We are running batch spark jobs using AWS EMR clusters. Those jobs run periodically and we would like to orchestrate those via AWS Step Functions.
As of November 2019 Step Functions has support for EMR natively. When adding a Step to the cluster we can use the following config:
"Some Step": {
"Type": "Task",
"Resource": "arn:aws:states:::elasticmapreduce:addStep.sync",
"Parameters": {
"ClusterId.$": "$.cluster.ClusterId",
"Step": {
"Name": "FirstStep",
"ActionOnFailure": "CONTINUE",
"HadoopJarStep": {
"Jar": "command-runner.jar",
"Args": [
"spark-submit",
"--class",
"com.some.package.Class",
"JarUri",
"--startDate",
"$.time",
"--daysToLookBack",
"$.daysToLookBack"
]
}
}
},
"Retry" : [
{
"ErrorEquals": [ "States.ALL" ],
"IntervalSeconds": 1,
"MaxAttempts": 1,
"BackoffRate": 2.0
}
],
"ResultPath": "$.firstStep",
"End": true
}
Within the Args List of the HadoopJarStep we would like to set arguments dynamically. e.g. if the input of the state machine execution is:
{
"time": "2020-01-08",
"daysToLookBack": 2
}
The strings in the config starting with "$." should be replaced accordingly when executing the State Machine, and the step on the EMR cluster should run command-runner.jar spark-submit --class com.some.package.Class JarUri --startDate 2020-01-08 --daysToLookBack 2
. But instead it runs command-runner.jar spark-submit --class com.some.package.Class JarUri --startDate $.time --daysToLookBack $.daysToLookBack
.
Does anyone know if there is a way to do this?