Hey Gianmarco,
I agree that most people view Samza as a compute layer on top of Kafka and
that is not actually a bad thing. We have kind of built things as if they
were totally separate which kind of makes things harder for people which
is, I think, the important thing to correct.
As to your question about whether Samza should be a sub-project of Kafka. I
don't know, but it is worth thinking about.
I think there are a lot of good software engineering reasons to want a
separate repository and committer base. I think the prototype I showed
demonstrates that core Samza could be quite compact if it kind of embraced
Kafka but there is a bunch of stuff going on around SQL support that is
pretty extensive in its own right. So that kind of argues for keeping
things separate.
But from a branding and user experience point of view I think Samza would
really benefit from closer alignment. If it were just a light
transformation library that was configured, monitored, etc just like Kafka
that would make it a very light-weight adoption decision for processing if
you are going to be using Kafka for data. I think calling it something like
"Kafka Streams" would really help express what it is, and I think it would
be great to integrate the documentation with the main Kafka docs so people
could discover it in the natural course of things. I think this would help
a ton with adoption and really sell the point that it is a light-weight
adoption decision once you have Kafka.
-Jay
On Thu, Jul 2, 2015 at 3:55 AM, Gianmarco De Francisci Morales <
g...@apache.org> wrote:
> Very interesting thoughts.
> From outside, I have always perceived Samza as a computing layer over
> Kafka.
>
> The question, maybe a bit provocative, is "should Samza be a sub-project of
> Kafka then?"
> Or does it make sense to keep it as a separate project with a separate
> governance?
>
> Cheers,
>
> --
> Gianmarco
>
> On 2 July 2015 at 08:59, Yan Fang <yanfang...@gmail.com> wrote:
>
> > Overall, I agree to couple with Kafka more tightly. Because Samza de
> facto
> > is based on Kafka, and it should leverage what Kafka has. At the same
> time,
> > Kafka does not need to reinvent what Samza already has. I also like the
> > idea of separating the ingestion and transformation.
> >
> > But it is a little difficult for me to image how the Samza will look
> like.
> > And I feel Chris and Jay have a little difference in terms of how Samza
> > should look like.
> >
> > *** Will it look like what Jay's code shows (A client of Kakfa) ? And
> > user's application code calls this client?
> >
> > 1. If we make Samza be a library of Kafka (like what the code shows), how
> > do we implement auto-balance and fault-tolerance? Are they taken care by
> > the Kafka broker or other mechanism, such as "Samza worker" (just make up
> > the name) ?
> >
> > 2. What about other features, such as auto-scaling, shared state,
> > monitoring?
> >
> >
> > *** If we have Samza standalone, (is this what Chris suggests?)
> >
> > 1. we still need to ingest data from Kakfa and produce to it. Then it
> > becomes the same as what Samza looks like now, except it does not rely on
> > Yarn anymore.
> >
> > 2. if it is standalone, how can it leverage Kafka's metrics, logs, etc?
> Use
> > Kafka code as the dependency?
> >
> >
> > Thanks,
> >
> > Fang, Yan
> > yanfang...@gmail.com
> >
> > On Wed, Jul 1, 2015 at 5:46 PM, Guozhang Wang <wangg...@gmail.com>
> wrote:
> >
> > > Read through the code example and it looks good to me. A few thoughts
> > > regarding deployment:
> > >
> > > Today Samza deploys as executable runnable like:
> > >
> > > deploy/samza/bin/run-job.sh --config-factory=...
> --config-path=file://...
> > >
> > > And this proposal advocate for deploying Samza more as embedded
> libraries
> > > in user application code (ignoring the terminology since it is not the
> > same
> > > as the prototype code):
> > >
> > > StreamTask task = new MyStreamTask(configs);
> > > Thread thread = new Thread(task);
> > > thread.start();
> > >
> > > I think both of these deployment modes are important for different
> types
> > of
> > > users. That said, I think making Samza purely standalone is still
> > > sufficient for either runnable or library modes.
