OK, so this looks like your Yarn cluster does not allocate containers which you supposed should be 50. Does the yarn cluster have enough resource after allocating AM container, if not, that is the problem.
The problem not lies in dynamic allocation from my guess of your description. I said I'm OK with min and max executors to the same number. On Tue, Nov 24, 2015 at 11:54 AM, 谢廷稳 <[email protected]> wrote: > Hi Saisai, > I'm sorry for did not describe it clearly,YARN debug log said I have 50 > executors,but ResourceManager showed that I only have 1 container for the > AppMaster. > > I have checked YARN RM logs,after AppMaster changed state from ACCEPTED to > RUNNING,it did not have log about this job any more.So,the problem is I did > not have any executor but ExecutorAllocationManager think I have.Would you > minding having a test in your cluster environment? > Thanks, > Weber > > 2015-11-24 11:00 GMT+08:00 Saisai Shao <[email protected]>: > >> I think this behavior is expected, since you already have 50 executors >> launched, so no need to acquire additional executors. You change is not >> solid, it is just hiding the log. >> >> Again I think you should check the logs of Yarn and Spark to see if >> executors are started correctly. Why resource is still not enough where you >> already have 50 executors. >> >> On Tue, Nov 24, 2015 at 10:48 AM, 谢廷稳 <[email protected]> wrote: >> >>> Hi SaiSai, >>> I have changed "if (numExecutorsTarget >= maxNumExecutors)" to "if >>> (numExecutorsTarget > maxNumExecutors)" of the first line in the >>> ExecutorAllocationManager#addExecutors() and it rans well. >>> In my opinion,when I was set minExecutors equals maxExecutors,when the >>> first time to add Executors,numExecutorsTarget equals maxNumExecutors and >>> it repeat printe "DEBUG ExecutorAllocationManager: Not adding executors >>> because our current target total is already 50 (limit 50)". >>> Thanks >>> Weber >>> >>> 2015-11-23 21:00 GMT+08:00 Saisai Shao <[email protected]>: >>> >>>> Hi Tingwen, >>>> >>>> Would you minding sharing your changes in >>>> ExecutorAllocationManager#addExecutors(). >>>> >>>> From my understanding and test, dynamic allocation can be worked when >>>> you set the min to max number of executors to the same number. >>>> >>>> Please check your Spark and Yarn log to make sure the executors are >>>> correctly started, the warning log means currently resource is not enough >>>> to submit tasks. >>>> >>>> Thanks >>>> Saisai >>>> >>>> >>>> On Mon, Nov 23, 2015 at 8:41 PM, 谢廷稳 <[email protected]> wrote: >>>> >>>>> Hi all, >>>>> I ran a SparkPi on YARN with Dynamic Allocation enabled and set >>>>> spark.dynamicAllocation.maxExecutors >>>>> equals >>>>> spark.dynamicAllocation.minExecutors,then I submit an application >>>>> using: >>>>> ./bin/spark-submit --class org.apache.spark.examples.SparkPi --master >>>>> yarn-cluster --driver-memory 4g --executor-memory 8g >>>>> lib/spark-examples*.jar 200 >>>>> >>>>> then, this application was submitted successfully, but the AppMaster >>>>> always saying “15/11/23 20:13:08 WARN cluster.YarnClusterScheduler: >>>>> Initial job has not accepted any resources; check your cluster UI to >>>>> ensure >>>>> that workers are registered and have sufficient resources” >>>>> and when I open DEBUG,I found “15/11/23 20:24:00 DEBUG >>>>> ExecutorAllocationManager: Not adding executors because our current target >>>>> total is already 50 (limit 50)” in the console. >>>>> >>>>> I have fixed it by modifying code in >>>>> ExecutorAllocationManager.addExecutors,Does this a bug or it was designed >>>>> that we can’t set maxExecutors equals minExecutors? >>>>> >>>>> Thanks, >>>>> Weber >>>>> >>>> >>>> >>> >> >
