One other possibility is to use shared memory with something like Chronicle Map <http://openhft.net/products/chronicle-map/>.
On Wed, Oct 29, 2014 at 12:53 PM, Jordan Lewis <[email protected]> wrote: > 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/ > > >> >> > > >> >> > > >> > > >> > > > > >
