Fix distribution bottleneck for higher dimensional grids

src/mdio/segy/_workers.py

@@ -6,15 +6,16 @@
 from typing import TYPE_CHECKING
 
 import numpy as np
+from segy import SegyFile
 from segy.arrays import HeaderArray
+from zarr import open_group as zarr_open_group
 
 from mdio.core.config import MDIOSettings
 from mdio.segy._raw_trace_wrapper import SegyFileRawTraceWrapper
 from mdio.segy.file import SegyFileArguments
 from mdio.segy.file import SegyFileWrapper
 
 if TYPE_CHECKING:
-    from segy import SegyFile
     from zarr import Array as zarr_Array
 
 from zarr.core.config import config as zarr_config
@@ -68,30 +69,70 @@ def header_scan_worker(
     return HeaderArray(trace_header)  # wrap back so we can use aliases
 
 
-def trace_worker(  # noqa: PLR0913
-    segy_file: SegyFile,
-    data_array: zarr_Array,
-    header_array: zarr_Array | None,
-    raw_header_array: zarr_Array | None,
-    region: dict[str, slice],
+# Per-worker process state populated once by `trace_worker_init`. Keeping the SEG-Y handle,
+# Zarr array handles, and the (compressed, in-memory) grid map here lets us pickle them a single
+# time per worker via the pool initializer instead of once per submitted block. The grid map is
+# retained as a compressed in-memory Zarr array and sliced lazily per region, so each worker only
+# materializes its own block rather than the full dense map.
+_worker_state: dict[str, object] = {}
+
+
+def trace_worker_init(  # noqa: PLR0913
+    segy_file_kwargs: SegyFileArguments,
+    output_path: str,
+    storage_options: dict[str, object] | None,
+    use_consolidated: bool,
+    data_variable_name: str,
     grid_map: zarr_Array,
-) -> SummaryStatistics | None:
+) -> None:
+    """Initialize per-process state for trace ingestion workers.
+
+    Used as the `ProcessPoolExecutor` initializer so the SEG-Y file, Zarr output handles, and grid
+    map are opened/transferred once per worker process rather than re-pickled for every block.
+
+    Args:
+        segy_file_kwargs: Arguments to open the SegyFile instance.
+        output_path: POSIX path to the output MDIO Zarr store.
+        storage_options: fsspec storage options for the output store.
+        use_consolidated: Whether to open the group with consolidated metadata (Zarr V2).
+        data_variable_name: Name of the data variable in the dataset.
+        grid_map: Compressed in-memory Zarr array mapping live traces to their positions.
+    """
+    # Setting the zarr config to 1 thread to ensure we honor the `MDIO__IMPORT__CPU_COUNT` environment variable.
+    # The Zarr 3 engine utilizes multiple threads. This can lead to resource contention and unpredictable memory usage.
+    zarr_config.set({"threading.max_workers": 1})
+
+    zarr_group = zarr_open_group(
+        output_path,
+        mode="r+",
+        storage_options=storage_options,
+        use_consolidated=use_consolidated,
+    )
+
+    _worker_state["segy_file"] = SegyFile(**segy_file_kwargs)
+    _worker_state["data_array"] = zarr_group[data_variable_name]
+    _worker_state["header_array"] = zarr_group.get("headers")
+    _worker_state["raw_header_array"] = zarr_group.get("raw_headers")
+    _worker_state["grid_map"] = grid_map
+
+
+def trace_worker(region: dict[str, slice]) -> SummaryStatistics | None:
     """Writes a subset of traces from a region of the dataset of Zarr file.
 
+    Reads its shared inputs (SEG-Y handle, Zarr arrays, grid map) from the per-process state set up
+    by `trace_worker_init`, so only the lightweight `region` is pickled per block.
+
     Args:
-        segy_file: The opened SEG-Y file.
-        data_array: Zarr array for writing trace data.
-        header_array: Zarr array for writing trace headers (or None if not needed).
-        raw_header_array: Zarr array for writing raw headers (or None if not needed).
         region: Region of the dataset to write to.
-        grid_map: Zarr array mapping live traces to their positions in the dataset.
 
