Book Image

Hadoop MapReduce Cookbook

By : Srinath Perera, Thilina Gunarathne
Book Image

Hadoop MapReduce Cookbook

By: Srinath Perera, Thilina Gunarathne

Overview of this book

<p>We are facing an avalanche of data. The unstructured data we gather can contain many insights that might hold the key to business success or failure. Harnessing the ability to analyze and process this data with Hadoop MapReduce is one of the most highly sought after skills in today's job market.<br /><br />"Hadoop MapReduce Cookbook" is a one-stop guide to processing large and complex data sets using the Hadoop ecosystem. The book introduces you to simple examples and then dives deep to solve in-depth big data use cases.</p> <p>"Hadoop MapReduce Cookbook" presents more than 50 ready-to-use Hadoop MapReduce recipes in a simple and straightforward manner, with step-by-step instructions and real world examples.<br /><br />Start with how to install, then configure, extend, and administer Hadoop. Then write simple examples, learn MapReduce patterns, harness the Hadoop landscape, and finally jump to the cloud.<br /><br />The book deals with many exciting topics such as setting up Hadoop security, using MapReduce to solve analytics, classifications, on-line marketing, recommendations, and searching use cases. You will learn how to harness components from the Hadoop ecosystem including HBase, Hadoop, Pig, and Mahout, then learn how to set up cloud environments to perform Hadoop MapReduce computations.<br /><br />"Hadoop MapReduce Cookbook" teaches you how process large and complex data sets using real examples providing a comprehensive guide to get things done using Hadoop MapReduce.</p>

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Hadoop MapReduce Cookbook
Srinath Perera | Thilina Gunarathne
Jan, 2013 | 300 pages
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Table of Contents (17 chapters)
Hadoop MapReduce Cookbook
Hadoop MapReduce Cookbook
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Hadoop Up and Running in a Cluster
Getting Hadoop Up and Running in a Cluster
Introduction
Setting up Hadoop on your machine
Writing a WordCount MapReduce sample, bundling it, and running it using standalone Hadoop
Adding the combiner step to the WordCount MapReduce program
Setting up HDFS
Using HDFS monitoring UI
HDFS basic command-line file operations
Setting Hadoop in a distributed cluster environment
Running the WordCount program in a distributed cluster environment
Using MapReduce monitoring UI
2
Advanced HDFS
Advanced HDFS
Introduction
Benchmarking HDFS
Adding a new DataNode
Decommissioning DataNodes
Using multiple disks/volumes and limiting HDFS disk usage
Setting HDFS block size
Setting the file replication factor
Using HDFS Java API
Using HDFS C API (libhdfs)
Mounting HDFS (Fuse-DFS)
Merging files in HDFS
3
Advanced Hadoop MapReduce Administration
Advanced Hadoop MapReduce Administration
Introduction
Tuning Hadoop configurations for cluster deployments
Running benchmarks to verify the Hadoop installation
Reusing Java VMs to improve the performance
Fault tolerance and speculative execution
Debug scripts – analyzing task failures
Setting failure percentages and skipping bad records
Shared-user Hadoop clusters – using fair and other schedulers
Hadoop security – integrating with Kerberos
Using the Hadoop Tool interface
4
Developing Complex Hadoop MapReduce Applications
Developing Complex Hadoop MapReduce Applications
Introduction
Choosing appropriate Hadoop data types
Implementing a custom Hadoop Writable data type
Implementing a custom Hadoop key type
Emitting data of different value types from a mapper
Choosing a suitable Hadoop InputFormat for your input data format
Adding support for new input data formats – implementing a custom InputFormat
Formatting the results of MapReduce computations – using Hadoop OutputFormats
Hadoop intermediate (map to reduce) data partitioning
Broadcasting and distributing shared resources to tasks in a MapReduce job – Hadoop DistributedCache
Using Hadoop with legacy applications – Hadoop Streaming
Adding dependencies between MapReduce jobs
Hadoop counters for reporting custom metrics
5
Hadoop Ecosystem
Hadoop Ecosystem
Introduction
Installing HBase
Data random access using Java client APIs
Running MapReduce jobs on HBase (table input/output)
Installing Pig
Running your first Pig command
Set operations (join, union) and sorting with Pig
Installing Hive
Running a SQL-style query with Hive
Performing a join with Hive
Installing Mahout
Running K-means with Mahout
Visualizing K-means results
6
Analytics
Analytics
Introduction
Simple analytics using MapReduce
Performing Group-By using MapReduce
Calculating frequency distributions and sorting using MapReduce
Plotting the Hadoop results using GNU Plot
Calculating histograms using MapReduce
Calculating scatter plots using MapReduce
Parsing a complex dataset with Hadoop
Joining two datasets using MapReduce
7
Searching and Indexing
Searching and Indexing
Introduction
Generating an inverted index using Hadoop MapReduce
Intra-domain web crawling using Apache Nutch
Indexing and searching web documents using Apache Solr
Configuring Apache HBase as the backend data store for Apache Nutch
Deploying Apache HBase on a Hadoop cluster
Whole web crawling with Apache Nutch using a Hadoop/HBase cluster
ElasticSearch for indexing and searching
Generating the in-links graph for crawled web pages
8
Classifications, Recommendations, and Finding Relationships
Classifications, Recommendations, and Finding Relationships
Introduction
Content-based recommendations
Hierarchical clustering
Clustering an Amazon sales dataset
Collaborative filtering-based recommendations
Classification using Naive Bayes Classifier
Assigning advertisements to keywords using the Adwords balance algorithm
9
Mass Text Data Processing
Mass Text Data Processing
Introduction
Data preprocessing (extract, clean, and format conversion) using Hadoop Streaming and Python
Data de-duplication using Hadoop Streaming
Loading large datasets to an Apache HBase data store using importtsv and bulkload tools
Creating TF and TF-IDF vectors for the text data
Clustering the text data
Topic discovery using Latent Dirichlet Allocation (LDA)
Document classification using Mahout Naive Bayes classifier
10
Cloud Deployments: Using Hadoop on Clouds
Cloud Deployments: Using Hadoop on Clouds
Introduction
Running Hadoop MapReduce computations using Amazon Elastic MapReduce (EMR)
Saving money by using Amazon EC2 Spot Instances to execute EMR job flows
Executing a Pig script using EMR
Executing a Hive script using EMR
Creating an Amazon EMR job flow using the Command Line Interface
Deploying an Apache HBase Cluster on Amazon EC2 cloud using EMR
Using EMR Bootstrap actions to configure VMs for the Amazon EMR jobs
Using Apache Whirr to deploy an Apache Hadoop cluster in a cloud environment
Using Apache Whirr to deploy an Apache HBase cluster in a cloud environment
Index
Index

Formatting the results of MapReduce computations – using Hadoop OutputFormats

Often times the output of your MapReduce computation will be consumed by other applications. Hence, it is important to store the result of a MapReduce computation in a format that can be consumed efficiently by the target application. It is also important to store and organize the data in a location that is efficiently accessible by your target application. We can use Hadoop OutputFormat interface to define the data storage format, data storage location and the organization of the output data of a MapReduce computation. A OutputFormat prepares the output location and provides a RecordWriter implementation to perform the actual serialization and storage of the data.

Hadoop uses the org.apache.hadoop.mapreduce.lib.output.TextOutputFormat<K,V> as the default OutputFormat for the MapReduce computations. TextOutputFormat writes the records of the output data to plain text files in HDFS using a separate line for...

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