CODEWITHSUNDEEP
apache-spark
Rich
Rich

Reputation: 2885

How to allocate more executors per worker in Standalone cluster mode?

I use Spark 1.3.0 in a cluster of 5 worker nodes with 36 cores and 58GB of memory each. I'd like to configure Spark's Standalone cluster with many executors per worker.

I have seen the merged SPARK-1706, however it is not immediately clear how to actually configure multiple executors.

Here is the latest configuration of the cluster:

spark.executor.cores = "15"
spark.executor.instances = "10"
spark.executor.memory = "10g"

These settings are set on a SparkContext when the Spark application is submitted to the cluster.

Upvotes: 11

Views: 37148

Answers (4)

void
void

Reputation: 2533

In stand-alone mode, by default, all the resources on the cluster are acquired as you launch an application. You need to specify the number of executors you need using the --executor-cores and the --total-executor-cores configs.

For example, if there is 1 worker (1 worker == 1 machine in your cluster, it's a good practice to have only 1 worker per machine) in your cluster which has 3 cores and 3G available in its pool (this is specified in spark-env.sh), when you submit an application with --executor-cores 1 --total-executor-cores 2 --executor-memory 1g, two executors are launched for the application with 1 core and 1g each. Hope this helps!

Upvotes: 2

Yugerten
Yugerten

Reputation: 898

Until nowaday, Apache Spark 2.2 Standalone Cluster Mode Deployment don't resolve the issue of the number of EXECUTORS per WORKER,.... but there is an alternative for this, which is: launch Spark Executors Manually:

[usr@lcl ~spark/bin]# ./spark-class org.apache.spark.executor.CoarseGrainedExecutorBackend --driver-url spark://CoarseGrainedScheduler@DRIVER-URL:PORT --executor-id val --hostname localhost-val --cores 41 --app-id app-20170914105902-0000-just-exemple --worker-url spark://Worker@localhost-exemple:34117

I hope that help you !

Upvotes: 1

Rich
Rich

Reputation: 2885

Starting in Spark 1.4 it should be possible to configure this:

Setting: spark.executor.cores

Default: 1 in YARN mode, all the available cores on the worker in standalone mode.

Description: The number of cores to use on each executor. For YARN and standalone mode only. In standalone mode, setting this parameter allows an application to run multiple executors on the same worker, provided that there are enough cores on that worker. Otherwise, only one executor per application will run on each worker.

http://spark.apache.org/docs/1.4.0/configuration.html#execution-behavior

Upvotes: 1

ngtrkhoa
ngtrkhoa

Reputation: 764

You first need to configure your spark standalone cluster, then set the amount of resources needed for each individual spark application you want to run.

In order to configure the cluster, you can try this:

  • In conf/spark-env.sh:

    • Set the SPARK_WORKER_INSTANCES = 10 which determines the number of Worker instances (#Executors) per node (its default value is only 1)
    • Set the SPARK_WORKER_CORES = 15 # number of cores that one Worker can use (default: all cores, your case is 36)
    • Set SPARK_WORKER_MEMORY = 55g # total amount of memory that can be used on one machine (Worker Node) for running Spark programs.

  • Copy this configuration file to all Worker Nodes, on the same folder

  • Start your cluster by running the scripts in sbin (sbin/start-all.sh, ...)

As you have 5 workers, with the above configuration you should see 5 (workers) * 10 (executors per worker) = 50 alive executors on the master's web interface (http://localhost:8080 by default)

When you run an application in standalone mode, by default, it will acquire all available Executors in the cluster. You need to explicitly set the amount of resources for running this application: Eg:

val conf = new SparkConf()
             .setMaster(...)
             .setAppName(...)
             .set("spark.executor.memory", "2g")
             .set("spark.cores.max", "10")

Upvotes: 29

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