Custom Data Type in Hadoop Example

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Well data type in any programming language is very important aspect and without knowing data types you can't do programming, although, I had already shared the built in data types of Hadoop in our previous post with proper example here at Hadoop built in data types for MapReduce

And in this post going to show you an example of custom data type in Hadoop sometimes built in data types would not solve our problem, so wee need custom one that's why, I am planning to post an example of custom data type in Hadoop for MapReduce programming.

Here is a stock exchange sample data format, having below columns with tab separator
exchange, stock_symbol, date, stock_price_open, stock_price_high, stock_price_low, stock_price_close, stock_volume, stock_price_adj_close
Actual Data :

NYSE AEA 2010-02-08 4.42 4.42 4.21 4.24 205500 4.24
NYSE AEA 2010-02-05 4.42 4.54 4.22 4.41 194300 4.41
NYSE AEA 2010-02-04 4.55 4.69 4.39 4.42 233800 4.42
NYSE AEA 2010-02-03 4.65 4.69 4.5 4.55 182100 4.55
NYSE AEA 2010-02-02 4.74 5 4.62 4.66 222700 4.66
NYSE AEA 2010-02-01 4.84 4.92 4.68 4.75 194800 4.75
NYSE AEA 2010-01-29 4.97 5.05 4.76 4.83 222900 4.83
NYSE AEA 2010-01-28 5.12 5.22 4.81 4.98 283100 4.98
NYSE AEA 2010-01-27 4.82 5.16 4.79 5.09 243500 5.09
NYSE AEA 2010-01-26 5.18 5.18 4.81 4.84 554800 4.84

Problem : From the above dataset, we want to print the stock data corresponding to date(column[2]), we all know that Map Reduce works in key value pattern, so the expected out needs to be in date(key), value(stockInfo) pair.

Solution : To solve above problem, we need custom data type to hold the different columns(fields) value like class with above fields. So to create custom data type for MapReduce, we have Writable and WritableComparable interface, to solve our problem we need custom data type for value not for key, because key is a simple Text type with date text from the above data set.

To create a custom data type for value, we must have to implement Writable interface and provide the implementation of below methods,

public void write(DataOutput out) throws IOException
public void readFields(DataInput in) throws IOException

OK, lets implement and solve the above problem, of-course at the end you will find an attachment with complete source code and sample data.
Prerequisites for this Tutorial-
Hadoop must be installed, if not you may follow Hadoop Single Node Setup guide.
Better to have working knowledge of Word Count example in Map Reduce, if not you may follow Hello World in Map Reduce step by step guide.

Tools and Technologies we are using here:

• Java 8
• Eclipse Mars
• Hadoop 2.7.1
• Maven 3.3
• Ubuntu 14(Linux OS)

Overview of the project structure:

OUT PUT:

Main Objects of this project are:

• StockInfo.java
• StockMapper.java
• StockReducer.java
• StockDriver.java
• pom.xml

Step 1. Create a new maven project
Go to File Menu then New->Maven Project, and provide the required details, see the below attached screen.

Step 2. Edit pom.xml
Double click on your project's pom.xml file, it will looks like this with very limited information.

<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.javamakeuse.hadoop.dt</groupId>
 <artifactId>CustomWritable</artifactId>
 <version>1.0</version>
 <name>CustomWritable</name>
 <description>Custom data type example using Writable</description>
</project>

Now add Hadoop dependencies entry inside your pom.xml, below is the complete pom.xml
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.javamakeuse.hadoop.dt</groupId>
 <artifactId>CustomWritable</artifactId>
 <version>1.0</version>
 <name>CustomWritable</name>
 <description>Custom data type example using Writable</description>
 <dependencies>
  <dependency>
   <groupId>org.apache.hadoop</groupId>
   <artifactId>hadoop-client</artifactId>
   <version>2.7.1</version>
  </dependency>
  <dependency>
   <groupId>org.apache.hadoop</groupId>
   <artifactId>hadoop-mapreduce-client-core</artifactId>
   <version>2.7.1</version>
  </dependency>
 </dependencies>
 <build>
  <plugins>
   <plugin>
    <groupId>org.apache.maven.plugins</groupId>
    <artifactId>maven-compiler-plugin</artifactId>
    <configuration>
     <source>1.7</source>
     <target>1.7</target>
    </configuration>
   </plugin>
  </plugins>
 </build>
</project>

