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From "Umesh Agashe (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HBASE-19163) "Maximum lock count exceeded" from region server's batch processing
Date Mon, 13 Nov 2017 20:57:00 GMT

    [ https://issues.apache.org/jira/browse/HBASE-19163?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16250228#comment-16250228

Umesh Agashe commented on HBASE-19163:

[~huaxiang], getRowLock() is reentrant. So, acquiring the lock multiple times on a same row
from same thread may not be an issue here. I am not sure if this is the case here but this
is something to keep in mind while considering the solution. Thanks!

> "Maximum lock count exceeded" from region server's batch processing
> -------------------------------------------------------------------
>                 Key: HBASE-19163
>                 URL: https://issues.apache.org/jira/browse/HBASE-19163
>             Project: HBase
>          Issue Type: Bug
>          Components: regionserver
>    Affects Versions: 1.2.7
>            Reporter: huaxiang sun
>            Assignee: huaxiang sun
> In one of use cases, we found the following exception and replication is stuck.
> {code}
> 2017-10-25 19:41:17,199 WARN  [hconnection-0x28db294f-shared--pool4-t936] client.AsyncProcess:
#3, table=foo, attempt=5/5 failed=262836ops, last exception: java.io.IOException: java.io.IOException:
Maximum lock count exceeded
>         at org.apache.hadoop.hbase.ipc.RpcServer.call(RpcServer.java:2215)
>         at org.apache.hadoop.hbase.ipc.CallRunner.run(CallRunner.java:109)
>         at org.apache.hadoop.hbase.ipc.RpcExecutor$Handler.run(RpcExecutor.java:185)
>         at org.apache.hadoop.hbase.ipc.RpcExecutor$Handler.run(RpcExecutor.java:165)
> Caused by: java.lang.Error: Maximum lock count exceeded
>         at java.util.concurrent.locks.ReentrantReadWriteLock$Sync.fullTryAcquireShared(ReentrantReadWriteLock.java:528)
>         at java.util.concurrent.locks.ReentrantReadWriteLock$Sync.tryAcquireShared(ReentrantReadWriteLock.java:488)
>         at java.util.concurrent.locks.AbstractQueuedSynchronizer.tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1327)
>         at java.util.concurrent.locks.ReentrantReadWriteLock$ReadLock.tryLock(ReentrantReadWriteLock.java:871)
>         at org.apache.hadoop.hbase.regionserver.HRegion.getRowLock(HRegion.java:5163)
>         at org.apache.hadoop.hbase.regionserver.HRegion.doMiniBatchMutation(HRegion.java:3018)
>         at org.apache.hadoop.hbase.regionserver.HRegion.batchMutate(HRegion.java:2877)
>         at org.apache.hadoop.hbase.regionserver.HRegion.batchMutate(HRegion.java:2819)
>         at org.apache.hadoop.hbase.regionserver.RSRpcServices.doBatchOp(RSRpcServices.java:753)
>         at org.apache.hadoop.hbase.regionserver.RSRpcServices.doNonAtomicRegionMutation(RSRpcServices.java:715)
>         at org.apache.hadoop.hbase.regionserver.RSRpcServices.multi(RSRpcServices.java:2148)
>         at org.apache.hadoop.hbase.protobuf.generated.ClientProtos$ClientService$2.callBlockingMethod(ClientProtos.java:33656)
>         at org.apache.hadoop.hbase.ipc.RpcServer.call(RpcServer.java:2170)
>         ... 3 more
> {code}
> While we are still examining the data pattern, it is sure that there are too many mutations
in the batch against the same row, this exceeds the maximum 64k shared lock count and it throws
an error and failed the whole batch.
> There are two approaches to solve this issue.
> 1). Let's say there are mutations against the same row in the batch, we just need to
acquire the lock once for the same row vs to acquire the lock for each mutation.
> 2). We catch the error and start to process whatever it gets and loop back.
> With HBASE-17924, approach 1 seems easy to implement now. 
> Create the jira and will post update/patch when investigation moving forward.

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