noc19-cs33 Lec 07-Hadoop MapReduce 2.0 (Part-II)

Introduction

This tutorial provides a comprehensive guide on Hadoop MapReduce 2.0 as outlined in the lecture by IIT Kanpur. It aims to equip you with essential knowledge about how MapReduce works, its components, and practical applications. Understanding MapReduce is crucial for processing large datasets efficiently, making it relevant for data scientists and software engineers.

Step 1: Understanding MapReduce Concepts

• MapReduce Overview:

  • MapReduce is a programming model designed for processing large data sets with a distributed algorithm on a cluster.
  • It consists of two main functions: the Map function and the Reduce function.

• Key Components:

  • Input Data: Raw data that needs to be processed.
  • Mapper: Processes input data and produces key-value pairs.
  • Reducer: Takes the output from the mapper and combines it to produce final results.

Step 2: Setting Up Hadoop Environment

• Install Hadoop:

  • Download the Hadoop distribution from the official Apache website.
  • Follow the installation instructions specific to your operating system (Linux, Windows, etc.).

• Configure Hadoop:

  • Edit the configuration files (core-site.xml, hdfs-site.xml, mapred-site.xml) to set up the cluster properties.
  • Ensure you have a Java runtime environment installed, as Hadoop requires Java.

Step 3: Writing a MapReduce Program

• Create a Mapper Class:

  • Define a class that extends the Mapper class.
  • Implement the map method to define how input records are transformed into output key-value pairs.

  public class MyMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
      public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
          // Your mapping logic here
      }
  }
  

• Create a Reducer Class:

  • Define a class that extends the Reducer class.
  • Implement the reduce method to define how to aggregate the output from the mapper.

  public class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
      public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
          // Your reducing logic here
      }
  }
  

Step 4: Executing the MapReduce Job

• Set Up the Job Configuration:

  • Create a job configuration object and set the mapper, reducer, and output types.

  Configuration conf = new Configuration();
  Job job = Job.getInstance(conf, "my job");
  job.setJarByClass(MyJob.class);
  job.setMapperClass(MyMapper.class);
  job.setReducerClass(MyReducer.class);
  job.setOutputKeyClass(Text.class);
  job.setOutputValueClass(IntWritable.class);
  

• Run the Job:

  • Use the Job.waitForCompletion(true) method to execute the MapReduce job.

  System.exit(job.waitForCompletion(true) ? 0 : 1);
  

Step 5: Analyzing Output

• Output Format:

  • By default, the output is stored in HDFS (Hadoop Distributed File System).
  • You can specify the output directory in your job configuration.

• View Results:

  • Use the Hadoop command line tools to view the output files.
  • Analyze the results to ensure the MapReduce job has processed the data correctly.

Conclusion

In this tutorial, we explored the fundamental concepts of Hadoop MapReduce, set up the Hadoop environment, wrote a basic MapReduce program, executed the job, and analyzed the output. Mastering these steps will empower you to efficiently process large datasets. As a next step, consider implementing more complex algorithms using MapReduce or exploring other Hadoop ecosystem tools like Hive or Pig for data analysis.