Spark Backend

This backend adds support for execution of spark jobs in a workflow.

It supports the following Spark deploy modes:

  • Client deploy mode using the spark standalone cluster manager
  • Cluster deploy mode using the spark standalone cluster manager
  • Client deploy mode using Yarn resource manager
  • Cluster deploy mode using Yarn resource manager

Configuring Spark Project

Cromwell's default configuration file is located at core/src/main/resources/reference.conf

To customize configuration it is recommended that one copies relevant stanzas from core/src/main/resources/reference.conf into a new file, modify it as appropriate, then pass it to Cromwell via:

java -Dconfig.file=/path/to/yourOverrides.conf cromwell.jar

Spark configuration stanza is as follows:

Spark {
       actor-factory = "cromwell.backend.impl.spark.SparkBackendFactory"
       config {
         # Root directory where Cromwell writes job results.  This directory must be
         # visible and writeable by the Cromwell process as well as the jobs that Cromwell
         # launches.
         root: "cromwell-executions"

         filesystems {
           local {
             localization: [
               "hard-link", "soft-link", "copy"
        master: "local"
        deployMode: "client"


and add backend provider as Spark.

backend {
  default = "Spark"
  providers {

Configuring Spark Master and Deploy Mode

Default configuration is as follows:

Spark {
        master: "local"
        deployMode: "client"


However to use Spark in standalone cluster mode change master: spark://hostname:6066 and deployMode: cluster similarly, for yarn change master: yarn and deployMode: cluster or deployMode: client to run in cluster or client mode respectively.

Spark runtime attributes

Supported runtime attributes for a Spark Job is as follows:

  • executorCores (default value is 1)
  • executorMemory (default value is "1GB", Unit in MB or GB or TB.. )
  • appMainClass ( Spark app/job entry point)
  • numberOfExecutors ( Specific to cluster deploy mode)
  • additionalArgs ( i.e to add additional configuration or parameters to spark-submit)

Sample usage:

task sparkjob_with_yarn_cluster {

        runtime {
                appMainClass: "${entry_point}"
                executorMemory: "4GB"
                executorCores: "2"
                additionalArgs: "--conf '-Dsamjdk.compression_level=1 -Dsnappy.disable=true' ...."


Spark Environment

The Spark backend assumes Spark is already installed, and it constructs the spark submit command with the SPARK_HOME environment variable if set. Otherwise backend creates command spark-submit without a fully qualified path to spark-submit.Also, it is important to set environment variable HOSTNAME to master machine ip or hostname, that is accessible by spark backend. That can be done by setting either in ~/.bashrc or profile like "export HOSTNAME=<machine ip>"

Supported File Systems as follows:

  • Local File System
  • Network File System
  • Distributed file system

Next, create a WDL, and its JSON input like so:

Sample WDL

task sparkjob_with_yarn_cluster {
        File input_jar
        String input_1
        String output_base
        String entry_point
        Int cores
        String memory

        command {
                ${input_jar} ${input_1} ${output_base}

        runtime {
                appMainClass: "${entry_point}"
                executorMemory: "${memory}"
                executorCores: "${cores}"
        output {
                File out = "${output_base}"

and its accompanying JSON input as:

    "sparkWithYarnCluster.sparkjob_with_yarn_cluster.memory": "4G",
    "sparkWithYarnCluster.sparkjob_with_yarn_cluster.entry_point": "",
    "sparkWithYarnCluster.sparkjob_with_yarn_cluster.cores": "12",
    "sparkWithYarnCluster.sparkjob_with_yarn_cluster.input_1": "/mnt/lustre/hadoop/home/inputfiles/sample.txt",
    "sparkWithYarnCluster.sparkjob_with_yarn_cluster.input_jar": "/mnt/lustre/hadoop/home/inputjars/spark_hdfs.jar"