     Returns:
         SummaryStatistics object containing statistics about the written traces.
     """
-    # Setting the zarr config to 1 thread to ensure we honor the `MDIO__IMPORT__CPU_COUNT` environment variable.
-    # The Zarr 3 engine utilizes multiple threads. This can lead to resource contention and unpredictable memory usage.
-    zarr_config.set({"threading.max_workers": 1})
+    segy_file: SegyFile = _worker_state["segy_file"]
+    data_array: zarr_Array = _worker_state["data_array"]
+    header_array: zarr_Array | None = _worker_state["header_array"]
+    raw_header_array: zarr_Array | None = _worker_state["raw_header_array"]
+    grid_map: zarr_Array = _worker_state["grid_map"]
 
     region_slices = tuple(region.values())
     local_grid_map = grid_map[region_slices[:-1]]  # minus last (vertical) axis

src/mdio/segy/blocked_io.py

@@ -12,7 +12,6 @@
 import zarr
 from dask.array import Array
 from dask.array import map_blocks
-from segy import SegyFile
 from tqdm.auto import tqdm
 from zarr import open_group as zarr_open_group
 
@@ -23,6 +22,7 @@
 from mdio.core.config import MDIOSettings
 from mdio.core.indexing import ChunkIterator
 from mdio.segy._workers import trace_worker
+from mdio.segy._workers import trace_worker_init
 from mdio.segy.creation import SegyPartRecord
 from mdio.segy.creation import concat_files
 from mdio.segy.creation import serialize_to_segy_stack
@@ -82,47 +82,44 @@ def to_zarr(  # noqa: PLR0913, PLR0915
     num_chunks = chunk_iter.num_chunks
 
     zarr_format = zarr.config.get("default_zarr_format")
+    use_consolidated = zarr_format == ZarrFormat.V2
 
-    # Open zarr group once in main process
+    # Open zarr group once in main process (used for final stats update below).
     storage_options = _normalize_storage_options(output_path)
     zarr_group = zarr_open_group(
         output_path.as_posix(),
         mode="r+",
         storage_options=storage_options,
-        use_consolidated=zarr_format == ZarrFormat.V2,
+        use_consolidated=use_consolidated,
     )
 
-    # Get array handles from the opened group
-    data_array = zarr_group[data_variable_name]
-    header_array = zarr_group.get("headers")
-    raw_header_array = zarr_group.get("raw_headers")
-
     # For Unix async writes with s3fs/fsspec & multiprocessing, use 'spawn' instead of default
     # 'fork' to avoid deadlocks on cloud stores. Slower but necessary. Default on Windows.
     num_workers = min(num_chunks, settings.import_cpus)
     context = mp.get_context("spawn")
 
-    # Use initializer to open segy file once per worker
+    # Open the SEG-Y file, Zarr output handles, and transfer the compressed grid map once per worker
+    # via the initializer. The grid map stays a compressed in-memory Zarr array and is sliced lazily
+    # inside each worker, so we avoid both re-pickling per block and materializing the full dense map.
     executor = ProcessPoolExecutor(
         max_workers=num_workers,
         mp_context=context,
+        initializer=trace_worker_init,
+        initargs=(
+            segy_file_kwargs,
+            output_path.as_posix(),
+            storage_options,
+            use_consolidated,
+            data_variable_name,
+            grid_map,
+        ),
     )
 
-    segy_file = SegyFile(**segy_file_kwargs)
-
     with executor:
         futures = []
         for region in chunk_iter:
-            # Pass zarr array handles directly to workers
-            future = executor.submit(
-                trace_worker,
-                segy_file,
-                data_array,
-                header_array,
-                raw_header_array,
-                region,
-                grid_map,
-            )
+            # Only the lightweight region is pickled per block; shared inputs live in worker state.
+            future = executor.submit(trace_worker, region)
             futures.append(future)
 
         iterable = tqdm(