 / 2049 | -| random, indexes, narrow | 312,042,598 | 312,042,598 | 513 / 33025 | 257 / 513 | -| clustered, no indexes, wide | 1,278,042,437 | 1,278,042,437 | 512 / 2048 | 256 / 2048 | -| clustered, no indexes, narrow | 271,810,662 | 271,810,662 | 512 / 512 | 256 / 512 | -| clustered, indexes, wide | 28,883,141 | 1,286,958,261 | 513 / 4097 | 257 / 2049 | -| clustered, indexes, narrow | 13,160,491 | 280,726,486 | 513 / 1025 | 257 / 513 | - -
    - -For random matches without indexes, upfront reads reduce 512 reader calls to 256 while fetching the same bytes. With indexes, batching also removes the many small selected-page ranges. For indexed clustered matches, progressive fetching reads only selected pages: upfront reads instead fetch **44.6×** as many bytes for wide output and **21.3×** for narrow output. The wide upfront configurations take approximately 2.4–3.0× the progressive time across these runs. - -To isolate overlap, compare **Upfront** with **Upfront +PF**, which fetch the same chunks. For random wide output without indexes, prefetch reduced the median from 1717.3 to 1551.5 ms (9.7%) in the first run and from 1642.4 to 1542.2 ms (6.1%) in the repeat. This is evidence of potential overlap benefit in this local workload, with the baseline and timing caveats above. Some cases regress: clustered wide output without indexes improved by 11.6% in the first run but regressed by 3.7% in the repeat. - -All groups fit the 16 MiB prefetch budget in this fixture; 255 future groups were prefetched. Peak compressed prefetch reservations were about 5.1 MB for wide output and 1.1 MB for narrow output. These are exact reservation peaks from `PeakRecordingPool`, not total reader/process memory. Upfront demand allocations are outside that prefetch reservation. - -The full-chunk prefetch strategy loses much of page pruning's byte savings even when progressive demand reads are selected. Both options therefore remain opt-in. A remote-object-store benchmark with real latency, bandwidth limits, backend request counters, and concurrent scans is still needed before an upstream performance claim. - -
    +On the patch, add `-c datafusion.execution.parquet.progressive_io=false` for +upfront reads. For the off control on either revision, omit `--pushdown` and add +`-c datafusion.execution.parquet.pushdown_filters=false`. 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zR`DSDm*S^9#s1>)_P>5h+2Y%0`qKW5UY%xui0*^+bD8$*S5JV-7|34ycij{9jb>Dy zh<2hBU~fNv0ATaAe*fPI8tz4ao8R^Oub-tPi~jJe{QZyB(t&2l`WYCe6>hkClBsZF z=&-QD$R)xU{N4FvVkzAJ`$Ikdot!0?tHVW}Et!9h;s57dP8k_YzWmp}{eS=ZzhM;r f9}S~e4SB@euV#H+kNICi`ouqf5ib6u Result<(), Box> { .collect::>(), )); let directory = tempfile::tempdir()?; + let data_dir = std::env::args() + .nth(2) + .map(std::path::PathBuf::from) + .unwrap_or_else(|| directory.path().to_path_buf()); + std::fs::create_dir_all(&data_dir)?; println!( "distribution,indexes,projection,mode,prefetch,round,file_bytes,elapsed_ms,rows,checksum,bytes_scanned,reader_calls,reader_ranges,page_rows_pruned,prefetch_peak,prefetch_bytes,prefetch_row_groups" ); for clustered in [false, true] { for indexed in [false, true] { - let path = directory.path().join("scan.parquet"); + let path = + data_dir.join(format!("scan-{clustered}-{indexed}-{groups}.parquet")); let props = WriterProperties::builder() .set_compression(Compression::SNAPPY) .set_dictionary_enabled(false) @@ -153,11 +162,15 @@ async fn main() -> Result<(), Box> { }) .set_offset_index_disabled(!indexed) .build(); - let mut writer = ArrowWriter::try_new( - std::fs::File::create(&path)?, - Arc::clone(&schema), - Some(props), - )?; + let mut writer = if path.exists() { + None + } else { + Some(ArrowWriter::try_new( + std::fs::File::create(&path)?, + Arc::clone(&schema), + Some(props), + )?) + }; let mut random = 42u64; let mut expected_rows = 0; let mut expected_wide = 0i64; @@ -193,16 +206,24 @@ async fn main() -> Result<(), Box> { } } } - writer.write(&RecordBatch::try_new( - Arc::clone(&schema), - columns - .into_iter() - .map(|v| Arc::new(Int64Array::from(v)) as _) - .collect(), - )?)?; - writer.flush()?; + if let Some(writer) = &mut writer { + writer.write(&RecordBatch::try_new( + Arc::clone(&schema), + columns + .into_iter() + .map(|v| Arc::new(Int64Array::from(v)) as _) + .collect(), + )?)