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Distribution of Executors, Cores and Memory for a Spark Application running in Yarn:

spark-submit --class <CLASS_NAME> --num-executors ? --executor-cores ? --executor-memory ? ....

Ever wondered how to configure --num-executors, --executor-memory and --execuor-cores spark config params for your cluster?

Let’s find out how..

  1. Lil bit theory: Let’s see some key recommendations that will help understand it better
  2. Hands on: Next, we’ll take an example cluster and come up with recommended numbers to these spark params

Lil bit theory:

Following list captures some recommendations to keep in mind while configuring them:

Two things to make note of from this picture:

 Full memory requested to yarn per executor =
          spark-executor-memory + spark.yarn.executor.memoryOverhead.
 spark.yarn.executor.memoryOverhead = 
        	Max(384MB, 7% of spark.executor-memory)

So, if we request 20GB per executor, AM will actually get 20GB + memoryOverhead = 20 + 7% of 20GB = ~23GB memory for us.

Enough theory.. Let’s go hands-on..

Now, let’s consider a 10 node cluster with following config and analyse different possibilities of executors-core-memory distribution:

**Cluster Config:**
10 Nodes
16 cores per Node
64GB RAM per Node

First Approach: Tiny executors [One Executor per core]:

Tiny executors essentially means one executor per core. Following table depicts the values of our spar-config params with this approach:

- `--num-executors` = `In this approach, we'll assign one executor per core`
                    = `total-cores-in-cluster`
                   = `num-cores-per-node * total-nodes-in-cluster` 
                   = 16 x 10 = 160
- `--executor-cores` = 1 (one executor per core)
- `--executor-memory` = `amount of memory per executor`
                     = `mem-per-node/num-executors-per-node`
                     = 64GB/16 = 4GB

Analysis: With only one executor per core, as we discussed above, we’ll not be able to take advantage of running multiple tasks in the same JVM. Also, shared/cached variables like broadcast variables and accumulators will be replicated in each core of the nodes which is 16 times. Also, we are not leaving enough memory overhead for Hadoop/Yarn daemon processes and we are not counting in ApplicationManager. NOT GOOD!

Second Approach: Fat executors (One Executor per node):

Fat executors essentially means one executor per node. Following table depicts the values of our spark-config params with this approach:

- `--num-executors` = `In this approach, we'll assign one executor per node`
                    = `total-nodes-in-cluster`
                   = 10
- `--executor-cores` = `one executor per node means all the cores of the node are assigned to one executor`
                     = `total-cores-in-a-node`
                     = 16
- `--executor-memory` = `amount of memory per executor`
                     = `mem-per-node/num-executors-per-node`
                     = 64GB/1 = 64GB

Analysis: With all 16 cores per executor, apart from ApplicationManager and daemon processes are not counted for, HDFS throughput will hurt and it’ll result in excessive garbage results. Also,NOT GOOD!

Third Approach: Balance between Fat (vs) Tiny

According to the recommendations which we discussed above:

So, recommended config is: 29 executors, 18GB memory each and 5 cores each!!

Analysis: It is obvious as to how this third approach has found right balance between Fat vs Tiny approaches. Needless to say, it achieved parallelism of a fat executor and best throughputs of a tiny executor!!


We’ve seen:

--num-executors, --executor-cores and --executor-memory.. these three params play a very important role in spark performance as they control the amount of CPU & memory your spark application gets. This makes it very crucial for users to understand the right way to configure them. Hope this blog helped you in getting that perspective…

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