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https://issues.apache.org/jira/browse/KAFKA-2092?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14589634#comment-14589634
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Gianmarco De Francisci Morales commented on KAFKA-2092:
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Thanks for your comment [~jkreps].
Indeed, this uses the load estimated at the producer to infer the load at the
consumer. You might think this does not work but indeed it does in most cases
(see [1] for details). I am not sure whether the lifecycle of the producer has
any impact here. The goal is simply to send balanced partitions out of the
producer.
Regarding the key=>partition mapping, yes this breaks the 1 key to 1 partition
mapping. That's exactly the point, to offer a new primitive for stream
partitioning. If you are doing word count you need a final aggregator as you
say, but the aggregation is O(1) rather than O(W) [where W is the number of
workers, i.e., parallelism of the operator]. Also, if you imagine building
views out of these partitions, you can query 2 views rather than 1 to obtain
the final answer (again, compared to shuffle grouping where you need p queries).
I disagree with your last point (and the results do too). Given that you have 2
options, the imbalance is reduced much more than just by 2 times, because you
create options to offload part of the load on a heavy partition to the second
choice, thus creating a network of "backup/offload" options to move to when one
key becomes hot. It's as creating interconnected pipes where you pump a fluid
into.
What is true is that if the single heavy key is larger than (2/W)% of the
stream, then this technique cannot help you to achieve perfect load balance.
> New partitioning for better load balancing
> ------------------------------------------
>
> Key: KAFKA-2092
> URL: https://issues.apache.org/jira/browse/KAFKA-2092
> Project: Kafka
> Issue Type: Improvement
> Components: producer
> Reporter: Gianmarco De Francisci Morales
> Assignee: Jun Rao
> Attachments: KAFKA-2092-v1.patch
>
>
> We have recently studied the problem of load balancing in distributed stream
> processing systems such as Samza [1].
> In particular, we focused on what happens when the key distribution of the
> stream is skewed when using key grouping.
> We developed a new stream partitioning scheme (which we call Partial Key
> Grouping). It achieves better load balancing than hashing while being more
> scalable than round robin in terms of memory.
> In the paper we show a number of mining algorithms that are easy to implement
> with partial key grouping, and whose performance can benefit from it. We
> think that it might also be useful for a larger class of algorithms.
> PKG has already been integrated in Storm [2], and I would like to be able to
> use it in Samza as well. As far as I understand, Kafka producers are the ones
> that decide how to partition the stream (or Kafka topic).
> I do not have experience with Kafka, however partial key grouping is very
> easy to implement: it requires just a few lines of code in Java when
> implemented as a custom grouping in Storm [3].
> I believe it should be very easy to integrate.
> For all these reasons, I believe it will be a nice addition to Kafka/Samza.
> If the community thinks it's a good idea, I will be happy to offer support in
> the porting.
> References:
> [1]
> https://melmeric.files.wordpress.com/2014/11/the-power-of-both-choices-practical-load-balancing-for-distributed-stream-processing-engines.pdf
> [2] https://issues.apache.org/jira/browse/STORM-632
> [3] https://github.com/gdfm/partial-key-grouping
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