How to Configure Spark Application ( Scala and Java 8 Version with Maven ) in Eclipse.

Apache Spark is becoming very popular among organizations looking to leverage its fast, in-memory computing capability for big-data processing. This article is for beginners to get started with Spark Setup on Eclipse/Scala IDE and getting familiar with Spark terminologies in general –

Hope you have read the previous article on RDD basics, to get a basic understanding of Spark RDD.

Tools Used :

• Scala IDE for Eclipse – Download the latest version of Scala IDE from here. Here, I used Scala IDE 4.7.0 Release, which support both Scala and Java
• Scala Version – 2.11 ( make sure scala compiler is set to this version as well)
• Spark Version 2.2 ( provided in maven dependency)
• Java Version 1.8
• Maven Version 3.3.9 ( Embedded in Eclipse)
• winutils.exe

For running in Windows environment , you need hadoop binaries in windows format. winutils provides that and we need to set hadoop.home.dir system property to bin path inside which winutils.exe is present. You can download winutils.exe here and place at path like this – c:/hadoop/bin/winutils.exe . Read this for more information.

Creating a Sample Application in Eclipse –

In Scala IDE, create a new Maven Project –

Replace POM.XML as below –

POM.XML

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>com.saurzcode.spark</groupId>
    <artifactId>spark-app</artifactId>
    <version>0.0.1-SNAPSHOT</version>
    <dependencies>
        <dependency> <!-- Spark dependency -->
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>2.2.0</version>
            <scope>provided</scope>
        </dependency>
    </dependencies>
</project>

For creating a Java WordCount program, create a new Java Class and copy the code below –

Java Code for WordCount

package com.saurzcode.spark;

import java.util.Arrays;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;

import scala.Tuple2;

public class JavaWordCount {
public static void main(String[] args) throws Exception {

String inputFile = “src/main/resources/input.txt”;

//To set HADOOP_HOME.
System.setProperty(“hadoop.home.dir”, “c://hadoop//”);

//Initialize Spark Context
JavaSparkContext sc = new JavaSparkContext(new SparkConf().setAppName(“wordCount”).setMaster(“local[4]”));

// Load data from Input File.
JavaRDD<String> input = sc.textFile(inputFile);

// Split up into words.
JavaPairRDD<String, Integer> counts = input.flatMap(line -> Arrays.asList(line.split(” “)).iterator())
.mapToPair(word -> new Tuple2<>(word, 1)).reduceByKey((a, b) -> a + b);

System.out.println(counts.collect());

sc.stop();
sc.close();
}
}

Scala Version

For running the Scala version of WordCount program in scala, create a new Scala Object and use the code below –

You may need to set project as scala project to run this, and make sure scala compiler version matches Scala version in your Spark dependency, by setting in build path –

package com.saurzcode.spark

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext

object ScalaWordCount {

def main(args: Array[String]) {

//To set HADOOP_HOME.
System.setProperty(“hadoop.home.dir”, “c://hadoop//”);
// create Spark context with Spark configuration
val sc = new SparkContext(new SparkConf().setAppName(“Spark WordCount”).setMaster(“local[4]”))

//Load inputFile
val inputFile = sc.textFile(“src/main/resources/input.txt”)
val counts = inputFile.flatMap(line => line.split(” “)).map(word => (word, 1)).reduceByKey((a, b) => a + b)
counts.foreach(println)

sc.stop()
}

}

So, your final setup will look like this –

Running the code in Eclipse

You can run the above code in Scala or Java as simple Run As Scala or Java Application in eclipse to see the output.

Output

Now you should be able to see the word count output, along with log lines generated using default Spark log4j properties.

In the next post, I will explain how you can open Spark WebUI and look at various stages, tasks on Spark code execution internally.

You may also be interested in some other BigData posts –

• Concept of RDDs in Spark
• Getting started with MapReduce development
• Top 20 BigData Books
• Spark Dataframe Operations – Part I
• Spark ; How to Run Spark Applications on Windows