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From Yi Pan <nickpa...@gmail.com>
Subject Re: Thoughts and obesrvations on Samza
Date Thu, 02 Jul 2015 21:35:39 GMT
@Jay, yes, the current function in the JobCoordinator is just partition
management. Maybe we should just call it PartitionManager to make it
explicit.

-Yi

On Thu, Jul 2, 2015 at 2:24 PM, Jay Kreps <jay@confluent.io> wrote:

> Hey Yi,
>
> What does the JobCoordinator do? YARN/Mesos/etc would be doing the actual
> resource assignment, process restart, etc, right? Is the additional value
> add of the JobCoordinator just partition management?
>
> -Jay
>
> On Thu, Jul 2, 2015 at 11:32 AM, Yi Pan <nickpan47@gmail.com> wrote:
>
> > Hi, all,
> >
> >
> > Thanks Chris for sending out this proposal and Jay for sharing the
> > extremely illustrative prototype code.
> >
> >
> > I have been thinking it over many times and want to list out my personal
> > opinions below:
> >
> > 1. Generally, I agree with most of the people here on the mailing list on
> > two points:
> >
> >    a. Deeper integration w/ Kafka is great. No more confusing mapping
> from
> > SystemStreamPartition to TopicPartition etc.
> >
> >    b. Separation the ingestion vs transformation greatly simplify the
> > systems APIs
> >
> > Having the above two changes would allow us to remove many unnecessary
> > complexities introduced by those pluggable interfaces Chris’ pointed out,
> > e.g. pluggable streaming systems and serde.
> >
> >
> > To recall one of Chris’s statement on difficulties in dynamic
> deployment, I
> > believe that the difficulties are mainly the result of tight-coupling of
> > partition assignment vs the container deployment in the current system.
> The
> > current container deployment requires a pre-defined partition assignment
> > strategy coupled together w/ the deployment configuration before we can
> > submit to YARN and start the Samza container, which makes the launching
> > process super long. Also, fault-tolerance and the embedded JobCoordinator
> > code in YARN AppMaster is another way of  making dynamic deployment more
> > complex and difficult.
> >
> >
> > First, borrowing Yan’s term, let’s call the Samza standalone process a
> > Samza worker. Here is what I have been thinking:
> >
> > 1. Separate the execution framework from partition assignment/load
> > balancing:
> >
> >     a. a Samza worker should be launched by execution framework that only
> > deals w/ process placement to available nodes. The execution framework
> now
> > should only deal w/ how many such processes are needed, where to put
> them,
> > and how to keep them alive.
> >
> >     b. Partition assignment/load balancing can be a pluggable interface
> in
> > Samza that allows the Samza workers to ask for partition assignments.
> Let’s
> > borrow the name JobCoordinator for now. To allow fault-tolerance in case
> of
> > failure, the partition assignments to workers need to be dynamic. Hence,
> > the abstract interface would be much like what Jay’s code illustrate:
> > get()/onAssigned()/onRevoke(). The implementation of the partition
> > assignment can be either:
> >
> >         a) completely rely on Kafka.
> >
> >         b) explicit partition assignment via JobCoordinator. Chris’s work
> > in SAMZA-516 can be easily incorporated here. The use case in SAMZA-41
> that
> > runs Samza ProcessJob w/ static partition assignment can be implemented
> of
> > JobCoordinator via any home-grown implementation of distributed
> > coordinator. All the work we did in LinkedIn to support dynamic partition
> > assignment and host-affinity SAMZA-617 can be nicely reused as an
> > implementation of JobCoordinator.
> >
> >
> > When we did the above work, I can see three usage patterns in Samza:
> >
> >    a. Samza as a library: Samza has a set of libraries to provide stream
> > processing, just like a third Kafka client (as illustrated in Jay’s
> > example). The execution/deployment is totally controlled by the
> application
> > and the partition coordination is done via Kafka
> >
> >    b. Samza as a process: Samza runs as a standalone process. There may
> not
> > be a execution framework to launch and deploy Samza processes. The
> > partition assignment is pluggable JobCoordinator.
> >
> >    c. Samza as a service: Samza runs as a collection of processes. There
> > will be an execution framework to allocate resource, launch and deploy
> > Samza workers and keep them alive. The same pluggable JobCoordinator is
> > desirable here as well.
> >
> >
> > Lastly, I would argue that CopyCat in KIP-26 should probably follow the
> > same model. Hence, in Samza as a service model as in LinkedIn, we can use
> > the same fault tolerance execution framework to run CopyCat and Samza w/o
> > the need to operate two service platforms, which should address Sriram’s
> > comment in the email thread.
> >
> >
> > Hope the above makes sense. Thanks all!
> >
> >
> > -Yi
> >
> > On Thu, Jul 2, 2015 at 9:53 AM, Sriram <sriram.sub@gmail.com> wrote:
> >
> > > One thing that is worth exploring is to have a transformation and
