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From Stephan Ewen <se...@apache.org>
Subject Re: Distribution of sinks among the nodes
Date Thu, 04 Feb 2016 14:10:24 GMT
Hi Gwen!

You actually need not 24 slots, but only as many as the highest parallelism
is (16). Slots do not hold individual tasks, but "pipelines".

Here is an illustration how that works.
https://ci.apache.org/projects/flink/flink-docs-release-0.10/setup/config.html#configuring-taskmanager-processing-slots

You can control whether a task can share the slot with the previous task
with the function "startNewResourceGroup()" in the streaming API. Sharing
lots makes a few things easier to reason about, especially when adding
operators to a program, you need not immediately add new machines.


How to solve your program case
--------------------------------------------

We can actually make a pretty simple addition to Flink that will cause the
tasks to be locally connected, which in turn will cause the scheduler to
distribute them like you intend.
Rather than let the 4 sources rebalance across all 16 mappers, each one
should redistribute to 4 local mappers, and these 4 mappers should send
data to one local sink each.

We'll try and add that today and ping you once it is in.

The following would be sample code to use this:

env.setParallelism(4);

env
    .addSource(kafkaSource)
    .partitionFan()
    .map(mapper).setParallelism(16);
    .partitionFan()
    .addSink(kafkaSink);



A bit of background why the mechanism is the way that it is right now
----------------------------------------------------------------------------------------------

You can think of a slot as a slice of resources. In particular, an amount
of memory from the memory manager, but also memory in the network stack.

What we want to do quite soon is to make streaming programs more elastic.
Consider for example the case that you have 16 slots on 4 machines, a
machine fails, and you have no spare resources. In that case Flink should
recognize that no spare resource can be acquired, and scale the job in.
Since you have only 12 slots left, the parallelism of the mappers is
reduced to 12, and the source task that was on the failed machine is moved
to a slot on another machine.

It is important that the guaranteed resources for each task do not change
when scaling in, to keep behavior predictable. In this case, each slot will
still at most host 1 source, 1 mapper, and 1 sink, as did some of the slots
before. That is also the reason why the slots are per TaskManager, and not
global, to associate them with a constant set of resources (mainly memory).


Greetings,
Stephan



On Thu, Feb 4, 2016 at 9:54 AM, Gwenhael Pasquiers <
gwenhael.pasquiers@ericsson.com> wrote:

> Don’t we need to set the number of slots to 24 (4 sources + 16 mappers + 4
> sinks) ?
>
>
>
> *Or is there a way not to set the number of slots per TaskManager instead
> of globally so that they are at least equally dispatched among the nodes ?*
>
>
>
> As for the sink deployment : that’s not good news ; I mean we will have a
> non-negligible overhead : all the data generated by 3 of the 4 nodes will
> be sent to a third node instead of being sent to the “local” sink. Network
> I/O have a price.
>
>
>
> Do you have some sort of “topology” feature coming in the roadmap ? Maybe
> a listener on the JobManager / env that would be trigerred, asking usk on
> which node we would prefer each node to be deployed. That way you keep the
> standard behavior, don’t have to make a complicated generic-optimized
> algorithm, and let the user make it’s choices. *Should I create a JIRA ?*
>
>
>
> For the time being we could start the application 4 time : one time per
> node, put that’s not pretty at all J
>
>
>
> B.R.
>
>
>
> *From:* Till Rohrmann [mailto:trohrmann@apache.org]
> *Sent:* mercredi 3 février 2016 17:58
>
> *To:* user@flink.apache.org
> *Subject:* Re: Distribution of sinks among the nodes
>
>
>
> Hi Gwenhäel,
>
> if you set the number of slots for each TaskManager to 4, then all of
> your mapper will be evenly spread out. The sources should also be evenly
> spread out. However, for the sinks since they depend on all mappers, it
> will be most likely random where they are deployed. So you might end up
> with 4 sink tasks on one machine.
>
> Cheers,
> Till
>
> ​
>
>
>
> On Wed, Feb 3, 2016 at 4:31 PM, Gwenhael Pasquiers <
> gwenhael.pasquiers@ericsson.com> wrote:
>
> It is one type of mapper with a parallelism of 16
> It's the same for the sinks and sources (parallelism of 4)
>
> The settings are
> Env.setParallelism(4)
> Mapper.setPrallelism(env.getParallelism() * 4)
>
> We mean to have X mapper tasks per source / sink
>
> The mapper is doing some heavy computation and we have only 4 kafka
> partitions. That's why we need more mappers than sources / sinks
>
>
>
> -----Original Message-----
> From: Aljoscha Krettek [mailto:aljoscha@apache.org]
> Sent: mercredi 3 février 2016 16:26
> To: user@flink.apache.org
> Subject: Re: Distribution of sinks among the nodes
>
> Hi Gwenhäel,
> when you say 16 maps, are we talking about one mapper with parallelism 16
> or 16 unique map operators?
>
> Regards,
> Aljoscha
> > On 03 Feb 2016, at 15:48, Gwenhael Pasquiers <
> gwenhael.pasquiers@ericsson.com> wrote:
> >
> > Hi,
> >
> > We try to deploy an application with the following “architecture” :
> >
> > 4 kafka sources => 16 maps => 4 kafka sinks, on 4 nodes, with 24 slots
> (we disabled operator chaining).
> >
> > So we’d like on each node :
> > 1x source => 4x map => 1x sink
> >
> > That way there are no exchanges between different instances of flink and
> performances would be optimal.
> >
> > But we get (according to the flink GUI and the Host column when looking
> at the details of each task) :
> >
> > Node 1 : 1 source =>  2 map
> > Node 2 : 1 source =>  1 map
> > Node 3 : 1 source =>  1 map
> > Node 4 : 1 source =>  12 maps => 4 sinks
> >
> > (I think no comments are needed J)
> >
> > The the Web UI says that there are 24 slots and they are all used but
> they don’t seem evenly dispatched …
> >
> > How could we make Flink deploy the tasks the way we want ?
> >
> > B.R.
> >
> > Gwen’
>
>
>

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