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From "Joydeep Sen Sarma (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HIVE-105) estimate number of required reducers and other map-reduce parameters automatically
Date Sat, 06 Dec 2008 01:13:44 GMT

    [ https://issues.apache.org/jira/browse/HIVE-105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12654012#action_12654012

Joydeep Sen Sarma commented on HIVE-105:

don't understand why we are capping at mapred.reduce.tasks? what's the intention here? If
we are introducing a new parameter to set a max reducer count for all hadoop jobs - then i
would argue that such a control needs to be provided as part of hadoop administration options
- not hive.

will the explain plan be enhanced to show input size? (at least for the root tasks?)

for the per-query variable - we should change HiveConf to annotate some variables as 'per-query'
- and perhaps show it categorized such (somehow) when the user displays all the variables

> 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
> 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.

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