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Posted to issues@arrow.apache.org by "Wes McKinney (Jira)" <ji...@apache.org> on 2020/04/27 02:47:00 UTC
[jira] [Updated] (ARROW-7556) [Python][Parquet] Performance
regression in pyarrow-0.15.1 vs pyarrow-0.12.1 when reading a "partitioned
parquet table" ?
[ https://issues.apache.org/jira/browse/ARROW-7556?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Wes McKinney updated ARROW-7556:
--------------------------------
Labels: dataset-parquet-read parquet (was: parquet)
> [Python][Parquet] Performance regression in pyarrow-0.15.1 vs pyarrow-0.12.1 when reading a "partitioned parquet table" ?
> -------------------------------------------------------------------------------------------------------------------------
>
> Key: ARROW-7556
> URL: https://issues.apache.org/jira/browse/ARROW-7556
> Project: Apache Arrow
> Issue Type: Bug
> Components: Benchmarking, Python
> Affects Versions: 0.15.1
> Reporter: Julien MASSIOT
> Priority: Major
> Labels: dataset-parquet-read, parquet
> Attachments: arrow-7756.tar.gz, load_df_from_pyarrow.py, load_df_pyarrow-0-15.1.png, load_df_pyarrow-0.12.1.png, load_pyarrow_0.12.1.cprof, load_pyarrow_0.15.1.cprof
>
>
> Hi,
> I am currently running a small test with pyarrow to load 2 "partitioned" parquet tables.
> The performance seems to be 2 times less with pyarrow-0.15.1 than with pyarrow-0.12.1.
> In my test:
> * the 'parquet' tables I am loading are called 'reports' & 'memory'
> * they have been generated through pandas.to_parquet by specifying the partitions columns
> * they are both partitioned by 2 columns 'p_type' and 'p_start'
> * it is small tables:
> ** reports
> *** 90 partitions (1 parquet file / partition)
> *** total size: 6.2MB
> ** memory
> *** 105 partitions (1 parquet file / partition)
> *** total size: 9.1MB
>
> Here is the code of my simple test that tries to read them (I'm using a filter on the p_start partition):
>
> {code:java}
> // code placeholder
> import os
> import sys
> import time
> import pyarrow
> from pyarrow.parquet import ParquetDataset
> def load_dataframe(data_dir, table, start_date, end_date):
> return ParquetDataset(os.path.join(data_dir, table),
> filters=[('p_start', '>=', start_date),
> ('p_start', '<=', end_date)
> ]).read().to_pandas()
> print(f'pyarrow version;{pyarrow.__version__}')
> data_dir = sys.argv[1]
> for i in range(1, 10):
> start = time.time()
> start_date = '201912230000'
> end_date = '202001080000'
> load_dataframe(sys.argv[1], 'reports', start_date, end_date)
> load_dataframe(sys.argv[1], 'memory', start_date, end_date)
> print(f'loaded;in;{time.time()-start}')
> {code}
>
>
> Here are the results:
> * with pyarrow-0.12.1
> $ python -m cProfile -o load_pyarrow_0.12.1.cprof load_df_from_pyarrow.py parquet/
> pyarrow version;0.12.1
> loaded;in;0.5566098690032959
> loaded;in;0.32605648040771484
> loaded;in;0.28951501846313477
> loaded;in;0.29279112815856934
> loaded;in;0.3474299907684326
> loaded;in;0.4075736999511719
> loaded;in;0.425199031829834
> loaded;in;0.34653329849243164
> loaded;in;0.300839900970459
> (~350ms to load the 2 tables)
>
> !load_df_pyarrow-0.12.1.png!
>
> * with pyarrow-0.15.1
>
> $ python -m cProfile -o load_pyarrow_0.15.1.cprof load_df_from_pyarrow.py parquet/
> pyarrow version;0.15.1
> loaded;in;1.1126022338867188
> loaded;in;0.8931224346160889
> loaded;in;1.3298325538635254
> loaded;in;0.8584625720977783
> loaded;in;0.9232609272003174
> loaded;in;1.0619215965270996
> loaded;in;0.8619768619537354
> loaded;in;0.8686420917510986
> loaded;in;1.1183602809906006
> (>800ms to load the 2 tables)
>
> !load_df_pyarrow-0-15.1.png!
>
> Is there a performance regression here ?
> Am I missing something ?
>
> In attachment, you can find the 2 .cprof files.
> [^load_pyarrow_0.12.1.cprof]
> [^load_pyarrow_0.15.1.cprof]
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