Cache PyArrow schema operations #58583
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Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: xgui <xgui@anyscale.com>
xinyuangui2
commented
Nov 13, 2025
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| # Remove metadata for hashability | ||
| schemas[0].remove_metadata() | ||
| schemas[0] = schemas[0].remove_metadata() |
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This remove_metadata() doesn't mutate
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I think we cannot remove metadata in place. Otherwise it would fail some release tests:
[2025-11-14T23:48:47Z] raise ValueError(msg.format(self.feature_names, feature_names))
[2025-11-14T23:48:47Z] ValueError: feature_names mismatch: ['feature_0', 'feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5', 'feature_6', 'feature_7', 'feature_8', 'feature_9', 'feature_10', 'feature_11', 'feature_12', 'feature_13', 'feature_14', 'feature_15', 'feature_16', 'feature_17', 'feature_18', 'feature_19', 'feature_20', 'feature_21', 'feature_22', 'feature_23', 'feature_24', 'feature_25', 'feature_26', 'feature_27', 'feature_28', 'feature_29', 'feature_30', 'feature_31', 'feature_32', 'feature_33', 'feature_34', 'feature_35', 'feature_36', 'feature_37', 'feature_38', 'feature_39', 'partition'] ['feature_0', 'feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5', 'feature_6', 'feature_7', 'feature_8', 'feature_9', 'feature_10', 'feature_11', 'feature_12', 'feature_13', 'feature_14', 'feature_15', 'feature_16', 'feature_17', 'feature_18', 'feature_19', 'feature_20', 'feature_21', 'feature_22', 'feature_23', 'feature_24', 'feature_25', 'feature_26', 'feature_27', 'feature_28', 'feature_29', 'feature_30', 'feature_31', 'feature_32', 'feature_33', 'feature_34', 'feature_35', 'feature_36', 'feature_37', 'feature_38', 'feature_39', '__index_level_0__', 'partition']
[2025-11-14T23:48:47Z] training data did not have the following fields: __index_level_0__
in https://buildkite.com/ray-project/premerge/builds/53867#019a84b7-88cd-4186-8a4e-b89b9e4604e1
I updated this a bit @goutamvenkat-anyscale
xinyuangui2
commented
Nov 13, 2025
| # NOTE: Type promotions aren't available in Arrow < 14.0 | ||
| subset_blocks = [] | ||
| for block in blocks: | ||
| cols_to_select = [ |
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Profiler shows col_name in block.schema.names is heavy. We use set here.
Signed-off-by: Xinyuan <43737116+xinyuangui2@users.noreply.github.com>
This reverts commit aece4fd.
xinyuangui2
changed the title
xinyuangui2
requested review from
goutamvenkat-anyscale,
raulchen and
srinathk10
Signed-off-by: Xinyuan <43737116+xinyuangui2@users.noreply.github.com>
Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: xgui <xgui@anyscale.com>
raulchen
reviewed
Nov 14, 2025
Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: xgui <xgui@anyscale.com>
raulchen
approved these changes
Nov 14, 2025
Signed-off-by: Xinyuan <43737116+xinyuangui2@users.noreply.github.com>
![cursor[bot]](https://avatars.githubusercontent.com/in/1210556?s=60&v=4){kind=small}
Signed-off-by: xgui <xgui@anyscale.com>
Signed-off-by: xgui <xgui@anyscale.com>
| with self._cache_lock: | ||
| if self._serialize_cache is None: | ||
| self._serialize_cache = self._arrow_ext_serialize_compute() | ||
| return self._serialize_cache |
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If it's serialized you can skip the lock
Aydin-ab
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Nov 19, 2025
## Description This PR adds caching for PyArrow schema operations to improve performance during batching operations, especially for tables with a large number of columns. ### Main Changes - **Caching for Tensor Type Serialization/Deserialization**: Added cache for tensor type serialization and deserialization operations. This significantly reduces overhead for frequently accessed tensor types during schema operations. ### Performance Impact This optimization is particularly beneficial during batching operations for tables with a large number of columns. In one of our tests with 200 columns, the batching time per batch decreased from **0.30s to 0.11s** (~63% improvement). #### Without cache: <img width="1719" height="464" alt="Screenshot 2025-11-13 at 9 49 33 PM" src="https://github.com/user-attachments/assets/46122634-dd09-40ed-a2a8-725d14f85728" /> We