Step 3. StockInfo.java(Custom Data Type)
This is our custom data type, here we have implemented the Writable interface and provided the implementation of methods.

package com.javamakeuse.hadoop.dt;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.Writable;

public class StockInfo implements Writable {

 private String exchange;
 private String stockSymbol;
 private double stockOpenPrice;
 private double stockPriceHigh;
 private double stockPriceLow;
 private double stockPriceClosed;
 private long stockVolume;

 public String getExchange() {
  return exchange;
 }
 public void setExchange(String exchange) {
  this.exchange = exchange;
 }
 public String getStockSymbol() {
  return stockSymbol;
 }
 public void setStockSymbol(String stockSymbol) {
  this.stockSymbol = stockSymbol;
 }
 public double getStockOpenPrice() {
  return stockOpenPrice;
 }
 public void setStockOpenPrice(double stockOpenPrice) {
  this.stockOpenPrice = stockOpenPrice;
 }
 public double getStockPriceHigh() {
  return stockPriceHigh;
 }
 public void setStockPriceHigh(double stockPriceHigh) {
  this.stockPriceHigh = stockPriceHigh;
 }
 public double getStockPriceLow() {
  return stockPriceLow;
 }
 public void setStockPriceLow(double stockPriceLow) {
  this.stockPriceLow = stockPriceLow;
 }
 public double getStockPriceClosed() {
  return stockPriceClosed;
 }
 public void setStockPriceClosed(double stockPriceClosed) {
  this.stockPriceClosed = stockPriceClosed;
 }
 public long getStockVolume() {
  return stockVolume;
 }
 public void setStockVolume(long stockVolume) {
  this.stockVolume = stockVolume;
 }
 @Override
 public void write(DataOutput out) throws IOException {
  out.writeUTF(exchange);
  out.writeUTF(stockSymbol);
  out.writeDouble(stockOpenPrice);
  out.writeDouble(stockPriceHigh);
  out.writeDouble(stockPriceLow);
  out.writeDouble(stockPriceClosed);
  out.writeLong(stockVolume);

 }
 @Override
 public void readFields(DataInput in) throws IOException {
  exchange = in.readUTF();
  stockSymbol = in.readUTF();
  stockOpenPrice = in.readDouble();
  stockPriceHigh = in.readDouble();
  stockPriceLow = in.readDouble();
  stockPriceClosed = in.readDouble();
  stockVolume = in.readLong();

 }
 @Override
 public String toString() {
  return "StockInfo [exchange=" + exchange + ", stockSymbol=" + stockSymbol + ", stockOpenPrice=" + stockOpenPrice
    + ", stockPriceHigh=" + stockPriceHigh + ", stockPriceLow=" + stockPriceLow + ", stockPriceClosed="
    + stockPriceClosed + ", stockVolume=" + stockVolume + "]";
 }

}

Step 4. StockMapper.java (Mapper class)

package com.javamakeuse.hadoop.dt;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class StockMapper extends Mapper<LongWritable, Text, Text, StockInfo> {
 private Text dateTxt = new Text();

 @Override
 protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, StockInfo>.Context context)
   throws IOException, InterruptedException {
  String[] columns = value.toString().split("\t");
  if (columns.length > 6) {
   StockInfo sinfo = new StockInfo();
   sinfo.setExchange(columns[0]);
   sinfo.setStockSymbol(columns[1]);
   sinfo.setStockOpenPrice(Double.parseDouble(columns[3]));
   sinfo.setStockPriceHigh(Double.parseDouble(columns[4]));
   sinfo.setStockPriceLow(Double.parseDouble(columns[5]));
   sinfo.setStockPriceClosed(Double.parseDouble(columns[6]));
   sinfo.setStockVolume(Long.parseLong(columns[7]));
   dateTxt.set(columns[2]);
   context.write(dateTxt, sinfo);

  }

 }
}

Step 5. StockReducer.java (Reducer class)

package com.javamakeuse.hadoop.dt;

import java.io.IOException;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class StockReducer extends Reducer<Text, StockInfo, Text, StockInfo>{
 
 @Override
 protected void reduce(Text key, Iterable<StockInfo> values, Reducer<Text, StockInfo, Text, StockInfo>.Context context)
   throws IOException, InterruptedException {
  // iterate the values
  for(StockInfo stockInfo:values){
   context.write(key, stockInfo);
  }
 }

