On Tue, Oct 28, 2014 at 5:39 PM, Chris Riccomini < [email protected]> wrote: > > The problem is that coordinator.commit doesn't take parameters. It just > tells Samza to commit the offset that *it* knows you've processed up to. > The way Samza knows which offsets you've processed up to is implicit: when > StreamTask.process returns, Samza assumes that your task has processed the > message, and the offset is therefore safe to commit. >
Oh, I see! That makes sense. I didn't realize that the coordinator only lets you request a commit in that way. > This is a big deal to us mostly because of the large object memory > >sharing I was talking about before, but also probably because JVMs have > >non-trivial overhead in memory and CPU. > > Ah! I think I understand now. The problem is you want a high level of > parallelism, but every time you add it with a container, you pay for it in > memory by having another copy of this large object. Yep - exactly. > > Yea, unfortunately, right now the best you can do is to run a thread pool > inside the container. > Okay. Are there any plans in the works to expose a thread-based parallelism model? In other words, keep the same mental model of one TaskInstance per partition, but have the RunLoop distribute work to the TaskInstances in a container in a concurrent manner instead of a serial one. I would be very interested in such a project. - Jordan > > Cheers, > Chris > > On 10/28/14 2:29 PM, "Jordan Lewis" <[email protected]> wrote: > > >On Tue, Oct 28, 2014 at 5:17 PM, Chris Riccomini < > >[email protected]> wrote: > > > >> Hey Jordan, > >> > >> > Couldn't you instead concurrently commit offsets for each owned > >> >partition by taking the minimum offset of the threads working on that > >> >partition, minus one? That way, in the worst case, you'd only screw up > >>by > >> >forgetting to commit some just-finished work until the next call to > >> >window(). > >> > >> Yes, you could, but this would require changes to Samza, itself. The > >> window() method can be done today with no changes to Samza. > >> > > > >I must be missing something - since in your suggested implementation the > >Task itself manages the thread pool, what's stopping window() from doing > >what I suggested without changing Samza? Oh, I guess the problem is that > >Samza makes one Task instance per partition regardless of your parallelism > >settings? So the thread pool you suggest is actually parallelism within a > >single partition? > > > > > > > >> One other random aside on the threading situation is that, if you care > >> about message ordering, you'll need to make sure that messages that are > >> handed off to threads are done so based on their key or the partition > >>they > >> came from. Otherwise, t2 could get m1, and t1 could get m2, and t1 might > >> finish processing first, which would lead to out-of-order processing > >> (multi-subscriber partitions within a single job). > > > > > >Right - that makes sense. > > > > > > > >> > However, we recently switched to having each machine have as many > >> >Kafka-managed consumer threads as cores, and did away with the separate > >> >thread pool. > >> > >> Unless I'm misunderstanding, this is exactly what Samza does, doesn't > >>it? > >> Each SamzaContainer is single threaded, so running N of them on a > >>machine, > >> where N is the number of cores, results in the exact same model (since > >> each SamzaContainer has its own consumer threads). > >> > > > >The only difference is that Samza has one JVM per core, each with a single > >(or perhaps more than one, but at least blocking on each other?) consumer > >thread, whereas what we've been working with is one thread per core. This > >is a big deal to us mostly because of the large object memory sharing I > >was > >talking about before, but also probably because JVMs have non-trivial > >overhead in memory and CPU. > > > > > > > >> > Since Samza was built with single-threaded containers in mind, it > >>seems > >> >to me that it might be tricky to get Samza to tell YARN that it wants n > >> >compute units for a single container. Is there a way to accomplish > >>this? > >> > >> > >> This trickiness is why we are encouraging the one core per container > >> model. You can get around this by using the yarn.container.cpu.cores > >> setting, though. Setting this to a higher number will tell YARN that > >>more > >> cores are being used. > >> > > > >Got it. > > > >Thanks, > >Jordan > > > > > >On 10/28/14 12:16 PM, "Jordan Lewis" <[email protected]> wrote: > >> > >> >Hey Chris, > >> > > >> >Thanks for the detailed response. > >> > > >> >Your proposed solution for adding parallelism makes sense, but I don't > >>yet > >> >understand the importance of the blocking step in window(). Couldn't > >>you > >> >instead concurrently commit offsets for each owned partition by taking > >>the > >> >minimum offset of the threads working on that partition, minus one? > >>That > >> >way, in the worst case, you'd only screw up by forgetting to commit > >>some > >> >just-finished work until the next call to window(). > >> > > >> >We've had some experience with this strategy before, actually. We used > >>to > >> >have each machine use a single Kafka worker thread that read from all > >>of > >> >the partitions that it owned, and send the messages it consumes to a > >> >worker > >> >pool (sized proportionally to the number of cores on the machine) for > >> >processing. As you mention it's tricky to do the offset management > >>right > >> >in > >> >this way. However, we recently switched to having each machine have as > >> >many > >> >Kafka-managed consumer threads as cores, and did away with the separate > >> >thread pool. We like this approach a lot - it's simple, easy to manage, > >> >and > >> >doesn't expose us to data loss. Have you considered adding this kind of > >> >partition/task based