MapReduce Tutorial

Purpose

This document comprehensively describes all user-facing facets of the Hadoop MapReduce framework and serves as a tutorial.

Prerequisites

Ensure that Hadoop is installed, configured and is running. More details:

Overview

Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.

A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.

Typically the compute nodes and the storage nodes are the same, that is, the MapReduce framework and the Hadoop Distributed File System (see HDFS Architecture Guide ) are running on the same set of nodes. This configuration allows the framework to effectively schedule tasks on the nodes where data is already present, resulting in very high aggregate bandwidth across the cluster.

The MapReduce framework consists of a single master JobTracker and one slave TaskTracker per cluster-node. The master is responsible for scheduling the jobs' component tasks on the slaves, monitoring them and re-executing the failed tasks. The slaves execute the tasks as directed by the master.

Minimally, applications specify the input/output locations and supply map and reduce functions via implementations of appropriate interfaces and/or abstract-classes. These, and other job parameters, comprise the job configuration. The Hadoop job client then submits the job (jar/executable etc.) and configuration to the JobTracker which then assumes the responsibility of distributing the software/configuration to the slaves, scheduling tasks and monitoring them, providing status and diagnostic information to the job-client.

Although the Hadoop framework is implemented in Java TM. MapReduce applications need not be written in Java.

• Hadoop Streaming is a utility which allows users to create and run jobs with any executables (e.g. shell utilities) as the mapper and/or the reducer.
• Hadoop Pipes is a SWIG - compatible C++ API to implement MapReduce applications (non JNI TM based).

Inputs and Outputs

The MapReduce framework operates exclusively on <,key, value>, pairs, that is, the framework views the input to the job as a set of <,key, value>, pairs and produces a set of <,key, value>, pairs as the output of the job, conceivably of different types.

The key and value classes have to be serializable by the framework and hence need to implement the Writable interface. Additionally, the key classes have to implement the WritableComparable interface to facilitate sorting by the framework.

Input and Output types of a MapReduce job:

(input) <,k1, v1>, ->, map ->, <,k2, v2>, ->, combine ->, <,k2, v2>, ->, reduce ->, <,k3, v3>, (output)

Example: WordCount v1.0

Before we jump into the details, lets walk through an example MapReduce application to get a flavour for how they work.

WordCount is a simple application that counts the number of occurences of each word in a given input set.

This works with a local-standalone, pseudo-distributed or fully-distributed Hadoop installation (Single Node Setup ).

Source