}

Step 6. StockDriver.java (Main class to execute)

package com.javamakeuse.hadoop.dt;

import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
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.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class StockDriver extends Configured implements Tool {
 public static void main(String[] args) {
  try {
   int exitCode = ToolRunner.run(new StockDriver(), args);
   System.exit(exitCode);
  } catch (Exception e) {
   e.printStackTrace();
  }
 }

 @Override
 public int run(String[] args) throws Exception {
  Job job = Job.getInstance();
  System.out.println("Starting job !!");
  job.setJarByClass(StockDriver.class);

  // input/output path
  FileInputFormat.addInputPath(job, new Path(args[0]));
  FileOutputFormat.setOutputPath(job, new Path(args[1]));

  // set mapper/reducer class
  job.setMapperClass(StockMapper.class);
  job.setReducerClass(StockReducer.class);

  job.setMapOutputKeyClass(Text.class);
  job.setMapOutputValueClass(StockInfo.class);

  job.setOutputKeyClass(Text.class);
  job.setOutputValueClass(StockInfo.class);
  
  int returnValue = job.waitForCompletion(true) ? 0 : 1;
  System.out.println("done !!");
  return returnValue;
 }

}

Step 7. Steps to execute our Map-Reduce Program
i. Start Hadoop components,open your terminal and type

subodh@subodh-Inspiron-3520:~/software$ start-all.sh

ii. Verify Hadoop started or not with jps command

subodh@subodh-Inspiron-3520:~/software$ jps
8385 NameNode
8547 DataNode
5701 org.eclipse.equinox.launcher_1.3.100.v20150511-1540.jar
9446 Jps
8918 ResourceManager
9054 NodeManager
8751 SecondaryNameNode

You may verify with web-ui also using "http://localhost:50070/explorer.html#/" url.
iii. Create input folder on HDFS with below command.

subodh@subodh-Inspiron-3520:~/software$ hadoop fs -mkdir /input

The above command will create an input folder on HDFS, you can verify it using web UI, Now time to move input file which we need to process, below is the command to copy the nyse_sample input file on HDFS inside input folder.

subodh@subodh-Inspiron-3520:~$ hadoop fs -copyFromLocal /home/subodh/programs/input/nyse_sample /input/

Note - You will find nyse_sample file inside our this project, you may download it from our this tutorial, at the end you will find a downloadable link.

Step 8. Create & Execute jar file
We almost done,now create jar file of CustomWritable source code. You can create jar file using eclipse or by using mvn package command.
To execute CustomWritable-1.0.jar file use below command

hadoop jar /home/subodh/CustomWritable-1.0.jar com.javamakeuse.hadoop.dt.StockDriver /input/nyse_sample /output

Above will generate below output and also create an output folder with output of CustomWritable project.