?; + writer.flush()?; + } } - let metadata = writer.close()?; + let metadata = if let Some(writer) = writer { + writer.close()? + } else { + SerializedFileReader::new(std::fs::File::open(&path)?)? + .metadata() + .clone() + }; assert_eq!(metadata.num_row_groups(), groups); for group in metadata.row_groups() { for column in group.columns() { From 801cb0e5222a3e7b9c8d3d2e41a22817173792f8 Mon Sep 17 00:00:00 2001 From: peterxcli Date: Thu, 10 Sep 2026 11:08:04 +0800 Subject: [PATCH 08/13] fix: preserve fully matched row-group semantics in I/O policies --- .../core/tests/parquet/filter_pushdown.rs | 2 +- .../datasource-parquet/src/push_decoder.rs | 83 ++++++++++++++++++- datafusion/pruning/src/pruning_predicate.rs | 36 +++++++- .../dynamic_filter_pushdown_config.slt | 2 +- .../test_files/explain_analyze.slt | 20 ++--- .../sqllogictest/test_files/limit_pruning.slt | 2 +- 6 files changed, 127 insertions(+), 18 deletions(-) diff --git a/datafusion/core/tests/parquet/filter_pushdown.rs b/datafusion/core/tests/parquet/filter_pushdown.rs index ea7c0d59114c..0f13cbd65537 100644 --- a/datafusion/core/tests/parquet/filter_pushdown.rs +++ b/datafusion/core/tests/parquet/filter_pushdown.rs @@ -865,7 +865,7 @@ async fn upfront_io_preserves_page_pruning_from_session_configuration() { .set_offset_index_disabled(!indexed) .build(); let mut writer = ArrowWriter::try_new( - std::fs::File::create(&path).unwrap(), + File::create(&path).unwrap(), Arc::clone(&schema), Some(props), ) diff --git a/datafusion/datasource-parquet/src/push_decoder.rs b/datafusion/datasource-parquet/src/push_decoder.rs index b5b80f8a8aa7..89a61885679c 100644 --- a/datafusion/datasource-parquet/src/push_decoder.rs +++ b/datafusion/datasource-parquet/src/push_decoder.rs @@ -707,10 +707,16 @@ impl PushDecoderStreamState { if self.upfront_row_group != Some(row_group) { decoder.clear_all_ranges(); self.prefetch_reservation = None; + let entry = + self.rg_plan.front().expect("current row group"); let Some(mut upfront_ranges) = column_ranges( &self.parquet_metadata, - self.rg_plan.front().expect("current row group"), - &self.fetch_projection, + entry, + if entry.fully_matched { + self.decoder_projection.projection_mask() + } else { + &self.fetch_projection + }, ) else { return Some(( internal_err!( @@ -780,7 +786,11 @@ impl PushDecoderStreamState { self.pending_prefetch = PrefetchedRowGroup::start( entry, &self.parquet_metadata, - &self.fetch_projection, + if entry.fully_matched { + self.decoder_projection.projection_mask() + } else { + &self.fetch_projection + }, options, Arc::clone(&self.reader), &self.prefetch_metrics, @@ -1025,6 +1035,7 @@ mod tests { #[derive(Debug, Default)] struct ReadControl { calls: std::sync::atomic::AtomicUsize, + bytes: std::sync::atomic::AtomicUsize, started: tokio::sync::Notify, release: tokio::sync::Notify, block_second: bool, @@ -1056,6 +1067,10 @@ mod tests { use futures::FutureExt; use std::sync::atomic::Ordering; async move { + self.control.bytes.fetch_add( + ranges.iter().map(|r| (r.end - r.start) as usize).sum(), + Ordering::SeqCst, + ); let call = self.control.calls.fetch_add(1, Ordering::SeqCst); if call == 1 { self.control.started.notify_one(); @@ -1131,6 +1146,9 @@ mod tests { let mut source = crate::source::ParquetSource::new(schema) .with_progressive_io(progressive) .with_row_group_prefetch(budget, pool) + // Page selections disable runtime pruning (#24355). Page-pruned + // I/O is covered by the session-configuration integration test. + .with_enable_page_index(false) .with_pushdown_filters(true) .with_parquet_file_reader_factory(Arc::new(TestReader { data, @@ -1445,6 +1463,65 @@ mod tests { } } + #[tokio::test] + async fn io_policies_preserve_fully_matched_filter_suppression() { + use datafusion_execution::memory_pool::GreedyMemoryPool; + use std::sync::atomic::Ordering; + + let (_, metadata, _) = build_three_rg_file_data(); + // Exercise both directions: fully matched -> filtered, and the reverse. + // The predicate column is absent from the output in both cases. + for (operator, expected, row_groups) in [ + (Operator::Lt, (0..1500).collect::>(), [0, 1]), + (Operator::GtEq, (1500..3000).collect::>(), [1, 2]), + ] { + let expected_bytes = row_groups + .iter() + .map(|&rg| metadata.row_group(rg).column(0).byte_range().1) + .sum::() + + metadata.row_group(1).column(1).byte_range().1; + for progressive in [true, false] { + for budget in [0, 1 << 20] { + let pool: Arc = + Arc::new(GreedyMemoryPool::new(1 << 20)); + let control = Arc::new(ReadControl::default()); + let predicate = Arc::new(BinaryExpr::new( + Arc::new(Column::new("w", 1)), + operator, + lit(1500i64), + )); + let mut stream = io_test_stream( + budget, + Arc::clone(&pool), + Arc::clone(&control), + None, + Some(predicate), + progressive, + ); + let mut values = Vec::new(); + while let Some(batch) = stream.next().await { + values.extend_from_slice( + batch + .unwrap() + .column(0) + .as_any() + .downcast_ref::() + .unwrap() + .values(), + ); + } + assert_eq!(values, expected); + assert_eq!( + control.bytes.load(Ordering::SeqCst) as u64, + expected_bytes, + "predicate-only chunks in fully matched groups must stay unread" + ); + assert_pool_released(&pool).await; + } + } + } + } + /// Controlled scheduling experiment, not a production throughput benchmark. #[tokio::test(flavor = "multi_thread", worker_threads = 2)] #[ignore = "manual benchmark with simulated I/O and batch processing latency"] diff --git a/datafusion/pruning/src/pruning_predicate.rs b/datafusion/pruning/src/pruning_predicate.rs index b49b72058e0c..9539a8892702 100644 --- a/datafusion/pruning/src/pruning_predicate.rs +++ b/datafusion/pruning/src/pruning_predicate.rs @@ -525,6 +525,13 @@ impl<'a> PruningPredicateBuilder<'a> { ) })?; + // A changing filter can tighten after this snapshot. Its current inverse + // cannot prove that every row will still match when the data is read. + let stable_predicate = !matches!( + phys_expr::DynamicFilterTracking::classify(&predicate), + phys_expr::DynamicFilterTracking::Watching(_) + ); + // Get a (simpler) snapshot of the physical expr here to use with `PruningPredicate`. // In particular this unravels any `DynamicFilterPhysicalExpr`s by snapshotting them // so that PruningPredicate can work with a static expression. @@ -567,7 +574,8 @@ impl<'a> PruningPredicateBuilder<'a> { orig_expr: predicate, literal_guarantees, max_in_list_size: self.max_in_list_size, - can_be_inverted_for_full_match: !properties.has_filter_semantics_only, + can_be_inverted_for_full_match: stable_predicate + && !properties.has_filter_semantics_only, }) } } @@ -746,7 +754,8 @@ impl PruningPredicate { } /// Returns whether pruning the logical inverse can safely prove that every - /// row in a container satisfies the original predicate. + /// row in a container satisfies the original predicate. Snapshots of dynamic + /// filters that can still change cannot provide this guarantee. pub fn can_be_inverted_for_full_match(&self) -> bool { self.can_be_inverted_for_full_match } @@ -3398,6 +3407,29 @@ mod tests { ); } + #[test] + fn full_match_requires_a_stable_dynamic_filter() -> Result<()> { + let schema = + Arc::new(Schema::new(vec![Field::new("c1", DataType::Int32, false)])); + let expression = logical2physical(&col("c1").gt(lit(5)), &schema); + let dynamic = Arc::new(DynamicFilterPhysicalExpr::new( + vec![Arc::new(phys_expr::Column::new("c1", 0))], + Arc::clone(&expression), + )); + let build = |expression| { + PruningPredicateBuilder::new() + .with_file_schema(Arc::clone(&schema)) + .try_build(expression) + }; + assert!(build(expression)?.can_be_inverted_for_full_match()); + let snapshot = build(Arc::clone(&dynamic) as _)?; + assert!(!snapshot.can_be_inverted_for_full_match()); + dynamic.mark_complete(); + assert!(build(dynamic)?.can_be_inverted_for_full_match()); + assert!