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From WangRamon <ramon_w...@hotmail.com>
Subject RE: Why most of the free reduce slots are NOT used for my Hadoop Jobs? Thanks.
Date Mon, 12 Mar 2012 08:05:02 GMT

Hi Satish I'm not sure about this, but it's a double Quad-Core CPU, each of them is Hyper
Threading, so 14 mapper and 14 reducer for a node should be OK, right? I find each task finishs
very quickly, no more than 20 seconds, so is this the root cause for this problem? ThanksRamon
 From: Satish.Setty@hcl.com
To: mapreduce-user@hadoop.apache.org; ramon_wang@hotmail.com
Date: Mon, 12 Mar 2012 09:00:49 +0530
Subject: RE: Why most of the free reduce slots are NOT used for my Hadoop Jobs? Thanks.












 
Just guessing if this has something to do with number of cores/cpus.
Myself noticed this for number of map tasks spawned - depending on number of  input files
and also number of tasks to run concurrently depends on number cores/cpus.
 
Thanks
 
 


From: WangRamon [ramon_wang@hotmail.com]

Sent: Saturday, March 10, 2012 5:35 PM

To: mapreduce-user@hadoop.apache.org

Subject: RE: Why most of the free reduce slots are NOT used for my Hadoop Jobs? Thanks.






Joey, here is the information:

 

Cluster Summary (Heap Size is 481.88 MB/1.74 GB)

Maps Reduces Total Submissions Nodes Map Task Capacity Reduce Task Capacity Avg. Tasks/Node
Blacklisted Nodes


0        6             11                          3         42                          
42                                 28.00                 & nbsp; 0 

 

 

Cheers

Ramon



 




Subject: Re: Why most of the free reduce slots are NOT used for my Hadoop Jobs? Thanks.

From: joey@cloudera.com

Date: Sat, 10 Mar 2012 07:00:26 -0500

To: mapreduce-user@hadoop.apache.org



What does the jobtracker web page say is the total reduce capacity?



-Joey








On Mar 10, 2012, at 5:39, WangRamon <ramon_wang@hotmail.com> wrote:







Hi All

 

I'm using Hadoop-0.20-append, the cluster contains 3 nodes, for each node I have 14 map and
14 reduce slots, here is the configuration:

 

 

    <property>

        <name>mapred.tasktracker.map.tasks.maximum</name>

        <value>14</value>

    </property>

    <property>

        <name>mapred.tasktracker.reduce.tasks.maximum</name>

        <value>14</value>

    </property>

    <property>

        <name>mapred.reduce.tasks</name>

        <value>73</value>

    </property>



 

When I submit 5 Jobs simultane ously (the input data for each job is not so big for the test,
it's about 2~5M in size), I assume the Jobs will use the slots as much as possible, each Job
did created 73 Reduce Tasks as configured above, so there will be 5 *
 73 Reduce Tasks in total, but, most of them are in pending state, only about 12 of them are
running, it's too small compared to the total slots number for reduce, 42 reduce slots for
the 3 nodes cluster.


 

What interestring is that it always about 12 of them are running, I tried a few times.

 

So, I thought it might because about the scheduler, I changed it to Fair Scheduler, I created
3 pools, the configure is as below:

 

<?xml version="1.0"?>

<allocations>

 <pool name="pool-a">

  <minMaps>14</minMaps>

  <minReduces>14</minReduces>

  <weight>1.0</weight>

 </pool>

 <pool name="pool -b">

  <minMaps>14</minMaps>

  <minReduces>14</minReduces>

  <weight>1.0</weight>

 </pool>

 <pool name="pool-c">

  <minMaps>14</minMaps>

  <minReduces>14</minReduces>

  <weight>1.0</weight>

 </pool>

 

</allocations> 

 

Then I submit the 5 Jobs simultaneously to these pools randomly again, I can see the jobs
were assigned to different pools, but, it's still the same problem only about 12 of the reduce
tasks from different pool are running, here is the output i copied from
 the Fair Scheduler monitor GUI:

 

pool-a 2 14 14 0 9

pool-b 0 14 14 0 0 

pool-c 2 14 14 0 3 

 

pool-a and pool-c have a total of 12 reduce tasks running, but I do have about 11 reduce slots
at least available in my cluster.

 

So can anyone please give me some suggestions, why NOT all my REDUCE SLOTS are working? Thanks
in advance.


 

Cheers 

Ramon











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