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 > > >> > > >
