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From "stack (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HBASE-13408) HBase In-Memory Memstore Compaction
Date Sun, 05 Apr 2015 23:55:07 GMT

    [ https://issues.apache.org/jira/browse/HBASE-13408?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14436431#comment-14436431
] 

stack commented on HBASE-13408:
-------------------------------

In the doc it says the proposal is for in-memory column families only and may not be generally
unless there are lots of instances of Cells at exact same coordinates. But as Lars says above,
the memstore is a costly data structure for keeping all in-memory state sorted; a compacted
version that was hfile sorted could make for better perf than the skiplist (as speculated
over in HBASE-5311).

Other comments:

bq. The data is kept in memory for as long as possible

What Duo says above...We need to flush to free up WALs to contain our WAL-burden of edits
to replay on crash.

bq. pull the last component of the compaction pipeline and shift it to snapshot

What is involved running above step?

bq. CellSetMgr

What is one of these? It is a skiplist?

What do you think of the attempt at lockless snapshotting suggested over in HBASE-5311

Thanks for taking this up



> 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.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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