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https://issues.apache.org/jira/browse/ARROW-11007?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17279763#comment-17279763
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Antoine Pitrou commented on ARROW-11007:
----------------------------------------
Ah, I didn't know that gRPC was involved. Since Arrow returned all the memory
it had allocated, it's quite possible that the memory is held at the gRPC level.
> If a `message` is `google.protobuf` message and a batch is created like
> below, will it be a zero-copy operation?
Hmm... I guess it probably should? But I think you may find more expertise
about this by asking the BigQuery developers / community.
> [Python] Memory leak in pq.read_table and table.to_pandas
> ---------------------------------------------------------
>
> Key: ARROW-11007
> URL: https://issues.apache.org/jira/browse/ARROW-11007
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 2.0.0
> Reporter: Michael Peleshenko
> Priority: Major
>
> While upgrading our application to use pyarrow 2.0.0 instead of 0.12.1, we
> observed a memory leak in the read_table and to_pandas methods. See below for
> sample code to reproduce it. Memory does not seem to be returned after
> deleting the table and df as it was in pyarrow 0.12.1.
> *Sample Code*
> {code:python}
> import io
> import pandas as pd
> import pyarrow as pa
> import pyarrow.parquet as pq
> from memory_profiler import profile
> @profile
> def read_file(f):
> table = pq.read_table(f)
> df = table.to_pandas(strings_to_categorical=True)
> del table
> del df
> def main():
> rows = 2000000
> df = pd.DataFrame({
> "string": ["test"] * rows,
> "int": [5] * rows,
> "float": [2.0] * rows,
> })
> table = pa.Table.from_pandas(df, preserve_index=False)
> parquet_stream = io.BytesIO()
> pq.write_table(table, parquet_stream)
> for i in range(3):
> parquet_stream.seek(0)
> read_file(parquet_stream)
> if __name__ == '__main__':
> main()
> {code}
> *Python 3.8.5 (conda), pyarrow 2.0.0 (pip), pandas 1.1.2 (pip) Logs*
> {code:java}
> Filename: C:/run_pyarrow_memoy_leak_sample.py
> Line # Mem usage Increment Occurences Line Contents
> ============================================================
> 9 161.7 MiB 161.7 MiB 1 @profile
> 10 def read_file(f):
> 11 212.1 MiB 50.4 MiB 1 table = pq.read_table(f)
> 12 258.2 MiB 46.1 MiB 1 df =
> table.to_pandas(strings_to_categorical=True)
> 13 258.2 MiB 0.0 MiB 1 del table
> 14 256.3 MiB -1.9 MiB 1 del df
> Filename: C:/run_pyarrow_memoy_leak_sample.py
> Line # Mem usage Increment Occurences Line Contents
> ============================================================
> 9 256.3 MiB 256.3 MiB 1 @profile
> 10 def read_file(f):
> 11 279.2 MiB 23.0 MiB 1 table = pq.read_table(f)
> 12 322.2 MiB 43.0 MiB 1 df =
> table.to_pandas(strings_to_categorical=True)
> 13 322.2 MiB 0.0 MiB 1 del table
> 14 320.3 MiB -1.9 MiB 1 del df
> Filename: C:/run_pyarrow_memoy_leak_sample.py
> Line # Mem usage Increment Occurences Line Contents
> ============================================================
> 9 320.3 MiB 320.3 MiB 1 @profile
> 10 def read_file(f):
> 11 326.9 MiB 6.5 MiB 1 table = pq.read_table(f)
> 12 361.7 MiB 34.8 MiB 1 df =
> table.to_pandas(strings_to_categorical=True)
> 13 361.7 MiB 0.0 MiB 1 del table
> 14 359.8 MiB -1.9 MiB 1 del df
> {code}
> *Python 3.5.6 (conda), pyarrow 0.12.1 (pip), pandas 0.24.1 (pip) Logs*
> {code:java}
> Filename: C:/run_pyarrow_memoy_leak_sample.py
> Line # Mem usage Increment Occurences Line Contents
> ============================================================
> 9 138.4 MiB 138.4 MiB 1 @profile
> 10 def read_file(f):
> 11 186.2 MiB 47.8 MiB 1 table = pq.read_table(f)
> 12 219.2 MiB 33.0 MiB 1 df =
> table.to_pandas(strings_to_categorical=True)
> 13 171.7 MiB -47.5 MiB 1 del table
> 14 139.3 MiB -32.4 MiB 1 del df
> Filename: C:/run_pyarrow_memoy_leak_sample.py
> Line # Mem usage Increment Occurences Line Contents
> ============================================================
> 9 139.3 MiB 139.3 MiB 1 @profile
> 10 def read_file(f):
> 11 186.8 MiB 47.5 MiB 1 table = pq.read_table(f)
> 12 219.2 MiB 32.4 MiB 1 df =
> table.to_pandas(strings_to_categorical=True)
> 13 171.5 MiB -47.7 MiB 1 del table
> 14 139.1 MiB -32.4 MiB 1 del df
> Filename: C:/run_pyarrow_memoy_leak_sample.py
> Line # Mem usage Increment Occurences Line Contents
> ============================================================
> 9 139.1 MiB 139.1 MiB 1 @profile
> 10 def read_file(f):
> 11 186.8 MiB 47.7 MiB 1 table = pq.read_table(f)
> 12 219.2 MiB 32.4 MiB 1 df =
> table.to_pandas(strings_to_categorical=True)
> 13 171.8 MiB -47.5 MiB 1 del table
> 14 139.3 MiB -32.4 MiB 1 del df
> {code}
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