pishen / sbt-lighter   1.2.0

Apache License 2.0 GitHub

SBT plugin for Apache Spark on AWS EMR

Scala versions: 2.12
sbt plugins: 1.0


Build Status

SBT plugin for Spark on AWS EMR.

Getting started

  1. Add sbt-lighter in project/plugins.sbt
addSbtPlugin("net.pishen" % "sbt-lighter" % "1.2.0")
  1. Setup sbt version for your project in project/build.properties (requires sbt 1.0):
  1. Prepare your build.sbt
name := "sbt-lighter-demo"

scalaVersion := "2.11.12"

libraryDependencies ++= Seq(
  "org.apache.spark" %% "spark-core" % "2.3.1" % "provided"

sparkAwsRegion := "ap-northeast-1"

//Since we use cluster mode, we need a bucket to store our application's jar.
sparkS3JarFolder := "s3://my-emr-bucket/my-emr-folder/"

//(optional) Set the subnet id if you want to run Spark in VPC.
sparkSubnetId := Some("subnet-********")

//(optional) Additional security groups that will be attached to Master and Core instances.
sparkSecurityGroupIds := Seq("sg-********")

//(optional) Total number of instances, including master node. The default value is 1.
sparkInstanceCount := 2
  1. Write your application at src/main/scala/mypackage/Main.scala
package mypackage

import org.apache.spark._

object Main {
  def main(args: Array[String]): Unit = {
    //setup spark
    val sc = new SparkContext(new SparkConf())
    //your algorithm
    val n = 10000000
    val count = sc.parallelize(1 to n).map { i =>
      val x = scala.math.random
      val y = scala.math.random
      if (x * x + y * y < 1) 1 else 0
    }.reduce(_ + _)
    println("Pi is roughly " + 4.0 * count / n)
  1. Submit your Spark application
> sparkSubmit arg0 arg1 ...

Note that a cluster with the same name as your project's name will be created by this command. This cluster will terminate itself automatically if there's no further steps (beyond the one you just submitted) waiting in the queue. (You can submit multiple steps into the queue by running sparkSubmit multiple times.)

If you want a keep-alive cluster, run sparkCreateCluster before sparkSubmit, and remember to terminate it with sparkTerminateCluster when you are done:

> sparkCreateCluster
> sparkSubmit arg0 arg1 ...
== Wait for your job to finish ==
> sparkTerminateCluster

The id of the created cluster will be stored in a file named .cluster_id. This file will be used to find the cluster when running other commands.

Other available settings

//Your cluster's name. Default value is copied from your project's `name` setting.
sparkClusterName := "your-new-cluster-name"

sparkClusterIdFile := file(".cluster_id")

sparkEmrRelease := "emr-5.17.0"

sparkEmrServiceRole := "EMR_DefaultRole"

//EMR applications that will be installed, default value is Seq("Spark")
sparkEmrApplications := Seq("Spark", "Zeppelin")

sparkVisibleToAllUsers := true

//EC2 instance type of EMR Master node, default is m4.large
//Note that this is *not* the master node of Spark
sparkMasterType := "m4.large"

//EC2 instance type of EMR Core nodes, default is m4.large
sparkCoreType := "m4.large"

//EBS (disk) size of EMR Master node, default is 32GB
sparkMasterEbsSize := Some(32)

//EBS (disk) size of EMR Core nodes, default is 32GB
sparkCoreEbsSize := Some(32)

//Spot instance bid price of Master node, default is None.
sparkMasterPrice := Some(0.1)

//Spot instance bid price of Core nodes, default is None.
sparkCorePrice := Some(0.1)

sparkInstanceRole := "EMR_EC2_DefaultRole"

//EC2 keypair, default is None.
sparkInstanceKeyName := Some("your-keypair")

//EMR logging folder, default is None.
sparkS3LogUri := Some("s3://my-emr-bucket/my-emr-log-folder/")

//Configs of --conf when running spark-submit, default is an empty Map.
sparkSubmitConfs := Map("spark.executor.memory" -> "10G", "spark.executor.instances" -> "2")

//List of EMR bootstrap scripts and their parameters, if any, default is Seq.empty.
sparkEmrBootstrap := Seq(BootstrapAction("my-bootstrap", "s3://my-production-bucket/bootstrap.sh", "--full"))

Other available commands

> show sparkClusterId

> sparkListClusters

> sparkTerminateCluster

> sparkSubmitMain mypackage.Main arg0 arg1 ...

If you accidentally deleted your .cluster_id file, you can bind it back using:

> sparkBindCluster j-*************

Use EmrConfig to configure the applications

EMR provides a JSON syntax to configure the applications on cluster, including spark. Here we provide a helper class called EmrConfig, which lets you setup the configuration in an easier way.

