Backport #40412 to pyarrow-15.x
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…ay from Python list of dicts (apache#40412) ### Rationale for this change When creating Arrow arrays using `pa.array` from lists of dicts, memory usage is observed to increase over time despite the created arrays going out of scope. The issue appears to only happen for lists of dicts, as opposed to lists of numpy arrays or other types. ### What changes are included in this PR? This PR makes two changes to _python_to_arrow.cc_, to ensure that new references created by [`PyDict_Items`](https://docs.python.org/3/c-api/dict.html#c.PyDict_Items) and [`PySequence_GetItem`](https://docs.python.org/3/c-api/sequence.html#c.PySequence_GetItem) are properly reference counted via `OwnedRef`. ### Are these changes tested? The change was tested against the following reproduction script: ```python """Repro memory increase observed when creating pyarrow arrays.""" # System imports import logging # Third-party imports import numpy as np import psutil import pyarrow as pa LIST_LENGTH = 5 * (2**20) LOGGER = logging.getLogger(__name__) def initialize_logging() -> None: logging.basicConfig( format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO, ) def get_rss_in_mib() -> float: """Return the Resident Set Size of the current process in MiB.""" return psutil.Process().memory_info().rss / 1024 / 1024 def main() -> None: initialize_logging() for idx in range(100): data = np.random.randint(256, size=(LIST_LENGTH,), dtype=np.uint8) # data = "a" * LIST_LENGTH pa.array([{"data": data}]) if (idx + 1) % 10 == 0: LOGGER.info( "%d dict arrays created, RSS: %.2f MiB", idx + 1, get_rss_in_mib() ) LOGGER.info("---------") for idx in range(100): pa.array( [ np.random.randint(256, size=(LIST_LENGTH,), dtype=np.uint8).tobytes(), ] ) if (idx + 1) % 10 == 0: LOGGER.info( "%d non-dict arrays created, RSS: %.2f MiB", idx + 1, get_rss_in_mib() ) if __name__ == "__main__": main() ``` Prior to this change, the reproduction script produces the following output: ``` 2024-03-07 23:14:17,560 - __main__ - INFO - 10 dict arrays created, RSS: 121.05 MiB 2024-03-07 23:14:17,698 - __main__ - INFO - 20 dict arrays created, RSS: 171.07 MiB 2024-03-07 23:14:17,835 - __main__ - INFO - 30 dict arrays created, RSS: 221.09 MiB 2024-03-07 23:14:17,971 - __main__ - INFO - 40 dict arrays created, RSS: 271.11 MiB 2024-03-07 23:14:18,109 - __main__ - INFO - 50 dict arrays created, RSS: 320.86 MiB 2024-03-07 23:14:18,245 - __main__ - INFO - 60 dict arrays created, RSS: 371.65 MiB 2024-03-07 23:14:18,380 - __main__ - INFO - 70 dict arrays created, RSS: 422.18 MiB 2024-03-07 23:14:18,516 - __main__ - INFO - 80 dict arrays created, RSS: 472.20 MiB 2024-03-07 23:14:18,650 - __main__ - INFO - 90 dict arrays created, RSS: 522.21 MiB 2024-03-07 23:14:18,788 - __main__ - INFO - 100 dict arrays created, RSS: 572.23 MiB 2024-03-07 23:14:18,789 - __main__ - INFO - --------- 2024-03-07 23:14:19,001 - __main__ - INFO - 10 non-dict arrays created, RSS: 567.61 MiB 2024-03-07 23:14:19,211 - __main__ - INFO - 20 non-dict arrays created, RSS: 567.61 MiB 2024-03-07 23:14:19,417 - __main__ - INFO - 30 non-dict arrays created, RSS: 567.61 MiB 2024-03-07 23:14:19,623 - __main__ - INFO - 40 non-dict arrays created, RSS: 567.61 MiB 2024-03-07 23:14:19,832 - __main__ - INFO - 50 non-dict arrays created, RSS: 567.61 MiB 2024-03-07 23:14:20,047 - __main__ - INFO - 60 non-dict arrays created, RSS: 567.61 MiB 2024-03-07 23:14:20,253 - __main__ - INFO - 70 non-dict arrays created, RSS: 567.61 MiB 2024-03-07 23:14:20,499 - __main__ - INFO - 80 non-dict arrays created, RSS: 567.61 MiB 2024-03-07 23:14:20,725 - __main__ - INFO - 90 non-dict arrays created, RSS: 567.61 MiB 2024-03-07 23:14:20,950 - __main__ - INFO - 100 non-dict arrays created, RSS: 567.61 MiB ``` After this change, the output changes to the following. Notice that the Resident Set Size (RSS) no longer increases as more Arrow arrays are created from list of dict. ``` 2024-03-07 23:14:47,246 - __main__ - INFO - 10 dict arrays created, RSS: 81.73 MiB 2024-03-07 23:14:47,353 - __main__ - INFO - 20 dict arrays created, RSS: 76.53 MiB 2024-03-07 23:14:47,445 - __main__ - INFO - 30 dict arrays created, RSS: 82.20 MiB 2024-03-07 23:14:47,537 - __main__ - INFO - 40 dict arrays created, RSS: 86.59 MiB 2024-03-07 23:14:47,634 - __main__ - INFO - 50 dict arrays created, RSS: 