[
https://issues.apache.org/jira/browse/ARROW-1956?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16306461#comment-16306461
]
Wes McKinney commented on ARROW-1956:
-------------------------------------
If someone could propose a desired API for pyarrow, then a patch for this could
be written
> Support reading specific partitions from a partitioned parquet dataset
> ----------------------------------------------------------------------
>
> Key: ARROW-1956
> URL: https://issues.apache.org/jira/browse/ARROW-1956
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Format
> Affects Versions: 0.8.0
> Environment: Kernel: 4.14.8-300.fc27.x86_64
> Python: 3.6.3
> Reporter: Suvayu Ali
> Priority: Minor
> Labels: parquet
> Fix For: 0.9.0
>
> Attachments: so-example.py
>
>
> I want to read specific partitions from a partitioned parquet dataset. This
> is very useful in case of large datasets. I have attached a small script
> that creates a dataset and shows what is expected when reading (quoting
> salient points below).
> # There is no way to read specific partitions in Pandas
> # In pyarrow I tried to achieve the goal by providing a list of
> files/directories to ParquetDataset, but it didn't work:
> # In PySpark it works if I simply do:
> {code:none}
> spark.read.options('basePath', 'datadir').parquet(*list_of_partitions)
> {code}
> I also couldn't find a way to easily write partitioned parquet files. In the
> end I did it by hand by creating the directory hierarchies, and writing the
> individual files myself (similar to the implementation in the attached
> script). Again, in PySpark I can do
> {code:none}
> df.write.partitionBy(*list_of_partitions).parquet(output)
> {code}
> to achieve that.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
