each container gets 1GB at the moment.
can you try increasing memory per reducer ?
On Wed, Oct 31, 2012 at 9:15 PM, Eduard Skaley <[email protected]
<mailto:[email protected]>> wrote:
Hello,
I'm getting this Error through job execution:
16:20:26 INFO [main] Job - map 100% reduce 46%
16:20:27 INFO [main] Job - map 100% reduce 51%
16:20:29 INFO [main] Job - map 100% reduce 62%
16:20:30 INFO [main] Job - map 100% reduce 64%
16:20:32 INFO [main] Job - Task Id :
attempt_1351680008718_0018_r_000006_0, Status : FAILED
Error:
org.apache.hadoop.mapreduce.task.reduce.Shuffle$ShuffleError:
error in shuffle in fetcher#2
at
org.apache.hadoop.mapreduce.task.reduce.Shuffle.run(Shuffle.java:123)
at org.apache.hadoop.mapred.ReduceTask.run(ReduceTask.java:371)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:152)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:396)
at
org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1332)
at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:147)
Caused by: java.lang.OutOfMemoryError: Java heap space
at
org.apache.hadoop.io.BoundedByteArrayOutputStream.<init>(BoundedByteArrayOutputStream.java:58)
at
org.apache.hadoop.io.BoundedByteArrayOutputStream.<init>(BoundedByteArrayOutputStream.java:45)
at
org.apache.hadoop.mapreduce.task.reduce.MapOutput.<init>(MapOutput.java:97)
at
org.apache.hadoop.mapreduce.task.reduce.MergeManager.unconditionalReserve(MergeManager.java:286)
at
org.apache.hadoop.mapreduce.task.reduce.MergeManager.reserve(MergeManager.java:276)
at
org.apache.hadoop.mapreduce.task.reduce.Fetcher.copyMapOutput(Fetcher.java:384)
at
org.apache.hadoop.mapreduce.task.reduce.Fetcher.copyFromHost(Fetcher.java:319)
at
org.apache.hadoop.mapreduce.task.reduce.Fetcher.run(Fetcher.java:179)
16:20:33 INFO [main] Job - map 100% reduce 65%
16:20:36 INFO [main] Job - map 100% reduce 67%
16:20:39 INFO [main] Job - map 100% reduce 69%
16:20:41 INFO [main] Job - map 100% reduce 70%
16:20:43 INFO [main] Job - map 100% reduce 71%
I have no clue what the issue could be for this. I googled this
issue and checked several sources of possible solutions but
nothing does fit.
I saw this jira entry which could fit:
https://issues.apache.org/jira/browse/MAPREDUCE-4655.
Here somebody recommends to increase the value for the property
dfs.datanode.max.xcievers / dfs.datanode.max.receiver.threads to
4096, but this is the value for our cluster.
http://yaseminavcular.blogspot.de/2011/04/common-hadoop-hdfs-exceptions-with.html
The issue with the to small input files doesn't fit I think,
because the map phase reads 137 files with each 130MB. Block Size
is 128MB.
The cluster uses version 2.0.0-cdh4.1.1,
581959ba23e4af85afd8db98b7687662fe9c5f20.
Thx
--
Nitin Pawar
