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From "Joydeep Sen Sarma (JIRA)" <>
Subject [jira] Commented: (HIVE-105) estimate number of required reducers and other map-reduce parameters automatically
Date Wed, 21 Jan 2009 00:35:59 GMT


Joydeep Sen Sarma commented on HIVE-105:

> 1. If "mapred.reduce.tasks" is set and not less than 0, use that as the number of reducers,
and skip all following steps;

most hadoop installations have a non-zero value specified for this. hadoop-default.xml comes
with a value of 1. Did u mean if it is > 1?

> 2. Take a look at the total size of the input files, divide that by "hive.exec.bytes.per.reducer",
to get a number R;

for part-B - i think Prasad filed a jira for variables that are query specific (versus session
specific). that is easy to get done. I am a little uncomfortable with the query hinting thing
since the user doesn't even know when reduces will be used or not (depends on how we implement
group-bys and joins). It seems query hinting will have to first force a particular type of
plan (use group-by algo1) and then force reducer count for that particular algo.

> estimate number of required reducers and other map-reduce parameters automatically
> ----------------------------------------------------------------------------------
>                 Key: HIVE-105
>                 URL:
>             Project: Hadoop Hive
>          Issue Type: Improvement
>          Components: Query Processor
>            Reporter: Joydeep Sen Sarma
>            Assignee: Zheng Shao
> 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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