Thanks a lot for the help.

-Nirmal
From: Arun C Murthy [mailto:[email protected]]
Sent: Friday, January 17, 2014 9:09 PM
To: [email protected]
Subject: Re: Doubts: Deployment and Configuration of YARN cluster


On Jan 16, 2014, at 9:14 PM, Nirmal Kumar 
<[email protected]<mailto:[email protected]>> wrote:


Hi Arun,

Thanks a lot for the clarification.

I understand it like in *yarn-site.xml* first I can set the max. and min. 
container size GLOBALLY for all the nodes in the cluster through:
*         {yarn.scheduler.minimum-allocation-mb}
*         {yarn.scheduler.maximum-allocation-mb}

Then at each of the node I can set the NM memory using:
*         {yarn.nodemanager.resource.memory-mb}



Exactly! :)


My 2nd doubt is whether we can run Mappers\Reducers tasks with varying memory 
options at each of the slave nodes.
That is, can we change the following properties in *mapred-site.xml* at each of 
the slave nodes? This is because depending on the machine's power we can adjust 
the memory options for the M\R tasks.
*         {mapreduce.map.memory.mb}
*         {mapreduce.map.java.opts}
*         {mapreduce.reduce.memory.mb}
*         {mapreduce.reduce.java.opts}

You can change these for every single job, so each job can have different 
requirements.

$ bin/hadoop jar hadoop-examples.jar word count -Dmapreduce.map.memory.mb=1024 
...
$ bin/hadoop jar hadoop-examples.jar word count -Dmapreduce.map.memory.mb=2048 
...

hth,
Arun



Thanks,
-Nirmal

From: Arun C Murthy [mailto:[email protected]<http://hortonworks.com>]
Sent: Thursday, January 16, 2014 7:43 PM
To: [email protected]<mailto:[email protected]>
Subject: Re: Doubts: Deployment and Configuration of YARN cluster

No, you can set resources available in each node to be different...

For e.g. Node A: 10G, Node B: 12G.

Now, if min. container size is 1G, the RM will allocate 10 containers to Node A 
and 12 containers to Node B.

hth,
Arun

On Jan 15, 2014, at 11:03 PM, Nirmal Kumar 
<[email protected]<mailto:[email protected]>> wrote:

Hi German,

I went through the links for memory configuration settings/best-practices.
It considers the cluster to be homogenous i.e. same RAM size in all the nodes.

Also on the Yarn whitepaper(Section 3.2 Page 6) I see:
This resource model serves current applications well
in homogeneous environments, but we expect it to
evolve over time as the ecosystem matures and new requirements
emerge.

Does that mean in YARN in order to configure processing capacity like Container 
Size, No. of Containers, No. of Mappers\Reducers the cluster has to be 
homogenous?
How about if I have a *heterogeneous cluster* with varying RAM, disks , cores?

Thanks,
-Nirmal

From: Nirmal Kumar
Sent: Wednesday, January 15, 2014 8:22 PM
To: [email protected]<mailto:[email protected]>
Subject: RE: Doubts: Deployment and Configuration of YARN cluster

Thanks a lot German.

Will go through the links and see if that answers my questions\doubts.

-Nirmal

From: German Florez-Larrahondo [mailto:[email protected]]
Sent: Wednesday, January 15, 2014 7:20 PM
To: [email protected]<mailto:[email protected]>
Subject: RE: Doubts: Deployment and Configuration of YARN cluster

Nirmal

-A good summary regarding memory configuration settings/best-practices can be 
found here. Note that in YARN, the way you configure resource limits dictates 
number of containers in the nodes and in the cluster:
http://dev.hortonworks.com.s3.amazonaws.com/HDPDocuments/HDP2/HDP-2.0.6.0/bk_installing_manually_book/content/rpm-chap1-11.html

-A good intro to YARN configuration is this:
http://www.thecloudavenue.com/2012/01/getting-started-with-nextgen-mapreduce_11.html

Regards
.g



From: Nirmal Kumar [mailto:[email protected]]
Sent: Wednesday, January 15, 2014 7:22 AM
To: [email protected]<mailto:[email protected]>
Subject: Doubts: Deployment and Configuration of YARN cluster

All,

I am new to YARN and have certain doubts regarding the deployment and 
configuration of YARN on a cluster.

As per my understanding to deploy Hadoop 2.x using YARN on a cluster we need to 
distribute the below files to all the slave nodes in the cluster:
*         conf/core-site.xml
*         conf/hdfs-site.xml
*         conf/yarn-site.xml
*         conf/mapred-site.xml

Also we need to ONLY change the following file on each slave nodes:
*         conf/hdfs-site.xml
Need to mention the {dfs.datanode.name.dir} value

Do we need to change any other config file on the slave nodes?
Can I change {yarn.nodemanager.resource.memory-mb} for each NM running on the 
slave nodes?
This is since I might have a *heterogeneous environment* i.e. different nodes 
with different memory and cores. For NM1 I might have 40GB memory and for the 
other say 20GB.

Also,
{mapreduce.map.memory.mb}   specifies the *max. virtual memory* allowed by a 
Hadoop task subprocess.
{mapreduce.map.java.opts}         specify the *max. heap space* of the 
allocated jvm. If you exceed the max heap size, the JVM throws an OOM.
{mapreduce.reduce.memory.mb}
{mapreduce.reduce.java.opts}
are the above properties applicable to all the Map\Reduce tasks(from different 
Map Reduce applications) in general, running on different slave nodes?
or Can I change these for a particular slave node.? For e.g. say for a 
SlaveNode1 I run the map task with 4GB and for other SlaveNode2 I run the map 
task with 8GB. Same with the reduce task.

I need some understanding to *configure processing capacity* in the cluster 
like Container Size, No. of Containers, No. of Mappers\Reducers.

Thanks,
-Nirmal

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Hortonworks Inc.
http://hortonworks.com/


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--
Arun C. Murthy
Hortonworks Inc.
http://hortonworks.com/


CONFIDENTIALITY NOTICE
NOTICE: This message is intended for the use of the individual or entity to 
which it is addressed and may contain information that is confidential, 
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this message is not the intended recipient, you are hereby notified that any 
printing, copying, dissemination, distribution, disclosure or forwarding of 
this communication is strictly prohibited. If you have received this 
communication in error, please contact the sender immediately and delete it 
from your system. Thank You.

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