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From Ufuk Celebi <u...@data-artisans.com>
Subject Re: Forward Partitioning & same Parallelism: 1:1 communication?
Date Wed, 12 Aug 2015 09:33:38 GMT
Hey Marton,

out of curiosity: is this using Flink’s “point” connections underneath or
is there some custom logic for streaming jobs?

What happens if operator B has 2 times the parallelism of operator A? For
example if there were parallel tasks A1 and A2 and B1-B4: would A1 send to
B1 *and* B2 or just B1?

– Ufuk

On 12 Aug 2015, at 10:39, Márton Balassi <balassi.marton@gmail.com> wrote:

Dear Nica,

Yes, forward partitioning means that if subsequent operators share
parallelism then the output of an upstream operator is sent to exactly
one downstream operator. This makes sense for operators working on
individual records, e.g. a typical map-filter pair, because as a
consequence Flink may be able to collocate these operator pairs on the same
physical machine.



On Tue, Aug 11, 2015 at 11:41 PM, Nicaz <Walteran@students.uni-marburg.de
> wrote:

I have a question about forward partitioning in Flink.

If Operator A and Operator B have the same parallelism set and forward
partitioning is used for events coming from instances of A and going to
instances of B:

Will each instance of A send events to _exactly one_ instance of B?

That is, will all events coming from a specific instance of A go to the
_same_ specific instance of B, and will _all_ instances of B be used?
Or are there any situations where an instance of A will distribute events to
several different instances of B, or where two instances of A will send
events to the same instance of B (possibly leaving some other instance of B

I'd be very happy if someone were able to shed some light on this issue. :-)

Thanks in advance

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