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.

This message was sent by Atlassian JIRA

To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org

Reply via email to