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https://issues.apache.org/jira/browse/SPARK-6221?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17002085#comment-17002085
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Tian Tian edited comment on SPARK-6221 at 12/23/19 3:24 AM:
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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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