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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 01:43:04 GMT

    [ https://issues.apache.org/jira/browse/HBASE-14463?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14909969#comment-14909969
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Hiroshi Ikeda commented on HBASE-14463:
---------------------------------------

If you use non-fair mode, it is possible to unexpectedly long wait to lock a write lock when
reading is congest so that there is almost always an unrelated thread waiting an unrelated
read-lock. But fair mode might unexpectedly block everything and quite reduce performance.

Lock striping or using some tricks for ConcurrentMap is also applicable, but in this case
HBASE-14268 might help you, which uses weak references. If you prefer Google Guava, you can
create your own class using Google Guava's CacheBuilder.


> 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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