parallelism to Samza? It seems to me that this > >>isn't > >> >so hard to understand, and seems like it might produce less overhead. > >> >However, I can also see the appeal of having the simple one thread per > >> >container model. > >> > > >> >Let's pretend for a moment that cross-task memory sharing was > >>implemented, > >> >and that we also choose the dangerous road of adding multithreading to > >>our > >> >task implementations. Since Samza was built with single-threaded > >> >containers > >> >in mind, it seems to me that it might be tricky to get Samza to tell > >>YARN > >> >that it wants n compute units for a single container. Is there a way to > >> >accomplish this? > >> > > >> >Thanks, > >> >Jordan Lewis > >> > > >> >On Mon, Oct 27, 2014 at 5:51 PM, Chris Riccomini < > >> >[email protected]> wrote: > >> > > >> >> Hey Jordan, > >> >> > >> >> Your question touches on a couple of things: > >> >> > >> >> 1. Shared objects between Samza tasks within one container. > >> >> 2. Multi-threaded SamzaContainers. > >> >> > >> >> For (1), there is some discussion on shared state here: > >> >> > >> >> https://issues.apache.org/jira/browse/SAMZA-402 > >> >> > >> >> The outcome of this ticket was that it's something we want, but > >>aren't > >> >> implementing right now. The idea is to have a state shore that's > >>shared > >> >> amongst all tasks in a container. The store would be immutable, and > >> >>would > >> >> be restored on startup via a stream that had all required data. > >> >> > >> >> An alternative to this is to just have a static variable that all > >>tasks > >> >> use. This will allow all tasks within one container to use the > >>object. > >> >> We've done this before, and it works reasonably well for immutable > >> >> objects, which you have. > >> >> > >> >> For (2), we've actively tried to avoid adding threading to the > >> >> SamzaContainer. Having a single threaded container has worked out > >>pretty > >> >> well for us, and greatly simplifies the mental model that people > >>need to > >> >> have to use Samza. Our advice to people who ask about adding > >>parallelism > >> >> is to tell them to add more containers. > >> >> > >> >> That said, it is possible to run threads inside a StreamTask if you > >> >>really > >> >> want to increase your parallelism. Again, I would advise against > >>this. > >> >>If > >> >> not implemented properly, doing so can lead to data loss. The proper > >>way > >> >> to implement threading inside a StreamTask is to have an thread pool > >> >> execute, and give threads messages as process() is called. You must > >>then > >> >> disable offset checkpointing by setting task.commit.ms to -1. > Lastly, > >> >>your > >> >> task must implement WindowableTask. In the window method, it must > >>block > >> >>on > >> >> all threads that are currently processing a message. When all threads > >> >>have > >> >> finished processing, it's then safe to checkpoint offsets, and the > >> >>window > >> >> method must call coordinator.commit(). > >> >> > >> >> We've written a task that does this as well, and it works, but you > >>have > >> >>to > >> >> know what you're doing to get it right. > >> >> > >> >> So, I think the two state options are: > >> >> > >> >> 1. Wait for global state to be implemented (or implement it yourself > >> >>:)). > >> >> This could take a while. > >> >> 2. Use static objects to share state among StreamTasks in a given > >> >> SamzaContainer. > >> >> > >> >> And for parallelism: > >> >> > >> >> 1. Increase partition/container count for your job. > >> >> 2. Add threads to your StreamTasks. > >> >> > >> >> Cheers, > >> >> Chris > >> >> > >> >> On 10/27/14 12:52 PM, "Jordan Lewis" <[email protected]> wrote: > >> >> > >> >> >Hi, > >> >> > > >> >> >My team is interested in trying out Samza to augment or replace our > >> >> >hand-rolled Kafka-based stream processing system. I have a question > >> >>about > >> >> >sharing memory across task instances. > >> >> > > >> >> >Currently, our main stream processing application has some large, > >> >> >immutable > >> >> >objects that need to be loaded into JVM heap memory in order to > >>process > >> >> >messages on any partition of certain topics. We use thread-based > >> >> >parallelism in our system, so that the Kafka consumer threads on > >>each > >> >> >machine listening to these topics can use the same instance of these > >> >>large > >> >> >heap objects. This is very convenient, as these objects are so large > >> >>that > >> >> >storing multiple copies of them would be quite wasteful. > >> >> > > >> >> >To use Samza, it seems as though each JVM would have to store > >>copies of > >> >> >these objects separately, even if we were to use LevelDB's off-heap > >> >> >storage > >> >> >- each JVM would eventually have to inflate the off-heap memory into > >> >> >regular Java objects to be usable. One solution to this problem > >>could > >> >>be > >> >> >using something like Google's Flatbuffers [0] for these large > >>objects > >> >>- so > >> >> >that we could use accessors on large, off-heap ByteBuffers without > >> >>having > >> >> >to actually deserialize them. However, we think that doing this for > >> >>all of > >> >> >the relevant data we have would be a lot of work. > >> >> > > >> >> >Have you guys considered implementing a thread-based parallelism > >>model > >> >>for > >> >> >Samza, whether as a replacement or alongside the current JVM-based > >> >> >parallelism approach? What obstacles are there to making this > >>happen, > >> >> >assuming that decided not to do it? This approach would be > >>invaluable > >> >>for > >> >> >our use case, since we rely so heavily (perhaps unfortunately so) on > >> >>these > >> >> >shared heap data structures. > >> >> > > >> >> >Thanks, > >> >> >Jordan Lewis > >> >> > > >> >> >[0]: http://google.github.io/flatbuffers/ > >> >> > >> >> > >> > >> > >
