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Posted to issues@spark.apache.org by "Dongjoon Hyun (JIRA)" <ji...@apache.org> on 2019/06/09 20:08:00 UTC
[jira] [Reopened] (SPARK-27870) Flush each batch for pandas UDF
(for improving pandas UDFs pipeline)
[ https://issues.apache.org/jira/browse/SPARK-27870?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Dongjoon Hyun reopened SPARK-27870:
-----------------------------------
Assignee: (was: Weichen Xu)
This is reverted via the following PR by [~hyukjin.kwon].
- https://github.com/apache/spark/pull/24827
> Flush each batch for pandas UDF (for improving pandas UDFs pipeline)
> --------------------------------------------------------------------
>
> Key: SPARK-27870
> URL: https://issues.apache.org/jira/browse/SPARK-27870
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, SQL
> Affects Versions: 3.0.0
> Reporter: Weichen Xu
> Priority: Major
> Fix For: 3.0.0
>
>
> Flush each batch for pandas UDF.
> This could improve performance when multiple pandas UDF plans are pipelined.
> When batch being flushed in time, downstream pandas UDFs will get pipelined as soon as possible, and pipeline will help hide the donwstream UDFs computation time. For example:
> When the first UDF start computing on batch-3, the second pipelined UDF can start computing on batch-2, and the third pipelined UDF can start computing on batch-1.
> If we do not flush each batch in time, the donwstream UDF's pipeline will lag behind too much, which may increase the total processing time.
>
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