> > >
> > > Guozhang
> > >
> > > On Tue, Jun 30, 2015 at 11:33 PM, Jay Kreps <j...@confluent.io> wrote:
> > >
> > > > Looks like gmail mangled the code example, it was supposed to look
> like
> > > > this:
> > > >
> > > > Properties props = new Properties();
> > > > props.put("bootstrap.servers", "localhost:4242");
> > > > StreamingConfig config = new StreamingConfig(props);
> > > > config.subscribe("test-topic-1", "test-topic-2");
> > > > config.processor(ExampleStreamProcessor.class);
> > > > config.serialization(new StringSerializer(), new
> StringDeserializer());
> > > > KafkaStreaming container = new KafkaStreaming(config);
> > > > container.run();
> > > >
> > > > -Jay
> > > >
> > > > On Tue, Jun 30, 2015 at 11:32 PM, Jay Kreps <j...@confluent.io>
> wrote:
> > > >
> > > > > Hey guys,
> > > > >
> > > > > This came out of some conversations Chris and I were having around
> > > > whether
> > > > > it would make sense to use Samza as a kind of data ingestion
> > framework
> > > > for
> > > > > Kafka (which ultimately lead to KIP-26 "copycat"). This kind of
> > > combined
> > > > > with complaints around config and YARN and the discussion around
> how
> > to
> > > > > best do a standalone mode.
> > > > >
> > > > > So the thought experiment was, given that Samza was basically
> already
> > > > > totally Kafka specific, what if you just embraced that and turned
> it
> > > into
> > > > > something less like a heavyweight framework and more like a third
> > Kafka
> > > > > client--a kind of "producing consumer" with state management
> > > facilities.
> > > > > Basically a library. Instead of a complex stream processing
> framework
> > > > this
> > > > > would actually be a very simple thing, not much more complicated to
> > use
> > > > or
> > > > > operate than a Kafka consumer. As Chris said we thought about it a
> > lot
> > > of
> > > > > what Samza (and the other stream processing systems were doing)
> > seemed
> > > > like
> > > > > kind of a hangover from MapReduce.
> > > > >
> > > > > Of course you need to ingest/output data to and from the stream
> > > > > processing. But when we actually looked into how that would work,
> > Samza
> > > > > isn't really an ideal data ingestion framework for a bunch of
> > reasons.
> > > To
> > > > > really do that right you need a pretty different internal data
> model
> > > and
> > > > > set of apis. So what if you split them and had an api for Kafka
> > > > > ingress/egress (copycat AKA KIP-26) and a separate api for Kafka
> > > > > transformation (Samza).
> > > > >
> > > > > This would also allow really embracing the same terminology and
> > > > > conventions. One complaint about the current state is that the two
> > > > systems
> > > > > kind of feel bolted on. Terminology like "stream" vs "topic" and
> > > > different
> > > > > config and monitoring systems means you kind of have to learn
> Kafka's
> > > > way,
> > > > > then learn Samza's slightly different way, then kind of understand
> > how
> > > > they
> > > > > map to each other, which having walked a few people through this is
> > > > > surprisingly tricky for folks to get.
> > > > >
> > > > > Since I have been spending a lot of time on airplanes I hacked up
> an
> > > > > ernest but still somewhat incomplete prototype of what this would
> > look
> > > > > like. This is just unceremoniously dumped into Kafka as it
> required a
> > > few
> > > > > changes to the new consumer. Here is the code:
> > > > >
> > > > >
> > > >
> > >
> >
> https://github.com/jkreps/kafka/tree/streams/clients/src/main/java/org/apache/kafka/clients/streaming
> > > > >
> > > > > For the purpose of the prototype I just liberally renamed
> everything
> > to
> > > > > try to align it with Kafka with no regard for compatibility.
