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From Harsh J <ha...@cloudera.com>
Subject Re: Why my tests shows Yarn is worse than MRv1 for terasort?
Date Sat, 08 Jun 2013 15:09:54 GMT
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.ryza@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 <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
>>
>>
>



-- 
Harsh J

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