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From "Eshcar Hillel (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HBASE-13408) HBase In-Memory Memstore Compaction
Date Sun, 19 Jul 2015 13:15:06 GMT

    [ https://issues.apache.org/jira/browse/HBASE-13408?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14632806#comment-14632806

Eshcar Hillel commented on HBASE-13408:

Snapshot active set and the pipeline components are all memstore segments, it's an abstraction
that allows to treat all these parts equally.

The memstore compaction should work also with flush-by-column-family. However, even when flushing
by column the WAL sequence id is defined per region (right?) so WAL truncation is not trivial.

forceflushsize is not a new config, instead we take the average of flush size and the blocking
flush size: flush-size < forceflushsize < blockingflushsize.
When considering a flush-by-column-family mode, if the active segment is greater than flush
size then flush is invoked and the active segment is pushed to the pipeline. If the active
+pipeline segments are greater the forceflushsize then the flush is forced and snapshot is
flushed to disk.

All entries (active, pipeline, snapshot) are stored in a skip-list. The performance gain comes
from accessing only memory and not the disk. The skip lists are not too large as multiple
versions of the same key are removed within the compacted pipeline, but are not too small
either, e.g., active is pushed to pipeline only when it gets to 128MB.

When there is no duplication, i.e., a large set of active keys and no multiple versions per
active key compaction is of no help, data is flushed to disk anyway but the compaction pipeline
consumes memory and cpu. We don't see slow down in our experiments but in a setting where
the memory/cpu resources are limited and contended for might show slow down.

> 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,
> 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
> 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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