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From Jakob Homan <jgho...@gmail.com>
Subject Re: How to deal with scaling?
Date Fri, 26 Sep 2014 21:21:40 GMT
This is pretty close to how things are laid out.  The data from the 'public
facing' kafka clusters are Mirror-made into Samza-specific Kafka clusters,
which are colocated (though not necessarily on the same box) as the YARN
resources.  Data produced through the Samza jobs is written to the Samza
cluster and then  mirror-made to other clusters for consumption.  This
approach has the advantage of keeping the Samza processes separate,
controlled and out of the production path.  The disadvantage is more
complexity, machines and a tiny bit of latency via the mirror making, but
overall this approach is pretty rock solid.

-Jakob


On Fri, Sep 26, 2014 at 2:14 PM, Roger Hoover <roger.hoover@gmail.com>
wrote:

> Chris,
>
> Would mind giving some advice on my deployment question below?
>
> "Do you recommend having two separate Kafka clusters?  In the "public"
> cluster, brokers would be deployed on machines by themselves.  Then you
> have another Kafka cluster for Samza in which the brokers are co-located
> with YARN NodeManagers on each machine.  With this approach, Samza
> topologies would consume from and ultimately publish to topics on the
> "public" cluster.  All of the internal topics like repartitioning,
> changelog, etc. would be hidden away in the Kafka cluster dedicated to
> Samza."
>
> Thanks,
>
> Roger
>
> On Fri, Sep 26, 2014 at 11:20 AM, Roger Hoover <roger.hoover@gmail.com>
> wrote:
>
> > Chris,
> >
> > Thanks for the great answers.  It's helping me clear up my thinking...
> >
> > On Fri, Sep 26, 2014 at 9:10 AM, Chris Riccomini <
> > criccomini@linkedin.com.invalid> wrote:
> >
> >> Hey Roger,
> >>
> >> > If the job's input topics are partitioned by key, then you cannot add
> >> >more partitions without corrupting existing state.
> >>
> >> This is correct.
> >>
> >> > Does this come up for people in practice?
> >>
> >> It does come up occasionally for us. Thus far, we usually just run a
> Kafka
> >> topic-partition expansion (thereby trashing the semantics of the
> >> partitioning) and restart the job. Inconsistent output is then emitted
> for
> >> a while. We do this only when we agree that inconsistent output is
> >> tolerable.
> >>
> >
> > Thanks.  This might be a reasonable in many cases (not sure yet).
> >
> >
> >>
> >> Another thing we do for this is over-partition our Kafka topics when
> we're
> >> concerned about growth.
> >>
> >> Both of these solutions are admittedly hacky. As you said, the ideal
> >> solution would be some kind of automatic migration. It seems possible
> that
> >> the AM (job coordinator) might be able to manage this, especially of we
> >> had a pre-packaged "repartition job" that it could trigger. I haven't
> >> thought about this in detail, though.
> >>
> >> > Deploy jobs to repartition inputs and changelog topics into the new
> >> >topics
> >>
> >> The changelog topic seems problematic to me. It seems that they key used
> >> in the changelog might not always be directly related to the
> partitioning
> >> of the input topic. For example,  if you have a StreamTask that is
> >> consuming a single input partition, and keeping a count in the state
> store
> >> of all messages that it sees, how do you repartition this changelog? In
> >> the new world, the keys for the single partition that it's consuming
> could
> >> be spread across many different partitions, and the count is pretty much
> >> meaningless, since it can't be split up by key.
> >>
> >> It almost feels like state has to be totally reset to safely do an input
> >> partition expansion under all cases. In a sense, you have to treat the
> new
> >> job as a job that's completely new, and start it from scratch.
> >>
> >
> > Ah, you're right.  I think there's no way to migrate state in general.
> If
> > a job is saving any kind of aggregate state then that's an irreversible
> > operation that was done on the old partition.  There's not enough
> > information to "repartition" the results.
> >
> > Just to be more explicit about  "starting it from scratch".  The only way
> > to do this theoretically correctly, I think, would be to have the newly
> > partitioned job start with no state and playback it's input topics from
> the
> > beginning of time.
> >
> >
> >
> >>
> >> > Change job config to point to new topics and restart the job
> >>
> >> One problem with this is going to be the case where you don't control
> the
> >> producers for the old input topic. They'd either have to be migrated to
> >> produce to the new input topic for your job, or you'd have to
> permanently
> >> run the repartition job to move data from the original topic to the
> >> currently expanded topic. Keeping the repartition job is not all that
> wild
> >> of an idea. Most Samza topologies we run have some form of a repartition
> >> job that runs permanently at the beginning of their flow.
