Hey Sam, Thanks for sharing your results. I'm definitely curious about what's causing the difference.
A couple observations: It looks like you've got yarn.nodemanager.resource.memory-mb in there twice with two different values. Your max JVM memory of 1000 MB is (dangerously?) close to the default mapreduce.map/reduce.memory.mb of 1024 MB. Are any of your tasks getting killed for running over resource limits? -Sandy On Thu, Jun 6, 2013 at 10:21 PM, sam liu <[email protected]> wrote: > The terasort execution log shows that reduce spent about 5.5 mins from 33% > to 35% as below. > 13/06/10 08:02:22 INFO mapreduce.Job: map 100% reduce 31% > 13/06/10 08:02:25 INFO mapreduce.Job: map 100% reduce 32% > 13/06/10 *08:02:46* INFO mapreduce.Job: map 100% reduce 33% > 13/06/10 *08:08:16* INFO mapreduce.Job: map 100% reduce 35% > 13/06/10 08:08:19 INFO mapreduce.Job: map 100% reduce 40% > 13/06/10 08:08:22 INFO mapreduce.Job: map 100% reduce 43% > > Any way, below are my configurations for your reference. Thanks! > *(A) core-site.xml* > only define 'fs.default.name' and 'hadoop.tmp.dir' > > *(B) hdfs-site.xml* > <property> > <name>dfs.replication</name> > <value>1</value> > </property> > > <property> > <name>dfs.name.dir</name> > <value>/opt/hadoop-2.0.4-alpha/temp/hadoop/dfs_name_dir</value> > </property> > > <property> > <name>dfs.data.dir</name> > <value>/opt/hadoop-2.0.4-alpha/temp/hadoop/dfs_data_dir</value> > </property> > > <property> > <name>dfs.block.size</name> > <value>134217728</value><!-- 128MB --> > </property> > > <property> > <name>dfs.namenode.handler.count</name> > <value>64</value> > </property> > > <property> > <name>dfs.datanode.handler.count</name> > <value>10</value> > </property> > > *(C) mapred-site.xml* > <property> > <name>mapreduce.cluster.temp.dir</name> > <value>/opt/hadoop-2.0.4-alpha/temp/hadoop/mapreduce_temp</value> > <description>No description</description> > <final>true</final> > </property> > > <property> > <name>mapreduce.cluster.local.dir</name> > <value>/opt/hadoop-2.0.4-alpha/temp/hadoop/mapreduce_local_dir</value> > <description>No description</description> > <final>true</final> > </property> > > <property> > <name>mapreduce.child.java.opts</name> > <value>-Xmx1000m</value> > </property> > > <property> > <name>mapreduce.framework.name</name> > <value>yarn</value> > </property> > > <property> > <name>mapreduce.tasktracker.map.tasks.maximum</name> > <value>8</value> > </property> > > <property> > <name>mapreduce.tasktracker.reduce.tasks.maximum</name> > <value>4</value> > </property> > > > <property> > <name>mapreduce.tasktracker.outofband.heartbeat</name> > <value>true</value> > </property> > > *(D) yarn-site.xml* > <property> > <name>yarn.resourcemanager.resource-tracker.address</name> > <value>node1:18025</value> > <description>host is the hostname of the resource manager and > port is the port on which the NodeManagers contact the Resource > Manager. > </description> > </property> > > <property> > <description>The address of the RM web application.</description> > <name>yarn.resourcemanager.webapp.address</name> > <value>node1:18088</value> > </property> > > > <property> > <name>yarn.resourcemanager.scheduler.address</name> > <value>node1:18030</value> > <description>host is the hostname of the resourcemanager and port is > the port > on which the Applications in the cluster talk to the Resource Manager. > </description> > </property> > > > <property> > <name>yarn.resourcemanager.address</name> > <value>node1:18040</value> > <description>the host is the hostname of the ResourceManager and the > port is the port on > which the clients can talk to the Resource Manager. </description> > </property> > > <property> > <name>yarn.nodemanager.local-dirs</name> > <value>/opt/hadoop-2.0.4-alpha/temp/hadoop/yarn_nm_local_dir</value> > <description>the local directories used by the > nodemanager</description> > </property> > > <property> > <name>yarn.nodemanager.address</name> > <value>0.0.0.0:18050</value> > <description>the nodemanagers bind to this port</description> > </property> > > <property> > <name>yarn.nodemanager.resource.memory-mb</name> > <value>10240</value> > <description>the amount of memory on the NodeManager