(!snapshot.can_be_inverted_for_full_match()); + Ok(()) + } + /// Integration test demonstrating that a dynamic filter with replaced children as literals will be snapshotted, simplified and then pruned correctly. #[test] fn row_group_predicate_dynamic_filter_with_literals() { diff --git a/datafusion/sqllogictest/test_files/dynamic_filter_pushdown_config.slt b/datafusion/sqllogictest/test_files/dynamic_filter_pushdown_config.slt index be7cf71768ce..a7870ae02bf6 100644 --- a/datafusion/sqllogictest/test_files/dynamic_filter_pushdown_config.slt +++ b/datafusion/sqllogictest/test_files/dynamic_filter_pushdown_config.slt @@ -104,7 +104,7 @@ Plan with Metrics 03)----ProjectionExec: expr=[id@0 as id, value@1 as v, value@1 + id@0 as name], metrics=[output_rows=10, ] 04)------FilterExec: value@1 > 3, metrics=[output_rows=10, , selectivity=100% (10/10)] 05)--------RepartitionExec: partitioning=RoundRobinBatch(4), input_partitions=1, metrics=[output_rows=10, ] -06)----------DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/dynamic_filter_pushdown_config/test_data.parquet]]}, projection=[id, value], file_type=parquet, predicate=value@1 > 3 AND DynamicFilter [ value@1 IS NULL OR value@1 > 800 ], dynamic_rg_pruning=eligible, pruning_predicate=value_null_count@1 != row_count@2 AND value_max@0 > 3 AND (value_null_count@1 > 0 OR value_null_count@1 != row_count@2 AND value_max@0 > 800), required_guarantees=[], metrics=[output_rows=10, elapsed_compute=, output_bytes=80.0 B, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=1 total → 1 matched -> 1 fully matched, row_groups_pruned_bloom_filter=1 total → 1 matched, page_index_pages_pruned=0 total → 0 matched, page_index_pages_skipped_by_fully_matched=1, limit_pruned_row_groups=0 total → 0 matched, bytes_processed=1147.0 B, bytes_scanned=210.0 B, page_index_load_skipped=1, row_groups_pruned_dynamic_filter=0, metadata_load_time=, scan_efficiency_ratio=18.31% (210/1.15 K)] +06)----------DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/dynamic_filter_pushdown_config/test_data.parquet]]}, projection=[id, value], file_type=parquet, predicate=value@1 > 3 AND DynamicFilter [ value@1 IS NULL OR value@1 > 800 ], dynamic_rg_pruning=eligible, pruning_predicate=value_null_count@1 != row_count@2 AND value_max@0 > 3 AND (value_null_count@1 > 0 OR value_null_count@1 != row_count@2 AND value_max@0 > 800), required_guarantees=[], metrics=[output_rows=10, elapsed_compute=, output_bytes=80.0 B, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=1 total → 1 matched, row_groups_pruned_bloom_filter=1 total → 1 matched, page_index_pages_pruned=1 total → 1 matched, limit_pruned_row_groups=0 total → 0 matched, bytes_processed=1147.0 B, bytes_scanned=210.0 B, row_groups_pruned_dynamic_filter=0, metadata_load_time=, scan_efficiency_ratio=18.31% (210/1.15 K)] statement ok set datafusion.explain.analyze_level = dev; diff --git a/datafusion/sqllogictest/test_files/explain_analyze.slt b/datafusion/sqllogictest/test_files/explain_analyze.slt index 81b274d6c137..cdf7bd9c54f8 100644 --- a/datafusion/sqllogictest/test_files/explain_analyze.slt +++ b/datafusion/sqllogictest/test_files/explain_analyze.slt @@ -247,7 +247,7 @@ explain analyze select * from cat_tracking where species > 'M' AND s >= 50 order ---- Plan with Metrics 01)SortExec: TopK(fetch=3), expr=[species@0 ASC NULLS LAST], preserve_partitioning=[false], filter=[species@0 < Nlpine Sheep], metrics=[output_rows=3] -02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched -> 1 fully matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=2 total → 2 matched, page_index_pages_skipped_by_fully_matched=1, limit_pruned_row_groups=0 total → 0 matched, scan_efficiency_ratio=21.75% (485/2.23 K)] +02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=3 total → 3 matched, limit_pruned_row_groups=0 total → 0 matched, scan_efficiency_ratio=21.75% (485/2.23 K)] statement ok reset datafusion.explain.analyze_categories; @@ -262,7 +262,7 @@ explain analyze select * from cat_tracking where species > 'M' AND s >= 50 order ---- Plan with Metrics 01)SortExec: TopK(fetch=3), expr=[species@0 ASC NULLS LAST], preserve_partitioning=[false], filter=[species@0 < Nlpine Sheep], metrics=[output_rows=3, output_bytes=] -02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, output_bytes=, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched -> 1 fully matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=2 total → 2 matched, page_index_pages_skipped_by_fully_matched=1, limit_pruned_row_groups=0 total → 0 matched, bytes_processed=, bytes_scanned=, scan_efficiency_ratio=] +02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, output_bytes=, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=3 