spark-deployer

Deploy Spark cluster in an easy way.

Programming language: Scala

License: Apache License 2.0

Tags: Big Data    

Latest version: v3.0.2

README

spark-deployer

• A Scala tool which helps deploying Apache Spark stand-alone cluster on EC2 and submitting job.
• Currently supports Spark 2.0.0+.
• There are two modes when using spark-deployer: SBT plugin mode and embedded mode.

SBT plugin mode

Here are the basic steps to run a Spark job (all the sbt commands support TAB-completion):

1. Set the environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY.
2. Prepare a project with structure like below:

  project-root
  ├── build.sbt
  ├── project
  │   └── plugins.sbt
  └── src
      └── main
          └── scala
              └── mypackage
                  └── Main.scala

1. Add one line in project/plugins.sbt:

  addSbtPlugin("net.pishen" % "spark-deployer-sbt" % "3.0.2")

1. Write your Spark project's build.sbt (Here we give a simple example):

  name := "my-project-name"

  scalaVersion := "2.11.8"

  libraryDependencies ++= Seq(
    "org.apache.spark" %% "spark-core" % "2.0.0" % "provided"
  )

1. Write your job's algorithm in src/main/scala/mypackage/Main.scala:

  package mypackage

  import org.apache.spark._

  object Main {
    def main(args: Array[String]) {
      //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. Enter sbt, and build a config by:

  > sparkBuildConfig

(Most settings have default values, just hit Enter to go through it.)

1. Create a cluster with 1 master and 2 workers by:

  > sparkCreateCluster 2

1. See your cluster's status by:

  > sparkShowMachines

1. Submit your job by:

  > sparkSubmit

1. When your job is done, destroy your cluster with

  > sparkDestroyCluster

Advanced functions

• To build config with different name or build a config based on old one:

  > sparkBuildConfig <new-config-name>
  > sparkBuildConfig <new-config-name> from <old-config-name>

All the configs are stored as .deployer.json files in the conf/ folder. You can modify it if you know what you're doing.

• To change the current config:

  > sparkChangeConfig <config-name>

• To submit a job with arguments or with a main class:

  > sparkSubmit <args>
  > sparkSubmitMain mypackage.Main <args>

• To add or remove worker machines dynamically:

  > sparkAddWorkers <num-of-workers>
  > sparkRemoveWorkers <num-of-workers>

Embedded mode

If you don't want to use sbt, or if you would like to trigger the cluster creation from within your Scala application, you can include the library of spark-deployer directly:

libraryDependencies += "net.pishen" %% "spark-deployer-core" % "3.0.2"

Then, from your Scala code, you can do something like this:

import sparkdeployer._

// build a ClusterConf
val clusterConf = ClusterConf.build()

// save and load ClusterConf
clusterConf.save("path/to/conf.deployer.json")
val clusterConfReloaded = ClusterConf.load("path/to/conf.deployer.json")

// create cluster and submit job
val sparkDeployer = new SparkDeployer()(clusterConf)

val workers = 2
sparkDeployer.createCluster(workers)

val jar = new File("path/to/job.jar")
val mainClass = "mypackage.Main"
val args = Seq("arg0", "arg1")
sparkDeployer.submit(jar, mainClass, args)

sparkDeployer.destroyCluster()

• Environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY should also be set.
• You may prepare the job.jar by sbt-assembly from other sbt project with Spark.
• For other available functions, check SparkDeployer.scala in our source code.

spark-deployer uses slf4j, remember to add your own backend to see the log. For example, to print the log on screen, add

libraryDependencies += "org.slf4j" % "slf4j-simple" % "1.7.14"

FAQ

Could I use other ami?

Yes, just specify the ami id when running sparkBuildConfig. The image should be HVM EBS-Backed with Java 7+ installed. You can also run some commands before Spark start on each machine by editing the preStartCommands in json config. For example:

"preStartCommands": [
  "sudo bash -c \"echo -e 'LC_ALL=en_US.UTF-8\\nLANG=en_US.UTF-8' >> /etc/environment\"",
  "sudo apt-get -qq install openjdk-8-jre",
  "cd spark/conf/ && cp log4j.properties.template log4j.properties && echo 'log4j.rootCategory=WARN, console' >> log4j.properties"
]

When using custom ami, the root device should be your root volume's name (/dev/sda1 for Ubuntu) that can be enlarged by disk size settings in master and workers.

Could I use custom Spark tarball?

Yes, just change the tgz url when running sparkBuildConfig, the tgz will be extracted as a spark/ folder in each machine's home folder.

What rules should I set on my security group?

Assuming your security group id is sg-abcde123, the basic settings is:

Type	Protocol	Port Range	Source
All traffic	All	All	sg-abcde123
SSH	TCP	22	<your-allowed-ip>
Custom TCP Rule	TCP	8080-8081	<your-allowed-ip>
Custom TCP Rule	TCP	4040	<your-allowed-ip>

How do I upgrade the config to new version of spark-deployer?

Change to the config you want to upgrade, and run sparkUpgradeConfig to build a new config based on settings in old one. If this doesn't work or you don't mind rebuilding one from scratch, it's recommended to directly create a new config by sparkBuildConfig.

Could I change the directory where configurations are saved?

You can change it by add the following line to your build.sbt:

sparkConfigDir := "path/to/my-config-dir"

How to contribute

• Please report issue or ask on gitter if you meet any problem.
• Pull requests are welcome.