The core is equivalent to the number of logical processeors

cat /proc/cpuinfo|grep processor|wc -l


That will tell you how many logical processors you have.

I gave an explanation for this a while back. As you are running in Standalone mode, this is my take:


Standalone mode

Resources are managed by Spark resource manager itself. You start your master and slaves/worker processes As far as I have worked it out the following applies

num-executors         --> It does not care about this. The number of executors will be the number of workers on each node
executor-memory       --> If you have set up SPARK_WORKER_MEMORY in spark-env.sh, this will be the memory used by the executor
executor-cores        --> If you have set up SPARK_WORKER_CORES in spark-env.sh, this will be the number of cores used by each executor
SPARK_WORKER_CORES=n ##, total number of cores to be used by executors by each worker
SPARK_WORKER_MEMORY=mg ##, to set how much total memory workers have to give executors (e.g. 1000m, 2g)


HTH






Dr Mich Talebzadeh

 

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On 3 November 2016 at 04:48, map reduced <k3t.git.1@gmail.com> wrote:
Hi,

I am noticing that when there are N cores per executor, each executor only starts N threads to process the data (so 1 thread per core). Is there a way to increase more than N threads, i.e. say N+m threads per core?

So I assigned 7 cores/executor, so I see 7 Active Tasks at all times.

Inline image 1

And only 7 threads doing all the work:

Inline image 2

Is there any way to make it atleast 2 threads/core?

P.S.: Long running Streaming job, Standalone 2.0.0 cluster

Thanks,
KP