Hi, Lukas,
I currently use one process in a Docker container. My assumption was that > it already spawned multiple processes so I was comfortable with that > solution until we have to scale some job beyond one machine. > > I assume that you are referring "spawned multiple *threads*" instead of "spawned multiple processes"? I don't think that is right, if you are referring to "multiple threads for multiple tasks in a Samza job". The ProcessJobFactory will launch another process w/ a single container to run the actual job. That container is still single threaded and is assigned all input partitions. If you are looking to scale one job beyond one machine, you will see that the current ProcessJobFactory does not support that. > Docker containers are restarted automatically on failure and we collect > metrics from the metrics log in Kafka in case there is any serious fault > and we need to be alerted. So far this worked fine, but our set up is > fairly small. I would like to see the ProcessJobFactory spawn multiple > processes automatically since the amount of data we're processing is > growing quickly. This is aligned w/ Yan's comment to enhance the ProcessJobFactory to improve the parallelism. In addition, you may want to look into Monal's patch (from Netflix's team) on SAMZA-41. That will allow an intermediate solution to scale ProcessJob across multiple machines. > > > Lukas > > -----Original Message----- From: Yan Fang > Sent: Wednesday, September 16, 2015 3:45 PM > > To: dev@samza.apache.org > Subject: Re: Runtime Execution Model > > -- Hi Lukas, > > I want to learn more from your production environment. How do you use > ProcessJobFactory > in Docker containers? Do you use one ProcessJobFactory process all the > tasks, or spawn out as many threads as the task number? How is the > fault-tolerance? > > > -- Hi Yi, > > * Any progress in your side, in terms of the standalone job? (Chris' patch > is big, :) > > * Invert the JobCoordinator to the standalone Samza process s.t. the > leader > process of the Samza job becomes the JobCoordinator > Currently, we run the JobCoordinator first, and then Yarn talks to > the JobCoordinator. Isn't it enough so far? > > * Make the partition assignment as pluggable model to distribute the > tasks to > all Samza processes in a job-group in coordination. > I think the reason for this is for the Kafka's new feature.The API > design needs to be compatible with Kafka. > > * Make Samza process multi-threaded while maintaining the per-task > single-threaded > programming model for the users > Do we already have this, or need to add that? This I think can be > done in current ProcessJob. We can have the same number of threads as the > tasks. > > Thanks, > > Fang, Yan > yanfang...@gmail.com > > On Tue, Sep 15, 2015 at 10:54 AM, Yi Pan <nickpa...@gmail.com> wrote: > > Hi, all, >> >> Thanks for pitching in for the improvement plan. We have actually >> discussed >> this for a while now. In a complete view, I think that there are the >> following issues need to be addressed: >> 1) Currently, the steps involved to launch a Samza process are too complex >> and intertwined with YARN. >> 2) The Samza partition assignment is embedded within YARN AppMaster >> implementation, which makes it difficult to run the job outside YARN >> environment >> >> We have actually already started some work to address the above issues: >> 1) SAMZA-516: support standalone Samza jobs. Chris has started this work >> and has a proto-type patch available. This allows a ZK-based coordination >> to start standalone Samza processes w/o YARN >> >> There are also planned changes to allow de-coupling of Samza job >> coordination logic from YARN AppMaster: >> 1) SAMZA-680 Invert the JobCoordinator and AM logic. This would allow us >> to >> keep the Samza-specific JobCoordinator logic independent from >> cluster-management systems. >> >> There is one more thing I am thinking: we may want to make the partition >> assignment logic as a pluggable module, such that we can choose different >> coordination mechanism in partition assignment as needed (e.g. ZK-based, >> cluster-management