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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 Tue, 17 May 2016 21:39:13 GMT

    [ https://issues.apache.org/jira/browse/SQOOP-2920?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15287637#comment-15287637

Attila Szabo commented on SQOOP-2920:

Hi [~venkatnrangan],

Sorry for the delayed answer but we did have a national holiday in Hungary.

So I've already raised this patch/question/change to the community for consideration over
the dev list, but now I'd also attached the review ticket here too. Next time I'll do both
of them.

If you have got any questions please raise it to me, and I'll try to answer ASAP.


> 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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