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From "Hiroshi Ikeda (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HBASE-14463) Severe performance downgrade when parallel reading a single key from BucketCache
Date Mon, 28 Sep 2015 05:24:05 GMT

    [ https://issues.apache.org/jira/browse/HBASE-14463?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14910045#comment-14910045
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Hiroshi Ikeda commented on HBASE-14463:
---------------------------------------

Sorry I still don't understand the relation between read/write locks of {{lockOnMap}} and
"one write many read" block cache. It seems enough to cause contention around {{lockOnMap}}
by many reading block cache.

> Severe performance downgrade when parallel reading a single key from BucketCache
> --------------------------------------------------------------------------------
>
>                 Key: HBASE-14463
>                 URL: https://issues.apache.org/jira/browse/HBASE-14463
>             Project: HBase
>          Issue Type: Bug
>    Affects Versions: 0.98.14, 1.1.2
>            Reporter: Yu Li
>            Assignee: Yu Li
>             Fix For: 2.0.0, 1.2.0, 1.3.0, 1.0.3, 1.1.3, 0.98.16
>
>         Attachments: HBASE-14463.patch, HBASE-14463_v2.patch, HBASE-14463_v3.patch, HBASE-14463_v4.patch,
HBASE-14463_v5.patch, TestBucketCache-new_with_IdLock.png, TestBucketCache-new_with_IdReadWriteLock.png,
TestBucketCache_with_IdLock.png, TestBucketCache_with_IdReadWriteLock-resolveLockLeak.png,
TestBucketCache_with_IdReadWriteLock.png
>
>
> We store feature data of online items in HBase, do machine learning on these features,
and supply the outputs to our online search engine. In such scenario we will launch hundreds
of yarn workers and each worker will read all features of one item(i.e. single rowkey in HBase),
so there'll be heavy parallel reading on a single rowkey.
> We were using LruCache but start to try BucketCache recently to resolve gc issue, and
just as titled we have observed severe performance downgrade. After some analytics we found
the root cause is the lock in BucketCache#getBlock, as shown below
> {code}
>       try {
>         lockEntry = offsetLock.getLockEntry(bucketEntry.offset());
>         // ...
>         if (bucketEntry.equals(backingMap.get(key))) {
>           // ...
>           int len = bucketEntry.getLength();
>           Cacheable cachedBlock = ioEngine.read(bucketEntry.offset(), len,
>               bucketEntry.deserializerReference(this.deserialiserMap));
> {code}
> Since ioEnging.read involves array copy, it's much more time-costed than the operation
in LruCache. And since we're using synchronized in IdLock#getLockEntry, parallel read dropping
on the same bucket would be executed in serial, which causes a really bad performance.
> To resolve the problem, we propose to use ReentranceReadWriteLock in BucketCache, and
introduce a new class called IdReadWriteLock to implement it.



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