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

["Kevin Glasson (Jira)" <ji...@apache.org>]

Kevin Glasson created ARROW-8980:
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             Summary: [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
         Attachments: growing_metadata.py, test.pq

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.
{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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