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
> >
>