in > GB</description> > </property> > > <property> > <name>yarn.nodemanager.remote-app-log-dir</name> > <value>/opt/hadoop-2.0.4-alpha/temp/hadoop/yarn_nm_app-logs</value> > <description>directory on hdfs where the application logs are moved to > </description> > </property> > > <property> > <name>yarn.nodemanager.log-dirs</name> > <value>/opt/hadoop-2.0.4-alpha/temp/hadoop/yarn_nm_log</value> > <description>the directories used by Nodemanagers as log > directories</description> > </property> > > <property> > <name>yarn.nodemanager.aux-services</name> > <value>mapreduce.shuffle</value> > <description>shuffle service that needs to be set for Map Reduce to > run </description> > </property> > > <property> > <name>yarn.resourcemanager.client.thread-count</name> > <value>64</value> > </property> > > <property> > <name>yarn.nodemanager.resource.cpu-cores</name> > <value>24</value> > </property> > > <property> > <name>yarn.nodemanager.vcores-pcores-ratio</name> > <value>3</value> > </property> > > <property> > <name>yarn.nodemanager.resource.memory-mb</name> > <value>22000</value> > </property> > > <property> > <name>yarn.nodemanager.vmem-pmem-ratio</name> > <value>2.1</value> > </property> > > > > 2013/6/7 Harsh J <[email protected]> > >> Not tuning configurations at all is wrong. YARN uses memory resource >> based scheduling and hence MR2 would be requesting 1 GB minimum by >> default, causing, on base configs, to max out at 8 (due to 8 GB NM >> memory resource config) total containers. Do share your configs as at >> this point none of us can tell what it is. >> >> Obviously, it isn't our goal to make MR2 slower for users and to not >> care about such things :) >> >> On Fri, Jun 7, 2013 at 8:45 AM, sam liu <[email protected]> wrote: >> > At the begining, I just want to do a fast comparision of MRv1 and Yarn. >> But >> > they have many differences, and to be fair for comparison I did not tune >> > their configurations at all. So I got above test results. After >> analyzing >> > the test result, no doubt, I will configure them and do comparison >> again. >> > >> > Do you have any idea on current test result? I think, to compare with >> MRv1, >> > Yarn is better on Map phase(teragen test), but worse on Reduce >> > phase(terasort test). >> > And any detailed suggestions/comments/materials on Yarn performance >> tunning? >> > >> > Thanks! >> > >> > >> > 2013/6/7 Marcos Luis Ortiz Valmaseda <[email protected]> >> >> >> >> Why not to tune the configurations? >> >> Both frameworks have many areas to tune: >> >> - Combiners, Shuffle optimization, Block size, etc >> >> >> >> >> >> >> >> 2013/6/6 sam liu <[email protected]> >> >>> >> >>> Hi Experts, >> >>> >> >>> We are thinking about whether to use Yarn or not in the near future, >> and >> >>> I ran teragen/terasort on Yarn and MRv1 for comprison. >> >>> >> >>> My env is three nodes cluster, and each node has similar hardware: 2 >> >>> cpu(4 core), 32 mem. Both Yarn and MRv1 cluster are set on the same >> env. To >> >>> be fair, I did not make any performance tuning on their >> configurations, but >> >>> use the default configuration values. >> >>> >> >>> Before testing, I think Yarn will be much better than MRv1, if they >> all >> >>> use default configuration, because Yarn is a better framework than >> MRv1. >> >>> However, the test result shows some differences: >> >>> >> >>> MRv1: Hadoop-1.1.1 >> >>> Yarn: Hadoop-2.0.4 >> >>> >> >>> (A) Teragen: generate 10 GB data: >> >>> - MRv1: 193 sec >> >>> - Yarn: 69 sec >> >>> Yarn is 2.8 times better than MRv1 >> >>> >> >>> (B) Terasort: sort 10 GB data: >> >>> - MRv1: 451 sec >> >>> - Yarn: 1136 sec >> >>> Yarn is 2.5 times worse than MRv1 >> >>> >> >>> After a fast analysis, I think the direct cause might be that Yarn is >> >>> much faster than MRv1 on Map phase, but much worse on Reduce phase. >> >>> >> >>> Here I have two questions: >> >>> - Why my tests shows Yarn is worse than MRv1 for terasort? >> >>> - What's the stratage for tuning Yarn performance? Is any materials? >> >>> >> >>> Thanks! >> >> >> >> >> >> >> >> >> >> -- >> >> Marcos Ortiz Valmaseda >> >> Product Manager at PDVSA >> >> http://about.me/marcosortiz >> >> >> > >> >> >> >> -- >> Harsh J >> > >
