Let me rephrase it. In scoped mode, there's multiple Interpreter Group (Personally I prefer to call it multiple sessions) in ones JVM (For spark interpreter, there's multiple SparkInterpreter instances). And there's one SparkContext in this JVM which is shared by all the SparkInterpreter instances. Regarding Scheduler, there's multiple Scheduler in scoped mode in this JVM, each SparkInterpreter instance own its own scheduler. Let me know if you have any other question.
Ankit Jain <ankitjain....@gmail.com>于2018年7月25日周三 下午10:27写道： > Jeff, what you said seems to be in conflict with what is detailed here - > https://medium.com/@leemoonsoo/apache-zeppelin-interpreter-mode-explained-bae0525d0555 > > "In *Scoped* mode, Zeppelin still runs single interpreter JVM process but > multiple *Interpreter Group* serve each Note." > > In practice as well we see one Interpreter process for scoped mode. > > Can you please clarify? > > Adding Moon too. > > Thanks > Ankit > > On Tue, Jul 24, 2018 at 11:09 PM, Ankit Jain <ankitjain....@gmail.com> > wrote: > >> Aah that makes sense - so only all jobs from one user will block in >> FIFOScheduler. >> >> By moving to ParallelScheduler, only gain achieved is jobs from same user >> can also be run in parallel but may have dependency resolution issues. >> >> Just to confirm I have it right - If "Run all" notebook is not a >> requirement and users run one paragraph at a time from different notebooks, >> ParallelScheduler >> should be ok? >> >> Thanks >> Ankit >> >> On Tue, Jul 24, 2018 at 10:38 PM, Jeff Zhang <zjf...@gmail.com> wrote: >> >>> >>> 1. Zeppelin-3563 force FAIR scheduling and just allow to specify the pool >>> 2. scheduler can not to figure out the dependencies between paragraphs. >>> That's why SparkInterpreter use FIFOScheduler. >>> If you use per user scoped mode. SparkContext is shared between users >>> but SparkInterpreter is not shared. That means there's multiple >>> SparkInterpreter instances that share the same SparkContext but they >>> doesn't share the same FIFOScheduler, each SparkInterpreter use its own >>> FIFOScheduler. >>> >>> Ankit Jain <ankitjain....@gmail.com>于2018年7月25日周三 下午12:58写道： >>> >>>> Thanks for the quick feedback Jeff. >>>> >>>> Re:1 - I did see Zeppelin-3563 but we are not on .8 yet and also we may >>>> want to force FAIR execution instead of letting user control it. >>>> >>>> Re:2 - Is there an architecture issue here or we just need better >>>> thread safety? Ideally scheduler should be able to figure out the >>>> dependencies and run whatever can be parallel. >>>> >>>> Re:Interpreter mode, I may not have been clear but we are running per >>>> user scoped mode - so Spark context is shared among all users. >>>> >>>> Doesn't that mean all jobs from different users go to one FIFOScheduler >>>> forcing all small jobs to block on a big one? That is specifically we are >>>> trying to avoid. >>>> >>>> Thanks >>>> Ankit >>>> >>>> On Tue, Jul 24, 2018 at 5:40 PM, Jeff Zhang <zjf...@gmail.com> wrote: >>>> >>>>> Regarding 1. ZEPPELIN-3563 should be helpful. See >>>>> https://github.com/apache/zeppelin/blob/master/docs/interpreter/spark.md#running-spark-sql-concurrently >>>>> for more details. >>>>> https://issues.apache.org/jira/browse/ZEPPELIN-3563 >>>>> >>>>> Regarding 2. If you use ParallelScheduler for SparkInterpreter, you >>>>> may hit weird issues if your paragraph has dependency between each other. >>>>> e.g. paragraph 1 will use variable v1 which is defined in paragraph p2. >>>>> Then the order of paragraph execution matters here, and ParallelScheduler >>>>> can not guarantee the order of execution. >>>>> That's why we use FIFOScheduler for SparkInterpreter. >>>>> >>>>> In your scenario where multiple users share the same sparkcontext, I >>>>> would suggest you to use scoped per user mode. Then each user will share >>>>> the same sparkcontext which means you can save resources, and also they >>>>> are >>>>> in each FIFOScheduler which is isolated from each other. >>>>> >>>>> Ankit Jain <ankitjain....@gmail.com>于2018年7月25日周三 上午8:14写道： >>>>> >>>>>> Forgot to mention this is for shared scoped mode, so same Spark >>>>>> application and context for all users on a single Zeppelin instance. >>>>>> >>>>>> Thanks >>>>>> Ankit >>>>>> >>>>>> On Jul 24, 2018, at 4:12 PM, Ankit Jain <ankitjain....@gmail.com> >>>>>> wrote: >>>>>> >>>>>> Hi, >>>>>> I am playing around with execution policy of Spark jobs(and all >>>>>> Zeppelin paragraphs actually). >>>>>> >>>>>> Looks like there are couple of control points- >>>>>> 1) Spark scheduling - FIFO vs Fair as documented in >>>>>> https://spark.apache.org/docs/2.1.1/job-scheduling.html#fair-scheduler-pools >>>>>> . >>>>>> >>>>>> Since we are still on .7 version and don't have >>>>>> https://issues.apache.org/jira/browse/ZEPPELIN-3563, I am forcefully >>>>>> doing sc.setLocalProperty("spark.scheduler.pool", "fair"); >>>>>> in both SparkInterpreter.java and SparkSqlInterpreter.java. >>>>>> >>>>>> Also because we are exposing Zeppelin to multiple users we may not >>>>>> actually want users to hog the cluster and always use FAIR. >>>>>> >>>>>> This may complicate our merge to .8 though. >>>>>> >>>>>> 2. On top of Spark scheduling, each Zeppelin Interpreter itself seems >>>>>> to have a scheduler queue. Each task is submitted to a FIFOScheduler >>>>>> except >>>>>> SparkSqlInterpreter which creates a ParallelScheduler ig concurrentsql >>>>>> flag >>>>>> is turned on. >>>>>> >>>>>> I am changing SparkInterpreter.java to use ParallelScheduler too and >>>>>> that seems to do the trick. >>>>>> >>>>>> Now multiple notebooks are able to run in parallel. >>>>>> >>>>>> My question is if other people have tested SparkInterpreter with >>>>>> ParallelScheduler? >>>>>> Also ideally this should be configurable. User should be specify fifo or >>>>>> parallel. >>>>>> >>>>>> Executing all paragraphs does add more complication and maybe >>>>>> >>>>>> https://issues.apache.org/jira/browse/ZEPPELIN-2368 will help us >>>>>> keep the execution order sane. >>>>>> >>>>>> >>>>>> Thoughts? >>>>>> >>>>>> -- >>>>>> Thanks & Regards, >>>>>> Ankit. >>>>>> >>>>>> >>>> >>>> >>>> -- >>>> Thanks & Regards, >>>> Ankit. >>>> >>> >> >> >> -- >> Thanks & Regards, >> Ankit. >> > > > > -- > Thanks & Regards, > Ankit. >
