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From "Zheng Shao (JIRA)" <j...@apache.org>
Subject [jira] Updated: (HIVE-105) estimate number of required reducers and other map-reduce parameters automatically
Date Sat, 02 May 2009 00:41:30 GMT

     [ https://issues.apache.org/jira/browse/HIVE-105?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Zheng Shao updated HIVE-105:
----------------------------

    Description: 
currently users have to specify number of reducers. In a multi-user environment - we generally
ask users to be prudent in selecting number of reducers (since they are long running and block
other users). Also - large number of reducers produce large number of output files - which
puts pressure on namenode resources.

there are other map-reduce parameters - for example the min split size and the proposed use
of combinefileinputformat that are also fairly tricky for the user to determine (since they
depend on map side selectivity and cluster size). This will become totally critical when there
is integration with BI tools since there will be no opportunity to optimize job settings and
there will be a wide variety of jobs.

This jira calls for automating the selection of such parameters - possibly by a best effort
at estimating map side selectivity/output size using sampling and determining such parameters
from there.

Configs:
hive.exec.reducers.bytes.per.reducer
hive.exec.reducers.max
mapred.reduce.tasks



  was:
currently users have to specify number of reducers. In a multi-user environment - we generally
ask users to be prudent in selecting number of reducers (since they are long running and block
other users). Also - large number of reducers produce large number of output files - which
puts pressure on namenode resources.

there are other map-reduce parameters - for example the min split size and the proposed use
of combinefileinputformat that are also fairly tricky for the user to determine (since they
depend on map side selectivity and cluster size). This will become totally critical when there
is integration with BI tools since there will be no opportunity to optimize job settings and
there will be a wide variety of jobs.

This jira calls for automating the selection of such parameters - possibly by a best effort
at estimating map side selectivity/output size using sampling and determining such parameters
from there.


> estimate number of required reducers and other map-reduce parameters automatically
> ----------------------------------------------------------------------------------
>
>                 Key: HIVE-105
>                 URL: https://issues.apache.org/jira/browse/HIVE-105
>             Project: Hadoop Hive
>          Issue Type: Improvement
>          Components: Query Processor
>            Reporter: Joydeep Sen Sarma
>            Assignee: Zheng Shao
>             Fix For: 0.2.0
>
>         Attachments: HIVE-105.1.patch, HIVE-105.2.patch, HIVE-105.3.patch, HIVE-105.4.patch
>
>
> currently users have to specify number of reducers. In a multi-user environment - we
generally ask users to be prudent in selecting number of reducers (since they are long running
and block other users). Also - large number of reducers produce large number of output files
- which puts pressure on namenode resources.
> there are other map-reduce parameters - for example the min split size and the proposed
use of combinefileinputformat that are also fairly tricky for the user to determine (since
they depend on map side selectivity and cluster size). This will become totally critical when
there is integration with BI tools since there will be no opportunity to optimize job settings
and there will be a wide variety of jobs.
> This jira calls for automating the selection of such parameters - possibly by a best
effort at estimating map side selectivity/output size using sampling and determining such
parameters from there.
> Configs:
> hive.exec.reducers.bytes.per.reducer
> hive.exec.reducers.max
> mapred.reduce.tasks

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