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From ihorps <ihor...@gmail.com>
Subject Re: Task management - MapReduce & ForkJoin performance penalty
Date Mon, 04 Sep 2017 18:52:14 GMT
hi @ezhuravlev

Thank you for your reply, very appreciated!

I can confirm that by adding real business logic to Jobs it's actually
scales horizontally quite well and by adding more nodes the whole task
finishes just faster.

One more think, which I'm looking now on is running tasks with help of
MapReduce API in collocated fashion. As far as I understood from
documentation ( Collocate Computing and Data
<https://apacheignite.readme.io/docs/collocate-compute-and-data>  ) this is
possible only by calling affinityCall(...) or affinityRun(...), which take
IgniteCallable or IgniteRunnable.
I'd like to create a ComputeTask (ComputeTaskAdapter or
ComputeTaskSplitAdapter), which would spawn ComputeJob with affinity key
(let's say in constructor) and execute them on node with co-located data.

So is this possible to do such somehow? I couldn't find for now how it can
be done in elegant way...

Thank you in advance.

Sent from: http://apache-ignite-users.70518.x6.nabble.com/

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