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From "Attila Szabo (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SQOOP-2920) sqoop performance deteriorates significantly on wide datasets; sqoop 100% on cpu
Date Wed, 11 May 2016 14:23:12 GMT

    [ https://issues.apache.org/jira/browse/SQOOP-2920?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15280199#comment-15280199

Attila Szabo commented on SQOOP-2920:

According to this ticket I've also raised a discussion in the community around this changeset:

Just pure on the mapper class level it's 250x faster than the previous solution, and in E2E
use cases (with wide tables with 800+columns) I've also measured 4-5X better performance,
and only 50% of the CPU is busy with that version of setField not 92%.


> sqoop performance deteriorates significantly on wide datasets; sqoop 100% on cpu
> --------------------------------------------------------------------------------
>                 Key: SQOOP-2920
>                 URL: https://issues.apache.org/jira/browse/SQOOP-2920
>             Project: Sqoop
>          Issue Type: Bug
>          Components: connectors/oracle, hive-integration, metastore
>    Affects Versions: 1.4.5
>         Environment: - sqoop export on a very wide dataset (over 700 columns)
> - sqoop export to oracle
> - subset of columns is exported (using --columns argument)
> - parquet files
> - --table --hcatalog-database --hcatalog-table options are used
>            Reporter: Ruslan Dautkhanov
>            Priority: Critical
>              Labels: columns, hive, oracle, perfomance
>         Attachments: jstack.zip, top - sqoop mappers hog cpu.png
> We sqoop export from datalake to Oracle quite often.
> Every time we sqoop "narrow" datasets, Oracle always have scalability issues (3-node
all-flash Oracle RAC) normally can't keep up with more than 45-55 sqoop mappers. Map-reduce
framework shows sqoop mappers are not so loaded. 
> On wide datasets, this picture is quite opposite. Oracle shows 95% of sessions are bored
and waiting for new INSERTs. Even when we go over hundred of mappers. Sqoop has serious scalability
issues on very wide datasets. (Our company normally has very wide datasets)
> For example, on the last sqoop export:
> Started ~2.5 hours ago and 95 mappers already accumulated
> CPU time spent (ms)	1,065,858,760
> (looking at this metric through map-reduce framework stats)
> 1 million seconds of CPU time.
> Or 11219.57 per mapper. Which is roughly 3.11 hours of CPU time per mapper. 
> So they are 100% on cpu.
> Will also attach jstack files.

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