Oh! I though distcp works on complete files rather then mappers per
So I guess parallelism would still be there if there are multipel files..
please correct if ther is anything wrong.
On Sun, May 12, 2013 at 5:39 PM, Mohammad Tariq <[EMAIL PROTECTED]> 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,
> On Sat, May 11, 2013 at 11:07 PM, Mohammad Tariq <[EMAIL PROTECTED]>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,
>> On Sat, May 11, 2013 at 8:57 PM, Rahul Bhattacharjee <
>> [EMAIL PROTECTED]> wrote:
>>> 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.
>>> On Sat, May 11, 2013 at 8:02 PM, Shashidhar Rao <
>>> [EMAIL PROTECTED]> wrote:
>>>> 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