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MapReduce >> mail # user >> Shuffle phase replication factor


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Re: Shuffle phase replication factor
Hi John,
   1. for the number of  simultaneous connection limitations. You can
configure this using the mapred.reduce.parallel.copies flag. the default
 is 5.

   2. For the aggressively disconnect implication, I am afraid it is only a
little. Normally, each reducer will connect to each mapper task, and asking
for the partions of the map output file.   Because there are about 5
simultaneous connections to fetch the map output for each reducer. For a
large MR cluster with 1000 node, and a Huge MR job with 1000 Mapper, and
1000 reducer, for each node, there are only about 5 connections. So the
imply is only a little.
  3.  What happens to the pending/ failing coonection, the short answer is:
just try to reconnect.    There is a List<>, which maintain all the output
of the Mapper that need to copied, and the element will be removed iff the
map output is successfully copied.  A forever loop will keep on look into
the List, and fetch the corrsponding map output.
  All the above answer is based on the Hadoop 1.0.4 source code, especially
the ReduceTask.java file.

yours,
Ling Kun
On Wed, May 22, 2013 at 10:57 PM, John Lilley <[EMAIL PROTECTED]>wrote:

>  Ummmm, is that also the limit for the number of simultaneous
> connections?  In general, one does not need a 1:1 map between threads and
> connections.****
>
> If this is the connection limit, does it imply  that the client or server
> side aggressively disconnects after a transfer?  ****
>
> What happens to the pending/failing connection attempts that exceed the
> limit?****
>
> Thanks!****
>
> john****
>
> ** **
>
> *From:* Rahul Bhattacharjee [mailto:[EMAIL PROTECTED]]
> *Sent:* Wednesday, May 22, 2013 8:52 AM
>
> *To:* [EMAIL PROTECTED]
> *Subject:* Re: Shuffle phase replication factor****
>
> ** **
>
> There are properties/configuration to control the no. of copying threads
> for copy.
> tasktracker.http.threads=40
> Thanks,
> Rahul****
>
> ** **
>
> On Wed, May 22, 2013 at 8:16 PM, John Lilley <[EMAIL PROTECTED]>
> wrote:****
>
> This brings up another nagging question I’ve had for some time.  Between
> HDFS and shuffle, there seems to be the potential for “every node
> connecting to every other node” via TCP.  Are there explicit mechanisms in
> place to manage or limit simultaneous connections?  Is the protocol simply
> robust enough to allow a server-side to disconnect at any time to free up
> slots and the client-side will retry the request?****
>
> Thanks****
>
> john****
>
>  ****
>
> *From:* Shahab Yunus [mailto:[EMAIL PROTECTED]]
> *Sent:* Wednesday, May 22, 2013 8:38 AM****
>
>
> *To:* [EMAIL PROTECTED]
> *Subject:* Re: Shuffle phase replication factor****
>
>  ****
>
> As mentioned by Bertrand, Hadoop, The Definitive Guide, is well... really
> definitive :) place to start. It is pretty thorough for starts and once you
> are gone through it, the code will start making more sense too.****
>
>  ****
>
> Regards,****
>
> Shahab****
>
>  ****
>
> On Wed, May 22, 2013 at 10:33 AM, John Lilley <[EMAIL PROTECTED]>
> wrote:****
>
> Oh I see.  Does this mean there is another service and TCP listen port for
> this purpose?****
>
> Thanks for your indulgence… I would really like to read more about this
> without bothering the group but not sure where to start to learn these
> internals other than the code.****
>
> john****
>
>  ****
>
> *From:* Kai Voigt [mailto:[EMAIL PROTECTED]]
> *Sent:* Tuesday, May 21, 2013 12:59 PM
> *To:* [EMAIL PROTECTED]
> *Subject:* Re: Shuffle phase replication factor****
>
>  ****
>
> The map output doesn't get written to HDFS. The map task writes its output
> to its local disk, the reduce tasks will pull the data through HTTP for
> further processing.****
>
>  ****
>
> Am 21.05.2013 um 19:57 schrieb John Lilley <[EMAIL PROTECTED]>:****
>
>  ****
>
> When MapReduce enters “shuffle” to partition the tuples, I am assuming
> that it writes intermediate data to HDFS.  What replication factor is used
> for those temporary files?****

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NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB