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Multiple HPROF files are getting generated with hdfs user

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Expert Contributor

Hi All,

 

I have 3 node cluster running on CentOS 6.7.

Namenode has been facing issue of .hprof files in /tmp directory leading to 100% disk usage on / mount. The owner of these files are hdfs:hadoop.

hprof.PNG

 

I know hprof is created when we have a heap dump of the process at the time of the failure. This is typically seen in scenarios with "java.lang.OutOfMemoryError".

Hence I increased the RAM of my NN to 112GB from 56GB. 

My configs are:

 

yarn.nodemanager.resource.memory-mb - 12GB

yarn.scheduler.maximum-allocation-mb - 16GB

mapreduce.map.memory.mb - 4GB

mapreduce.reduce.memory.mb - 4GB

mapreduce.map.java.opts.max.heap - 3GB

mapreduce.reduce.java.opts.max.heap - 3GB

namenode_java_heapsize - 6GB

secondarynamenode_java_heapsize - 6GB

dfs_datanode_max_locked_memory - 3GB

 

The datanode log on NN has below error but they are also present on other DN (on all 3 nodes basically):

 

2017-05-03 10:03:17,914 ERROR org.apache.hadoop.hdfs.server.datanode.DataNode: DataNode{data=FSDataset{dirpath='[/bigdata/dfs/dn/current]'}, localName='XXXX.azure.com:50010', datanodeUuid='4ea75665-b223-4456-9308-1defcad54c89', xmitsInProgress=0}:Exception transfering block BP-939287337-X.X.X.4-148408516
3925:blk_1077604623_3864267 to mirror X.X.X.5:50010: java.net.SocketTimeoutException: 65000 millis timeout while waiting for channel to be ready for read. ch : java.ni
o.channels.SocketChannel[connected local=/X.X.X.4:43801 remote=X.X.X.5:50010]
2017-05-03 10:03:17,922 ERROR org.apache.hadoop.hdfs.server.datanode.DataNode: XXXX.azure.com:50010:DataXceiver error processing WRITE_BLO
CK operation  src: /X.X.X.4:53902 dst: /X.X.X.4:50010
java.net.SocketTimeoutException: 65000 millis timeout while waiting for channel to be ready for read. ch : java.nio.channels.SocketChannel[connected local=/X.X.X.4:438
01 remote=/X.X.X.5:50010]
        at org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java:164)
        at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:161)
        at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:131)
        at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:118)
        at java.io.FilterInputStream.read(FilterInputStream.java:83)
        at java.io.FilterInputStream.read(FilterInputStream.java:83)
        at org.apache.hadoop.hdfs.protocolPB.PBHelper.vintPrefixed(PBHelper.java:2241)
        at org.apache.hadoop.hdfs.server.datanode.DataXceiver.writeBlock(DataXceiver.java:743)
        at org.apache.hadoop.hdfs.protocol.datatransfer.Receiver.opWriteBlock(Receiver.java:169)
        at org.apache.hadoop.hdfs.protocol.datatransfer.Receiver.processOp(Receiver.java:106)
        at org.apache.hadoop.hdfs.server.datanode.DataXceiver.run(DataXceiver.java:246)
        at java.lang.Thread.run(Thread.java:745)
2017-05-03 10:04:52,371 ERROR org.apache.hadoop.hdfs.server.datanode.DataNode: XXXX.azure.com:50010:DataXceiver error processing WRITE_BLO
CK operation  src: /X.X.X.4:54258 dst: /X.X.X.4:50010
java.io.IOException: Premature EOF from inputStream
        at org.apache.hadoop.io.IOUtils.readFully(IOUtils.java:201)
        at org.apache.hadoop.hdfs.protocol.datatransfer.PacketReceiver.doReadFully(PacketReceiver.java:213)
        at org.apache.hadoop.hdfs.protocol.datatransfer.PacketReceiver.doRead(PacketReceiver.java:134)
        at org.apache.hadoop.hdfs.protocol.datatransfer.PacketReceiver.receiveNextPacket(PacketReceiver.java:109)
        at org.apache.hadoop.hdfs.server.datanode.BlockReceiver.receivePacket(BlockReceiver.java:500)
        at org.apache.hadoop.hdfs.server.datanode.BlockReceiver.receiveBlock(BlockReceiver.java:896)

This log is getting such errors even during night time or early morning when nothing is running.

 

My cluster is used to getting webpage info using wget and then processing the data using SparkR.

 

Apart from this, I am also getting Block count more than threshold, for which I have another thread. http://community.cloudera.com/t5/Storage-Random-Access-HDFS/Datanodes-report-block-count-more-than-t...

 

Please help! I am worried about my cluster.

 

Cluster configs(after recent upgrades) -

NN: RAM- 112GB, Core 16, Disk : 500GB

DN1: RAM- 56GB, Core 8, Disk: 400GB

DN2: RAM- 28GB, Core 4, Disk: 400GB

 

Thanks,

Shilpa

 

1 ACCEPTED SOLUTION

avatar
Super Collaborator

The dump refer to the data-node role. Is their a data-node role on the host you call name-node ?

If yes, it is the memory of that role you need to increase.

 

I guess the memory allocated to that role is too low.

HDFS > configuration > DataNode DefaultGroup > Resource Management > Java Heap Size of DataNode in Bytes in Cloudera Manager.

 

Also, if you don't investigate the content of the dump you can desactivate the generation of the dump in case of OOM in order to not fill up your disk.

View solution in original post

4 REPLIES 4

avatar
Super Collaborator

The dump refer to the data-node role. Is their a data-node role on the host you call name-node ?

If yes, it is the memory of that role you need to increase.

 

I guess the memory allocated to that role is too low.

HDFS > configuration > DataNode DefaultGroup > Resource Management > Java Heap Size of DataNode in Bytes in Cloudera Manager.

 

Also, if you don't investigate the content of the dump you can desactivate the generation of the dump in case of OOM in order to not fill up your disk.

avatar
Expert Contributor
Thanks @mathieu.d.

As a work around what I have done is, have changed the HeapDump Path to /dev/null for datanode.

I will check what you have suggested and get back as my NN has a datanode role.

avatar
Expert Contributor

Java Heap for Datanode was 1GB for all 3 DNs. Hence I changed the Heap for only NN's Datanode to 3GB. Also, changed the OOM heap dump back to /tmp Just to see if HPROF files are getting generated or not.

 

Java heap DN.PNGThanks,

Shilpa

avatar
Expert Contributor

After increasing the Heap size of Datanode role on my NN, I have not seen Hprof files getting created. The issue is resolved.

 

Thanks @mathieu.d