[jira] [Resolved] (SPARK-24425) Regression from 1.6 to 2.x - Spark no longer respects input partitions, unnecessary shuffle required

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

     [ https://issues.apache.org/jira/browse/SPARK-24425?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-24425.
----------------------------------
    Resolution: Incomplete

> Regression from 1.6 to 2.x - Spark no longer respects input partitions, unnecessary shuffle required
> ----------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-24425
>                 URL: https://issues.apache.org/jira/browse/SPARK-24425
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 2.0.2
>            Reporter: sam
>            Priority: Major
>              Labels: bulk-closed
>
> I think this is a regression. We used to be able to easily control the number of output files / tasks based on num files and coalesce. Now I have to use `repartition` to get the desired num files / partitions which is unnecessarily expensive.
> I've tried playing with spark.sql.files.maxPartitionBytes and spark.sql.files.openCostInBytes to see if I can force the conventional behaviour.
> {code:java}
> val ss = SparkSession.builder().appName("uber-cp").master(conf.master())
>              .config("spark.sql.files.maxPartitionBytes", 1)
>              .config("spark.sql.files.openCostInBytes", Long.MaxValue)
> {code}
> This didn't work. Spark just squashes all my parquet files into less partitions.
> Suggest a simple `option` on DataFrameReader that can disable this (or enable it, default behaviour should be same as 1.6).
>  
> This relates to https://issues.apache.org/jira/browse/SPARK-5997, in that if SPARK-5997 was implemented this ticket wouldn't really be necessary.
>  
>  



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