@Jay, yes, that's the point.
{quote}
allow the job to control partition assignment without having to deploy a
custom partition assignment strategy to the Kafka broker
{quote}Thanks! -Yi On Thu, Jul 2, 2015 at 2:57 PM, Jay Kreps <j...@confluent.io> wrote: > Hey Yi, > > I think the goal of this is to allow the job to control partition > assignment without having to deploy a custom partition assignment strategy > to the Kafka broker, is that right? > > The regex support and dynamic topic discovery you get for free as the > consumer needs to do that anyway. > > -Jay > > On Thu, Jul 2, 2015 at 2:52 PM, Yi Pan <nickpa...@gmail.com> wrote: > > > One more use case we had encountered that needs an explicit dynamic > > PartitionManager/JobCoordinator outside Kafka broker is: there are use > > cases that a Samza job needs to consume all Kafka topics matching a > certain > > regex, and users want the newly added topics to be assigned in runtime. > > There is a need to have a dynamic discovery module for new topics and > > assign the new topic partitions to the Samza workers. IMO, this should be > > the functionality in a PartitionManager outside the Kafka broker, since > it > > is part of the application logic. > > > > Said all that, my main point is simple: I am proposing that we need a > > pluggable partition management component, decoupled from the framework to > > do resource assignment, process restart, etc. > > > > On Thu, Jul 2, 2015 at 2:35 PM, Yi Pan <nickpa...@gmail.com> wrote: > > > > > @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 <j...@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 <nickpa...@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....@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 <j...@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.turking...@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:g...@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 <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=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 > > >> > > >> > > >> > > > > >> > > > >> > > > > > > > > >
