Although Hadoop Framework itself is created with Java the MapReduce jobs can be written in many different languages. In this post I show how to create a MapReduce job in Java based on a Maven project like any other Java project.
- Prepare the example input
- Create the Java MapReduce project
Lets start with a fictional business case. In this case we need a CSV file with English words from a dictionary and all translations in other languages added to it, separated by a ‘|’ symbol. I have based this example on this post. So the job will read dictionaries of different languages and match each English word with a translation in another language. The input dictionaries for the job is taken from here. I downloaded a few files in different languages and put them together in one file (Hadoop is better to process one large file than multiple small ones). My example file can be found here.
Next step is creating the Java code for the MapReduce job. Like I said before I use a Maven project for this so I created a new empty Maven project in my IDE, IntelliJ. I modified the default pom to add the necessary plugins and dependencies:
The dependency I added:
<dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-core</artifactId> <version>1.2.0</version> <scope>provided</scope> </dependency>
The Hadoop dependency is necessary to make use of the Hadoop classes in my MapReduce job. Since I want to run the job on AWS EMR I make sure I have a matching Hadoop version. Furthermore the scope can be set to ‘provided’ since the Hadoop framework will be available on the Hadoop cluster.
Beside the dependency I added the following two plugins to the pom.xml:
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-jar-plugin</artifactId>
<configuration>
<archive>
<manifest>
<addClasspath>true</addClasspath>
<mainClass>net.pascalalma.hadoop.Dictionary</mainClass>
</manifest>
</archive>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<configuration>
<source>1.6</source>
<target>1.6</target>
</configuration>
</plugin>
</plugins>
The first plugin is used to create an executable jar of our project. This makes the running of the JAR on the Hadoop cluster easier since we don’t have to state the main class.
The second plugin is necessary to make the created JAR compatible with the instances of the AWS EMR cluster. This AWS cluster comes with a JDK 1.6. If you omit this one the cluster will fail (I got a message like ‘Unsupported major.minor version 51.0’). I will show later in another post how to setup this AWS EMR cluster.
That is the basic project, just like a regular Java project. Lets implement the MapReduce jobs next.
I have described the functionality that we want to perform in the first step. To achieve this I created three Java classes in my Hadoop project. The first class is the ‘Mapper‘:
package net.pascalalma.hadoop;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
import java.util.StringTokenizer;
/**
* Created with IntelliJ IDEA.
* User: pascal
* Date: 16-07-13
* Time: 12:07
*/
public class WordMapper extends Mapper<Text,Text,Text,Text> {
private Text word = new Text();
public void map(Text key, Text value, Context context) throws IOException, InterruptedException
{
StringTokenizer itr = new StringTokenizer(value.toString(),",");
while (itr.hasMoreTokens())
{
word.set(itr.nextToken());
context.write(key, word);
}
}
}
This class isn’t very complicated. It just receives a row from the input file and creates a Map of it in which each key will have one value (and multiple keys are allowed at this stage).
The next class is the ‘Reducer‘ which reduces the map to the wanted output:
package net.pascalalma.hadoop;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
/**
* Created with IntelliJ IDEA.
* User: pascal
* Date: 17-07-13
* Time: 19:50
*/
public class AllTranslationsReducer extends Reducer<Text, Text, Text, Text> {
private Text result = new Text();
@Override
protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
String translations = "";
for (Text val : values) {
translations += "|" + val.toString();
}
result.set(translations);
context.write(key, result);
}
}
This Reduce steps collects all values for a given key and put them after each other separated with a ‘|’ symbol.
The final class left is the one that is putting it all together to make it a runnable job:
package net.pascalalma.hadoop;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.KeyValueTextInputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
/**
* Created with IntelliJ IDEA.
