[jira] [Commented] (HBASE-13408) HBase In-Memory Memstore Compaction

["Eshcar Hillel (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/HBASE-13408?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14626301#comment-14626301 ] 

Eshcar Hillel commented on HBASE-13408:
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Hi - we are back with an implementation of the basic feature (see link to the review board for the HBASE-13408-098 code), and some experimental results. We were able to show 30-65% performance gain for read accesses in high-churn workloads (comprising of 50% reads and 50% writes), and mainly to maintain predictable latency SLA (see performance evaluation document for full results). We’ve also adapted the design document to reflect the code, specifically renaming some classes and describing the changes we made in the region flushing policy. (see design document ver02).


> HBase In-Memory Memstore Compaction
> -----------------------------------
>
>                 Key: HBASE-13408
>                 URL: https://issues.apache.org/jira/browse/HBASE-13408
>             Project: HBase
>          Issue Type: New Feature
>            Reporter: Eshcar Hillel
>         Attachments: HBaseIn-MemoryMemstoreCompactionDesignDocument-ver02.pdf, HBaseIn-MemoryMemstoreCompactionDesignDocument.pdf, InMemoryMemstoreCompactionEvaluationResults.pdf
>
>
> A store unit holds a column family in a region, where the memstore is its in-memory component. The memstore absorbs all updates to the store; from time to time these updates are flushed to a file on disk, where they are compacted. Unlike disk components, the memstore is not compacted until it is written to the filesystem and optionally to block-cache. This may result in underutilization of the memory due to duplicate entries per row, for example, when hot data is continuously updated. 
> Generally, the faster the data is accumulated in memory, more flushes are triggered, the data sinks to disk more frequently, slowing down retrieval of data, even if very recent.
> In high-churn workloads, compacting the memstore can help maintain the data in memory, and thereby speed up data retrieval. 
> We suggest a new compacted memstore with the following principles:
> 1.	The data is kept in memory for as long as possible
> 2.	Memstore data is either compacted or in process of being compacted 
> 3.	Allow a panic mode, which may interrupt an in-progress compaction and force a flush of part of the memstore.
> We suggest applying this optimization only to in-memory column families.
> A design document is attached.
> This feature was previously discussed in HBASE-5311.



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