total → 3 matched, limit_pruned_row_groups=0 total → 0 matched, bytes_processed=, bytes_scanned=, scan_efficiency_ratio=] statement ok reset datafusion.explain.analyze_categories; @@ -277,7 +277,7 @@ explain analyze select * from cat_tracking where species > 'M' AND s >= 50 order ---- Plan with Metrics 01)SortExec: TopK(fetch=3), expr=[species@0 ASC NULLS LAST], preserve_partitioning=[false], filter=[species@0 < Nlpine Sheep], metrics=[output_rows=3, output_bytes=] -02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, output_bytes=, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched -> 1 fully matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=2 total → 2 matched, page_index_pages_skipped_by_fully_matched=1, limit_pruned_row_groups=0 total → 0 matched, bytes_processed=, bytes_scanned=, scan_efficiency_ratio=] +02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, output_bytes=, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=3 total → 3 matched, limit_pruned_row_groups=0 total → 0 matched, bytes_processed=, bytes_scanned=, scan_efficiency_ratio=] statement ok reset datafusion.explain.analyze_categories; @@ -559,7 +559,7 @@ EXPLAIN (ANALYZE, METRICS 'rows', LEVEL summary) select * from cat_tracking wher ---- Plan with Metrics 01)SortExec: TopK(fetch=3), expr=[species@0 ASC NULLS LAST], preserve_partitioning=[false], filter=[species@0 < Nlpine Sheep], metrics=[output_rows=3] -02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched -> 1 fully matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=2 total → 2 matched, page_index_pages_skipped_by_fully_matched=1, limit_pruned_row_groups=0 total → 0 matched, scan_efficiency_ratio=21.75% (485/2.23 K)] +02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=3 total → 3 matched, limit_pruned_row_groups=0 total → 0 matched, scan_efficiency_ratio=21.75% (485/2.23 K)] # ---- Quoted-string METRICS with multiple categories ---- @@ -568,7 +568,7 @@ EXPLAIN (ANALYZE, METRICS 'rows,bytes', LEVEL summary) select * from cat_trackin ---- Plan with Metrics 01)SortExec: TopK(fetch=3), expr=[species@0 ASC NULLS LAST], preserve_partitioning=[false], filter=[species@0 < Nlpine Sheep], metrics=[output_rows=3, output_bytes=] -02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, output_bytes=, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched -> 1 fully matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=2 total → 2 matched, page_index_pages_skipped_by_fully_matched=1, limit_pruned_row_groups=0 total → 0 matched, bytes_processed=, bytes_scanned=, scan_efficiency_ratio=] +02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, output_bytes=, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=3 total → 3 matched, limit_pruned_row_groups=0 total → 0 matched, bytes_processed=, bytes_scanned=, scan_efficiency_ratio=] # ---- (METRICS 'timing', LEVEL summary) — timing metrics only ---- @@ -588,7 +588,7 @@ EXPLAIN (ANALYZE, METRICS 'rows,bytes', TIMING off, LEVEL summary) select * from ---- Plan with Metrics 01)SortExec: TopK(fetch=3), expr=[species@0 ASC NULLS LAST], preserve_partitioning=[false], filter=[species@0 < Nlpine Sheep], metrics=[output_rows=3, output_bytes=] -02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, output_bytes=, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched -> 1 fully matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=2 total → 2 matched, page_index_pages_skipped_by_fully_matched=1, limit_pruned_row_groups=0 total → 0 matched, bytes_processed=, bytes_scanned=, scan_efficiency_ratio=] +02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, output_bytes=, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=3 total → 3 matched, limit_pruned_row_groups=0 total → 0 matched, bytes_processed=, bytes_scanned=, scan_efficiency_ratio=] # ---- TIMING sugar: `METRICS 'rows', TIMING on` ↔ rows + timing ---- @@ -597,7 +597,7 @@ EXPLAIN (ANALYZE, METRICS 'rows', TIMING on, LEVEL summary) select * from cat_tr ---- Plan with Metrics 01)SortExec: TopK(fetch=3), expr=[species@0 ASC NULLS LAST], preserve_partitioning=[false], filter=[species@0 < Nlpine Sheep], metrics=[output_rows=3, elapsed_compute=] -02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, elapsed_compute=, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched -> 1 fully matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=2 total → 2 matched, page_index_pages_skipped_by_fully_matched=1, limit_pruned_row_groups=0 total → 0 matched, metadata_load_time=, scan_efficiency_ratio=21.75% (485/2.23 K)] +02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, elapsed_compute=, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=3 total → 3 matched, limit_pruned_row_groups=0 total → 0 matched, metadata_load_time=, scan_efficiency_ratio=21.75% (485/2.23 K)] # ---- SUMMARY sugar: `SUMMARY on` ↔ `LEVEL summary` ---- # Equivalent to METRICS 'rows', LEVEL summary above. @@ -607,7 +607,7 @@ EXPLAIN (ANALYZE, METRICS 'rows', SUMMARY on) select * from cat_tracking where s ---- Plan with Metrics 01)SortExec: TopK(fetch=3), expr=[species@0 ASC NULLS LAST], preserve_partitioning=[false], filter=[species@0 < Nlpine Sheep], metrics=[output_rows=3] -02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched -> 1 fully matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=2 total → 2 matched, page_index_pages_skipped_by_fully_matched=1, limit_pruned_row_groups=0 total → 0 matched, scan_efficiency_ratio=21.75% (485/2.23 K)] +02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=3 total → 3 matched, limit_pruned_row_groups=0 total → 0 matched, scan_efficiency_ratio=21.75% (485/2.23 K)] # ---- Statement option overrides session config ---- # Session says 'timing' but statement-level `METRICS 'rows'` wins. @@ -620,7 +620,7 @@ EXPLAIN (ANALYZE, METRICS 'rows', LEVEL summary) select * from cat_tracking wher ---- Plan with Metrics 01)SortExec: TopK(fetch=3), expr=[species@0 ASC NULLS LAST], preserve_partitioning=[false], filter=[species@0 < Nlpine Sheep], metrics=[output_rows=3] -02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched -> 1 fully matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=2 total → 2 matched, page_index_pages_skipped_by_fully_matched=1, limit_pruned_row_groups=0 total → 0 matched, scan_efficiency_ratio=21.75% (485/2.23 K)] +02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=3 total → 3 matched, limit_pruned_row_groups=0 total → 0 matched, scan_efficiency_ratio=21.75% (485/2.23 K)] # ---- pgjson format: structural golden with no metrics ---- @@ -682,7 +682,7 @@ EXPLAIN (ANALYZE, METRICS rows, LEVEL summary) select * from cat_tracking where ---- Plan with Metrics 01)SortExec: TopK(fetch=3), expr=[species@0 ASC NULLS LAST], preserve_partitioning=[false], filter=[species@0 < Nlpine Sheep], metrics=[output_rows=3] -02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched -> 1 fully matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=2 total → 2 matched, page_index_pages_skipped_by_fully_matched=1, limit_pruned_row_groups=0 total → 0 matched, scan_efficiency_ratio=21.75% (485/2.23 K)] +02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/explain_analyze/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=3 total → 3 matched, limit_pruned_row_groups=0 total → 0 matched, scan_efficiency_ratio=21.75% (485/2.23 K)] statement ok reset datafusion.sql_parser.dialect; diff --git a/datafusion/sqllogictest/test_files/limit_pruning.slt b/datafusion/sqllogictest/test_files/limit_pruning.slt index 5f07c91b822b..4b082e877e4c 100644 --- a/datafusion/sqllogictest/test_files/limit_pruning.slt +++ b/datafusion/sqllogictest/test_files/limit_pruning.slt @@ -120,7 +120,7 @@ explain analyze select * from tracking_data where species > 'M' AND s >= 50 orde ---- Plan with Metrics 01)SortExec: TopK(fetch=3), expr=[species@0 ASC NULLS LAST], preserve_partitioning=[false], filter=[species@0 < Nlpine Sheep], metrics=[output_rows=3, elapsed_compute=, output_bytes=] -02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/limit_pruning/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, elapsed_compute=, output_bytes=, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched -> 1 fully matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=2 total → 2 