[
https://issues.apache.org/jira/browse/HBASE-14463?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Yu Li updated HBASE-14463:
--------------------------
Attachment: GC_with_WeakObjectPool.png
Launched several YCSB workloadc testing against a 4 nodes dev cluster with
zipfian/hotspot requestdistributio, each round running for around 20 minutes
and watch the GC status with VirsualVM, the result shows no memory leak in the
new implementation with WeakObjectPool. See the attached screenshot for more
details.
> 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
>
>
> 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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
