Programming Hive

10/04/2012 · No Responses

Need to move a relational database application to Hadoop? This comprehensive guide introduces you to Apache Hive, Hadoop’s data warehouse infrastructure. You’ll quickly learn how to use Hive’s SQL dialect—HiveQL—to summarize, query, and analyze large datasets stored in Hadoop’s distributed filesystem.

This example-driven guide shows you how to set up and configure Hive in your environment, provides a detailed overview of Hadoop and MapReduce, and demonstrates how Hive works within the Hadoop ecosystem. You’ll also find real-world case studies that describe how companies have used Hive to solve unique problems involving petabytes of data.

Use Hive to create, alter, and drop databases, tables, views, functions, and indexes
Customize data formats and storage options, from files to external databases
Load and extract data from tables—and use queries, grouping, filtering, joining, and other conventional query methods
Gain best practices for creating user defined functions (UDFs)
Learn Hive patterns you should use and anti-patterns you should avoid
Integrate Hive with other data processing programs
Use storage handlers for NoSQL databases and other datastores
Learn the pros and cons of running Hive on Amazon’s Elastic MapReduce

Table of Contents
Chapter 1. Introduction
Chapter 2. Getting Started
Chapter 3. Data Types and File Formats
Chapter 4. HiveQL: Data Definition
Chapter 5. HiveQL: Data Manipulation
Chapter 6. HiveQL: Queries
Chapter 7. HiveQL: Views
Chapter 8. HiveQL: Indexes
Chapter 9. Schema Design
Chapter 10. Tuning
Chapter 11. Other File Formats and Compression
Chapter 12. Developing
Chapter 13. Functions
Chapter 14. Streaming
Chapter 15. Customizing Hive File and Record Formats
Chapter 16. Hive Thrift Service
Chapter 17. Storage Handlers and NoSQL
Chapter 18. Security
Chapter 19. Locking
Chapter 20. Hive Integration with Oozie
Chapter 21. Hive and Amazon Web Services (AWS)
Chapter 22. HCatalog
Chapter 23. Case Studies