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From "philipse (Jira)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-31588) merge small files may need more common setting
Date Fri, 08 May 2020 09:45:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-31588?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17102414#comment-17102414
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philipse commented on SPARK-31588:
----------------------------------

yes, the block size can be controlled in HDFS.i mean we just take the block size as one the
the condition. if we can control the target size in SPARK, we can control the real data in
HDFS,instand using repartition control the hard limit.

> merge small files may need more common setting
> ----------------------------------------------
>
>                 Key: SPARK-31588
>                 URL: https://issues.apache.org/jira/browse/SPARK-31588
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 2.4.5
>         Environment: spark:2.4.5
> hdp:2.7
>            Reporter: philipse
>            Priority: Major
>
> Hi ,
> SparkSql now allow us to use  repartition or coalesce to manually control the small
files like the following
> /*+ REPARTITION(1) */
> /*+ COALESCE(1) */
> But it can only be  tuning case by case ,we need to decide whether we need to use COALESCE
or REPARTITION,can we try a more common way to reduce the decision by set the target size 
as hive did
> *Good points:*
> 1)we will also the new partitions number
> 2)with an ON-OFF parameter  provided , user can close it if needed
> 3)the parmeter can be set at cluster level instand of user side,it will be more easier
to controll samll files.
> 4)greatly reduce the pressue of namenode
>  
> *Not good points:*
> 1)It will add a new task to calculate the target numbers by stastics the out files.
>  
> I don't know whether we have planned this in future.
>  
> Thanks



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