Edward Capriolo - Programming Hive
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Need to move a relational database application to Hadoop? This comprehensive guide introduces you to Apache Hive, Hadoops data warehouse infrastructure. Youll quickly learn how to use Hives SQL dialectHiveQLto summarize, query, and analyze large datasets stored in Hadoops 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. Youll 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 tablesand 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 Amazons Elastic MapReduce
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Programming Hive introduces Hive , an essential tool in the Hadoop ecosystem that provides an SQL (Structured Query Language) dialect for querying data stored in the Hadoop Distributed Filesystem (HDFS), other filesystems that integrate with Hadoop, such as MapR-FS and Amazons S3 and databases like HBase (the Hadoop database) and Cassandra .
Most data warehouse applications are implemented using relational databases that use SQL as the query language. Hive lowers the barrier for moving these applications to Hadoop. People who know SQL can learn Hive easily. Without Hive, these users must learn new languages and tools to become productive again. Similarly, Hive makes it easier for developers to port SQL-based applications to Hadoop, compared to other tool options. Without Hive, developers would face a daunting challenge when porting their SQL applications to Hadoop.
Still, there are aspects of Hive that are different from other SQL-based environments. Documentation for Hive users and Hadoop developers has been sparse. We decided to write this book to fill that gap. We provide a pragmatic, comprehensive introduction to Hive that is suitable for SQL experts, such as database designers and business analysts. We also cover the in-depth technical details that Hadoop developers require for tuning and customizing Hive.
You can learn more at the books catalog page (http://oreil.ly/Programming_Hive).
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This book is here to help you get your job done. In general, you may use the code in this book in your programs and documentation. You do not need to contact us for permission unless youre reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from OReilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your products documentation does require permission.
We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: Programming Hive by Edward Capriolo, Dean Wampler, and Jason Rutherglen (OReilly). Copyright 2012 Edward Capriolo, Aspect Research Associates, and Jason Rutherglen, 978-1-449-31933-5.
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The three of us arrived here from different directions.
When I first became involved with Hadoop, I saw the distributed filesystem and MapReduce as a great way to tackle computer-intensive problems. However, programming in the MapReduce model was a paradigm shift for me. Hive offered a fast and simple way to take advantage of MapReduce in an SQL-like world I was comfortable in. This approach also made it easy to prototype proof-of-concept applications and also to champion Hadoop as a solution internally. Even though I am now very familiar with Hadoop internals, Hive is still my primary method of working with Hadoop.
It is an honor to write a Hive book. Being a Hive Committer and a member of the Apache Software Foundation is my most valued accolade.
As a big data consultant at Think Big Analytics , I work with experienced data people who eat and breathe SQL. For them, Hive is a necessary and sufficient condition for Hadoop to be a viable tool to leverage their investment in SQL and open up new opportunities for data analytics.
Hive has lacked good documentation. I suggested to my previous editor at OReilly, Mike Loukides, that a Hive book was needed by the community. So, here we are
I work at Think Big Analytics as a software architect. My career has involved an array of technologies including search, Hadoop, mobile, cryptography, and natural language processing. Hive is the ultimate way to build a data warehouse using open technologies on any amount of data. I use Hive regularly on a variety of projects.
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