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From Sandy Ryza <sandy.r...@cloudera.com>
Subject Re: Why my tests shows Yarn is worse than MRv1 for terasort?
Date Fri, 07 Jun 2013 06:53:35 GMT
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 <samliuhadoop@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 <harsh@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 <samliuhadoop@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 <marcosluis2186@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 <samliuhadoop@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
>>
>
>

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