[jira] [Created] (ARROW-6059) Regression memory issue when calling pandas.read_parquet

["Francisco Sanchez (JIRA)" <ji...@apache.org>]

Francisco Sanchez created ARROW-6059:
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             Summary: Regression memory issue when calling pandas.read_parquet
                 Key: ARROW-6059
                 URL: https://issues.apache.org/jira/browse/ARROW-6059
             Project: Apache Arrow
          Issue Type: Bug
          Components: Python
    Affects Versions: 0.14.1, 0.14.0
            Reporter: Francisco Sanchez


I have a ~2MB parquet file with the next schema:
{code:java}
bag_stamp: timestamp[ns]
transforms_[]_.header.seq: list<item: int64>
  child 0, item: int64
transforms_[]_.header.stamp: list<item: timestamp[ns]>
  child 0, item: timestamp[ns]
transforms_[]_.header.frame_id: list<item: string>
  child 0, item: string
transforms_[]_.child_frame_id: list<item: string>
  child 0, item: string
transforms_[]_.transform.translation.x: list<item: double>
  child 0, item: double
transforms_[]_.transform.translation.y: list<item: double>
  child 0, item: double
transforms_[]_.transform.translation.z: list<item: double>
  child 0, item: double
transforms_[]_.transform.rotation.x: list<item: double>
  child 0, item: double
transforms_[]_.transform.rotation.y: list<item: double>
  child 0, item: double
transforms_[]_.transform.rotation.z: list<item: double>
  child 0, item: double
transforms_[]_.transform.rotation.w: list<item: double>
  child 0, item: double
{code}
 If I read it with *pandas.read_parquet()* using pyarrow 0.13.0 all seems fine and it takes no time to load. If I try the same with 0.14.0 or 0.14.1 it takes a lot of time and uses ~10GB of RAM. Many times if I don't have enough available memory it will just be killed OOM. Now, if I use the next code snippet instead it works perfectly with all the versions:
{code:java}
parquet_file = pq.ParquetFile(input_file)
tables = []
for row_group in range(parquet_file.num_row_groups):
    tables.append(parquet_file.read_row_group(row_group, columns=columns, use_pandas_metadata=True))
df = pa.concat_tables(tables).to_pandas()
{code}



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