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Re: How Yarn execute MRv1 job?
Appreciating for the detailed answers! Here are three further questions:

- Yarn maintains backwards compatibility, and MRv1 job could run on Yarn.
If yarn does not ask existing MRv1 job to do any code change, but why we
should recompile the MRv1 job?
- Which yarn jar files are required in the recompiling?
- In a cluster with Hadoop 1.1.1 and other Hadoop related components(HBase
0.94.3,  Hive 0.9.0, Zookeeper 3.4.5,...), if we want to replace Hadoop
1.1.1 with yarn, do we need to recompile all other Hadoop related
components again with yarn jar files? Without any code change?

Thanks in advance!

2013/6/19 Rahul Bhattacharjee <[EMAIL PROTECTED]>

> Thanks Arun and Devraj , good to know.
>
>
>
> On Wed, Jun 19, 2013 at 11:24 AM, Arun C Murthy <[EMAIL PROTECTED]>wrote:
>
>> Not true, the CapacityScheduler has support for both CPU & Memory now.
>>
>> On Jun 18, 2013, at 10:41 PM, Rahul Bhattacharjee <
>> [EMAIL PROTECTED]> wrote:
>>
>> Hi Devaraj,
>>
>> As for the container request request for yarn container , currently only
>> memory is considered as resource , not cpu. Please correct.
>>
>> Thanks,
>> Rahul
>>
>>
>> On Wed, Jun 19, 2013 at 11:05 AM, Devaraj k <[EMAIL PROTECTED]> wrote:
>>
>>>  Hi Sam,****
>>>
>>>   Please find the answers for your queries. ****
>>>
>>>
>>> >- 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?****
>>>
>>> ** **
>>>
>>> In Yarn, it is a concept of application. MR Job is one kind of
>>> application which makes use of MRAppMaster(i.e ApplicationMaster for the
>>> application). If we want to run different kinds of applications we should
>>> have ApplicationMaster for each kind of application.****
>>>
>>> ** **
>>>
>>> >- Do the MRv1 parameters still work for Yarn? Like
>>> mapreduce.task.io.sort.mb and mapreduce.map.sort.spill.percent?****
>>>
>>> These configurations still work for MR Job in Yarn.****
>>>
>>>
>>> >- What's the general process for ApplicationMaster of Yarn to execute a
>>> job?****
>>>
>>> MRAppMaster(Application Master for MR Job) does the Job life cycle which
>>> includes getting the containers for maps & reducers, launch the containers
>>> using NM, tacks the tasks status till completion, manage the failed tasks.
>>> ****
>>>
>>>
>>> >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?****
>>>
>>> Correct, these params don’t work in yarn. In Yarn it is completely based
>>> on the resources(memory, cpu). Application Master can request the RM for
>>> resources to complete the tasks for that application.****
>>>
>>>
>>> >- 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?****
>>>
>>> ApplicationMaster is responsible for getting the containers from RM by
>>> sending the resource requests. For MR Job, you can use
>>> "mapreduce.map.memory.mb" and “mapreduce.reduce.memory.mb" configurations
>>> for specifying the map & reduce container memory sizes.****
>>>
>>> ** **
>>>
>>> >- How to set the maximum size of physical mem of a container? By the
>>> parameter of 'mapred.child.java.opts'?****
>>>
>>> It can be set based on the resources requested for that container.****
>>>
>>> ** **
>>>
>>> ** **
>>>
>>> Thanks****
>>>
>>> Devaraj K****
>>>
>>> *From:* sam liu [mailto:[EMAIL PROTECTED]]
>>> *Sent:* 19 June 2013 08:16
>>> *To:* [EMAIL PROTECTED]
>>> *Subject:* How Yarn execute MRv1 job?****
>>>
>>> ** **
>>>
>>> Hi,
>>>
>>> 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