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From yypvsxf19870706 <yypvsxf19870...@gmail.com>
Subject Re: Need help about task slots
Date Sun, 12 May 2013 12:29:58 GMT
    The concept of task slots is used in MRv1.
     In the new version of Hadoop ,MRv2 uses yarn instead of slots.
      You can read it from Hadoop definitive 3rd.

发自我的 iPhone

在 2013-5-12,20:11,Mohammad Tariq <dontariq@gmail.com> 写道:

> Sorry for the blunder guys.
> Warm Regards,
> Tariq
> cloudfront.blogspot.com
> On Sun, May 12, 2013 at 5:39 PM, Mohammad Tariq <dontariq@gmail.com> wrote:
>> @Rahul : I'm sorry as I am not aware of any such document. But you could use distcp
for local to HDFS copy :
>> bin/hadoop  distcp  file:///home/tariq/in.txt  hdfs://localhost:9000/
>> And yes. When you use distcp from local to HDFS, you can't take the pleasure of parallelism
as the data is stored in a non distributed fashion.
>> Warm Regards,
>> Tariq
>> cloudfront.blogspot.com
>> On Sat, May 11, 2013 at 11:07 PM, Mohammad Tariq <dontariq@gmail.com> wrote:
>>> Hello guys, 
>>>             My 2 cents : 
>>> Actually no. of mappers is primarily governed by the no. of InputSplits created
by the InputFormat you are using and the no. of reducers by the no. of partitions you get
after the map phase. Having said that, you should also keep the no of slots, available per
slave, in mind, along with the available memory. But as a general rule you could use this
approach :
>>> Take the no. of virtual CPUs*.75 and that's the no. of slots you can configure.
For example, if you have 12 physical cores (or 24 virtual cores), you would have (24*.75)=18
slots. Now, based on your requirement you could choose how many mappers and reducers you want
to use. With 18 MR slots, you could have 9 mappers and 9 reducers or 12 mappers and 9 reducers
or whatever you think is OK with you. 
>>> I don't know if it ,makes much sense, but it helps me pretty decently.
>>> Warm Regards,
>>> Tariq
>>> cloudfront.blogspot.com
>>> On Sat, May 11, 2013 at 8:57 PM, Rahul Bhattacharjee <rahul.rec.dgp@gmail.com>
>>>> Hi,
>>>> I am also new to Hadoop world , here is my take on your question , if there
is something missing then others would surely correct that.
>>>> For per-YARN , the slots are fixed and computed based on the crunching capacity
of the datanode hardware , once the slots per data node is ascertained , they are divided
into Map and reducer slots and that goes into the config files and remain fixed , until changed.In
YARN , its decided at runtime based on the kind of requirement of particular task.Its very
much possible that a datanode at certain point of time running  10 tasks and another similar
datanode is only running 4 tasks.
>>>> Coming to your question. Based of the data set size , block size of dfs and
input formater , the number of map tasks are decided , generally for file based inputformats
its one mapper per data block , however there are way to change this using configuration settings.Reduce
tasks are set using job configuration.
>>>> General rule as I have read from various documents is that Mappers should
run atleast a minute , so you can run a sample to find out a good size of data block which
would make you mapper run more than a minute. Now it again depends on your SLA , in case you
are not looking for a very small SLA you can choose to run less mappers at the expense of
higher runtime.
>>>> But again its all theory , not sure how these things are handled in actual
prod clusters.
>>>> HTH,
>>>> Thanks,
>>>> Rahul
>>>> On Sat, May 11, 2013 at 8:02 PM, Shashidhar Rao <raoshashidhar123@gmail.com>
>>>>> Hi Users,
>>>>> I am new to Hadoop and confused about task slots in a cluster. How would
I know how many task slots would be required for a job. Is there any empirical formula or
on what basis should I set the number of task slots.
>>>>> Advanced Thanks

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