For example, to maximize the memory allocation for each Spark job, one can use the following JSON config:

    "Classification": "spark",
    "Properties": {
      "maximizeResourceAllocation": "true"

Instead of using this JSON config, one can add the following setting in build.sbt to achieve the same effect:

import sbtlighter.EmrConfig

sparkEmrConfigs := Seq(
  EmrConfig("spark").withProperties("maximizeResourceAllocation" -> "true")

For people who already have a JSON config, there's a parsing function EmrConfig.parseJson(jsonString: String) which can convert the JSON array into List[EmrConfig]. And, if your JSON is located on S3, you can also parse the file on S3 directly (note that this will read the file from S3 right after you execute sbt):

import sbtlighter.EmrConfig

sparkEmrConfigs := EmrConfig

Modify the configurations of underlying AWS objects

There are two settings called sparkJobFlowInstancesConfig and sparkRunJobFlowRequest, which corresponds to JobFlowInstancesConfig and RunJobFlowRequest in AWS Java SDK. Some default values are already configured in these settings, but you can modify it for your own purpose, for example:

To set the master and slave security groups separately (This requires you leaving sparkSecurityGroupIds as None in step 2):

sparkJobFlowInstancesConfig := sparkJobFlowInstancesConfig.value

To set the EMR auto-scaling role:

sparkRunJobFlowRequest := sparkRunJobFlowRequest.value.withAutoScalingRole("EMR_AutoScaling_DefaultRole")

To set the tags on cluster resources:

import com.amazonaws.services.elasticmapreduce.model.Tag

sparkRunJobFlowRequest := sparkRunJobFlowRequest.value.withTags(new Tag("Name", "my-cluster-name"))

To add some initial steps at cluster creation:

import com.amazonaws.services.elasticmapreduce.model._

sparkRunJobFlowRequest := sparkRunJobFlowRequest.value
    new StepConfig()
      .withName("Install components")
        new HadoopJarStepConfig()
          .withArgs(Seq("arg1", "arg2").asJava)

To add Server Side Encryption to Jar File and Add meta data support:

import com.amazonaws.services.s3.model.ObjectMetadata
sparkS3PutObjectDecorator := { req =>
  val metadata = new ObjectMetadata()

Use SBT's config to provide multiple setting combinations

If you have multiple environments (e.g. different subnet, different AWS region, ...etc) for your Spark project, you can use SBT's config to provide multiple setting combinations:

import sbtlighter._

//Since we don't use the global scope now, we can disable it.

//And setup your configurations
lazy val Testing = config("testing")
lazy val Production = config("production")

inConfig(Testing)(LighterPlugin.baseSettings ++ Seq(
  sparkAwsRegion := "ap-northeast-1",
  sparkSubnetId := Some("subnet-aaaaaaaa"),
  sparkSecurityGroupIds := Seq("sg-aaaaaaaa"),
  sparkInstanceCount := 1,
  sparkS3JarFolder := "s3://my-testing-bucket/my-emr-folder/"

inConfig(Production)(LighterPlugin.baseSettings ++ Seq(
  sparkAwsRegion := "us-west-2",
  sparkSubnetId := Some("subnet-bbbbbbbb"),
  sparkSecurityGroupIds := Seq("sg-bbbbbbbb"),
  sparkInstanceCount := 20,
  sparkS3JarFolder := "s3://my-production-bucket/my-emr-folder/",
  sparkS3LogUri := Some("s3://aws-logs-************-us-west-2/elasticmapreduce/")
  sparkCorePrice := Some(0.39),
  sparkEmrConfigs := Seq(EmrConfig("spark", Map("maximizeResourceAllocation" -> "true")))

Then, in sbt, activate different config by the <config>:<task/setting> syntax:

> testing:sparkSubmit

> production:sparkSubmit

Keep SBT monitoring the cluster status until it completes

There's a special command called

> sparkMonitor

which will poll on the cluster's status until it terminates or exceeds the time limit.

The time limit can be defined by:

import scala.concurrent.duration._

sparkTimeoutDuration := 90.minutes

(the default value of sparkTimeoutDuration is 90 minutes)

And this command will fall into one of the three following behaviors:

  1. If the cluster ran for a duration longer than sparkTimeoutDuration, terminate the cluster and throw an exception.
  2. If the cluster terminated within sparkTimeoutDuration but had some failed steps, throw an exception.
  3. If the cluster terminated without any failed step, return Unit (exit code == 0).

This command would be useful if you want to trigger some notifications. For example, a bash command like this

$ sbt 'sparkSubmit arg0 arg1' sparkMonitor

will exit with error if the job fail or running too long (Don't enter the sbt console here, just append the task names after sbt like above). You can then put this command into a cron job for scheduled computation, and let cron notify yourself when something go wrong.