> > > > >
> > > > > To use this would be something like this:
> > > > > Properties props = new Properties(); props.put("bootstrap.servers",
> > > > > "localhost:4242"); StreamingConfig config = new
> > StreamingConfig(props);
> > > > config.subscribe("test-topic-1",
> > > > > "test-topic-2"); config.processor(ExampleStreamProcessor.class);
> > > > config.serialization(new
> > > > > StringSerializer(), new StringDeserializer()); KafkaStreaming
> > > container =
> > > > > new KafkaStreaming(config); container.run();
> > > > >
> > > > > KafkaStreaming is basically the SamzaContainer; StreamProcessor is
> > > > > basically StreamTask.
> > > > >
> > > > > So rather than putting all the class names in a file and then
> having
> > > the
> > > > > job assembled by reflection, you just instantiate the container
> > > > > programmatically. Work is balanced over however many instances of
> > this
> > > > are
> > > > > alive at any time (i.e. if an instance dies, new tasks are added to
> > the
> > > > > existing containers without shutting them down).
> > > > >
> > > > > We would provide some glue for running this stuff in YARN via
> Slider,
> > > > > Mesos via Marathon, and AWS using some of their tools but from the
> > > point
> > > > of
> > > > > view of these frameworks these stream processing jobs are just
> > > stateless
> > > > > services that can come and go and expand and contract at will.
> There
> > is
> > > > no
> > > > > more custom scheduler.
> > > > >
> > > > > Here are some relevant details:
> > > > >
> > > > > 1. It is only ~1300 lines of code, it would get larger if we
> > > > > productionized but not vastly larger. We really do get a ton of
> > > > leverage
> > > > > out of Kafka.
> > > > > 2. Partition management is fully delegated to the new consumer.
> > This
> > > > > is nice since now any partition management strategy available to
> > > Kafka
> > > > > consumer is also available to Samza (and vice versa) and with
> the
> > > > exact
> > > > > same configs.
> > > > > 3. It supports state as well as state reuse
> > > > >
> > > > > Anyhow take a look, hopefully it is thought provoking.
> > > > >
> > > > > -Jay
> > > > >
> > > > >
> > > > >
> > > > > On Tue, Jun 30, 2015 at 6:55 PM, Chris Riccomini <
> > > criccom...@apache.org>
> > > > > wrote:
> > > > >
> > > > >> Hey all,
> > > > >>
> > > > >> I have had some discussions with Samza engineers at LinkedIn and
> > > > Confluent
> > > > >> and we came up with a few observations and would like to propose
> > some
> > > > >> changes.
> > > > >>
> > > > >> We've observed some things that I want to call out about Samza's
> > > design,
> > > > >> and I'd like to propose some changes.
> > > > >>
> > > > >> * Samza is dependent upon a dynamic deployment system.
> > > > >> * Samza is too pluggable.
> > > > >> * Samza's SystemConsumer/SystemProducer and Kafka's consumer APIs
> > are
> > > > >> trying to solve a lot of the same problems.
> > > > >>
> > > > >> All three of these issues are related, but I'll address them in
> > order.
> > > > >>
> > > > >> Deployment
> > > > >>
> > > > >> Samza strongly depends on the use of a dynamic deployment
> scheduler
> > > such
> > > > >> as
> > > > >> YARN, Mesos, etc. When we initially built Samza, we bet that there
> > > would
> > > > >> be
> > > > >> one or two winners in this area, and we could support them, and
> the
> > > rest
> > > > >> would go away. In reality, there are many variations. Furthermore,
> > > many
> > > > >> people still prefer to just start their processors like normal
> Java
> > > > >> processes, and use traditional deployment scripts such as Fabric,
> > > Chef,
> > > > >> Ansible, etc. Forcing a deployment system on users makes the Samza
> > > > >> start-up
> > > > >> process really painful for first time users.
