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From "Tian Tian (Jira)" <j...@apache.org>
Subject [jira] [Comment Edited] (SPARK-6221) SparkSQL should support auto merging output files
Date Mon, 23 Dec 2019 03:25:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-6221?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17002085#comment-17002085
] 

Tian Tian edited comment on SPARK-6221 at 12/23/19 3:24 AM:
------------------------------------------------------------

I encounterd this problem and find [issue-24940](https://issues.apache.org/jira/browse/SPARK-24940)

Use {quote}/*+ COALESCE(numPartitions) */{quote} or {quote}/*+ REPARTITION(numPartitions)
*/{quote} in spark sql query will control output file numbers.

In my parctice I recommend second parm for users, because it will generate a new stage to
do this job, while first parm won't which may lead the job dead because of fewer tasks in
the last stage.


was (Author: tian tian):
I encounterd this problem and find [issue-24940](https://issues.apache.org/jira/browse/SPARK-24940)

Use `/*+ COALESCE(numPartitions) */` or `/*+ REPARTITION(numPartitions) */` in spark sql query
will control output file numbers.

In my parctice I recommend second parm for users, because it will generate a new stage to
do this job, while first parm won't which may lead the job dead because of fewer tasks in
the last stage.

> SparkSQL should support auto merging output files
> -------------------------------------------------
>
>                 Key: SPARK-6221
>                 URL: https://issues.apache.org/jira/browse/SPARK-6221
>             Project: Spark
>          Issue Type: New Feature
>          Components: SQL
>            Reporter: Tianyi Wang
>            Priority: Major
>
> Hive has a feature that could automatically merge small files in HQL's output path. 
> This feature is quite useful for some cases that people use {{insert into}} to  handle
minute data from the input path to a daily table.
> In that case, if the SQL includes {{group by}} or {{join}} operation, we always set the
{{reduce number}} at least 200 to avoid the possible OOM in reduce side.
> That will cause this SQL output at least 200 files at the end of the execution. So the
daily table will finally contains more than 50000 files. 
> If we could provide the same feature in SparkSQL, it will extremely reduce hdfs operations
and spark tasks when we run other sql on this table.



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