Eclipse has recently announced the release of Eclipse Juno which brings new features such as Detecting resource leaks for both Java7 and Java6 etc.

Previous Plugins to Install

I firstly install all my plugins from previous eclipse version. The process is quite simple
  • import and install from existing installation
  • refer to old eclipse location
  • Eclipse will show the list of plugins available and then asked for installation
  • After accepting license, installation should complete smoothly.

New Features available

  • Java 7 coding support in an IDE. Since Java 7 contains many new feature including dynamically-typed languages support and other small enhancements from Coin project, eclipse Java development tooling will include support for these features of Java 7.
  • Detecting resource leaks of Closeable/Autocloseable resources. This feature is really cool and it also works with “old code”. The common IO and JDBC resources now implement the relevant interfaces and the warnings are good enough to be shown on these.
    Resource leaks can be occurred in the following situations:
         1. A resource opened but not closed
         2. A resource may not closed on all control flows
         3. A resource may not closed at a method exit point
         4. In a Java 7 program a resource is closed but the code could still be improved by using a try-with-resources statement.

  • The new version include the global search bar that gives user quick access to almost any Eclipse feature.
    Glabal search bar
  • Code Recommenders This is amazing feature that analyzes code of existing applications and extracts common patterns of how other developers have used. It helps deal with the complexity of large APIs using intelligent code completion attribute.

    More examples can be found from below links,

  • The addition of integrated debugging of JVM-based domain specific languages in the Xtext development framework, The below link provide extensive examples regarding the mentioned feature,

  • Eclipse for Mobile Developers - now developers can use eclipse more easily with a variety of mobile SDKs, including the Google Android SDK

Amazon Elastic MapReduce (EMR) is a web service using which developers can easily and efficiently process enormous amounts of data. It uses a hosted Hadoop framework running on the web-scale infrastructure of Amazon EC2 and Amazon S3.

Amazon EMR removes most of the cumbersome details of Hadoop, while take care for provisioning of Hadoop, running the job flow, terminating the job flow, moving the data between Amazon EC2 and Amazon S3, and optimising Hadoop.

In this tutorial, we will first going to develop WordCount java example using a MapReduce framework Hadoop and thereafter, we execute our program on Amazon Elastic MapReduce.


You must have valid AWS account credentials. You should also have a general familiarity with using the Eclipse IDE before you begin. The reader can also use any other IDE of their choice.

Step 1 – Develop Hadoop MapReduce WordCount Java Program

In this section, we will first develop an WordCount application. A WordCount program will determine how many times different words appear in a set of files.

  • 1. In Eclipse (or whatever the IDE you are using), create a simple Java project name "WordCount".
  • 2. Create a java class name Map and override the map method as follow:
    public class Map extends Mapper<longwritable, 
                               intwritable="" text,=""> {
     private final static IntWritable one = 
                              new IntWritable(1);
     private Text word = new Text();
     public void map(LongWritable key, Text value, 
                         Context context)
         throws IOException, InterruptedException {
       String line = value.toString();
       StringTokenizer tokenizer = new 
       while (tokenizer.hasMoreTokens()) {
           context.write(word, one);
  • 3.Create a java class name Reduce and override the reduce method as below:
    public class Reduce extends Reducer<text, 
                  intwritable,="" intwritable="" text,=""> {
     protected void reduce(
       Text key,
       java.lang.Iterable<intwritable> values,
               intwritable,="" intwritable="" text,="">.Context context)
       throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable value : values) {
       sum += value.get();
      context.write(key, new IntWritable(sum));
  • 4. Create a java class name WordCount and defined the main method as below:
    public static void main(String[] args) 
                          throws Exception {
      Configuration conf = new Configuration();
      Job job = new Job(conf, "wordcount");
      FileInputFormat.addInputPath(job, new Path(args[0]));
      FileOutputFormat.setOutputPath(job, new Path(args[1]));
  • 5. Export the WordCount program in a jar using eclipse and save it to some location on disk. Make sure that you have provided the Main Class (WordCount.jar) during extracting the jar file as shown below:

jar ready
                                 ur jar is ready!!

Step 2 – Upload the WordCount JAR and Input Files to Amazon S3

Now we are going to upload the WordCount jar to Amazon S3. First, visit the following URL: Next, click “Create Bucket”, give your bucket a name, and click the “Create” button. Select your new S3 bucket in the left-hand pane. Upload the WordCount JAR and sample input file for counting the words.

Step 3 – Running an Amazon Elastic MapReduce Hadoop job

Running Hadoop WordCount example

Now that the JAR is uploaded into S3, all we need to do is to create a new Job flow. let's execute the below steps. (I encourage reader to check out the following link for details regarding each step, How to Create a Job Flow Using a Custom JAR )

  • 1. Sign in to the AWS Management Console and open the Amazon Elastic MapReduce console at
  • 2. Click Create New Job Flow.
  • 3. In the DEFINE JOB FLOW page, enter the following details:

    a. Job Flow Name = WordCountJob
    b. Select Run your own application
    c. Select Custom JAR in the drop-down list
    d. Click Continue

  • 4. In the SPECIFY PARAMETERS page, enter values in the boxes using the following table as a guide, and then click Continue.
    JAR Location = bucketName/jarFileLocation
    JAR Arguments =

    Please note that the output path must be unique each time we execute the job. The Hadoop always create a folder with the same name specify here.

After executing a job, just wait and monitor your job that runs through the Hadoop flow. You can also look for errors by using the Debug button. The job should be complete within 10 to 15 minutes (can also depend on the size of the input). After completing a job, You can view results in the S3 Browser panel. You can also download the files from S3 and can analyse the outcome of the job.

Amazon Elastic MapReduce Resources