Cool, obviously we'd need to have a solution here work with connect and
streams to be viable.
On the linear hashing thing, what I am talking about is something
different. I am talking about splitting existing partitions incrementally.
E.g. if you have 100 partitions and want to move to 110. Obviously a naive
approach which added partitions would require you to reshuffle all data as
the hashing of all data would change. A linear hashing-like scheme gives an
approach by which you can split individual partitions one at a time to
avoid needing to reshuffle much data. This approach has the benefit that at
any time you have a fixed number of partitions and all data is fully
partitioned with whatever the partition count you choose is but also has
the benefit that you can dynamically scale up or down the partition count.
This seems like it simplifies things like log compaction etc.
On Sun, Feb 25, 2018 at 3:51 PM, Dong Lin <lindon...@gmail.com> wrote:
> Hey Jay,
> Thanks for the comment!
> I have not specifically thought about how this works with Streams and
> Connect. The current KIP w.r.t. the interface that our producer and
> consumer exposes to the user. It ensures that if there are two messages
> with the same key produced by the same producer, say messageA and messageB,
> and suppose messageB is produced after messageA to a different partition
> than messageA, then we can guarantee that the following sequence can happen
> in order:
> - Consumer of messageA can execute callback, in which user can flush state
> related to the key of messageA.
> - messageA is delivered by its consumer to the application
> - Consumer of messageB can execute callback, in which user can load the
> state related to the key of messageB.
> - messageB is delivered by its consumer to the application.
> So it seems that it should support Streams and Connect properly. But I am
> not entirely sure because I have not looked into how Streams and Connect
> works. I can think about it more if you can provide an example where this
> does not work for Streams and Connect.
> Regarding the second question, I think linear hashing approach provides a
> way to reduce the number of partitions that can "conflict" with a give
> partition to *log_2(n)*, as compares to *n* in the current KIP, where n is
> the total number of partitions of the topic. This will be useful when
> number of partition is large and asymptotic complexity matters.
> I personally don't think this optimization is worth the additional
> complexity in Kafka. This is because partition expansion or deletion should
> happen infrequently and the largest number of partitions of a single topic
> today is not that large -- probably 1000 or less. And when partitions of a
> topic changes, each consumer will likely need to query and wait for
> positions of a large percentage of partitions of the topic anyway even with
> this optimization. I think this algorithm is kind of orthogonal to this
> KIP. We can extend the KIP to support this algorithm in the future as well.
> On Thu, Feb 22, 2018 at 5:19 PM, Jay Kreps <j...@confluent.io> wrote:
> > Hey Dong,
> > Two questions:
> > 1. How will this work with Streams and Connect?
> > 2. How does this compare to a solution where we physically split
> > using a linear hashing approach (the partition number is equivalent to
> > hash bucket in a hash table)? https://en.wikipedia.org/wiki/
> > -Jay
> > On Sat, Feb 10, 2018 at 3:35 PM, Dong Lin <lindon...@gmail.com> wrote:
> > > Hi all,
> > >
> > > I have created KIP-253: Support in-order message delivery with
> > > expansion. See
> > > https://cwiki.apache.org/confluence/display/KAFKA/KIP-
> > > 253%3A+Support+in-order+message+delivery+with+partition+expansion
> > > .
> > >
> > > This KIP provides a way to allow messages of the same key from the same
> > > producer to be consumed in the same order they are produced even if we
> > > expand partition of the topic.
> > >
> > > Thanks,
> > > Dong
> > >