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From "Hive QA (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HIVE-20220) Incorrect result when hive.groupby.skewindata is enabled
Date Mon, 30 Jul 2018 06:38:00 GMT

    [ https://issues.apache.org/jira/browse/HIVE-20220?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16561469#comment-16561469
] 

Hive QA commented on HIVE-20220:
--------------------------------



Here are the results of testing the latest attachment:
https://issues.apache.org/jira/secure/attachment/12933540/HIVE-20220.patch

{color:green}SUCCESS:{color} +1 due to 2 test(s) being added or modified.

{color:green}SUCCESS:{color} +1 due to 14817 tests passed

Test results: https://builds.apache.org/job/PreCommit-HIVE-Build/12938/testReport
Console output: https://builds.apache.org/job/PreCommit-HIVE-Build/12938/console
Test logs: http://104.198.109.242/logs/PreCommit-HIVE-Build-12938/

Messages:
{noformat}
Executing org.apache.hive.ptest.execution.TestCheckPhase
Executing org.apache.hive.ptest.execution.PrepPhase
Executing org.apache.hive.ptest.execution.YetusPhase
Executing org.apache.hive.ptest.execution.ExecutionPhase
Executing org.apache.hive.ptest.execution.ReportingPhase
{noformat}

This message is automatically generated.

ATTACHMENT ID: 12933540 - PreCommit-HIVE-Build

> Incorrect result when hive.groupby.skewindata is enabled
> --------------------------------------------------------
>
>                 Key: HIVE-20220
>                 URL: https://issues.apache.org/jira/browse/HIVE-20220
>             Project: Hive
>          Issue Type: Bug
>          Components: Query Processor
>    Affects Versions: 3.0.0
>            Reporter: Ganesha Shreedhara
>            Assignee: Ganesha Shreedhara
>            Priority: Major
>         Attachments: HIVE-20220.patch
>
>
> hive.groupby.skewindata makes use of rand UDF to randomly distribute grouped by keys
to the reducers and hence avoids overloading a single reducer when there is a skew in data. 
> This random distribution of keys is buggy when the reducer fails to fetch the mapper
output due to a faulty datanode or any other reason. When reducer finds that it can't fetch
mapper output, it sends a signal to Application Master to reattempt the corresponding map
task. The reattempted map task will now get the different random value from rand function
and hence the keys that gets distributed now to the reducer will not be same as the previous
run. 
>  
> *Steps to reproduce:*
> create table test(id int);
> insert into test values (1),(2),(2),(3),(3),(3),(4),(4),(4),(4),(5),(5),(5),(5),(5),(6),(6),(6),(6),(6),(6),(7),(7),(7),(7),(7),(7),(7),(7),(8),(8),(8),(8),(8),(8),(8),(8),(9),(9),(9),(9),(9),(9),(9),(9),(9);
> SET hive.groupby.skewindata=true;
> SET mapreduce.reduce.reduces=2;
> //Add a debug port for reducer
> select count(1) from test group by id;
> //Remove mapper's intermediate output file when map stage is completed and one out of
2 reduce tasks is completed and then continue the run. This causes 2nd reducer to send event
to Application Master to rerun the map task. 
> The following is the expected result. 
> 1
> 2
> 3
> 4
> 5
> 6
> 8
> 8
> 9 
>  
> But you may get different result due to a different value returned by the rand function
in the second run causing different distribution of keys.
> This needs to be fixed such that the mapper distributes the same keys even if it is
reattempted multiple times. 



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