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https://issues.apache.org/jira/browse/HBASE-14463?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Yu Li updated HBASE-14463:
--------------------------
Attachment: HBASE-14463.patch
Attaching the initial patch
> 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
>
>
> 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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