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https://issues.apache.org/jira/browse/SPARK-9072?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14628557#comment-14628557
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Sean Owen commented on SPARK-9072:
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Yes, I'm really only suggesting this start at user@. If it turns out to be
something that needs to change in Spark, then we make a JIRA with details.
There may be no (new, separate) issue here, but something that's already
resolved or has an easy answer.
> Parquet : Writing data to S3 very slowly
> ----------------------------------------
>
> Key: SPARK-9072
> URL: https://issues.apache.org/jira/browse/SPARK-9072
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Reporter: Murtaza Kanchwala
> Priority: Critical
> Labels: parquet
>
> I've created spark programs through which I am converting the normal textfile
> to parquet and csv to S3.
> There is around 8 TB of data and I need to compress it into lower for further
> processing on Amazon EMR
> Results :
> 1) Text -> CSV took 1.2 hrs to transform 8 TB of data without any problems
> successfully to S3.
> 2) Text -> Parquet Job completed in the same time (i.e. 1.2 hrs) but still
> after the Job completion it is spilling/writing the data separately to S3
> which is making it slower and in starvation.
> Input : s3n://<SameBucket>/input
> Output : s3n://<SameBucket>/output/parquet
> Lets say If I have around 10K files then it is taking 1000 files / 20 min to
> write back in S3.
> Note :
> Also I found that program is creating temp folder on S3 output location, And
> in Logs I've seen S3ReadDelays.
> Can anyone tell me what am I doing wrong? or is there anything I need to add
> so that the Spark App cant create temp folder on S3 and do write ups fast
> from EMR to S3 just like saveAsTextFile. Thanks
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