80.28 MiB 2024-03-07 23:14:47,734 - __main__ - INFO - 60 dict arrays created, RSS: 85.44 MiB 2024-03-07 23:14:47,827 - __main__ - INFO - 70 dict arrays created, RSS: 85.44 MiB 2024-03-07 23:14:47,921 - __main__ - INFO - 80 dict arrays created, RSS: 85.44 MiB 2024-03-07 23:14:48,024 - __main__ - INFO - 90 dict arrays created, RSS: 82.94 MiB 2024-03-07 23:14:48,132 - __main__ - INFO - 100 dict arrays created, RSS: 87.84 MiB 2024-03-07 23:14:48,132 - __main__ - INFO - --------- 2024-03-07 23:14:48,229 - __main__ - INFO - 10 non-dict arrays created, RSS: 87.84 MiB 2024-03-07 23:14:48,324 - __main__ - INFO - 20 non-dict arrays created, RSS: 87.84 MiB 2024-03-07 23:14:48,420 - __main__ - INFO - 30 non-dict arrays created, RSS: 87.84 MiB 2024-03-07 23:14:48,516 - __main__ - INFO - 40 non-dict arrays created, RSS: 87.84 MiB 2024-03-07 23:14:48,613 - __main__ - INFO - 50 non-dict arrays created, RSS: 87.84 MiB 2024-03-07 23:14:48,710 - __main__ - INFO - 60 non-dict arrays created, RSS: 87.84 MiB 2024-03-07 23:14:48,806 - __main__ - INFO - 70 non-dict arrays created, RSS: 87.84 MiB 2024-03-07 23:14:48,905 - __main__ - INFO - 80 non-dict arrays created, RSS: 87.84 MiB 2024-03-07 23:14:49,009 - __main__ - INFO - 90 non-dict arrays created, RSS: 87.84 MiB 2024-03-07 23:14:49,108 - __main__ - INFO - 100 non-dict arrays created, RSS: 87.84 MiB ``` When this change is tested against the reproduction script provided in apache#37989 (comment), the reported memory increase is no longer observed. I have not added a unit test, but it may be possible to add one similar to the reproduction scripts used above, provided there's an accurate way to capture process memory usage on all the platforms that Arrow supports, and provided memory usage is not affected by concurrently running tests. If this code could be tested under valgrind, that may be an even better way to go. ### Are there any user-facing changes? * GitHub Issue: apache#37989 Authored-by: Chuck Yang <chuck.yang@getcruise.com> Signed-off-by: Joris Van den Bossche <jorisvandenbossche@gmail.com>
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Thanks for opening a pull request! If this is not a minor PR. Could you open an issue for this pull request on GitHub? https://github.com/apache/arrow/issues/new/choose Opening GitHub issues ahead of time contributes to the Openness of the Apache Arrow project. Then could you also rename the pull request title in the following format? or In the case of PARQUET issues on JIRA the title also supports: See also: |
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See #40412 (comment), we are currently not planning bug-fix releases for this, so going to close this PR. |
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This PR backports a critical bug fix: #40412
Rationale for this change
When creating Arrow arrays using
pa.arrayfrom lists of dicts, memory usage is observed to increase over time despite the created arrays going out of scope. The issue appears to only happen for lists of dicts, as opposed to lists of numpy arrays or other types.What changes are included in this PR?
This PR makes two changes to python_to_arrow.cc, to ensure that new references created by
PyDict_ItemsandPySequence_GetItemare properly reference counted viaOwnedRef.Are these changes tested?
The change was tested against the following reproduction script:
Prior to this change, the reproduction script produces the following output:
After this change, the output changes to the following. Notice that the Resident Set Size (RSS) no longer increases as more Arrow arrays are created from list of dict.
When this change is tested against the reproduction script provided in #37989 (comment), the reported memory increase is no longer observed.
I have not added a unit test, but it may be possible to add one similar to the reproduction scripts used above, provided there's an accurate way to capture process memory usage on all the platforms that Arrow supports, and provided memory usage is not affected by concurrently running tests. If this code could be tested under valgrind, that may be an even better way to go.
Are there any user-facing changes?
pyarrow.Table.from_pandas()causing memory leak #37989Authored-by: Chuck Yang chuck.yang@getcruise.com
Rationale for this change
What changes are included in this PR?
Are these changes tested?
Are there any user-facing changes?