16/02/20 23:22:40 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Starting job !!
16/02/20 23:22:41 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
16/02/20 23:22:41 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
16/02/20 23:22:41 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
16/02/20 23:22:41 INFO input.FileInputFormat: Total input paths to process : 1
16/02/20 23:22:41 INFO mapreduce.JobSubmitter: number of splits:1
16/02/20 23:22:41 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local1404022867_0001
16/02/20 23:22:41 INFO mapreduce.Job: The url to track the job: http://localhost:8080/
16/02/20 23:22:41 INFO mapreduce.Job: Running job: job_local1404022867_0001
16/02/20 23:22:41 INFO mapred.LocalJobRunner: OutputCommitter set in config null
16/02/20 23:22:41 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
16/02/20 23:22:41 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
16/02/20 23:22:41 INFO mapred.LocalJobRunner: Waiting for map tasks
16/02/20 23:22:41 INFO mapred.LocalJobRunner: Starting task: attempt_local1404022867_0001_m_000000_0
16/02/20 23:22:41 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
16/02/20 23:22:41 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
16/02/20 23:22:41 INFO mapred.MapTask: Processing split: hdfs://localhost:9000/input/nyse_sample:0+517
16/02/20 23:22:42 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
16/02/20 23:22:42 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
16/02/20 23:22:42 INFO mapred.MapTask: soft limit at 83886080
16/02/20 23:22:42 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
16/02/20 23:22:42 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
16/02/20 23:22:42 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
16/02/20 23:22:42 INFO mapred.LocalJobRunner: 
16/02/20 23:22:42 INFO mapred.MapTask: Starting flush of map output
16/02/20 23:22:42 INFO mapred.MapTask: Spilling map output
16/02/20 23:22:42 INFO mapred.MapTask: bufstart = 0; bufend = 620; bufvoid = 104857600
16/02/20 23:22:42 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26214360(104857440); length = 37/6553600
16/02/20 23:22:42 INFO mapred.MapTask: Finished spill 0
16/02/20 23:22:42 INFO mapred.Task: Task:attempt_local1404022867_0001_m_000000_0 is done. And is in the process of committing
16/02/20 23:22:42 INFO mapred.LocalJobRunner: map
16/02/20 23:22:42 INFO mapred.Task: Task 'attempt_local1404022867_0001_m_000000_0' done.
16/02/20 23:22:42 INFO mapred.LocalJobRunner: Finishing task: attempt_local1404022867_0001_m_000000_0
16/02/20 23:22:42 INFO mapred.LocalJobRunner: map task executor complete.
16/02/20 23:22:42 INFO mapred.LocalJobRunner: Waiting for reduce tasks
16/02/20 23:22:42 INFO mapred.LocalJobRunner: Starting task: attempt_local1404022867_0001_r_000000_0
16/02/20 23:22:42 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
16/02/20 23:22:42 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
16/02/20 23:22:42 INFO mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@6144d11f
16/02/20 23:22:42 INFO reduce.MergeManagerImpl: MergerManager: memoryLimit=334338464, maxSingleShuffleLimit=83584616, mergeThreshold=220663392, ioSortFactor=10, memToMemMergeOutputsThreshold=10
16/02/20 23:22:42 INFO reduce.EventFetcher: attempt_local1404022867_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
16/02/20 23:22:42 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local1404022867_0001_m_000000_0 decomp: 642 len: 646 to MEMORY
16/02/20 23:22:42 INFO reduce.InMemoryMapOutput: Read 642 bytes from map-output for attempt_local1404022867_0001_m_000000_0
16/02/20 23:22:42 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 642, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->642
16/02/20 23:22:42 INFO reduce.EventFetcher: EventFetcher is interrupted.. Returning
16/02/20 23:22:42 INFO mapred.LocalJobRunner: 1 / 1 copied.
16/02/20 23:22:42 INFO reduce.MergeManagerImpl: finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
16/02/20 23:22:42 INFO mapred.Merger: Merging 1 sorted segments
16/02/20 23:22:42 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 629 bytes
16/02/20 23:22:42 INFO reduce.MergeManagerImpl: Merged 1 segments, 642 bytes to disk to satisfy reduce memory limit
16/02/20 23:22:42 INFO reduce.MergeManagerImpl: Merging 1 files, 646 bytes from disk
16/02/20 23:22:42 INFO reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
16/02/20 23:22:42 INFO mapred.Merger: Merging 1 sorted segments
16/02/20 23:22:42 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 629 bytes
16/02/20 23:22:42 INFO mapred.LocalJobRunner: 1 / 1 copied.
16/02/20 23:22:42 INFO Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
16/02/20 23:22:42 INFO mapreduce.Job: Job job_local1404022867_0001 running in uber mode : false
16/02/20 23:22:42 INFO mapreduce.Job:  map 100% reduce 0%
16/02/20 23:22:43 INFO mapred.Task: Task:attempt_local1404022867_0001_r_000000_0 is done. And is in the process of committing
16/02/20 23:22:43 INFO mapred.LocalJobRunner: 1 / 1 copied.
16/02/20 23:22:43 INFO mapred.Task: Task attempt_local1404022867_0001_r_000000_0 is allowed to commit now
16/02/20 23:22:43 INFO output.FileOutputCommitter: Saved output of task 'attempt_local1404022867_0001_r_000000_0' to hdfs://localhost:9000/output9/_temporary/0/task_local1404022867_0001_r_000000
16/02/20 23:22:43 INFO mapred.LocalJobRunner: reduce > reduce
16/02/20 23:22:43 INFO mapred.Task: Task 'attempt_local1404022867_0001_r_000000_0' done.
16/02/20 23:22:43 INFO mapred.LocalJobRunner: Finishing task: attempt_local1404022867_0001_r_000000_0
16/02/20 23:22:43 INFO mapred.LocalJobRunner: reduce task executor complete.
16/02/20 23:22:43 INFO mapreduce.Job:  map 100% reduce 100%
16/02/20 23:22:43 INFO mapreduce.Job: Job job_local1404022867_0001 completed successfully
16/02/20 23:22:43 INFO mapreduce.Job: Counters: 35
 File System Counters
  FILE: Number of bytes read=15694
  FILE: Number of bytes written=570076
  FILE: Number of read operations=0
  FILE: Number of large read operations=0
  FILE: Number of write operations=0
  HDFS: Number of bytes read=1034
  HDFS: Number of bytes written=1588
  HDFS: Number of read operations=13
  HDFS: Number of large read operations=0
  HDFS: Number of write operations=4
 Map-Reduce Framework
  Map input records=11
  Map output records=10
  Map output bytes=620
  Map output materialized bytes=646
  Input split bytes=104
  Combine input records=0
  Combine output records=0
  Reduce input groups=10
  Reduce shuffle bytes=646
  Reduce input records=10
  Reduce output records=10
  Spilled Records=20
  Shuffled Maps =1
  Failed Shuffles=0
  Merged Map outputs=1
  GC time elapsed (ms)=7
  Total committed heap usage (bytes)=496500736
 Shuffle Errors
  BAD_ID=0
  CONNECTION=0
  IO_ERROR=0
  WRONG_LENGTH=0
  WRONG_MAP=0
  WRONG_REDUCE=0
 File Input Format Counters 
  Bytes Read=517
 File Output Format Counters 
  Bytes Written=1588
done !!

