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Yi Tian edited comment on SPARK-6221 at 3/10/15 3:25 AM:
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[~srowen], thanks for your comment.
I think we should add an extra job after the job generated by executing SQL to implement the
merge operation, just like HIVE does.
There are two benefits:
1. It will make the merge logic more independent.
2. It will be easy to merge all the output files with a same size, which is beneficial for
the following processing.
BTW, I agreed with your idea of making this argument for any output from Spark.
was (Author: tianyi):
[~srowen], thanks for your comment.
I think we should add an extra job after the job generated by SQL to implement the merge operation,
just like HIVE does.
There are two benefits:
1. It will make the merge logic more independent.
2. It will be easy to merge all the output files with same size, which is good for the next
operation.
BTW, I agreed with your idea of making this argument for any output from Spark.
> 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: Yi Tian
>
> 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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