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From "Vivek Ratan" <viv...@yahoo-inc.com>
Subject RE: Questions about the MapReduce libraries and job schedulers inside JobTracker and JobClient running on Hadoop
Date Fri, 15 Feb 2008 23:01:36 GMT
I read Andy's question a little differently. For a given job, the JobTracker
decides which tasks go to which TaskTracker (the TTs ask for a task to run
and the JT decides which task is the most appropriate). Currently, the JT
favors a task whose input data is on the same host as the TT (if there are
more than one such tasks, it picks the one with the largest input size). It
also looks at failed tasks and certain other criteria. This is very basic
scheduling and there is a lot of scope for improvement. There currently is a
proposal to support rack awareness, so that if the JT can't find a task
whose input data is on the same host as the TT, it looks for a task whose
data is on the same rack. 

You can clearly get more ambitious with your scheduling algorithm. As you
mention, you could use other criteria for scheduling a task: available CPU
or memory, for example. You could assign tasks to hosts that are the most
'free', or aim to distribute tasks across racks, or try some other load
balancing techniques. I believe there are a few discussions on these methods
on Jira, but I don't think there's anything concrete yet. 

BTW, the code that decides what task to run is primarily in

-----Original Message-----
From: Ted Dunning [mailto:tdunning@veoh.com] 
Sent: Friday, February 15, 2008 1:54 PM
To: core-user@hadoop.apache.org
Subject: Re: Questions about the MapReduce libraries and job schedulers
inside JobTracker and JobClient running on Hadoop

Core-user is the right place for this question.

Your description is mostly correct.  Jobs don't necessarily go to all of
your boxes in the cluster, but they may.

Non-uniform machine specs are a bit of a problem that is being (has been?)
addressed by allowing each machine to have a slightly different
hadoop-site.xml file.  That would allow different settings for storage
configuration and number of processes to run.

Even without that, you can level the load a bit by simply running more jobs
on the weak machines than you would otherwise prefer.  Most map reduce
programs are pretty light on memory usage so all that happens is that you
get less throughput on the weak machines.  Since there are normally more map
tasks than cores, this is no big deal; slow machines get fewer tasks and
toward the end of the job, their tasks are even replicated on other machines
in case they can be done more quickly.

On 2/15/08 1:25 PM, "Andrew_Lee@trendmicro.com" <Andrew_Lee@trendmicro.com>

> Hello,
> My first time posting this in the news group.    My question sounds more
> a MapReduce question
> instead of Hadoop HDFS itself.
> To my understanding, the JobClient will submit all Mapper and Reduce 
> class in a uniform way to the cluster?  Can I assume this is more like 
> a uniform scheduler for all the task?
> For example, if I have a 100 node cluster, 1 master (namenode), 99 
> slaves (datanodes).
> When I do
> "JobClient.runJob(jconf)"
> the JobClient will uniformly distributes all Mapper and Reduce class 
> to all 99 nodes.
> In the slaves, they will all have the same hadoop-site.xml and 
> hadoop-default.xml.
> Here comes the main concern, what if some of the nodes don't have the 
> same hardware spec such as memory or CPU speed?  E.g. different batch 
> purchase and repairment overtime that causes this.
> Is there any way that the JobClient can be aware of this and submit 
> different number of tasks to different slaves during start-up?
> For example, for some slaves, it has 16 cores CPU instead of 8 cores.  
> The problem I see here is that for the 16 cores, only 8 cores are 
> used.
> P.S. I'm looking into the JobClient source code and 
> JobProfile/JobTracker to see if this can be done.
> But not sure if I am on the right track.
> If this topic is more likely to be in the core-dev@hadoop.apache.org, 
> please let me know.  I'll send another one to that news group.
> Regards,
> -Andy
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