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I have written a sparkR code and wondering if I can submit it using spark-submit or sparkR on an EMR cluster.

I have tried several ways for example: sparkR mySparkRScript.r or sparkR --no-save mySparkScript.r etc.. but every time I am getting below error:

Error in sparkR.sparkContext(master, appName, sparkHome, sparkConfigMap,  : 
JVM is not ready after 10 seconds

Sample Code:

#Set the path for the R libraries you would like to use.
#You may need to modify this if you have custom R libraries.
.libPaths(c(.libPaths(), '/usr/lib/spark/R/lib'))

#Set the SPARK_HOME environment variable to the location on EMR
Sys.setenv(SPARK_HOME = '/usr/lib/spark')

#Load the SparkR library into R
library(SparkR, lib.loc = c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib")))

#Initiate a Spark context and identify where the master node is located.
#local is used here because the RStudio server
#was installed on the master node

sc <- sparkR.session(master = "local[*]", sparkEnvir = list(spark.driver.memory="2g"))

sqlContext <- sparkRSQL.init(sc)

Note: I am able to run my code in sparkr-shell by pasting directly or using source("mySparkRScript.R").

Ref:

  1. Crunching Statistics at Scale with SparkR on Amazon EMR
  2. SparkR Spark documentation
  3. R on Spark
  4. Executing-existing-r-scripts-from-spark-rutger-de-graaf
  5. Github
Shashwat Shekhar Shukla
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1 Answers1

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I was able to get this running via Rscript. There are a few things you need to do, and this may be a bit process intensive. If you are willing to give it a go, I would recommend:

  1. Figure out how to do an automated SparkR or sparklyR build. Via: https://github.com/UrbanInstitute/spark-social-science
  2. Use the AWS CLI to first create a cluster with the EMR template and bootstrap script you will create via following Step 1. (Make sure to put the EMR template and rstudio_sparkr_emrlyr_blah_blah.sh sripts into an S3 bucket)
  3. Place your R code into a single file and put this in another S3 bucket...the sample code you have provided would work just fine, but I would recommend actually doing some operation, say reading in data from S3, adding a value to it, then writing it back out (just to confirm it works before getting into the 'heavy' code you might have sitting around)

  4. Create another .sh file that copies the R file from the S3 bucket you have to the cluster, and then execute it via Rscript. Put this shell script in the same S3 bucket as your R code file (for simplicity). An example of the contents of this shell file might look like this: 

    #!/bin/bash
aws s3 cp s3://path/to/the/R/file/from/step3.R theNameOfTheFileToRun.R
Rscript theNameOfTheFileToRun.R

  5. In the AWS CLI, at the time of cluster creation, insert a --step to your cluster creation call, Use the CUSTOM JAR RUNNER provided by Amazon to run the shell script that copies and executes the R code

  6. Make sure to stop the Spark session at the end of your R code.

An example of the AWS CLI command might look like this (I'm using the us-east-1 zone on Amazon in my example, and throwing a 100GB disk on each worker in the cluster...just put your zone in wherever you see 'us-east-1' and pick whatever size disk you want instead)

aws emr create-cluster --name "MY COOL SPARKR OR SPARKLYR CLUSTER WITH AN RSCRIPT TO RUN SOME R CODE" --release-label emr-5.8.0 --applications Name=Spark Name=Ganglia Name=Hadoop --instance-groups InstanceGroupType=MASTER,InstanceCount=1,InstanceType=m4.xlarge 'InstanceGroupType=CORE,InstanceCount=2,InstanceType=m4.2xlarge,EbsConfiguration={EbsOptimized=true,EbsBlockDeviceConfigs=[{VolumeSpecification={VolumeType=gp2,SizeInGB=100}},{VolumeSpecification={VolumeType=io1,SizeInGB=100,Iops=100},VolumesPerInstance=1}]}' --log-uri s3://path/to/EMR/sparkr_logs --bootstrap-action Path=s3://path/to/EMR/sparkr_bootstrap/rstudio_sparkr_emr5lyr-proc.sh,Args=['--user','cool_dude','--user-pw','top_secret','--shiny','true','--sparkr','true','sparklyr','true'] --ec2-attributes KeyName=mykeyfilename,InstanceProfile=EMR_EC2_DefaultRole,AdditionalMasterSecurityGroups="sg-abc123",SubnetId="subnet-abc123" --service-role EMR_DefaultRole --scale-down-behavior TERMINATE_AT_TASK_COMPLETION --auto-terminate --region us-east-1 --steps Type=CUSTOM_JAR,Name=CustomJAR,ActionOnFailure=CONTINUE,Jar=s3://us-east-1.elasticmapreduce/libs/script-runner/script-runner.jar,Args=["s3://path/to/the/shell/file/from/step4.sh"]

Good luck! Cheers, Nate

nate
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