matched, page_index_pages_skipped_by_fully_matched=1, limit_pruned_row_groups=0 total → 0 matched, bytes_processed=, bytes_scanned=, row_groups_pruned_dynamic_filter=0, metadata_load_time=, scan_efficiency_ratio= (/)] +02)--DataSourceExec: file_groups={1 group: [[WORKSPACE_ROOT/datafusion/sqllogictest/test_files/scratch/limit_pruning/data.parquet]]}, projection=[species, s], file_type=parquet, predicate=species@0 > M AND s@1 >= 50 AND DynamicFilter [ species@0 < Nlpine Sheep ], sort_order_for_reorder=[species@0 ASC NULLS LAST], dynamic_rg_pruning=eligible, pruning_predicate=species_null_count@1 != row_count@2 AND species_max@0 > M AND s_null_count@4 != row_count@2 AND s_max@3 >= 50 AND species_null_count@1 != row_count@2 AND species_min@5 < Nlpine Sheep, required_guarantees=[], metrics=[output_rows=3, elapsed_compute=, output_bytes=, files_ranges_pruned_statistics=1 total → 1 matched, row_groups_pruned_statistics=4 total → 3 matched, row_groups_pruned_bloom_filter=3 total → 3 matched, page_index_pages_pruned=3 total → 3 matched, limit_pruned_row_groups=0 total → 0 matched, bytes_processed=, bytes_scanned=, row_groups_pruned_dynamic_filter=0, metadata_load_time=, scan_efficiency_ratio= (/)] statement ok drop table tracking_data; From a37616ddbce513855d3eee87c2bcc73ab17f78ad Mon Sep 17 00:00:00 2001 From: peterxcli Date: Thu, 10 Sep 2026 11:18:49 +0800 Subject: [PATCH 09/13] docs: refresh Parquet I/O benchmarks after upstream rebase --- .../datasource-parquet/IO_POLICY_BENCHMARK.md | 99 ++++++++++-------- .../parquet-main-clickbench.png | Bin 95539 -> 103188 bytes .../benchmark-charts/parquet-main-timings.png | Bin 177634 -> 181067 bytes 3 files changed, 53 insertions(+), 46 deletions(-) diff --git a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md index 62dca8737882..d0150d60f9d4 100644 --- a/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md +++ b/datafusion/datasource-parquet/IO_POLICY_BENCHMARK.md @@ -25,11 +25,11 @@ with 10 CPU cores and 24 GiB RAM. | Revision | DataFusion | Arrow/Parquet | | ----------------------------------------------------------------------------------------------------------------- | ---------- | ------------- | -| [Upstream main `40488988a`](https://github.com/apache/datafusion/commit/40488988ad596c9b093ad60e1453430d803ce33c) | 55.0.0 | 59.3.0 | -| [Current patch implementation `8411b35ba`](https://github.com/peterxcli/datafusion/commit/8411b35ba) | 55.0.0 | 59.2.0 | +| [Upstream main `408696966`](https://github.com/apache/datafusion/commit/4086969669f96c0d5de03f442f2dba1869d2ee80) | 55.0.0 | 59.3.0 | +| [Current patch implementation `801cb0e52`](https://github.com/peterxcli/datafusion/commit/801cb0e52) | 55.0.0 | 59.3.0 | -Each revision uses its checked-in dependency lockfile. The comparison includes -changes elsewhere in main and differences in dependency versions. +The patch is rebased onto the measured upstream commit. Both revisions use +the same dependency lockfile, with Arrow/Parquet 59.3.0. ## Synthetic Parquet scans @@ -47,12 +47,18 @@ including the indexed, clustered cases: 28.9 MB for wide output and 13.2 MB for narrow output. All patch configurations, including prefetch, requested the same bytes in this fixture. -**Elapsed-time comparisons were unstable across passes.** For example, main's -progressive scan of indexed, clustered narrow output measured 273.3 ms in pass 1 -and 26.4 ms in pass 2. Patch progressive measured 27.8 and 26.5 ms. The -pass-1 timing gap therefore does not establish a stable patch speedup. Desktop -processes were active during the run; this measurement cannot isolate their -contribution to the variation. +With prefetch off, indexed clustered wide output took **41.3/41.0 ms** with +upfront reads versus **48.2/45.1 ms** on main progressive across the two passes +(9–14% less elapsed time). Narrow output took **20.1/20.1 ms** versus +**25.5/23.7 ms** (15–21% less). + +Gains depended on the workload. Random wide output without indexes changed +direction: 1.2% faster in pass 1 and 2.2% slower in pass 2. Main's clustered +wide scan without indexes also moved from 361.1 to 305.8 ms between passes, +while patch upfront measured 298.6 and 297.1 ms. These warm desktop timings +retain