Step 9. Verify output
Now time to verify the output of the CustomWritable project, the above command will create an output folder inside HDFS and inside output folder it will create two file _SUCCESS and part-r-00000, let see the output using below cat command.

subodh@subodh-Inspiron-3520:~$ hadoop fs -cat /output/part*
16/02/20 23:25:35 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
2010-01-26 StockInfo [exchange=NYSE, stockSymbol=AEA, stockOpenPrice=5.18, stockPriceHigh=5.18, stockPriceLow=4.81, stockPriceClosed=4.84, stockVolume=554800]
2010-01-27 StockInfo [exchange=NYSE, stockSymbol=AEA, stockOpenPrice=4.82, stockPriceHigh=5.16, stockPriceLow=4.79, stockPriceClosed=5.09, stockVolume=243500]
2010-01-28 StockInfo [exchange=NYSE, stockSymbol=AEA, stockOpenPrice=5.12, stockPriceHigh=5.22, stockPriceLow=4.81, stockPriceClosed=4.98, stockVolume=283100]
2010-01-29 StockInfo [exchange=NYSE, stockSymbol=AEA, stockOpenPrice=4.97, stockPriceHigh=5.05, stockPriceLow=4.76, stockPriceClosed=4.83, stockVolume=222900]
2010-02-01 StockInfo [exchange=NYSE, stockSymbol=AEA, stockOpenPrice=4.84, stockPriceHigh=4.92, stockPriceLow=4.68, stockPriceClosed=4.75, stockVolume=194800]
2010-02-02 StockInfo [exchange=NYSE, stockSymbol=AEA, stockOpenPrice=4.74, stockPriceHigh=5.0, stockPriceLow=4.62, stockPriceClosed=4.66, stockVolume=222700]
2010-02-03 StockInfo [exchange=NYSE, stockSymbol=AEA, stockOpenPrice=4.65, stockPriceHigh=4.69, stockPriceLow=4.5, stockPriceClosed=4.55, stockVolume=182100]
2010-02-04 StockInfo [exchange=NYSE, stockSymbol=AEA, stockOpenPrice=4.55, stockPriceHigh=4.69, stockPriceLow=4.39, stockPriceClosed=4.42, stockVolume=233800]
2010-02-05 StockInfo [exchange=NYSE, stockSymbol=AEA, stockOpenPrice=4.42, stockPriceHigh=4.54, stockPriceLow=4.22, stockPriceClosed=4.41, stockVolume=194300]
2010-02-08 StockInfo [exchange=NYSE, stockSymbol=AEA, stockOpenPrice=4.42, stockPriceHigh=4.42, stockPriceLow=4.21, stockPriceClosed=4.24, stockVolume=205500]

You may also verify it on Web-UI on "http://localhost:50070/explorer.html#/output" below is the web ui screen shot

Note - To verify your logic you may directly run the CustomWritable from the eclipse also.

Done :)

Download the complete example from here Source Code

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