can see `__arrow_ext_deserialize__` and `__arrow_ext_serialize__` in different places. Each time `__arrow_ext_deserialize__` will create a new object and `__arrow_ext_serialize__` includes expensive pickle. #### With cache <img width="1717" height="476" alt="Screenshot 2025-11-13 at 9 41 15 PM" src="https://github.com/user-attachments/assets/50e77253-d69d-40d9-9e1f-56e9341bc131" /> The time on `__arrow_ext_deserialize__` and `__arrow_ext_serialize__` is not a bottleneck anymore. --------- Signed-off-by: xgui <xgui@anyscale.com> Signed-off-by: Xinyuan <43737116+xinyuangui2@users.noreply.github.com> Signed-off-by: Aydin Abiar <aydin@anyscale.com>
ykdojo
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Nov 27, 2025
## Description This PR adds caching for PyArrow schema operations to improve performance during batching operations, especially for tables with a large number of columns. ### Main Changes - **Caching for Tensor Type Serialization/Deserialization**: Added cache for tensor type serialization and deserialization operations. This significantly reduces overhead for frequently accessed tensor types during schema operations. ### Performance Impact This optimization is particularly beneficial during batching operations for tables with a large number of columns. In one of our tests with 200 columns, the batching time per batch decreased from **0.30s to 0.11s** (~63% improvement). #### Without cache: <img width="1719" height="464" alt="Screenshot 2025-11-13 at 9 49 33 PM" src="https://github.com/user-attachments/assets/46122634-dd09-40ed-a2a8-725d14f85728" /> We can see `__arrow_ext_deserialize__` and `__arrow_ext_serialize__` in different places. Each time `__arrow_ext_deserialize__` will create a new object and `__arrow_ext_serialize__` includes expensive pickle. #### With cache <img width="1717" height="476" alt="Screenshot 2025-11-13 at 9 41 15 PM" src="https://github.com/user-attachments/assets/50e77253-d69d-40d9-9e1f-56e9341bc131" /> The time on `__arrow_ext_deserialize__` and `__arrow_ext_serialize__` is not a bottleneck anymore. --------- Signed-off-by: xgui <xgui@anyscale.com> Signed-off-by: Xinyuan <43737116+xinyuangui2@users.noreply.github.com> Signed-off-by: YK <1811651+ykdojo@users.noreply.github.com>
SheldonTsen
pushed a commit
to SheldonTsen/ray
that referenced
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Dec 1, 2025
## Description This PR adds caching for PyArrow schema operations to improve performance during batching operations, especially for tables with a large number of columns. ### Main Changes - **Caching for Tensor Type Serialization/Deserialization**: Added cache for tensor type serialization and deserialization operations. This significantly reduces overhead for frequently accessed tensor types during schema operations. ### Performance Impact This optimization is particularly beneficial during batching operations for tables with a large number of columns. In one of our tests with 200 columns, the batching time per batch decreased from **0.30s to 0.11s** (~63% improvement). #### Without cache: <img width="1719" height="464" alt="Screenshot 2025-11-13 at 9 49 33 PM" src="https://github.com/user-attachments/assets/46122634-dd09-40ed-a2a8-725d14f85728" /> We can see `__arrow_ext_deserialize__` and `__arrow_ext_serialize__` in different places. Each time `__arrow_ext_deserialize__` will create a new object and `__arrow_ext_serialize__` includes expensive pickle. #### With cache <img width="1717" height="476" alt="Screenshot 2025-11-13 at 9 41 15 PM" src="https://github.com/user-attachments/assets/50e77253-d69d-40d9-9e1f-56e9341bc131" /> The time on `__arrow_ext_deserialize__` and `__arrow_ext_serialize__` is not a bottleneck anymore. --------- Signed-off-by: xgui <xgui@anyscale.com> Signed-off-by: Xinyuan <43737116+xinyuangui2@users.noreply.github.com>
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Description
This PR adds caching for PyArrow schema operations to improve performance during batching operations, especially for tables with a large number of columns.
Main Changes
Performance Impact
This optimization is particularly beneficial during batching operations for tables with a large number of columns. In one of our tests with 200 columns, the batching time per batch decreased from 0.30s to 0.11s (~63% improvement).
Without cache:
With cache