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. >
