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From "Michael Segel (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HBASE-12853) distributed write pattern to replace ad hoc 'salting'
Date Thu, 02 Apr 2015 13:58:53 GMT

    [ https://issues.apache.org/jira/browse/HBASE-12853?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14392726#comment-14392726
] 

Michael Segel  commented on HBASE-12853:
----------------------------------------

Sorry, I thought that the HM had the META table cached in memory. Didn't think that the META
was too large....

Ok, so then it looks like what I want to do is all client side then. 

The design is pretty straight forward. 

The number of buckets is fixed at the time of table creation. 
The row key is a composite key of  bucket_id | rowkey  and the bucket_id is derived from taking
the modulus N of the first byte of the row key. (Giving you 0xFF(255) max buckets. ) Then
when you want to fetch a single row given the rowkey, you can find the bucket and fetch the
single row. If you need to do a scan, given the start row, you can then create N parallel
threads and within each thread, start the scan by prepending the bucket_id |  to the start
rowkey.

When returning the result set, you can then strip off the bucket_id | and take the MIN(value(n))
value(n) is the next row from each scanner, popping it off the stack. This will give you a
single result that is guaranteed to still be within sort order. 

Its all client side and it abstracts the bucketing from the user/client code so that the same
code will run against either table without any changes.



> distributed write pattern to replace ad hoc 'salting'
> -----------------------------------------------------
>
>                 Key: HBASE-12853
>                 URL: https://issues.apache.org/jira/browse/HBASE-12853
>             Project: HBase
>          Issue Type: New Feature
>            Reporter: Michael Segel 
>            Priority: Minor
>
> In reviewing HBASE-11682 (Description of Hot Spotting), one of the issues is that while
'salting' alleviated  regional hot spotting, it increased the complexity required to utilize
the data.  
> Through the use of coprocessors, it should be possible to offer a method which distributes
the data on write across the cluster and then manages reading the data returning a sort ordered
result set, abstracting the underlying process. 
> On table creation, a flag is set to indicate that this is a parallel table. 
> On insert in to the table, if the flag is set to true then a prefix is added to the key.
 e.g. <region server#>- or <region server #|| where the region server # is an integer
between 1 and the number of region servers defined.  
> On read (scan) for each region server defined, a separate scan is created adding the
prefix. Since each scan will be in sort order, its possible to strip the prefix and return
the lowest value key from each of the subsets. 



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