Apache YARN is Yet Another Resource Negotiator for distributed systems. It’s a distributed system resource scheduler similar to mesos. Yarn was created as effort to diversify the hadoop for different use cases. Yarn is available in all hadoop 2.x releases.

In this post, we are going to discuss about how to run a scala program in yarn. You may have seen distributed shell example which run shell commands on yarn. This example extends that code to run scala programs in place of shell commands.

If you are new to yarn please go through YARN architecture before continuing.

Yarn and Scala

Yarn is written in Java. So the API it exposes is primarily in java. There is no special support for Scala. We just use the java api in our example.

tl;dr Access the complete code on github.

The following are the steps to write a yarn application which runs scala helloworld program on hadoop cluster.

Step 1 : Add yarn dependencies


I am adding version 2.2.0 as I have that version installed on my system. If you have different version of hadoop installed, please change accordingly.

hadoop-yarn-client dependency contains all protocols to talk to resource manager and node manager . We need hadoop-common to do hdfs operations.

Step 2 : Yarn Client

For every yarn application, there will be a client which will launch application specific master.

So let’s start implementing one

Step 2.1 : Start yarn client

First we have to start a YarnClient, which will talk to Resource manager on our behalf.

val client = YarnClient.createYarnClient()

Step 2.2 : Specify command to launch Application master

 val app = client.createApplication()
    val amContainer = Records.newRecord(classOf[ContainerLaunchContext])
    //application master is a just java program with given commands
      "$JAVA_HOME/bin/java" +
        " -Xmx256M" +
        " com.madhukaraphatak.yarn.helloworld.ApplicationMaster"+
        "  " +jarPath +"   "+ numberOfInstances + " "+
        " 1>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout" +
        " 2>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr"

Launching an application master is just running a command from shell. Yarn will not know anything about application or it’s environment. So you have to specify the complete command how to launch the application master.

Please note that we call asJava to convert scala list to java. The reason being all yarn API take Java collections.

Now you may be wondering, how yarn will get the code which contains this main class to launch. That’s the next step

Step 2.3 : Add the application jar to local resource

 val appMasterJar = Records.newRecord(classOf[LocalResource])
 setUpLocalResource(new Path(jarPath), appMasterJar)
 amContainer.setLocalResources(Collections.singletonMap("helloworld.jar", appMasterJar))

Here we instruct the yarn to make the specific jar available in class path when we launch the application master. These jars has to be there in HDFS not on your local system. How to copy and specify the path we will see in running section.

Step 2.4: Add hadoop and yarn jars to class path

As our code depends on hadoop and yarn api, we have to add them to class path. The following code does that.

def setUpEnv(env: collection.mutable.Map[String, String])
(implicit conf:YarnConfiguration) = {
 val classPath =  conf.getStrings(YarnConfiguration.YARN_APPLICATION_CLASSPATH,YarnConfiguration.

 for (c <- classPath){
    Apps.addToEnvironment(env.asJava, Environment.CLASSPATH.name(),
      Environment.PWD.$() + File.separator + "*")

We fill up our env map using the jar name from yarn classpath


Once we have map, set the map as environment for application master.

Step 2.5: Specifying resource requirement for Application master

Everything in yarn runs on a container which consumes part of resources on cluster. So before launching any container you have to specify how much resource it needs.

 val resource = Records.newRecord(classOf[Resource])

Here we are telling to yarn that we need 300 mb of memory and one cpu to run our application master.

Step 2.5: Setup the context and submit the application

Once everything is ready, create an application submission context which will request a new application id from RM. Then submit the application.

val appContext = app.getApplicationSubmissionContext

//submit the application
val appId = appContext.getApplicationId
println("submitting application id" + appId)

Access complete code here.

Step 3 : Application master

Application Master is a simple java program which runs in yarn container. Application master is responsible for talking to RM and NM to request for containers to run the tasks. Here our task is to run our hello world program.

Step 3.1 : Start RM and NM client

We have to start RM and NM client in order to talk to these components.

// Create a client to talk to the RM
val rmClient = AMRMClient.createAMRMClient().asInstanceOf[AMRMClient[ContainerRequest]]
rmClient.registerApplicationMaster("", 0, "")

//create a client to talk to NM
val nmClient = NMClient.createNMClient()

Step 3.2 : Request for containers

Once we have established communication to RM and NM, we will request for containers which allows us to run our program. No.of containers is specified as command line argument. If you specify more than 1, hello world runs more than ones.

for ( i <- 1 to n) {
 val containerAsk = new ContainerRequest(resource,null,null,priority)
 println("asking for " +s"$i")
 rmClient.addContainerRequest(containerAsk) }

Step 3.3 : Wait for container allocation

Whenever you request for containers in yarn, they will be not allocated immediately. If there is high traffic on cluster, your application has to wait till the resources are free.

Step 3.4 : Launch Hellworld on allocated container

Once resources are available, YARN will allocate requested containers. Once we have container we will launch the our hello world. Setting up jar and environment is exactly same like client.

while( completedContainers < n) {

val appMasterJar = Records.newRecord(classOf[LocalResource])
setUpLocalResource(new Path(jarPath),appMasterJar)

val env = collection.mutable.Map[String,String]()

val response = rmClient.allocate(responseId+1)

for (container <- response.getAllocatedContainers.asScala) {
val ctx =
"$JAVA_HOME/bin/java" +
" -Xmx256M " +
" com.madhukaraphatak.yarn.helloworld.HelloWorld" +
" 1>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stdout" +
" 2>" + ApplicationConstants.LOG_DIR_EXPANSION_VAR + "/stderr"


System.out.println("Launching container " + container)
nmClient.startContainer(container, ctx)


Access complete code here.

Step 4 : Hello world program

Our hello world is just simple scala class.

object HelloWorld {
 def main(args: Array[String]) {

Step 5 : Build

Download code from here and run mvn clean install

Step 6 : Running

Follow the following steps to run the example.

Step 6.1 : Create jars folder in HDFS

This folder will hold the jar built in the build step. As we discussed earlier, the jar containing application master has to be in HDFS in order to add as a local resource.

hdfs dfs -mkdir /jars

Step 6.2 : Put the jar file in /jars

Copy the jar from your local file system to HDFS.

 hdfs dfs -put <jar-path> /jars

Step 6.3 : Run the code

Replace jar-path with absolute path to jar on you system. Also put appropriate values for namenode-host and namenode-port. The last parameter specifies number of containers.

 hadoop jar <jar-path>  com.madhukaraphatak.yarn.helloworld.Client hdfs://<namenode-host:namenode-port>/jars/yarn-helloworld-scala-1.0-SNAPSHOT.jar 1

If everything runs fine, you should see hello world in logs, available at $HADOOP_HOME/logs/userlogs.