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Posted to issues@spark.apache.org by "Li Jin (JIRA)" <ji...@apache.org> on 2018/04/13 22:09:00 UTC
[jira] [Commented] (SPARK-23030) Decrease memory consumption with
toPandas() collection using Arrow
[ https://issues.apache.org/jira/browse/SPARK-23030?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16438001#comment-16438001 ]
Li Jin commented on SPARK-23030:
--------------------------------
Hey [~bryanc], did you by an chance have some process on this? I guess what's tricky here is you probably lose the parallelism if streaming each partitions one by one?
> 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
> Priority: Major
>
> 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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