Hi Bobby, You are right that the Map outputs when copied will be spilled to the disk, but in case the the reducer cannot accomodate the copy inmemory. (shuffleInMemory and shuffleToDisk are chosen by rammanager based on inmemory size)
But according to the stack trace provided by Mingxi, >org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.shuffleInMemory(ReduceTask.java:1592) > The problem has occured,after the inmemory copy was chosen, Regards, Ravi Teja ________________________________________ From: Robert Evans [ev...@yahoo-inc.com] Sent: 01 December 2011 21:44:50 To: common-dev@hadoop.apache.org Subject: Re: Hadoop - non disk based sorting? Mingxi, My understanding was that just like with the maps that when a reducer's in memory buffer fills up it too will spill to disk as part of the sort. In fact I think it uses the exact same code for doing the sort as the map does. There may be an issue where your sort buffer is some how too large for the amount of heap that you requested as part of the mapred.child.java.opts. I have personally run a reduce that took in 300GB of data, which it successfully sorted, to test this very thing. And no the box did not have 300 GB of RAM. --Bobby Evans On 12/1/11 4:12 AM, "Ravi teja ch n v" <raviteja.c...@huawei.com> wrote: Hi Mingxi , >So, why when map outputs are huge, reducer will not able to copy them? The Reducer will copy the Map output into its inmemory buffer. When the Reducer JVM doesnt have enough memory to accomodate the Map output, then it leads to OutOfMemoryException. >Can you please kindly explain what's the function of mapred.child.java.opts? >how does it relate to copy? The Maps and Reducers will be launched in separate child JVMs launched at the Tasktrackers. When the Tasktracker launches the Map or Reduce JVMs, it uses the mapred.child.java.opts as JVM arguments for the new child JVMs. Regards, Ravi Teja ________________________________________ From: Mingxi Wu [mingxi...@turn.com] Sent: 01 December 2011 12:37:54 To: common-dev@hadoop.apache.org Subject: RE: Hadoop - non disk based sorting? Thanks Ravi. So, why when map outputs are huge, reducer will not able to copy them? Can you please kindly explain what's the function of mapred.child.java.opts? how does it relate to copy? Thank you, Mingxi -----Original Message----- From: Ravi teja ch n v [mailto:raviteja.c...@huawei.com] Sent: Tuesday, November 29, 2011 9:46 PM To: common-dev@hadoop.apache.org Subject: RE: Hadoop - non disk based sorting? 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
