[jira] [Commented] (SPARK-31794) Incorrect distribution with repartitionByRange and repartition column expression

["Jungtaek Lim (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-31794?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17115080#comment-17115080 ] 

Jungtaek Lim commented on SPARK-31794:
--------------------------------------

Please read through the doc of these methods, which explain why the distribution may not be evenly. Only `Dataset.repartition(numPartition)` guarantees the rows are evenly distributed across partitions. It's neither a bug nor the thing which can be improved. (Unless we expose the interface to implement custom hash function.)

> Incorrect distribution with repartitionByRange and repartition column expression
> --------------------------------------------------------------------------------
>
>                 Key: SPARK-31794
>                 URL: https://issues.apache.org/jira/browse/SPARK-31794
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 2.3.2, 2.4.5, 3.0.1
>         Environment: Sample code for obtaining the above test results.
> import java.io.File 
> import java.io.PrintWriter 
> val logfile="/tmp/sparkdftest.log"
> val writer = new PrintWriter(logfile) 
> writer.println("Spark Version " + sc.version)
> val df= Range(1, 1002).toDF("val")
> writer.println("Default Partition Length:" + df.rdd.partitions.length)
> writer.println("Default Partition getNumPartitions:" + df.rdd.getNumPartitions)
> writer.println("Default Partition groupBy spark_partition_id:" + df.groupBy(spark_partition_id).count().rdd.partitions.length)
> val dfcount=df.mapPartitions\{part => Iterator(part.size)}
> writer.println("Default Partition:" + dfcount.collect().toList)
> val numparts=24
> val dfparts_range=df.withColumn("partid", $"val" % numparts).repartitionByRange(numparts, $"partid")
> writer.println("repartitionByRange Length:" + dfparts_range.rdd.partitions.length)
> writer.println("repartitionByRange getNumPartitions:" + dfparts_range.rdd.getNumPartitions)
> writer.println("repartitionByRange groupBy spark_partition_id:" + dfparts_range.groupBy(spark_partition_id).count().rdd.partitions.length)
> val dfpartscount=dfparts_range.mapPartitions\{part => Iterator(part.size)}
> writer.println("repartitionByRange: " + dfpartscount.collect().toList)
> val dfparts_expr=df.withColumn("partid", $"val" % numparts).repartition(numparts, $"partid")
> writer.println("repartition by column expr Length:" + dfparts_expr.rdd.partitions.length)
> writer.println("repartition by column expr getNumPartitions:" + dfparts_expr.rdd.getNumPartitions)
> writer.println("repartition by column expr groupBy spark_partitoin_id:" + dfparts_expr.groupBy(spark_partition_id).count().rdd.partitions.length)
> val dfpartscount=dfparts_expr.mapPartitions\{part => Iterator(part.size)}
> writer.println("repartition by column expr:" + dfpartscount.collect().toList)
> writer.close()
>            Reporter: Ramesha Bhatta
>            Priority: Major
>              Labels: performance
>
> Both repartitionByRange and  repartition(<num>, <column>)  resulting in wrong distribution within the resulting partition.  
>  
> In the Range partition one of the partition has 2x volume and last one with zero.  In repartition this is more problematic with some partition with 4x, 2x the avg and many partitions with zero volume.  
>  
> This distribution imbalance can cause performance problem in a concurrent environment.
> Details from testing in 3 different versions.
> |Verion 2.3.2|Version 2.4.5|Versoin 3.0 Preview2|
> |Spark Version 2.3.2.3.1.4.0-315|Spark Version 2.4.5|Spark Version 3.0.0-preview2|
> |Default Partition Length:2|Default Partition Length:2|Default Partition Length:80|
> |Default Partition getNumPartitions:2|Default Partition getNumPartitions:2|Default Partition getNumPartitions:80|
> |Default Partition groupBy spark_partition_id:200|Default Partition groupBy spark_partition_id:200|Default Partition groupBy spark_partition_id:200|
> |repartitionByRange Length:24|repartitionByRange Length:24|repartitionByRange Length:24|
> |repartitionByRange getNumPartitions:24|repartitionByRange getNumPartitions:24|repartitionByRange getNumPartitions:24|
> |repartitionByRange groupBy spark_partition_id:200|repartitionByRange groupBy spark_partition_id:200|repartitionByRange groupBy spark_partition_id:200|
> |repartitionByRange: List(83, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 41, 41, 41, 41, 41, 41, 0)|repartitionByRange: List(83, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 41, 41, 41, 41, 41, 41, 0)|repartitionByRange: List(83, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 41, 41, 41, 41, 41, 41, 0)|
> |repartition by column expr Length:24|repartition by column expr Length:24|repartition by column expr Length:24|
> |repartition by column expr getNumPartitions:24|repartition by column expr getNumPartitions:24|repartition by column expr getNumPartitions:24|
> |repartition by column expr groupBy spark_partitoin_id:200|repartition by column expr groupBy spark_partitoin_id:200|repartition by column expr groupBy spark_partitoin_id:200|
> |repartition by column expr:List(83, 42, 0, 84, 0, 42, 125, 0, 42, 84, 0, 42, 0, 82, 0, 124, 42, 83, 84, 42, 0, 0, 0, 0)|repartition by column expr:List(83, 42, 0, 84, 0, 42, 125, 0, 42, 84, 0, 42, 0, 82, 0, 124, 42, 83, 84, 42, 0, 0, 0, 0)|repartition by column expr:List(83, 42, 0, 84, 0, 42, 125, 0, 42, 84, 0, 42, 0, 82, 0, 124, 42, 83, 84, 42, 0, 0, 0, 0)|



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