Hi Harsh, Thanks for your detailed response! Now, the efficiency of my Yarn cluster improved a lot after increasing the reducer number(mapreduce.job.reduces) in mapred-site.xml. But I still have some questions about the way of Yarn to execute MRv1 job:
1.In Hadoop 1.x, a job will be executed by map task and reduce task together, with a typical process(map > shuffle > reduce). In Yarn, as I know, a MRv1 job will be executed only by ApplicationMaster. - Yarn could run multiple kinds of jobs(MR, MPI, ...), but, MRv1 job has special execution process(map > shuffle > reduce) in Hadoop 1.x, and how Yarn execute a MRv1 job? still include some special MR steps in Hadoop 1.x, like map, sort, merge, combine and shuffle? - Do the MRv1 parameters still work for Yarn? Like mapreduce.task.io.sort.mb and mapreduce.map.sort.spill.percent? - What's the general process for ApplicationMaster of Yarn to execute a job? 2. In Hadoop 1.x, we can set the map/reduce slots by setting 'mapred.tasktracker.map.tasks.maximum' and 'mapred.tasktracker.reduce.tasks.maximum' - For Yarn, above tow parameter do not work any more, as yarn uses container instead, right? - For Yarn, we can set the whole physical mem for a NodeManager using ' yarn.nodemanager.resource.memory-mb'. But how to set the default size of physical mem of a container? - How to set the maximum size of physical mem of a container? By the parameter of 'mapred.child.java.opts'? Thanks as always! 2013/6/9 Harsh J <ha...@cloudera.com> > Hi Sam, > > > - How to know the container number? Why you say it will be 22 containers > due to a 22 GB memory? > > The MR2's default configuration requests 1 GB resource each for Map > and Reduce containers. It requests 1.5 GB for the AM container that > runs the job, additionally. This is tunable using the properties > Sandy's mentioned in his post. > > > - My machine has 32 GB memory, how many memory is proper to be assigned > to containers? > > This is a general question. You may use the same process you took to > decide optimal number of slots in MR1 to decide this here. Every > container is a new JVM, and you're limited by the CPUs you have there > (if not the memory). Either increase memory requests from jobs, to > lower # of concurrent containers at a given time (runtime change), or > lower NM's published memory resources to control the same (config > change). > > > - In mapred-site.xml, if I set 'mapreduce.framework.name' to be 'yarn', > will other parameters for mapred-site.xml still work in yarn framework? > Like 'mapreduce.task.io.sort.mb' and 'mapreduce.map.sort.spill.percent' > > Yes, all of these properties will still work. Old properties specific > to JobTracker or TaskTracker (usually found as a keyword in the config > name) will not apply anymore. > > On Sun, Jun 9, 2013 at 2:21 PM, sam liu <samliuhad...@gmail.com> wrote: > > Hi Harsh, > > > > According to above suggestions, I removed the duplication of setting, and > > reduce the value of 'yarn.nodemanager.resource.cpu-cores', > > 'yarn.nodemanager.vcores-pcores-ratio' and > > 'yarn.nodemanager.resource.memory-mb' to 16, 8 and 12000. Ant then, the > > efficiency improved about 18%. I have questions: > > > > - How to know the container number? Why you say it will be 22 containers > due > > to a 22 GB memory? > > - My machine has 32 GB memory, how many memory is proper to be assigned > to > > containers? > > - In mapred-site.xml, if I set 'mapreduce.framework.name' to be 'yarn', > will > > other parameters for mapred-site.xml still work in yarn framework? Like > > 'mapreduce.task.io.sort.mb' and 'mapreduce.map.sort.spill.percent' > > > > Thanks! > > > > > > > > 2013/6/8 Harsh J <ha...@cloudera.com> > >> > >> Hey Sam, > >> > >> Did you get a chance to retry with Sandy's suggestions? The config > >> appears to be asking NMs to use roughly 22 total containers (as > >> opposed to 12 total tasks in MR1 config) due to a 22 GB memory > >> resource. This could impact much, given the CPU is still the same for > >> both test runs. > >> > >> On Fri, Jun 7, 2013 at 12:23 PM, Sandy Ryza <sandy.r...@cloudera.com> > >> wrote: > >> > 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 <samliuhad...@gmail.com> > 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 <ha...@cloudera.com> > >> >>> > >> >>> 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 <samliuhad...@gmail.com> > >> >>> 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 <marcosluis2...@gmail.com> > >> >>> >> > >> >>> >> Why not to tune the configurations? > >> >>> >> Both frameworks have many areas to tune: > >> >>> >> - Combiners, Shuffle optimization, Block size, etc > >> >>> >> > >> >>> >> > >> >>> >> > >> >>> >> 2013/6/6 sam liu <samliuhad...@gmail.com> > >> >>> >>> > >> >>> >>> 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 > >> >> > >> >> > >> > > >> > >> > >> > >> -- > >> Harsh J > > > > > > > > -- > Harsh J >