> >>
> >
> > I was thinking about repartitioning as a good design pattern as well.
> > Having your job always repartition the input decouples it from the it's
> > upstream topic dependencies.  This brings me to another question about
> > deployment.  Do you recommend having two separate Kafka clusters?  In the
> > "public" cluster, brokers would be deployed on machines by themselves.
> > Then you have another Kafka cluster for Samza in which the brokers are
> > co-located with YARN NodeManagers on each machine.  With this approach,
> > Samza topologies would consume from and ultimately publish to topics on
> the
> > "public" cluster.  All of the internal topics like repartitioning,
> > changelog, etc. would be hidden away in the Kafka cluster dedicated to
> > Samza.
> >
> >
> >>
> >> > All meaningfully-partitioned topics would need to include their keys
> in
> >> >the stream
> >>
> >> True. Somewhat tangential to this is the case where the key that's been
> >> used is not the one your job wishes to partition by. In this case, a
> >> repartition job would be required as well.
> >>
> >> > This would be problematic as the order of the dictionary keys can
> change
> >> >but would still mean the same thing.  In order to use JSON as a serde
> for
> >> >keys, you'd need to enforce a sort order on dictionaries.
> >>
> >> I struggled with this as well. We basically need a forced ordering for
> the
> >> JSON keys in SAMZA-348. Originally, I was thinking of making the
> key/value
> >> messages just a simple string with a delimiter. Something like
> >> <type>:<key> for the key and <host>:<source>:<blah>
for the value. This
> >> approach is also much more compact than JSON. The problem with the
> latter
> >> approach is that it doesn't easily allow for hierarchical key/value
> pairs.
> >>
> >
> > I've been constructing string keys in my jobs so far as you mentioned but
> > it adds extra boilerplate to the code.  It would be nice if there were an
> > automatic way to do it.
> >
> >
> >>
> >> Cheers,
> >> Chris
> >>
> >> On 9/24/14 4:55 PM, "Roger Hoover" <roger.hoover@gmail.com> wrote:
> >>
> >> >Hi all,
> >> >
> >> >So it seems like one of the first decisions that you have to make when
> >> >creating a Samza job is how many partitions to have in your input
> topics.
> >> >This will dictate how many tasks are created and how many changelog
> >> >partitions get created.  It's great that you can independently change
> the
> >> >number of Samza containers that get deployed but what do you do once
> you
> >> >reach the max (# containers == # tasks)?
> >> >
> >> >If the job's input topics are partitioned by key, then you cannot add
> >> more
> >> >partitions without corrupting existing state.  Does this come up for
> >> >people
> >> >in practice?  How do you handle it?
> >> >
> >> >Just trying to think it through, it seems like you need a procedure
> >> >something like this:
> >> >
> >> >1) Create new topics to hold the same data but with more partitions
> >> >(inputs, outputs, and changelog topics)
> >> >2) Deploy jobs to repartition inputs and changelog topics into the new
> >> >topics
> >> >3) When caught up, stop the running job
> >> >4) Change job config to point to new topics and restart the job (if all
> >> >topics are new, this can be done while previous job run is still active
> >> >using new job.id)
> >> >5) Change downstream jobs to use new output topic if necessary.  Doing
> >> >this
> >> >in a safe way might be hard.
> >> >
> >> >Ideally at some point, this process could be automated.  I was
> wondering
> >> >whether a generic task could be written for step #2 but I think it
> would
> >> >require a couple of constraints:
> >> >
> >> >1) All meaningfully-partitioned topics would need to include their keys
> >> in
> >> >the stream.  In Kafka, this is optional unless you enable compaction
> but
> >> >for this to work generically, it would have to be mandatory in Samza
> for
> >> >any stream for which partitions have meaning (not using random or
> >> >round-robin partitioning).
> >> >2) The partition keys should be re-hashable based on their raw byte
> >> >representation so that the repartition task would not have to know how
> to
> >> >deserialize the keys in order to compute their new partition.  At first
> >> >glance, this doesn't seem too onerous but I saw in the Config Stream
> >> >proposal (SAMZA-348) that keys might be JSON:
> >> >
> >>
> >>
> >{"type":"offset","key","my-long-system-name.my-even-longer-stream-name-tha
> >> >t-is-really-long.1000"
> >> >}
> >> >
> >> >This would be problematic as the order of the dictionary keys can
> change
> >> >but would still mean the same thing.  In order to use JSON as a serde
> for
> >> >keys, you'd need to enforce a sort order on dictionaries.
> >> >
> >> >I'm curious what others do about this or what your thoughts are.
> Thanks,
> >> >
> >> >Roger
> >>
> >>
> >
>

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