Welcome to Instant Mapreduce Patterns – Hadoop Essentials How-to. This book provides an introduction to Hadoop and discusses several Hadoop-based analysis implementations with Hadoop. It is intended to be a concise "hands-on" Hadoop guide for beginners.

Historically, data processing was completely done using database technologies. Most of the data had a well-defined structure and was often stored in databases. When handling such data, relational databases were the most common store choice. Those, datasets were small enough to be stored and queried using relational databases.

However, the datasets started to grow in size. Soon, high-tech companies like Google found many large datasets that were not amenable to databases. For example, Google was crawling and indexing the entire Internet, which soon reached terabytes and then petabytes. Google developed a new programming model called MapReduce to handle large-scale data analysis, and later they introduced the model through their seminal paper MapReduce: Simplified Data Processing on Large Clusters.

Hadoop, the Java-based open source project, is an implementation of the MapReduce programming model. It enables users to only write the processing logic, and MapReduce frameworks such as Hadoop can execute the logic while handling distributed aspects such as job scheduling, data movements, and failures transparently from the users.

Hadoop has become the de facto MapReduce implementation for Java. A wide spectrum of users from students to large enterprises use Hadoop to solve their data processing problems, and MapReduce has become one of the most sought after skill in the job market.

This book is an effort to provide a concise introduction to MapReduce and different problems you can solve using MapReduce. There are many resources on how to get started with Hadoop and run a word count example, which is the "Hello World" equivalent in the MapReduce world. However, there is not much resource that provides a concise introduction to solving different types of problems using MapReduce. This book tries to address that gap.

The first three recipes of the book focus on writing a simple MapReduce program and running it using Hadoop. The next recipe explains how to write a custom formatter that can be used to parse a complicated data structure from the input files. The next recipe explains how to use MapReduce to calculate basic analytics and how to use GNU plot to plot the results. This is one of the common use case of Hadoop.

The rest of the recipes cover different classes of problems that can be solved with MapReduce, and provide an example of the solution pattern common to that class. They cover the problem classes: set operations, cross correlation, search, graph and relational operations, and similarity clustering.

Throughout this book, we will use the public dataset on the Amazon sales data collected by Stanford University. Dataset provides information about books and users who have brought those books. An example data record is shows as follows:

Id:   3
ASIN: 0486287785
title: World War II Allied Fighter Planes Trading Cards
group: Book
salesrank: 1270652
similar: 0
categories: 1
   |Books[283155]|Subjects[1000]|Home & Garden[48]|Crafts & Hobbies[5126]|General[5144]
reviews: total: 1  downloaded: 1  avg rating: 5
    2003-7-10  cutomer: A3IDGASRQAW8B2  rating: 5  votes:   2  helpful:   2

The dataset is available at http://snap.stanford.edu/data/#amazon. It is about 1 gigabyte in size. Unless you have access to a large Hadoop cluster, it is recommended to use smaller subsets of the same dataset available with the sample directory while running the samples.