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https://issues.apache.org/jira/browse/ARROW-4470?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Francois Saint-Jacques updated ARROW-4470:
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Labels: dataset datasets parquet (was: datasets parquet)
> [Python] Pyarrow using considerable more memory when reading partitioned
> Parquet file
> -------------------------------------------------------------------------------------
>
> Key: ARROW-4470
> URL: https://issues.apache.org/jira/browse/ARROW-4470
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 0.12.0
> Reporter: Ivan SPM
> Priority: Major
> Labels: dataset, datasets, parquet
> Fix For: 1.0.0
>
>
> Hi,
> I have a partitioned Parquet table in Impala in HDFS, using Hive metastore,
> with the following structure:
> {{/data/myparquettable/year=2016}}{{/data/myparquettable/year=2016/myfile_1.prt}}
> {{/data/myparquettable/year=2016/myfile_2.prt}}
> {{/data/myparquettable/year=2016/myfile_3.prt}}
> {{/data/myparquettable/year=2017}}
> {{/data/myparquettable/year=2017/myfile_1.prt}}
> {{/data/myparquettable/year=2017/myfile_2.prt}}
> {{/data/myparquettable/year=2017/myfile_3.prt}}
> and so on. I need to work with one partition, so I copied one partition to a
> local filesystem:
> {{hdfs fs -get /data/myparquettable/year=2017 /local/}}
> so now I have some data on the local disk:
> {{/local/year=2017/myfile_1.prt }}{{/local/year=2017/myfile_2.prt }}
> etc.I tried to read it using Pyarrow:
> {{import pyarrow.parquet as pq}}{{pq.read_parquet('/local/year=2017')}}
> and it starts reading. The problem is that the local Parquet files are around
> 15GB total, and I blew up my machine memory a couple of times because when
> reading these files, Pyarrow is using more than 60GB of RAM, and I'm not sure
> how much it will take because it never finishes. Is this expected? Is there a
> workaround?
>
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