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https://issues.apache.org/jira/browse/ARROW-17590?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17599102#comment-17599102
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Yin commented on ARROW-17590:
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Hi Will,
I am using pandas.read_parquet, which goes to pyarrow.parquet.read_table.
Tried pre_buffer=False, and saw no difference.
I will try to use iter_batches and filtering.
Attached the sample code [^sample.py], with memory stats on a Windows machine.
Used Pyarrow 9.0.0 (and 7.0.0) and the latest pandas 1.4.4 and numpy 1.23.2.
Saw that Pyarrow 9.0.0 improved read_table and saved some memory, but to_pandas
is still about the same.
The main question here is why read_table uses the same amount memory when loads
all columns with filtering similar as without filtering.
By the way, wonder if memory allocation for repeating strings (e.g. empty) and
None can be more efficient in to_pandas as well, without requiring use
Categorical columns explicitly.
Thanks,
-Yin
> 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
> Priority: Major
> Attachments: sample.py
>
>
> Hi,
> When I read a parquet file (about 23MB with 250K rows and 600 object/string
> columns with lots of None) with filter on a not null column for a small
> number of rows (e.g. 1 to 500), the memory usage is pretty high (around 900MB
> to 1GB). The result table and dataframe have only a few rows (1 row 20kb, 500
> rows 20MB). 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).
> When reading the same parquet file for all columns without filtering, the
> memory usage is about the same at 900MB. It goes up to 2.3GB after to_pandas
> dataframe,. df.info(memory_usage='deep') shows 4.3GB maybe double counting
> something.
> It helps to limit the number of columns read. Read 1 column with filter for 1
> row or more or without filter, it takes about 10MB, which is quite smaller
> and better, but still bigger than the size of table or data frame with 1 or
> 500 rows of 1 columns (under 1MB)
> The filtered column is not a partition key, which functionally works to get
> the correct rows. But the memory usage is quite high even when the parquet
> file is not really large, partitioned or not. There were some references
> similar 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 read
> filtering?
> If not supported yet, can it be fixed/improved with priority?
> This is a blocking issue for us when we need to load all or many columns.
> I am not sure what improvement is possible with respect to how the parquet
> columnar format works, and if it can be patched somehow in the Pyarrow Python
> code, or need to change and build the arrow C++ code.
> Thanks!
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