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From "Kruse, Sebastian" <Sebastian.Kr...@hpi.de>
Subject RE: Load balancing
Date Mon, 15 Jun 2015 07:57:53 GMT
Hi Gianmarco,

The processing time is quadratic in the size of the single elements. I was already applying
that strategy that you also proposed, but tried to find out if there is a way of balancing
the subitems of these large items over the workers without shuffling the whole dataset. However,
I noticed the PKG strategy and maybe it will come in handy in some other place :)
So, thanks again for the pointers!

Cheers,
Sebastian

From: Gianmarco De Francisci Morales [mailto:gdfm@apache.org]
Sent: Freitag, 12. Juni 2015 19:02
To: user@flink.apache.org
Subject: Re: Load balancing

Hi Sebastian,

Maybe I misunderstood your problem.
Is the processing time quadratic in the size of the single element of the dataset?
Or is it quadratic in the number of elements of the dataset with a single key?
That is, is the element heavy or is it the key heavy?

In the second case you can use PKG.
In the first case, I don't think you really need any system level help.
Given that you can split up the work for each element, you can just transform the dataset
so that a single heavy element is replaced by a set of generated sub-elements, with the ID
of the original element as the key.
Then you can process the subelements in parallel, and finally group by key to aggregate the
result.

Cheers,

--
Gianmarco

On 11 June 2015 at 19:16, Kruse, Sebastian <Sebastian.Kruse@hpi.de<mailto:Sebastian.Kruse@hpi.de>>
wrote:
Hi Gianmarco,

Thanks for the pointer!

I had a quick look at the paper, but unfortunately I don’t see a connection to my problem.
I have a batch job and elements in my dataset, that need quadratic much processing time depending
on their size. The largest ones, that cause higher-than-average load, shall be split up and
the splits shall be distributed among the workers. Your paper says “In  principle,  depending
 on  the  application,  two  different messages might impose a different load on workers.
However, in  most  cases  these  differences  even  out  and  modeling  such application-specific
differences is not necessary.” Maybe, I am missing something, but doesn’t this assumption
render PKG inapplicable to my case? Objections to that are of course welcome :)

Cheers,
Sebastian

From: Gianmarco De Francisci Morales [mailto:gdfm@apache.org<mailto:gdfm@apache.org>]
Sent: Mittwoch, 10. Juni 2015 15:40
To: user@flink.apache.org<mailto:user@flink.apache.org>
Subject: Re: Load balancing

We have been working on an adaptive load balancing strategy that would address exactly the
issue you point out.
FLINK-1725 is the starting point for the integration.

Cheers,

--
Gianmarco

On 9 June 2015 at 20:31, Fabian Hueske <fhueske@gmail.com<mailto:fhueske@gmail.com>>
wrote:
Hi Sebastian,
I agree, shuffling only specific elements would be a very useful feature, but unfortunately
it's not supported (yet).
Would you like to open a JIRA for that?
Cheers, Fabian

2015-06-09 17:22 GMT+02:00 Kruse, Sebastian <Sebastian.Kruse@hpi.de<mailto:Sebastian.Kruse@hpi.de>>:
Hi folks,

I would like to do some load balancing within one of my Flink jobs to achieve good scalability.
The rebalance() method is not applicable in my case, as the runtime is dominated by the processing
of very few larger elements in my dataset. Hence, I need to distribute the processing work
for these elements among the nodes in the cluster. To do so, I subdivide those elements into
partial tasks and want to distribute these partial tasks to other nodes by employing a custom
partitioner.

Now, my question is the following: Actually, I do not need to shuffle the complete dataset
but only a few elements. So is there a way of telling within the partitioner, that data should
reside on the same task manager? Thanks!

Cheers,
Sebastian



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