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https://issues.apache.org/jira/browse/HIVE-988?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ning Zhang updated HIVE-988:
----------------------------
Attachment: HIVE-988_4.patch
According to offline discussions with Namit, here are the new changes:
1) change Operator.fatalError as a static variable so all operators share it.
2) change Operator.getDone() to check fatalError as well.
3) change ExecMapper.map() to check the operator.getDone() and early exit if so.
4) change the ExecDriver to hold a success variable and ExecDriver.progress
will set it status rather than getting it from RunningJob.isSuccessful(). So it
solves the case where the Counter was incrmented but the RunningJob is finished
without checking for the counter.
> mapjoin should throw an error if the input is too large
> -------------------------------------------------------
>
> Key: HIVE-988
> URL: https://issues.apache.org/jira/browse/HIVE-988
> Project: Hadoop Hive
> Issue Type: New Feature
> Components: Query Processor
> Reporter: Namit Jain
> Assignee: Ning Zhang
> Fix For: 0.5.0
>
> Attachments: HIVE-988.patch, HIVE-988_2.patch, HIVE-988_3.patch,
> HIVE-988_4.patch
>
>
> If the input to the map join is larger than a specific threshold, it may lead
> to a very slow execution of the join.
> It is better to throw an error, and let the user redo his query as a non
> map-join query.
> However, the current map-reduce framework will retry the mapper 4 times
> before actually killing the job.
> Based on a offline discussion with Dhruba, Ning and myself, we came up with
> the following algorithm:
> Keep a threshold in the mapper for the number of rows to be processed for
> map-join. If the number of rows
> exceeds that threshold, set a counter and kill that mapper.
> The client (ExecDriver) monitors that job continuously - if this counter is
> set, it kills the job and also
> shows an appropriate error message to the user, so that he can retry the
> query without the map join.
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