Yin created ARROW-17590:
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Summary: Lower memory usage with filters
Key: ARROW-17590
URL: https://issues.apache.org/jira/browse/ARROW-17590
Project: Apache Arrow
Issue Type: Improvement
Reporter: Yin
Hi,
When I read a large parquet file with filter for a small number of rows, the
memory usage is pretty high. The result table and dataframe have only a few
rows. Looks like it scans/loads many rows from the parquet file. Not only the
footprint or watermark of memory usage is high, but also it seems not releasing
the memory in time (such as after GC in Python, but may get used for subsequent
read).
The filtered column is not a partition key, which functionally works to get a
small number of rows. But the memory usage is high when the parquet
(partitioned or not) is large. There were some references related to this
issue, for example: [https://github.com/apache/arrow/issues/7338.]
Related classes/methods in (pyarrow 9.0.0)
_ParquetDatasetV2.read
self._dataset.to_table(columns=columns, filter=self._filter_expression,
use_threads=use_threads)
pyarrow._dataset.FileSystemDatase.to_table
I played with pyarrow._dataset.Scanner.to_table
self._dataset.scanner(columns=columns,
filter=self._filter_expression).to_table()
The memory usage is small to construct the scanner but then goes up after the
to_table call materializes it.
Is there some way or workaround to reduce the memory usage with filters?
If not supported yet, can it be fixed/improved with priority?
This is a blocking issue for us. I don't see if it can be patched in the Python
code.
Thanks!
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