Yeah, hey Yi, I get what you are saying now. At the risk of getting into the weeds a bit you are exactly right, a similar thing is needed for copycat/kip-26. At the risk of getting a bit into the weeds, I think it may be possible to rework the assignment feature in the consumer to make this always be a client-side concern so that Samza, the Kafka consumer, and Copycat can all use the same facility.
The original design for the kafka coordinator was that the coordinator would just coordinate *group* membership and the actual assignment of partitions to members of the group would be done client side. The advantage of this was that it was more general the disadvantage was that the server couldn't really check or monitor the partition assignment. Since we didn't have any other use case for generic group management we went with the more specific partition assignment. However a few things have changed since that original design: 1. We now have the additional use cases of copycat and Samza 2. We now realize that the assignment strategies don't actually necessarily ensure each partition is assigned to only one consumer--there are really valid use cases for broadcast or multiple replica assignment schemes--so we can't actually make the a hard assertion on the server. So it may make sense to revist this, I don't think it is necessarily a massive change and would give more flexibility for the variety of cases. -Jay On Thu, Jul 2, 2015 at 3:38 PM, Yi Pan <nickpa...@gmail.com> wrote: > @Guozhang, yes, that's what I meant. From Kafka consumers' point of view, > it pretty much boils down to answer the following question: > 1. For Kafka consumer in each container (i.e. a Samza worker), which topic > partitions it should consume. > Samza's current StreamTask model still makes sense to me and the > partition-to-task mapping is the debatable point that whether that should > be in Kafka or stays in a separate module. As we discussed earlier, some > simple partition-to-task mapping maybe expressed as co-partition > distribution among different topics in Kafka (forgive me if I had make > mistakes here since I am not 100% sure about how partition distribution > policies work in Kafka). However, more complex and application-specific > partition-to-task mapping would need to stay outside of Kafka. One example > is the discussion on SQL tasks: > > https://issues.apache.org/jira/browse/SAMZA-516?focusedCommentId=14288685&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14288685 > > On Thu, Jul 2, 2015 at 2:47 PM, Guozhang Wang <wangg...@gmail.com> wrote: > > > Since the resource scheduling systems like YARN / Mesos only gives Samza > a > > couple of resource units (or "containers") to run processes, while Samza > > still needs to handle task assignment / scheduling like which tasks > should > > be allocated to which containers that consume from which partitions, > etc. I > > think this is want Yi meant for "partition management"? > > > > 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 > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > -- > > -- Guozhang > > >