> > > ingestion library in Kafka but use the same framework for fault
> > tolerance,
> > > resource isolation and management. The biggest difference I see in
> these
> > > two use cases is the API and data model.
> > >
> > >
> > > > On Jul 2, 2015, at 8:59 AM, Jay Kreps <jay@confluent.io> wrote:
> > > >
> > > > Hey Garry,
> > > >
> > > > Yeah that's super frustrating. I'd be happy to chat more about this
> if
> > > > you'd be interested. I think Chris and I started with the idea of
> "what
> > > > would it take to make Samza a kick-ass ingestion tool" but ultimately
> > we
> > > > kind of came around to the idea that ingestion and transformation had
> > > > pretty different needs and coupling the two made things hard.
> > > >
> > > > For what it's worth I think copycat (KIP-26) actually will do what
> you
> > > are
> > > > looking for.
> > > >
> > > > With regard to your point about slider, I don't necessarily disagree.
> > > But I
> > > > think getting good YARN support is quite doable and I think we can
> make
> > > > that work well. I think the issue this proposal solves is that
> > > technically
> > > > it is pretty hard to support multiple cluster management systems the
> > way
> > > > things are now, you need to write an "app master" or "framework" for
> > each
> > > > and they are all a little different so testing is really hard. In the
> > > > absence of this we have been stuck with just YARN which has fantastic
> > > > penetration in the Hadoopy part of the org, but zero penetration
> > > elsewhere.
> > > > Given the huge amount of work being put in to slider, marathon, aws
> > > > tooling, not to mention the umpteen related packaging technologies
> > people
> > > > want to use (Docker, Kubernetes, various cloud-specific deploy tools,
> > > etc)
> > > > I really think it is important to get this right.
> > > >
> > > > -Jay
> > > >
> > > > On Thu, Jul 2, 2015 at 4:17 AM, Garry Turkington <
> > > > g.turkington@improvedigital.com> wrote:
> > > >
> > > >> Hi all,
> > > >>
> > > >> I think the question below re does Samza become a sub-project of
> Kafka
> > > >> highlights the broader point around migration. Chris mentions
> Samza's
> > > >> maturity is heading towards a v1 release but I'm not sure it feels
> > > right to
> > > >> launch a v1 then immediately plan to deprecate most of it.
> > > >>
> > > >> From a selfish perspective I have some guys who have started working
> > > with
> > > >> Samza and building some new consumers/producers was next up. Sounds
> > like
> > > >> that is absolutely not the direction to go. I need to look into the
> > KIP
> > > in
> > > >> more detail but for me the attractiveness of adding new Samza
> > > >> consumer/producers -- even if yes all they were doing was really
> > getting
> > > >> data into and out of Kafka --  was to avoid  having to worry about
> the
> > > >> lifecycle management of external clients. If there is a generic
> Kafka
> > > >> ingress/egress layer that I can plug a new connector into and have a
> > > lot of
> > > >> the heavy lifting re scale and reliability done for me then it gives
> > me
> > > all
> > > >> the pushing new consumers/producers would. If not then it
> complicates
> > my
> > > >> operational deployments.
> > > >>
> > > >> Which is similar to my other question with the proposal -- if we
> > build a
> > > >> fully available/stand-alone Samza plus the requisite shims to
> > integrate
> > > >> with Slider etc I suspect the former may be a lot more work than we
> > > think.
> > > >> We may make it much easier for a newcomer to get something running
> but
> > > >> having them step up and get a reliable production deployment may
> still
> > > >> dominate mailing list  traffic, if for different reasons than today.
> > > >>
> > > >> Don't get me wrong -- I'm comfortable with making the Samza
> dependency
> > > on
> > > >> Kafka much more explicit and I absolutely see the benefits  in the
> > > >> reduction of duplication and clashing terminologies/abstractions
> that
> > > >> Chris/Jay describe. Samza as a library would likely be a very nice
> > tool
> > > to
> > > >> add to the Kafka ecosystem. I just have the concerns above re the
> > > >> operational side.
> > > >>
> > > >> Garry
> > > >>
> > > >> -----Original Message-----
> > > >> From: Gianmarco De Francisci Morales [mailto:gdfm@apache.org]
> > > >> Sent: 02 July 2015 12:56
> > > >> To: dev@samza.apache.org
> > > >> Subject: Re: Thoughts and obesrvations on Samza
> > > >>
> > > >> 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 <yanfang724@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
> > > >>> yanfang724@gmail.com
> > > >>>
> > > >>>> On Wed, Jul 1, 2015 at 5:46 PM, Guozhang Wang <wangguoz@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 <jay@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 <jay@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 <
> > > >>>> criccomini@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=58851
> > > >>> 767
> > > >>>>>>> )
> > > >>>>>>> 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
> > > >>
> > >
> >
>

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