Steps for Deploying Spark App on Amazon EMR

• Step 1

  Test your application using Scala - ide using sample data.

  

• Step 2

  • Remove all local path and Spark Context master local reference from Scala file.

  • Use SBT to package your application

    • Create and empty directory sbt
    • sbt new scala/hello-world.g8.
    • add your scale files under sbt\movies\src\main\scala directory
    • edit sbt\movies\built.sbt

  • name := "MostRatedMovies100k"
    
    version := "1.0"
    
    organization := "com.forsynet.sparkemr"
    
    scalaVersion := "2.11.12"
    
    libraryDependencies ++= Seq(
    "org.apache.spark" %% "spark-core" % "2.4.5" % "provided"
    )

  • at command prompt run command "sbt assembly"

  • This will create jar files with all dependencies under sbt\movies\target\scala-2.11\MostRatedMovies100k-assembly-1.0.jar

• Step 3

  • upload the jar files and the data files to s3 bucket .Use UI or below cli commands

  • aws configure
    aws s3api create-bucket --bucket rupeshemr
    aws s3 sync data/
    

    • Verify the data is uploaded to s3 bucket
    • 

• Step 4

• Use the aws cli or the UI to create the cluster.

• aws emr create-cluster \
      --instance-type m3.xlarge \
      --release-label emr-5.10.0 \
      --service-role EMR_DefaultRole \
      --ec2-attributes InstanceProfile=EMR_EC2_DefaultRole \
      --security-configuration mySecurityConfiguration \
      --kerberos-attributes file://kerberos_attributes.json

  • Verify Cluster Creation

  • 

  • Step5

  • Add SSH Inbound rule to security groups

  • 

• Step 6

• ssh into emr master node

• 

• copy the jar file from s3 bucker

• aws s3 cp s3://rupeshemr/MostRatedMovies-1.0.jar ./
  

  

• Step 7

  • spark-submit MostRatedMovies-1.0.jar

• Verify Results of top rated movies

  • 

• Use the Spark History Server UI to see the Spark Job History for submitted job

• 

• Verify Amazon s3 bucket for logs created for the job