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https://issues.apache.org/jira/browse/SPARK-25274?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16596860#comment-16596860
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Bryan Cutler commented on SPARK-25274:
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This is a followup to SPARK-23030 that is now possible since Arrow stream
format is being used
> Improve `toPandas` with Arrow by sending out-of-order record batches
> --------------------------------------------------------------------
>
> Key: SPARK-25274
> URL: https://issues.apache.org/jira/browse/SPARK-25274
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark, SQL
> Affects Versions: 2.4.0
> Reporter: Bryan Cutler
> Priority: Major
>
> When executing {{toPandas}} with Arrow enabled, partitions that arrive in the
> JVM out-of-order must be buffered before they can be send to Python. This
> causes an excess of memory to be used in the driver JVM and increases the
> time it takes to complete because data must sit in the JVM waiting for
> preceding partitions to come in.
> This can be improved by sending out-of-order partitions to Python as soon as
> they arrive in the JVM, followed by a list of indices so that Python can
> assemble the data in the correct order. This way, data is not buffered at the
> JVM and there is no waiting on particular partitions so performance will be
> increased.
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