variation that two reversed passes cannot eliminate. Patch progressive +was also 3.4%/9.7% slower than main progressive on indexed random wide output; +this control warrants profiling. The [benchmark example](examples/io_policy.rs) uses four Snappy Parquet files, each containing 256 row groups of 131,072 rows and eight Int64 columns: @@ -86,27 +92,27 @@ artificial I/O or downstream delay. Other desktop activity was uncontrolled. | Workload | Main off | Patch off | Patch off +PF | Main progressive | Patch progressive | Patch progressive +PF | Patch upfront | Patch upfront +PF | | ----------------------------- | -------: | --------: | ------------: | ---------------: | ----------------: | --------------------: | ------------: | ----------------: | -| random, no indexes, wide | 1607.1 | 2496.6 | 2148.2 | 1625.1 | 2684.2 | 2258.6 | 2511.7 | 2082.2 | -| random, no indexes, narrow | 379.0 | 388.4 | 352.2 | 384.2 | 386.5 | 346.6 | 380.9 | 348.0 | -| random, indexes, wide | 1928.1 | 1478.8 | 1384.9 | 3096.5 | 1728.4 | 1432.1 | 1532.2 | 1427.5 | -| random, indexes, narrow | 755.0 | 357.2 | 336.9 | 946.1 | 376.8 | 334.8 | 357.3 | 332.6 | -| clustered, no indexes, wide | 2453.4 | 1566.2 | 1489.5 | 832.4 | 305.1 | 293.1 | 295.7 | 317.6 | -| clustered, no indexes, narrow | 781.1 | 378.6 | 350.2 | 687.1 | 226.0 | 222.6 | 224.7 | 225.8 | -| clustered, indexes, wide | 58.7 | 54.8 | 44.9 | 64.9 | 55.2 | 40.6 | 49.2 | 40.3 | -| clustered, indexes, narrow | 154.4 | 22.7 | 21.4 | 273.3 | 27.8 | 25.7 | 22.6 | 25.7 | +| random, no indexes, wide | 1521.5 | 1506.8 | 1398.6 | 1543.6 | 1542.5 | 1414.5 | 1524.4 | 1418.9 | +| random, no indexes, narrow | 363.1 | 357.0 | 330.1 | 356.3 | 360.0 | 327.0 | 352.4 | 325.5 | +| random, indexes, wide | 1539.8 | 1512.4 | 1400.2 | 1765.3 | 1825.8 | 1431.5 | 1550.0 | 1547.5 | +| random, indexes, narrow | 366.4 | 361.7 | 335.7 | 402.4 | 380.2 | 334.2 | 361.8 | 336.2 | +| clustered, no indexes, wide | 1551.3 | 1504.2 | 1400.6 | 361.1 | 310.7 | 299.4 | 298.6 | 300.0 | +| clustered, no indexes, narrow | 358.3 | 369.9 | 319.8 | 218.9 | 209.5 | 204.7 | 204.4 | 206.4 | +| clustered, indexes, wide | 45.6 | 43.3 | 35.7 | 48.2 | 46.0 | 34.7 | 41.3 | 34.8 | +| clustered, indexes, narrow | 20.9 | 20.0 | 17.7 | 25.5 | 24.1 | 22.1 | 20.1 | 22.0 | ### Pass 2 | Workload | Main off | Patch off | Patch off +PF | Main progressive | Patch progressive | Patch progressive +PF | Patch upfront | Patch upfront +PF | | ----------------------------- | -------: | --------: | ------------: | ---------------: | ----------------: | --------------------: | ------------: | ----------------: | -| random, no indexes, wide | 1629.0 | 1574.1 | 1429.3 | 1623.3 | 1550.0 | 1553.2 | 1639.8 | 1457.3 | -| random, no indexes, narrow | 380.9 | 380.2 | 340.2 | 379.3 | 373.8 | 325.0 | 366.7 | 343.3 | -| random, indexes, wide | 1543.6 | 1568.6 | 1448.3 | 1754.4 | 1813.3 | 1495.8 | 1617.8 | 1478.8 | -| random, indexes, narrow | 441.4 | 378.3 | 346.0 | 404.6 | 386.7 | 349.8 | 382.4 | 343.9 | -| clustered, no indexes, wide | 1696.9 | 1555.7 | 1494.5 | 350.0 | 320.6 | 314.6 | 312.3 | 308.0 | -| clustered, no indexes, narrow | 371.7 | 384.2 | 349.8 | 220.4 | 219.3 | 224.5 | 221.3 | 220.6 | -| clustered, indexes, wide | 48.5 | 45.8 | 38.2 | 49.7 | 49.5 | 38.4 | 46.3 | 38.1 | -| clustered, indexes, narrow | 21.1 | 22.1 | 19.1 | 26.4 | 26.5 | 24.1 | 22.2 | 24.0 | +| random, no indexes, wide | 1527.1 | 1499.0 | 1392.5 | 1559.8 | 1520.8 | 1469.7 | 1594.3 | 1408.3 | +| random, no indexes, narrow | 355.4 | 355.1 | 329.0 | 357.1 | 355.3 | 323.4 | 351.5 | 324.4 | +| random, indexes, wide | 1504.0 | 1495.0 | 1400.6 | 1726.7 | 1894.5 | 1424.5 | 1557.1 | 1429.4 | +| random, indexes, narrow | 359.6 | 365.1 | 337.1 | 378.2 | 381.6 | 334.3 | 362.3 | 333.0 | +| clustered, no indexes, wide | 1497.1 | 1502.2 | 1398.0 | 305.8 | 305.9 | 294.3 | 297.1 | 294.1 | +| clustered, no indexes, narrow | 345.4 | 343.4 | 320.5 | 208.1 | 209.7 | 206.2 | 204.0 | 206.4 | +| clustered, indexes, wide | 43.1 | 43.2 | 35.8 | 45.1 | 46.2 | 34.3 | 41.0 | 34.0 | +| clustered, indexes, narrow | 19.7 | 20.3 | 19.1 | 23.7 | 24.2 | 21.9 | 20.1 | 22.7 |

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