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