Re: Multiple vcores per container when running Spark applications in Yarn cluster mode

; is actually meaningless. If you want to also calculate cpu resource, you > should choose DominantResourceCalculator. > > Thanks > Jerry > > On Sat, Sep 9, 2017 at 6:54 AM, Xiaoye Sun <sunxiaoy...@gmail.com> wrote: > >> Hi, >> >> I am using Spark 1

Multiple vcores per container when running Spark applications in Yarn cluster mode

Hi, I am using Spark 1.6.1 and Yarn 2.7.4. I want to submit a Spark application to a Yarn cluster. However, I found that the number of vcores assigned to a container/executor is always 1, even if I set spark.executor.cores=2. I also found the number of tasks an executor runs concurrently is 2.

Re: about broadcast join of base table in spark sql

you may need to check if spark can get the size of your table. If spark cannot get the table size, it won't do broadcast. On Sat, Jul 1, 2017 at 11:37 PM Paley Louie wrote: > Thank you for your reply, I have tried to add broadcast hint to the base > table, but it just

Spark Application hangs without trigger SparkShutdownHook

Hi all, I am running a Spark (v1.6.1) application using the ./bin/spark-submit script. I made some changes to the HttpBroadcast module. However, after the application finishes completely, the spark master program hangs at the end of the application. The ShutdownHook is supposed to be called at

How to interpret the Time Line on "Details for Stage" Spark UI page

Hi, I am using Spark 1.6.1, and I am looking at the Event Timeline on "Details for Stage" Spark UI web page in detail. I found that the "scheduler delay" on event timeline is somehow misrepresented. I want to confirm if my understanding is correct. Here is the detailed description: In Spark's

large scheduler delay in OnlineLDAOptimizer, (MLlib and LDA)

Hi, I am running some experiments with OnlineLDAOptimizer in Spark 1.6.1. My Spark cluster has 30 machines. However, I found that the Scheduler delay at job/stage "reduce at LDAOptimizer.scala:452" is extremely large when the LDA model is large. The delay could be tens of seconds. Does anyone

one executor runs multiple parallel tasks VS multiple excutors each runs one task

Hi, Currently, I am running Spark using the standalone scheduler with 3 machines in our cluster. For these three machines, one runs Spark Master and the other two run Spark Worker. We run a machine learning application on this small-scale testbed. A particular stage in my application is divided

How to know WHO are the slaves for an application

Hi all, I am currently making some changes in Spark in my research project. In my development, after an application has been submitted to the spark master, the master needs to get the IP addresses of all the slaves used by that application, so that the spark master is able to talk to the slave

Re: Can I assign affinity for spark executor processes?

going on in > the JRE, I don't see how spark could affect cpu affinity. > > regards, > --Jakob > > On Tue, Sep 13, 2016 at 7:56 PM, Xiaoye Sun <sunxiaoy...@gmail.com> wrote: > > Hi, > > > > In my experiment, I pin one very important process on a fixed

Can I assign affinity for spark executor processes?

Hi, In my experiment, I pin one very important process on a fixed CPU. So the performance of Spark task execution will be affected if the executors or the worker uses that CPU. I am wondering if it is possible to let the Spark executors not using a particular CPU. I tried to 'taskset -p

Can I assign affinity for spark executor processes?

Hi, In my experiment, I pin one very important process on a fixed CPU. So the performance of Spark task execution will be affected if the executors or the worker uses that CPU. I am wondering if it is possible to let the Spark executors not using a particular CPU. I tried to 'taskset -p

How to know how are the slaves for an application

Hi all, I am currently making some changes in Spark in my research project. In my development, after an application has been submitted to the spark master, I want to get the IP addresses of all the slaves used by that application, so that the spark master is able to talk to the slave machines

Re: Spark on Yarn with Dynamic Resource Allocation. Container always marked as failed

ed after the change in yarn-site.xml > > On Thu, Mar 3, 2016 at 6:00 AM, Jeff Zhang <zjf...@gmail.com> wrote: > >> The executor may fail to start. You need to check the executor logs, if >> there's no executor log then you need to check node manager log. >> >

Spark on Yarn with Dynamic Resource Allocation. Container always marked as failed

Hi all, I am very new to spark and yarn. I am running a BroadcastTest example application using spark 1.6.0 and Hadoop/Yarn 2.7.1. in a 5 nodes cluster. I configured my configuration files according to https://spark.apache.org/docs/latest/job-scheduling.html#dynamic-resource-allocation 1. copy

Spark on Yarn with Dynamic Resource Allocation. Container always marked as failed

Hi all, I am very new to spark and yarn. I am running a BroadcastTest example application using spark 1.6.0 and Hadoop/Yarn 2.7.1. in a 5 nodes cluster. I configured my configuration files according to https://spark.apache.org/docs/latest/job-scheduling.html#dynamic-resource-allocation 1. copy