[jira] [Updated] (ARROW-8980) [Python] Metadata grows exponentially when using schema from disk

["ASF GitHub Bot (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/ARROW-8980?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

ASF GitHub Bot updated ARROW-8980:
----------------------------------
    Labels: metadata parquet pull-request-available pyarrow python schema  (was: metadata parquet pyarrow python schema)

> [Python] Metadata grows exponentially when using schema from disk
> -----------------------------------------------------------------
>
>                 Key: ARROW-8980
>                 URL: https://issues.apache.org/jira/browse/ARROW-8980
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: Python
>    Affects Versions: 0.16.0
>         Environment: python: 3.7.3 | packaged by conda-forge | (default, Dec 6 2019, 08:36:57)
> [Clang 9.0.0 (tags/RELEASE_900/final)]
> pa version: 0.16.0
> pd version: 0.25.2
>            Reporter: Kevin Glasson
>            Assignee: Wes McKinney
>            Priority: Major
>              Labels: metadata, parquet, pull-request-available, pyarrow, python, schema
>             Fix For: 1.0.0
>
>         Attachments: growing_metadata.py, test.pq
>
>          Time Spent: 10m
>  Remaining Estimate: 0h
>
> When overwriting parquet files we first read the schema that is already on disk this is mainly to deal with some type harmonizing between pyarrow and pandas (that I wont go into).
> Regardless here is a simple example (below) with no weirdness. If I continously re-write the same file by first fetching the schema from disk, creating a writer with that schema and then writing same dataframe the file size keeps growing even though the amount of rows has not changed.
> Note: My solution was to remove `b'ARROW:schema'` data from the `schema.metadata.` this seems to stop the file size growing. So I wonder if the writer keeps appending to it or something? TBH I'm not entirely sure but I have a hunch that the ARROW:schema is just the metadata serialised or something.
> I should also note that once the metadata gets to big this leads to a buffer overflow in another part of the code 'thrift' which was referenced here: https://issues.apache.org/jira/browse/PARQUET-1345
> {code:java}
> import pyarrow as pa
> import pyarrow.parquet as pq
> import pyarrow as pa
> import pandas as pd
> import pathlib
> import sys
> def main():
>     print(f"python: {sys.version}")
>     print(f"pa version: {pa.__version__}")
>     print(f"pd version: {pd.__version__}")    fname = "test.pq"
>     path = pathlib.Path(fname)    df = pd.DataFrame({"A": [0] * 100000})
>     df.to_parquet(fname)    print(f"Wrote test frame to {fname}")
>     print(f"Size of {fname}: {path.stat().st_size}")    for _ in range(5):
>         file = pq.ParquetFile(fname)
>         tmp_df = file.read().to_pandas()
>         print(f"Number of rows on disk: {tmp_df.shape}")
>         print("Reading schema from disk")
>         schema = file.schema.to_arrow_schema()
>         print("Creating new writer")
>         writer = pq.ParquetWriter(fname, schema=schema)
>         print("Re-writing the dataframe")
>         writer.write_table(pa.Table.from_pandas(df))
>         writer.close()
>         print(f"Size of {fname}: {path.stat().st_size}")
> if __name__ == "__main__":
>     main()
> {code}
> {code:java}
> (sdm) ➜ ~ python growing_metadata.py
> python: 3.7.3 | packaged by conda-forge | (default, Dec 6 2019, 08:36:57)
> [Clang 9.0.0 (tags/RELEASE_900/final)]
> pa version: 0.16.0
> pd version: 0.25.2
> Wrote test frame to test.pq
> Size of test.pq: 1643
> Number of rows on disk: (100000, 1)
> Reading schema from disk
> Creating new writer
> Re-writing the dataframe
> Size of test.pq: 3637
> Number of rows on disk: (100000, 1)
> Reading schema from disk
> Creating new writer
> Re-writing the dataframe
> Size of test.pq: 8327
> Number of rows on disk: (100000, 1)
> Reading schema from disk
> Creating new writer
> Re-writing the dataframe
> Size of test.pq: 19301
> Number of rows on disk: (100000, 1)
> Reading schema from disk
> Creating new writer
> Re-writing the dataframe
> Size of test.pq: 44944
> Number of rows on disk: (100000, 1)
> Reading schema from disk
> Creating new writer
> Re-writing the dataframe
> Size of test.pq: 104815{code}



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