Gianmarco De Francisci Morales created KAFKA-2092:
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Summary: 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
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). Even after doing a
bit of reading, I am still not sure if I should be writing this email here or
on the Samza dev list. Anyway, my first guess is Kafka.
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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