Hey Steve,

One thing I'd add is that whereas Map/Reduce partitions tasks by file
split, Samza partitions tasks by input stream partition (i.e. Kafka topic
partition). It's true that a given key maps to just one partition in
Samza, but it's not a 1:1 relationship--multiple keys map to the same
input stream partition, and thus the same task. For example, task1 might
receive messages from partition0 of the input stream, which contains
messages for keys 0,2,4,6,8, etc..

Cheers,
Chris

On 4/22/14 10:46 PM, "Zhijie Shen" <[email protected]> wrote:

>AM is the master of an distributed application on YARN. It's supposed to
>negotiate with YARN for the cluster resources and monitor the status of
>the
>application. It's not associated with MapReduce. MapReduce V2 has its own
>AM, while Samza has one iteself as well.
>
>
>On Tue, Apr 22, 2014 at 3:40 PM, Steve Yates
><[email protected]>wrote:
>
>> Guys is it fair to say that YARN exposes an extension mechanism called
>>the
>> ApplicationMaster and by default in yarn this master is a map-reduce
>> application master.
>>
>> In the case of samza we have implemented a streaming case of this AM
>>which
>> takes full advantage of the parrallel / fault tolerant mechanisms built
>> into hadoop.
>>
>> So instead where we partition map reduce tasks bases on file size splits
>> in hdfs, we split a stream into stream tasks based on some filter key?
>>Is
>> this correct.
>>
>> -S
>
>
>
>
>-- 
>Zhijie Shen
>Hortonworks Inc.
>http://hortonworks.com/
>
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