> > > > >>
> > > > >> Dynamic deployment as a requirement was also a bit of a mis-fire
> > > because
> > > > >> of
> > > > >> a fundamental misunderstanding between the nature of batch jobs
> and
> > > > stream
> > > > >> processing jobs. Early on, we made conscious effort to favor the
> > > Hadoop
> > > > >> (Map/Reduce) way of doing things, since it worked and was well
> > > > understood.
> > > > >> One thing that we missed was that batch jobs have a definite
> > > beginning,
> > > > >> and
> > > > >> end, and stream processing jobs don't (usually). This leads to a
> > much
> > > > >> simpler scheduling problem for stream processors. You basically
> just
> > > > need
> > > > >> to find a place to start the processor, and start it. The way we
> run
> > > > >> grids,
> > > > >> at LinkedIn, there's no concept of a cluster being "full". We
> always
> > > add
> > > > >> more machines. The problem with coupling Samza with a scheduler is
> > > that
> > > > >> Samza (as a framework) now has to handle deployment. This pulls
> in a
> > > > bunch
> > > > >> of things such as configuration distribution (config stream),
> shell
> > > > scrips
> > > > >> (bin/run-job.sh, JobRunner), packaging (all the .tgz stuff), etc.
> > > > >>
> > > > >> Another reason for requiring dynamic deployment was to support
> data
> > > > >> locality. If you want to have locality, you need to put your
> > > processors
> > > > >> close to the data they're processing. Upon further investigation,
> > > > though,
> > > > >> this feature is not that beneficial. There is some good discussion
> > > about
> > > > >> some problems with it on SAMZA-335. Again, we took the Map/Reduce
> > > path,
> > > > >> but
> > > > >> there are some fundamental differences between HDFS and Kafka.
> HDFS
> > > has
> > > > >> blocks, while Kafka has partitions. This leads to less
> optimization
> > > > >> potential with stream processors on top of Kafka.
> > > > >>
> > > > >> This feature is also used as a crutch. Samza doesn't have any
> built
> > in
> > > > >> fault-tolerance logic. Instead, it depends on the dynamic
> deployment
> > > > >> scheduling system to handle restarts when a processor dies. This
> has
> > > > made
> > > > >> it very difficult to write a standalone Samza container
> (SAMZA-516).
> > > > >>
> > > > >> Pluggability
> > > > >>
> > > > >> In some cases pluggability is good, but I think that we've gone
> too
> > > far
> > > > >> with it. Currently, Samza has:
> > > > >>
> > > > >> * Pluggable config.
> > > > >> * Pluggable metrics.
> > > > >> * Pluggable deployment systems.
> > > > >> * Pluggable streaming systems (SystemConsumer, SystemProducer,
> etc).
> > > > >> * Pluggable serdes.
> > > > >> * Pluggable storage engines.
> > > > >> * Pluggable strategies for just about every component
> > (MessageChooser,
> > > > >> SystemStreamPartitionGrouper, ConfigRewriter, etc).
> > > > >>
> > > > >> There's probably more that I've forgotten, as well. Some of these
> > are
> > > > >> useful, but some have proven not to be. This all comes at a cost:
> > > > >> complexity. This complexity is making it harder for our users to
> > pick
> > > up
> > > > >> and use Samza out of the box. It also makes it difficult for Samza
> > > > >> developers to reason about what the characteristics of the
> container
> > > > >> (since
> > > > >> the characteristics change depending on which plugins are use).
> > > > >>
> > > > >> The issues with pluggability are most visible in the System APIs.
> > What
> > > > >> Samza really requires to be functional is Kafka as its transport
> > > layer.
> > > > >> But
> > > > >> we've conflated two unrelated use cases into one API:
> > > > >>
> > > > >> 1. Get data into/out of Kafka.
> > > > >> 2. Process the data in Kafka.
> > > > >>
> > > > >> The current System API supports both of these use cases. The
> problem
> > > is,
> > > > >> we
> > > > >> actually want different features for each use case. By papering
> over
> > > > these
> > > > >> two use cases, and providing a single API, we've introduced a ton
> of
> > > > leaky
> > > > >> abstractions.
