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 > >> > > >> > >> > > > >> > > >> > > > > >