8000 ray: fix handling large chunks by ranandfigma · Pull Request #53535 · ray-project/ray · GitHub
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ray: fix handling large chunks #53535

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ray: fix handling large chunks #53535

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ray: fix handling large chunks
ranandfigma Jun 3, 2025
7f3a56f
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ranandfigma Jun 5, 2025
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3 changes: 2 additions & 1 deletion python/ray/data/_internal/arrow_ops/transform_pyarrow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -863,7 +863,8 @@ def _try_combine_chunks_safe(
chunk_size = chunk.nbytes

if cur_slice_size_bytes + chunk_size > max_chunk_size:
slices.append(array.chunks[cur_slice_start:i])
if cur_slice_start != i:
slices.append(array.chunks[cur_slice_start:i])

cur_slice_start = i
cur_slice_size_bytes = 0
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15 changes: 15 additions & 0 deletions python/ray/data/tests/test_transform_pyarrow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
from ray.air.util.tensor_extensions.arrow import ArrowTensorTypeV2
from ray.data import DataContext
from ray.data._internal.arrow_ops.transform_pyarrow import (
MIN_NUM_CHUNKS_TO_TRIGGER_COMBINE_CHUNKS,
MIN_PYARROW_VERSION_TYPE_PROMOTION,
concat,
hash_partition,
Expand Down Expand Up @@ -49,6 +50,20 @@ def test_try_defragment_table():
assert dt == t


def test_defragment_large_table():

big = pa.array(list(range(800_000_000)), type=pa.int32()) # ~2GiB
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Copilot AI Jun 3, 2025

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Constructing a Python list of 800 million ints can OOM or be very slow. Consider using numpy.arange or a PyArrow streaming interface to build the array more efficiently.

Suggested change
big = pa.array(list(range(800_000_000)), type=pa.int32()) # ~2GiB
big = pa.array(np.arange(800_000_000), type=pa.int32()) # ~2GiB

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That looks like a good suggestion actually

chunked = [big]
for _ in range(MIN_NUM_CHUNKS_TO_TRIGGER_COMBINE_CHUNKS):
chunked.append(pa.array([1, 2, 3], type=pa.int32())) # a little tail chunk
chunked = pa.chunked_array(chunked)

table = pa.Table.from_arrays([chunked], names=["col"])

data = try_combine_chunked_columns(table)
assert len(data["col"].chunks) == 2


def test_hash_partitioning():
# Test hash-partitioning of the empty table
empty_table = pa.Table.from_pydict({"idx": []})
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