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From "Anoop Sam John (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HBASE-14463) Severe performance downgrade when parallel reading a single key from BucketCache
Date Tue, 27 Oct 2015 07:03:27 GMT

    [ https://issues.apache.org/jira/browse/HBASE-14463?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14975845#comment-14975845
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Anoop Sam John commented on HBASE-14463:
----------------------------------------

bq.So the most fair way is to test with the same random keys
Yes. I can do that tonight..

Yes with random keys, with run to run there can be different completion time..  That is why
with and with out patch doing the run at least 3 times.  So with out patch itself, we get
slightly different times. But the deviation in not much..  5% down is a big number IMO.  So
wanted to see why we perform poor with the patch. What are the reasons for that.

> 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, 0.98.16
>
>         Attachments: GC_with_WeakObjectPool.png, HBASE-14463.patch, HBASE-14463_v11.patch,
HBASE-14463_v12.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-latest.png,
TestBucketCache_with_IdLock.png, TestBucketCache_with_IdReadWriteLock-latest.png, TestBucketCache_with_IdReadWriteLock-resolveLockLeak.png,
TestBucketCache_with_IdReadWriteLock.png, pe_use_same_keys.patch, test-results.tar.gz
>
>
> 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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