Book Image

Hadoop MapReduce v2 Cookbook - Second Edition: RAW

Book Image

Hadoop MapReduce v2 Cookbook - Second Edition: RAW

Overview of this book

Related Content you might be interested in

Current Title:

Small book image
Hadoop MapReduce v2 Cookbook - Second Edition: RAW
Feb, 2015 | 322 pages
No titles found
Table of Contents (19 chapters)
Hadoop MapReduce v2 Cookbook Second Edition
Hadoop MapReduce v2 Cookbook Second Edition
Credits
Credits
About the Author
About the Author
Acknowledgments
Acknowledgments
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with Hadoop v2
Getting Started with Hadoop v2
Introduction
Setting up Hadoop v2 on your local machine
Writing a WordCount MapReduce application, bundling it, and running it using the Hadoop local mode
Adding a combiner step to the WordCount MapReduce program
Setting up HDFS
Setting up Hadoop YARN in a distributed cluster environment using Hadoop v2
Setting up Hadoop ecosystem in a distributed cluster environment using a Hadoop distribution
HDFS command-line file operations
Running the WordCount program in a distributed cluster environment
Benchmarking HDFS using DFSIO
Benchmarking Hadoop MapReduce using TeraSort
2
Cloud Deployments – Using Hadoop YARN on Cloud Environments
Cloud Deployments – Using Hadoop YARN on Cloud Environments
Introduction
Running Hadoop MapReduce v2 computations using Amazon Elastic MapReduce
Saving money 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 AWS Command Line Interface
Deploying an Apache HBase cluster on Amazon EC2 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
3
Hadoop Essentials – Configurations, Unit Tests, and Other APIs
Hadoop Essentials – Configurations, Unit Tests, and Other APIs
Introduction
Optimizing Hadoop YARN and MapReduce configurations for cluster deployments
Shared user Hadoop clusters – using Fair and Capacity schedulers
Setting classpath precedence to user-provided JARs
Speculative execution of straggling tasks
Unit testing Hadoop MapReduce applications using MRUnit
Integration testing Hadoop MapReduce applications using MiniYarnCluster
Adding a new DataNode
Decommissioning DataNodes
Using multiple disks/volumes and limiting HDFS disk usage
Setting the HDFS block size
Setting the file replication factor
Using the HDFS Java API
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
Writing multiple outputs from a MapReduce computation
Hadoop intermediate data partitioning
Secondary sorting – sorting Reduce input values
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 to report custom metrics
5
Analytics
Analytics
Introduction
Simple analytics using MapReduce
Performing GROUP BY using MapReduce
Calculating frequency distributions and sorting using MapReduce
Plotting the Hadoop MapReduce results using gnuplot
Calculating histograms using MapReduce
Calculating Scatter plots using MapReduce
Parsing a complex dataset with Hadoop
Joining two datasets using MapReduce
6
Hadoop Ecosystem – Apache Hive
Hadoop Ecosystem – Apache Hive
Introduction
Getting started with Apache Hive
Creating databases and tables using Hive CLI
Simple SQL-style data querying using Apache Hive
Creating and populating Hive tables and views using Hive query results
Utilizing different storage formats in Hive - storing table data using ORC files
Using Hive built-in functions
Hive batch mode - using a query file
Performing a join with Hive
Creating partitioned Hive tables
Writing Hive User-defined Functions (UDF)
HCatalog – performing Java MapReduce computations on data mapped to Hive tables
HCatalog – writing data to Hive tables from Java MapReduce computations
7
Hadoop Ecosystem II – Pig, HBase, Mahout, and Sqoop
Hadoop Ecosystem II – Pig, HBase, Mahout, and Sqoop
Introduction
Getting started with Apache Pig
Joining two datasets using Pig
Accessing a Hive table data in Pig using HCatalog
Getting started with Apache HBase
Data random access using Java client APIs
Running MapReduce jobs on HBase
Using Hive to insert data into HBase tables
Getting started with Apache Mahout
Running K-means with Mahout
Importing data to HDFS from a relational database using Apache Sqoop
Exporting data from HDFS to a relational database using Apache Sqoop
8
Searching and Indexing
Searching and Indexing
Introduction
Generating an inverted index using Hadoop MapReduce
Intradomain web crawling using Apache Nutch
Indexing and searching web documents using Apache Solr
Configuring Apache HBase as the backend data store for Apache Nutch
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
9
Classifications, Recommendations, and Finding Relationships
Classifications, Recommendations, and Finding Relationships
Introduction
Performing content-based recommendations
Classification using the naïve Bayes classifier
Assigning advertisements to keywords using the Adwords balance algorithm
10
Mass Text Data Processing
Mass Text Data Processing
Introduction
Data preprocessing using Hadoop streaming and Python
De-duplicating data using Hadoop streaming
Loading large datasets to an Apache HBase data store – importtsv and bulkload
Creating TF and TF-IDF vectors for the text data
Clustering text data using Apache Mahout
Topic discovery using Latent Dirichlet Allocation (LDA)
Document classification using Mahout Naive Bayes Classifier
Index
Index

Getting started with Apache Pig

Apache Pig is a high-level language framework for Hadoop that makes it easy to analyze very large datasets stored in HDFS without having to implement complex Java MapReduce applications. The language of Pig is called Pig Latin, which is a data flow language. While the goal of both Pig and Hive frameworks is similar, the language layers of these two frameworks complement each other by providing a procedural language and a declarative language, respectively.

Pig converts Pig Latin queries in to a series of one or more MapReduce jobs in the background.

In order to install Pig, we recommend you use one of the freely available commercial Hadoop distributions as described in Chapter 1, Getting Started with Hadoop v2. Another alternative is to use Apache Bigtop to install Pig. Refer to the Bigtop-related recipe in Chapter 1, Getting Started with Hadoop v2 for steps on installing Pig using the Apache Bigtop distribution.

Note

In case you don't have a working Pig and...

Previous Section
End of Section 3
Next Section