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From "Yu Li (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (HBASE-14463) Severe performance downgrade when parallel reading a single key from BucketCache
Date Tue, 22 Sep 2015 13:59:04 GMT

     [ https://issues.apache.org/jira/browse/HBASE-14463?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Yu Li updated HBASE-14463:
--------------------------
    Attachment: TestBucketCache_with_IdReadWriteLock.png
                TestBucketCache_with_IdLock.png

Actually the performance issue also shows in our UT case, say TestBucketCache#testCacheMultiThreadedSingleKey.
Times to run this case w/ and w/o patch are as follows:

w/ IdReadWriteLock and blocksize=16384: 0.172s
w/ IdLock and blocksize=16384: 19.676s

Also attach the screenshots of JUnit result

> 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
>         Attachments: HBASE-14463.patch, TestBucketCache_with_IdLock.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 BlockCache 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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