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  1. DZone
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  4. WordCount on Hadoop With Scala

WordCount on Hadoop With Scala

We use Scala and Java to implement a simple map reduce job and then run it using HDInsight using WordCount as an example.

Emmanouil Gkatziouras user avatar by
Emmanouil Gkatziouras
CORE ·
Mar. 24, 17 · Tutorial
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Today, we will use Scala to implement a simple map reduce job and then run it using HDInsight.

We shall add the assembly plugin on our assembly.sbt:

addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.14.3") 
Then we will add the Hadoop core dependency on our build.sbt file. 
Also will we apply some configuration in the merge strategy to avoid deduplicate
errors. assembly
MergeStrategy in 
assembly := { case PathList("META-INF", xs @ _*) => MergeStrategy.discard 
case x => MergeStrategy.first } 
libraryDependencies += "org.apache.hadoop" % "hadoop-core" % "1.2.1" 

We will use WordCount as an example.

The original Java class shall be transformed to a Scala class:

package com.gkatzioura.scala

import java.lang.Iterable
import java.util.StringTokenizer

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.Path
import org.apache.hadoop.io.{IntWritable, Text}
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat
import org.apache.hadoop.mapreduce.{Job, Mapper, Reducer}
import scala.collection.JavaConverters._

/**
  * Created by gkatzioura on 2/14/17.
  */
package object WordCount {

  class TokenizerMapper extends Mapper[Object, Text, Text, IntWritable] {

    val one = new IntWritable(1)
    val word = new Text()

    override def map(key: Object, value: Text, context: Mapper[Object, Text, Text, IntWritable]#Context): Unit = {
      val itr = new StringTokenizer(value.toString)
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken())
        context.write(word, one)
      }
    }
  }

  class IntSumReader extends Reducer[Text,IntWritable,Text,IntWritable] {
    override def reduce(key: Text, values: Iterable[IntWritable], context: Reducer[Text, IntWritable, Text, IntWritable]#Context): Unit = {
      var sum = values.asScala.foldLeft(0)(_ + _.get)
      context.write(key, new IntWritable(sum))
    }
  }


  def main(args: Array[String]): Unit = {
    val configuration = new Configuration
    val job = Job.getInstance(configuration,"word count")
    job.setJarByClass(this.getClass)
    job.setMapperClass(classOf[TokenizerMapper])
    job.setCombinerClass(classOf[IntSumReader])
    job.setReducerClass(classOf[IntSumReader])
    job.setOutputKeyClass(classOf[Text])
    job.setOutputKeyClass(classOf[Text]);
    job.setOutputValueClass(classOf[IntWritable]);
    FileInputFormat.addInputPath(job, new Path(args(0)))
    FileOutputFormat.setOutputPath(job, new Path(args(1)))
    System.exit(if(job.waitForCompletion(true))  0 else 1)
  }

}


Then, we will build our example:

sbt clean compile assembly 


Our new JAR will reside on target/scala-2.12/ScalaHadoop-assembly-1.0.jar. 

In the next post, we shall run our code using Azure’s HDInsight.

You can find the code on GitHub.

Scala (programming language) hadoop

Published at DZone with permission of Emmanouil Gkatziouras, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

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