Book Image

Instant MapReduce Patterns - Hadoop Essentials How-to

By : Liyanapathirannahelage H Perera
Book Image

Instant MapReduce Patterns - Hadoop Essentials How-to

By: Liyanapathirannahelage H Perera

Overview of this book

MapReduce is a technology that enables users to process large datasets and Hadoop is an implementation of MapReduce. We are beginning to see more and more data becoming available, and this hides many insights that might hold key to success or failure. However, MapReduce has the ability to analyze this data and write code to process it.Instant MapReduce Patterns – Hadoop Essentials How-to is a concise introduction to Hadoop and programming with MapReduce. It is aimed to get you started and give you an overall feel for programming with Hadoop so that you will have a well-grounded foundation to understand and solve all of your MapReduce problems as needed.Instant MapReduce Patterns – Hadoop Essentials How-to will start with the configuration of Hadoop before moving on to writing simple examples and discussing MapReduce programming patterns.We will start simply by installing Hadoop and writing a word count program. After which, we will deal with the seven styles of MapReduce programs: analytics, set operations, cross correlation, search, graph, Joins, and clustering. For each case, you will learn the pattern and create a representative example program. The book also provides you with additional pointers to further enhance your Hadoop skills.
Table of Contents (7 chapters)

Set operations with MapReduce (Intermediate)

Set operations are a useful tool we use when we want to understand a dataset. This recipe will explain how to use MapReduce to perform a set operation on a large dataset. The following MapReduce job calculates the set difference between the customers who have bought the items that have an amazon sales rank less than 100 and most frequent customers which we calculated in the earlier recipe.

Getting ready

  1. This assumes that you have installed Hadoop and started it. Refer to the Writing a word count application using Java (Simple) and Installing Hadoop in a distributed setup and running a word count application (Simple) recipes for more information. We will use HADOOP_HOME to refer to the Hadoop installation directory.

  2. This recipe assumes you are aware of how Hadoop processing works. If you have not already done so, you should follow the Writing a word count application with MapReduce and running it (Simple) recipe.

  3. Download the sample code for the chapter and download the data files as described in the Writing a word count application with MapReduce and running it (Simple) recipe. Select a subset of data from the Amazon dataset if you are running this with few computers. You can find the smaller dataset with the sample directory.

  4. This sample uses the data created from earlier recipes. If you have not already run it, please run it first.

How to do it...

  1. If you have not already done so, let us upload the amazon dataset to the HDFS filesystem using the following commands:

    > bin/hadoop dfs -mkdir /data/
    > bin/hadoop dfs -mkdir /data/amazon-dataset
    > bin/hadoop dfs -put <SMAPLE_DIR>/amazon-meta.txt /data/amazon-dataset
    > bin/Hadoop dfs –mkdir /data/set-input
  2. Copy the output from earlier recipes to the output directory.

    >bin/hadoop dfs -cp 
  3. Copy the hadoop-microbook.jar file from SAMPLE_DIR to HADOOP_HOME.

  4. Run the first MapReduce job. To do that run the following command from HADOOP_HOME:

    $ bin/hadoop jar hadoop-microbook.jar  microbook.set.FindCustomersBroughtFirst100Items /data/amazon-dataset /data/set-output1
  5. Copy the output of the MapReduce job and output of the earlier recipe to the input directory.

    > bin/hadoop dfs -cp /data/set-output1/part-r-00000  /data/set-input/ 
  6. Run the second MapReduce job. To do that run the following command from HADOOP_HOME:

    $ bin/hadoop jar hadoop-microbook.jar  microbook.set.BuyersSetDifference /data/set-input /data/set-output2
  7. You can find the results from the output directory at /data/set-output2.

How it works...

You can find the mapper and reducer code at src/microbook/

We define the set difference between the two sets S1 and S2, written as S1-S2, as the items that are in set S1 but not in set S2.

To perform set difference, we label each element at the mapper with the set it came from. Then send the search to a reducer, which emits an item only if it is in the first set, but not in the second set. The preceding figure shows the execution of the MapReduce job. Also the following code listing shows the map function and the reduce function.

Let us look at the execution in detail.

Here we put files for both sets into the same input directory. Hadoop will read the input files from the input folder and read records from each file. It invokes the mapper once per each record passing the record as input.

When the mapper receives an input, we finds out which line belongs to which set by getting the filename using InputSplit available through the Hadoop context. Then we emit elements in the set as the key and the set name (1 or 2) as the value.

public void map(Object key, Text value, Context context) {
    String currentFile =  ((FileSplit)context.getInputSplit()).getPath().getName();

    Matcher matcher = 
    if (matcher.find()) {
        String propName =;
        String propValue =;
            context.write(new Text(propName), 
            new IntWritable(1));
}else{ if(currentFile.equals("")){
            int count = Integer.parseInt(propValue);
            if(count > 100){
                context.write(new Text(propName), 
new IntWritable(2));                        
            throw new IOException("Unexpected file "
+ currentFile); 
    } else {
+ ":Unprocessed Line " + value);

Hadoop will sort the key-value pairs by the key and invoke the reducer once for each unique key, passing the list of values as the second argument. The reducer inspects the list of values, which contain the name of sets the values comes from, and then emits the key only if the given value is in the first set, but not in the second.

public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException,
        InterruptedException {
    boolean has1 = false;
    boolean has2 = false;
    System.out.print(key + "=");
    for (IntWritable val : values) {
            case 1:
                has1 = true;
            case 2:
                has2 = true;
    if(has1 && !has2){
        context.write(key, new IntWritable(1));    

There's more...

We can use MapReduce to implement most set operations by labeling elements against the sets they came from using a similar method and changing the reducer logic to emit only relevant elements. For example, we can implement the set intersection by changing the reducer to emit only elements that have both sets as values.