based, or Kafka-based coordination). >> >> >> Ultimately, I think that we should try to refactor the current job >> launching model to the following: >> 1) Make standalone Samza process the standard Samza process model >> 2) Invert the JobCoordinator to the standalone Samza process s.t. the >> leader process of the Samza job becomes the JobCoordinator >> 3) Make the partition assignment as pluggable model to distribute the >> tasks >> to all Samza processes in a job-group in coordination >> 4) Make launching of Samza process agnostic of cluster-management systems. >> The cluster-management systems will simply provide the functionality of >> placing the standard Samza processes to actual available nodes >> 5) Make Samza process multi-threaded while maintaining the per-task >> single-threaded programming model for the users. >> >> Thoughts? >> >> -Yi >> >> >> >> >> >> On Tue, Sep 15, 2015 at 9:50 AM, Hannes Stockner < >> hannes.stock...@gmail.com> >> wrote: >> >> > +1 >> > >> > >> > On Tue, Sep 15, 2015 at 5:43 PM, Bruno Bonacci <bruno.bona...@gmail.com >> > >> > wrote: >> > >> > > Hi, >> > > >> > > I support what Lukas saying. Samza packaging requirements are not >> > friendly, >> > > I use the ThreadJobFactory for the same reason. >> > > >> > > Bruno >> > > >> > > On Tue, Sep 15, 2015 at 5:39 PM, Lukas Steiblys <lu...@doubledutch.me >> > >> > > wrote: >> > > >> > > > Hi Yan, >> > > > >> > > > We use Samza in a production environment using ProcessJobFactory in >> > > Docker >> > > > containers because it greatly simplifies our deployment process and >> > makes >> > > > much better use of resources. >> > > > >> > > > Is there any plan to make the ThreadJobFactory or ProcessJobFactory >> > > > multithreaded? I will look into doing that myself, but I think it >> might >> > > be >> > > > useful to implement this for everyone. I am sure there are plenty of >> > > cases >> > > > where people do not want to use YARN, but want more parallelism in >> > their >> > > > tasks. >> > > > >> > > > Lukas >> > > > >> > > > -----Original Message----- From: Yan Fang >> > > > Sent: Monday, September 14, 2015 11:08 AM >> > > > To: dev@samza.apache.org >> > > > Subject: Re: Runtime Execution Model >> > > > >> > > > >> > > > Hi Bruno, >> > > > >> > > > AFAIK, there is no existing JobFactory that brings as many threads >> > > > as >> > the >> > > > partition number. But I think nothing stops you to implement this: >> you >> > > can >> > > > get the partition information from the JobCoordinator, and then > > >> > bring >> > as >> > > > many threads as the partition/task number. >> > > > >> > > > Since the two local factories (ThreadJobFactory and >> ProcessJobFactory) >> > > are >> > > > mainly for development, there is no additional document. But most of >> > the >> > > > code here >> > > > < >> > > > >> > > >> > >> >> https://github.com/apache/samza/tree/master/samza-core/src/main/scala/org/apache/samza/job/local >> > > > > >> > > > is >> > > > self-explained. >> > > > >> > > > Thanks, >> > > > >> > > > Fang, Yan >> > > > yanfang...@gmail.com >> > > > >> > > > On Sat, Sep 12, 2015 at 1:47 PM, Bruno Bonacci < >> > bruno.bona...@gmail.com> >> > > > wrote: >> > > > >> > > > Hi, >> > > >> I'm looking for additional documentation on the different RUNTIME >> > > >> EXECUTION MODELS of the different `job.factory.class`. >> > > >> >> > > >> I'm particularly interested on how each factory (ThreadJobFactory, >> > > >> ProcessJobFactory and YarnJobFactory) will create tasks consume and >> > > >> process >> > > >> messages out of Kafka and the thread model used. >> > > >> >> > > >> I did a few tests with the ThreadJob factory consuming out of a >> kafka >> > > >> topic with 5 partitions and I was expecting that it would use >> multiple >> > > >> threads to consume/process the different partitions, however it is >> > > >> using only one thread at runtime. >> > > >> >> > > >> Is there any way to tell Samza to use multiple processing threads >> > > >> (1 >> > per >> > > >> partition)?? >> > > >> >> > > >> >> > > >> Thanks >> > > >> Bruno >> > > >> >> > > >> >> > > > >> > > >> > >> >> >
