编程Hive权威指南：Edward Capriolo, Dean Wampler, Jason Rutherglen 联合出品

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"《编程Hive》是一本由Edward Capriolo、Dean Wampler和Jason Rutherglen合著的专业书籍，针对Hive编程语言进行了深入的指导。该书专为那些希望在大数据处理领域运用Hive技术的读者设计，Hive作为Apache Hadoop生态系统中的一个重要组件，被广泛用于数据仓库管理和SQL-like查询。本书旨在提供一个全面的学习路径，从基础概念到高级实践，涵盖了Hive的数据模型、查询语法、表设计、优化策略以及与Hadoop其他组件的集成。作者们以其丰富的经验和深入理解，确保了内容的实用性和准确性。书中不仅包含了理论知识，还提供了大量的实战案例，帮助读者通过实际操作掌握Hive的使用技巧。对于初学者来说，它是一个理想的入门教程，而对于经验丰富的开发者，它则是一本提升技能和解决实际问题的参考书。版权信息表明，这本书是在2012年首次出版，并持续进行修订，以保持与最新技术和最佳实践的一致性。书中附有在线版本供读者随时随地查阅，同时O'Reilly Media作为知名的出版商，提供了完善的客户服务和支持，包括教育、商业或销售推广用途的购买选项。编辑团队包括Mike Loukides和Courtney Nash，他们确保了内容的质量和结构。此外，书中还经过了多位专业人员如Proofreaders、Indexer和Cover Designer等的精心校对和设计，为读者提供了清晰易懂和赏心悦目的阅读体验。《编程Hive》的出版日期为2012年10月，第一版于同年9月17日发布，后续不断更新以纠正错误并补充新知识。无论你是数据分析师、数据工程师还是系统管理员，这都是一本不可多得的Hive编程指南，值得在整个职业生涯中参考和学习。"

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Using Code Examples

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What Brought Us to Hive?

The three of us arrived here from different directions.

Edward Capriolo

When I first became involved with Hadoop, I saw the distributed filesystem and Map-

Reduce 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

Preface | xv

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.

Dean Wampler

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 op-

portunities for data analytics.

Hive has lacked good documentation. I suggested to my previous editor at O’Reilly,

Mike Loukides, that a Hive book was needed by the community. So, here we are…

Jason Rutherglen

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.

Acknowledgments

Everyone involved with Hive. This includes committers, contributors, as well as end

users.

Mark Grover wrote the chapter on Hive and Amazon Web Services. He is a contributor

to the Apache Hive project and is active helping others on the Hive IRC channel.

David Ha and Rumit Patel, at M6D, contributed the case study and code on the Rank

function. The ability to do Rank in Hive is a significant feature.

Ori Stitelman, at M6D, contributed the case study, Data Science using Hive and R,

which demonstrates how Hive can be used to make first pass on large data sets and

produce results to be used by a second R process.

David Funk contributed three use cases on in-site referrer identification, sessionization,

and counting unique visitors. David’s techniques show how rewriting and optimizing

Hive queries can make large scale map reduce data analysis more efficient.

Ian Robertson read the entire first draft of the book and provided very helpful feedback

on it. We’re grateful to him for providing that feedback on short notice and a tight

schedule.

xvi | Preface

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