[jira] [Resolved] (SPARK-23030) Decrease memory consumption with toPandas() collection using Arrow

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-23030?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-23030.
----------------------------------
       Resolution: Fixed
    Fix Version/s: 2.4.0

Issue resolved by pull request 21546
[https://github.com/apache/spark/pull/21546]

> Decrease memory consumption with toPandas() collection using Arrow
> ------------------------------------------------------------------
>
>                 Key: SPARK-23030
>                 URL: https://issues.apache.org/jira/browse/SPARK-23030
>             Project: Spark
>          Issue Type: Sub-task
>          Components: PySpark, SQL
>    Affects Versions: 2.3.0
>            Reporter: Bryan Cutler
>            Assignee: Bryan Cutler
>            Priority: Major
>             Fix For: 2.4.0
>
>
> Currently with Arrow enabled, calling {{toPandas()}} results in a collection of all partitions in the JVM in the form of batches of Arrow file format.  Once collected in the JVM, they are served to the Python driver process. 
> I believe using the Arrow stream format can help to optimize this and reduce memory consumption in the JVM by only loading one record batch at a time before sending it to Python.  This might also reduce the latency between making the initial call in Python and receiving the first batch of records.



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