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
RE: Hadoop - non disk based sorting?
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
RE: Hadoop - non disk based sorting?
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
RE: Hadoop - non disk based sorting?
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" 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
407 error while building Hadoop
Hi Team, I have got a problem building Hadoop with the proxy settings. My linux machine has maven proxy settings configured and working fine, but the build fails with the following error, inspite of passing the username and pwd. mvn package -Pdist -Dtar -Dhttp.proxyHost=***.com -Dhttp.proxyPort=8080 -Dhttp.proxyUser= -Dhttp.proxyPass= main: [INFO] Executed tasks [INFO] [INFO] --- maven-antrun-plugin:1.6:run (xprepare-package-hadoop-daemon) @ hadoop-hdfs --- [INFO] Executing tasks main: [get] Getting: http://archive.apache.org/dist/commons/daemon/binaries/1.0.3/linux/commons-daemon-1.0.3-bin-linux-i686.tar.gz [get] To: /home/isap/.hudson/jobs/Hadoop/workspace/hadoop-hdfs-project/hadoop-hdfs/downloads/commons-daemon-1.0.3-bin-linux-i686.tar.gz [get] Error opening connection java.io.IOException: Server returned HTTP response code: 407 for URL: http://archive.apache.org/dist/commons/daemon/binaries/1.0.3/linux/commons-daemon-1.0.3-bin-linux-i686.tar.gz [get] Error opening connection java.io.IOException: Server returned HTTP response code: 407 for URL: http://archive.apache.org/dist/commons/daemon/binaries/1.0.3/linux/commons-daemon-1.0.3-bin-linux-i686.tar.gz [get] Error opening connection java.io.IOException: Server returned HTTP response code: 407 for URL: http://archive.apache.org/dist/commons/daemon/binaries/1.0.3/linux/commons-daemon-1.0.3-bin-linux-i686.tar.gz [get] Can't get http://archive.apache.org/dist/commons/daemon/binaries/1.0.3/linux/commons-daemon-1.0.3-bin-linux-i686.tar.gz to /home/isap/.hudson/jobs/Hadoop/workspace/hadoop-hdfs-project/hadoop-hdfs/downloads/commons-daemon-1.0.3-bin-linux-i686.tar.gz [INFO] [INFO] Reactor Summary: [INFO] [INFO] Apache Hadoop HDFS FAILURE [1:36.333s] [INFO] Apache Hadoop HttpFS .. SKIPPED [INFO] Apache Hadoop HDFS BookKeeper Journal . SKIPPED Any help will be highly appreciable... Thanks and Regards, Ravi Teja
[jira] [Created] (HADOOP-13124) Support weights/priority for user in Faircallqueue
Ravi Teja Ch N V created HADOOP-13124: - Summary: Support weights/priority for user in Faircallqueue Key: HADOOP-13124 URL: https://issues.apache.org/jira/browse/HADOOP-13124 Project: Hadoop Common Issue Type: New Feature Affects Versions: 2.6.0 Reporter: Ravi Teja Ch N V Fair call queue evaluates fairness across all the user submissions. This might be unfair for the users with more priority/importance than others and users whose usage is higher than others. Having priorities or weights in faircallqueue will enable the weighted fair share , which will enable these scenarios. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: common-dev-unsubscr...@hadoop.apache.org For additional commands, e-mail: common-dev-h...@hadoop.apache.org
[jira] [Created] (HADOOP-7926) Test-patch should have maven.test.failure.ignore,maven.test.error.ignore to run all the tests even in case of failure/error.
Test-patch should have maven.test.failure.ignore,maven.test.error.ignore to run all the tests even in case of failure/error. Key: HADOOP-7926 URL: https://issues.apache.org/jira/browse/HADOOP-7926 Project: Hadoop Common Issue Type: Bug Components: build Affects Versions: 0.23.0 Reporter: Ravi Teja Ch N V This approach will help to know all the failures even if some testcase fails. -- This message is automatically generated by JIRA. If you think it was sent incorrectly, please contact your JIRA administrators: https://issues.apache.org/jira/secure/ContactAdministrators!default.jspa For more information on JIRA, see: http://www.atlassian.com/software/jira