* User: pascal
* Date: 16-07-13
* Time: 12:07
*/
public class Dictionary {
public static void main(String[] args) throws Exception
{
Configuration conf = new Configuration();
Job job = new Job(conf, "dictionary");
job.setJarByClass(Dictionary.class);
job.setMapperClass(WordMapper.class);
job.setReducerClass(AllTranslationsReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
job.setInputFormatClass(KeyValueTextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
}
In this main method we put together a Job and run it. Please note that I simply expect the args[0] and args[1] to be the name of the input file and output directory (non existing). I didn’t add any check for this. Here is my ‘Run Configuration’ in IntelliJ:
Just make sure the output directory is not existing at the time you run the class. The logging output created by the job looks like this:
2013-08-15 21:37:00.595 java[73982:1c03] Unable to load realm info from SCDynamicStore aug 15, 2013 9:37:01 PM org.apache.hadoop.util.NativeCodeLoader <clinit> WARNING: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable aug 15, 2013 9:37:01 PM org.apache.hadoop.mapred.JobClient copyAndConfigureFiles WARNING: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same. aug 15, 2013 9:37:01 PM org.apache.hadoop.mapred.JobClient copyAndConfigureFiles WARNING: No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String). aug 15, 2013 9:37:01 PM org.apache.hadoop.mapreduce.lib.input.FileInputFormat listStatus INFO: Total input paths to process : 1 aug 15, 2013 9:37:01 PM org.apache.hadoop.io.compress.snappy.LoadSnappy <clinit> WARNING: Snappy native library not loaded aug 15, 2013 9:37:01 PM org.apache.hadoop.mapred.JobClient monitorAndPrintJob INFO: Running job: job_local_0001 aug 15, 2013 9:37:01 PM org.apache.hadoop.mapred.Task initialize INFO: Using ResourceCalculatorPlugin : null aug 15, 2013 9:37:01 PM org.apache.hadoop.mapred.MapTask$MapOutputBuffer <init> INFO: io.sort.mb = 100 aug 15, 2013 9:37:01 PM org.apache.hadoop.mapred.MapTask$MapOutputBuffer <init> INFO: data buffer = 79691776/99614720 aug 15, 2013 9:37:01 PM org.apache.hadoop.mapred.MapTask$MapOutputBuffer <init> INFO: record buffer = 262144/327680 aug 15, 2013 9:37:02 PM org.apache.hadoop.mapred.MapTask$MapOutputBuffer flush INFO: Starting flush of map output aug 15, 2013 9:37:02 PM org.apache.hadoop.mapred.MapTask$MapOutputBuffer sortAndSpill INFO: Finished spill 0 aug 15, 2013 9:37:02 PM org.apache.hadoop.mapred.Task done INFO: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting aug 15, 2013 9:37:02 PM org.apache.hadoop.mapred.JobClient monitorAndPrintJob INFO: map 0% reduce 0% aug 15, 2013 9:37:04 PM org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate INFO: aug 15, 2013 9:37:04 PM org.apache.hadoop.mapred.Task sendDone INFO: Task 'attempt_local_0001_m_000000_0' done. aug 15, 2013 9:37:04 PM org.apache.hadoop.mapred.Task initialize INFO: Using ResourceCalculatorPlugin : null aug 15, 2013 9:37:04 PM org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate INFO: aug 15, 2013 9:37:04 PM org.apache.hadoop.mapred.Merger$MergeQueue merge INFO: Merging 1 sorted segments aug 15, 2013 9:37:04 PM org.apache.hadoop.mapred.Merger$MergeQueue merge INFO: Down to the last merge-pass, with 1 segments left of total size: 524410 bytes aug 15, 2013 9:37:04 PM org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate INFO: aug 15, 2013 9:37:05 PM org.apache.hadoop.mapred.Task done INFO: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting aug 15, 2013 9:37:05 PM org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate INFO: aug 15, 2013 9:37:05 PM org.apache.hadoop.mapred.Task commit INFO: Task attempt_local_0001_r_000000_0 is allowed to commit now aug 15, 2013 9:37:05 PM org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter commitTask INFO: Saved output of task 'attempt_local_0001_r_000000_0' to /Users/pascal/output aug 15, 2013 9:37:05 PM org.apache.hadoop.mapred.JobClient monitorAndPrintJob INFO: map 100% reduce 0% aug 15, 2013 9:37:07 PM org.apache.hadoop.mapred.LocalJobRunner$Job statusUpdate INFO: reduce > reduce aug 15, 2013 9:37:07 PM org.apache.hadoop.mapred.Task sendDone INFO: Task 'attempt_local_0001_r_000000_0' done. aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.JobClient monitorAndPrintJob INFO: map 100% reduce 100% aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.JobClient monitorAndPrintJob INFO: Job complete: job_local_0001 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Counters: 17 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: File Output Format Counters aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Bytes Written=423039 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: FileSystemCounters aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: FILE_BYTES_READ=1464626 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: FILE_BYTES_WRITTEN=1537251 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: File Input Format Counters aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Bytes Read=469941 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Map-Reduce Framework aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Reduce input groups=11820 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Map output materialized bytes=524414 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Combine output records=0 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Map input records=20487 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Reduce shuffle bytes=0 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Reduce output records=11820 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Spilled Records=43234 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Map output bytes=481174 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Total committed heap usage (bytes)=362676224 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Combine input records=0 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Map output records=21617 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: SPLIT_RAW_BYTES=108 aug 15, 2013 9:37:08 PM org.apache.hadoop.mapred.Counters log INFO: Reduce input records=21617 Process finished with exit code 0
The output file created by this job can be found in the supplied output directory as can be seen in the next screenshot:
As you have seen we can run this main method in an IDE (or from the command line) but I would like to see some unit tests performed on the Mapper and Reducer before we go there. I will show this in another post how to do that.
The Pragmatic Integrator 
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Hey,
Could you post up more complete look at your Pom file? I am not sure what the correct place to put the plug in section in.
Great guide though!
Figured out I had to place the plug in section inside of