> > > > >>
> > > > >> For example, what we'd really like in (2) is to have monotonically
> > > > >> increasing longs for offsets (like Kafka). This would be at odds
> > with
> > > > (1),
> > > > >> though, since different systems have different
> > > > SCNs/Offsets/UUIDs/vectors.
> > > > >> There was discussion both on the mailing list and the SQL JIRAs
> > about
> > > > the
> > > > >> need for this.
> > > > >>
> > > > >> The same thing holds true for replayability. Kafka allows us to
> > rewind
> > > > >> when
> > > > >> we have a failure. Many other systems don't. In some cases,
> systems
> > > > return
> > > > >> null for their offsets (e.g. WikipediaSystemConsumer) because they
> > > have
> > > > no
> > > > >> offsets.
> > > > >>
> > > > >> Partitioning is another example. Kafka supports partitioning, but
> > many
> > > > >> systems don't. We model this by having a single partition for
> those
> > > > >> systems. Still, other systems model partitioning differently (e.g.
> > > > >> Kinesis).
> > > > >>
> > > > >> The SystemAdmin interface is also a mess. Creating streams in a
> > > > >> system-agnostic way is almost impossible. As is modeling metadata
> > for
> > > > the
> > > > >> system (replication factor, partitions, location, etc). The list
> > goes
> > > > on.
> > > > >>
> > > > >> Duplicate work
> > > > >>
> > > > >> At the time that we began writing Samza, Kafka's consumer and
> > producer
> > > > >> APIs
> > > > >> had a relatively weak feature set. On the consumer-side, you had
> two
> > > > >> options: use the high level consumer, or the simple consumer. The
> > > > problem
> > > > >> with the high-level consumer was that it controlled your offsets,
> > > > >> partition
> > > > >> assignments, and the order in which you received messages. The
> > problem
> > > > >> with
> > > > >> the simple consumer is that it's not simple. It's basic. You end
> up
> > > > having
> > > > >> to handle a lot of really low-level stuff that you shouldn't. We
> > > spent a
> > > > >> lot of time to make Samza's KafkaSystemConsumer very robust. It
> also
> > > > >> allows
> > > > >> us to support some cool features:
> > > > >>
> > > > >> * Per-partition message ordering and prioritization.
> > > > >> * Tight control over partition assignment to support joins, global
> > > state
> > > > >> (if we want to implement it :)), etc.
> > > > >> * Tight control over offset checkpointing.
> > > > >>
> > > > >> What we didn't realize at the time is that these features should
> > > > actually
> > > > >> be in Kafka. A lot of Kafka consumers (not just Samza stream
> > > processors)
> > > > >> end up wanting to do things like joins and partition assignment.
> The
> > > > Kafka
> > > > >> community has come to the same conclusion. They're adding a ton of
> > > > >> upgrades
> > > > >> into their new Kafka consumer implementation. To a large extent,
> > it's
> > > > >> duplicate work to what we've already done in Samza.
> > > > >>
> > > > >> On top of this, Kafka ended up taking a very similar approach to
> > > Samza's
> > > > >> KafkaCheckpointManager implementation for handling offset
> > > checkpointing.
> > > > >> Like Samza, Kafka's new offset management feature stores offset
> > > > >> checkpoints
> > > > >> in a topic, and allows you to fetch them from the broker.
> > > > >>
> > > > >> A lot of this seems like a waste, since we could have shared the
> > work
> > > if
> > > > >> it
> > > > >> had been done in Kafka from the get-go.
> > > > >>
> > > > >> Vision
> > > > >>
> > > > >> All of this leads me to a rather radical proposal. Samza is
> > relatively
> > > > >> stable at this point. I'd venture to say that we're near a 1.0
> > > release.
> > > > >> I'd
> > > > >> like to propose that we take what we've learned, and begin
> thinking
> > > > about
> > > > >> Samza beyond 1.0. What would we change if we were starting from
> > > scratch?
