[jira] [Commented] (HBASE-12590) A solution for data skew in HBase-Mapreduce Job

["Jonathan Hsieh (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/HBASE-12590?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14230024#comment-14230024 ] 

Jonathan Hsieh commented on HBASE-12590:
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Nice description of the problem in the slide deck.  I did a quick scan of the docs and the code and had a few questions.

1) The world "split" is ambiguous.  Need to make it clear in java doc that this is only a "MR input split" and not an "hbase region split" operation that would trigger a lot io.
2) Why do we only split by 2? Why not split further so that we have n mr input splits that are 1gb (in your example) instead of a 2x 3gb, 2x 2.5gb and a 2x 1gb "artificial" mr input splits?
3) To make this easier for users, do you think it might may sense to use something other than a constant size (which assumes the user knows the the server side region size property)? can we look at all of the regions sizes (we have the info already with the RegionSizeCalculator), and just add new MR inputsplits for the regions that are proportionately too large?  Maybe we have the setting be a ratio (maybe 5x-10x) larger than the median median region size?  That way the job won't have to change if the server side setting changes.

> A solution for data skew in HBase-Mapreduce Job
> -----------------------------------------------
>
>                 Key: HBASE-12590
>                 URL: https://issues.apache.org/jira/browse/HBASE-12590
>             Project: HBase
>          Issue Type: Improvement
>          Components: mapreduce
>            Reporter: Weichen Ye
>         Attachments: A Solution for Data Skew in HBase-MapReduce Job.pdf, HBase-12590-v1.patch
>
>
> 1, Motivation
> In production environment, data skew is a very common case. A HBase table always contains a lot of small regions and several large regions. Small regions waste a lot of computing resources. If we use a job to scan a table with 3000 small regions, we need a job with 3000 mappers. Large regions always block the job. If in a 100-region table, one region is far larger then the other 99 regions. When we run a job with the table as input, 99 mappers will be completed very quickly, and we need to wait for the last mapper for a long time.
> 2, Configuration
> Add two new configuration. 
> hbase.mapreduce.split.autobalance = true means enabling the “auto balance” in HBase-MapReduce jobs. The default value is false. 
> hbase.mapreduce.split.targetsize = 1073741824 (default 1GB). The target size of mapreduce splits. 
> If a region size is large than the target size, cut the region into two split.If the sum of several small continuous region size less than the target size, combine these regions into one split.
> Example:
> In attachment
> Welcome to the Review Board.
> https://reviews.apache.org/r/28494/diff/#



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