Hi Mingxi, >From your stacktrace, I understand that the OutOfMemoryError has actually >occured while copying the MapOutputs, not while sorting them.
Since your Mapoutputs are huge and your reducer does have enough heap memory, you got the problem. When you have made the reducers to 200, your Map outputs have got partitioned amoung 200 reducers, so you didnt get this problem. By setting the max memory of your reducer with mapred.child.java.opts, you can get over this problem. Regards, Ravi teja ________________________________________ From: Mingxi Wu [mingxi...@turn.com] Sent: 30 November 2011 05:14:49 To: common-dev@hadoop.apache.org Subject: Hadoop - non disk based sorting? Hi, I have a question regarding the shuffle phase of reducer. It appears when there are large map output (in my case, 5 billion records), I will have out of memory Error like below. Error: java.lang.OutOfMemoryError: Java heap space at org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.shuffleInMemory(ReduceTask.java:1592) at org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.getMapOutput(ReduceTask.java:1452) at org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.copyOutput(ReduceTask.java:1301) at org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.run(ReduceTask.java:1233) However, I thought the shuffling phase is using disk-based sort, which is not constraint by memory. So, why will user run into this outofmemory error? After I increased my number of reducers from 100 to 200, the problem went away. Any input regarding this memory issue would be appreciated! Thanks, Mingxi