> > > > >> My
> > > > >> proposal is to:
> > > > >>
> > > > >> 1. Make Samza standalone the *only* way to run Samza processors,
> and
> > > > >> eliminate all direct dependences on YARN, Mesos, etc.
> > > > >> 2. Make a definitive call to support only Kafka as the stream
> > > processing
> > > > >> layer.
> > > > >> 3. Eliminate Samza's metrics, logging, serialization, and config
> > > > systems,
> > > > >> and simply use Kafka's instead.
> > > > >>
> > > > >> This would fix all of the issues that I outlined above. It should
> > also
> > > > >> shrink the Samza code base pretty dramatically. Supporting only a
> > > > >> standalone container will allow Samza to be executed on YARN
> (using
> > > > >> Slider), Mesos (using Marathon/Aurora), or most other in-house
> > > > deployment
> > > > >> systems. This should make life a lot easier for new users. Imagine
> > > > having
> > > > >> the hello-samza tutorial without YARN. The drop in mailing list
> > > traffic
> > > > >> will be pretty dramatic.
> > > > >>
> > > > >> Coupling with Kafka seems long overdue to me. The reality is,
> > everyone
> > > > >> that
> > > > >> I'm aware of is using Samza with Kafka. We basically require it
> > > already
> > > > in
> > > > >> order for most features to work. Those that are using other
> systems
> > > are
> > > > >> generally using it for ingest into Kafka (1), and then they do the
> > > > >> processing on top. There is already discussion (
> > > > >>
> > > >
> > >
> >
> https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=58851767
> > > > >> )
> > > > >> in Kafka to make ingesting into Kafka extremely easy.
> > > > >>
> > > > >> Once we make the call to couple with Kafka, we can leverage a ton
> of
> > > > their
> > > > >> ecosystem. We no longer have to maintain our own config, metrics,
> > etc.
> > > > We
> > > > >> can all share the same libraries, and make them better. This will
> > also
> > > > >> allow us to share the consumer/producer APIs, and will let us
> > leverage
> > > > >> their offset management and partition management, rather than
> having
> > > our
> > > > >> own. All of the coordinator stream code would go away, as would
> most
> > > of
> > > > >> the
> > > > >> YARN AppMaster code. We'd probably have to push some partition
> > > > management
> > > > >> features into the Kafka broker, but they're already moving in that
> > > > >> direction with the new consumer API. The features we have for
> > > partition
> > > > >> assignment aren't unique to Samza, and seem like they should be in
> > > Kafka
> > > > >> anyway. There will always be some niche usages which will require
> > > extra
> > > > >> care and hence full control over partition assignments much like
> the
> > > > Kafka
> > > > >> low level consumer api. These would continue to be supported.
> > > > >>
> > > > >> These items will be good for the Samza community. They'll make
> Samza
> > > > >> easier
> > > > >> to use, and make it easier for developers to add new features.
> > > > >>
> > > > >> Obviously this is a fairly large (and somewhat backwards
> > incompatible
> > > > >> change). If we choose to go this route, it's important that we
> > openly
> > > > >> communicate how we're going to provide a migration path from the
> > > > existing
> > > > >> APIs to the new ones (if we make incompatible changes). I think
> at a
> > > > >> minimum, we'd probably need to provide a wrapper to allow existing
> > > > >> StreamTask implementations to continue running on the new
> container.
> > > > It's
> > > > >> also important that we openly communicate about timing, and stages
> > of
> > > > the
> > > > >> migration.
> > > > >>
> > > > >> If you made it this far, I'm sure you have opinions. :) Please
> send
> > > your
> > > > >> thoughts and feedback.
> > > > >>
> > > > >> Cheers,
> > > > >> Chris
> > > > >>
> > > > >
> > > > >
> > > >
> > >
> > >
> > >
> > > --
> > > -- Guozhang
> > >
> >
>
