Big Data/BI Zone is brought to you in partnership with:

Pascal is a senior JEE Developer and Architect at 4Synergy in The Netherlands. Pascal has been designing and building J2EE applications since 2001. He is particularly interested in Open Source toolstack (Mule, Spring Framework, JBoss) and technologies like Web Services, SOA and Cloud technologies. Specialties: JEE XML Web Services Mule ESB Maven Cloud Technology Pascal is a DZone MVB and is not an employee of DZone and has posted 54 posts at DZone. You can read more from them at their website. View Full User Profile

Unit Testing a Java Hadoop Job

08.29.2013
| 2829 views |
  • submit to reddit

In my previous post I showed how to setup a complete Maven based project to create a Hadoop job in Java. Of course it wasn’t complete because it is missing the unit test part . In this post I show how to add MapReduce unit tests to the project I started previously. For the unit test I make use of the MRUnit framework.

  • Add the necessary dependency to the pom
  • Add the following dependency to the pom:

    <dependency>
    <groupId>org.apache.mrunit</groupId>
    <artifactId>mrunit</artifactId>
    <version>1.0.0</version>
    <classifier>hadoop1</classifier>
    <scope>test</scope>
    </dependency>


    This will made the MRunit framework available to the project.

  • Add Unit tests for testing the Map Reduce logic
  • The use of this framework is quite straightforward, especially in our business case. So I will just show the unit test code and some comments if necessary but I think it is quite obvious how to use it.
    The unit test for the Mapper ‘MapperTest’:

    package net.pascalalma.hadoop;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mrunit.mapreduce.MapDriver;
    import org.junit.Before;
    import org.junit.Test;
    import java.io.IOException;
    /**
    * Created with IntelliJ IDEA.
    * User: pascal
    */
    public class MapperTest {
    MapDriver<Text, Text, Text, Text> mapDriver;
    @Before
    public void setUp() {
    WordMapper mapper = new WordMapper();
    mapDriver = MapDriver.newMapDriver(mapper);
    }
    @Test
    public void testMapper() throws IOException {
    mapDriver.withInput(new Text("a"), new Text("ein"));
    mapDriver.withInput(new Text("a"), new Text("zwei"));
    mapDriver.withInput(new Text("c"), new Text("drei"));
    mapDriver.withOutput(new Text("a"), new Text("ein"));
    mapDriver.withOutput(new Text("a"), new Text("zwei"));
    mapDriver.withOutput(new Text("c"), new Text("drei"));
    mapDriver.runTest();
    }
    }

    This test class is actually even simpler than the Mapper implementation itself. You just define the input of the mapper and the expected output and then let the configured MapDriver run the test. In our case the Mapper doesn’t do anything specific but you see how easy it is to setup a testcase.
    For completeness here is the test class of the Reducer:

    package net.pascalalma.hadoop;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mrunit.mapreduce.ReduceDriver;
    import org.junit.Before;
    import org.junit.Test;
    import java.io.IOException;
    import java.util.ArrayList;
    import java.util.List;
    /**
    * Created with IntelliJ IDEA.
    * User: pascal
    */
    public class ReducerTest {
    ReduceDriver<Text, Text, Text, Text> reduceDriver;
    @Before
    public void setUp() {
    AllTranslationsReducer reducer = new AllTranslationsReducer();
    reduceDriver = ReduceDriver.newReduceDriver(reducer);
    }
    @Test
    public void testReducer() throws IOException {
    List<Text> values = new ArrayList<Text>();
    values.add(new Text("ein"));
    values.add(new Text("zwei"));
    reduceDriver.withInput(new Text("a"), values);
    reduceDriver.withOutput(new Text("a"), new Text("|ein|zwei"));
    reduceDriver.runTest();
    }
    }
  • Run the unit tests it
  • With the Maven command “mvn clean test” we can run the tests:
    Screen Shot 2013-08-23 at 20.12.50

With the unit tests in place I would say we are ready to build the project and deploy it to an Hadoop cluster, which I will describe in the next post.

Published at DZone with permission of Pascal Alma, author and DZone MVB. (source)

(Note: Opinions expressed in this article and its replies are the opinions of their respective authors and not those of DZone, Inc.)