John Russell - Getting Started with Impala: Interactive SQL for Apache Hadoop
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Learn how to write, tune, and port SQL queries and other statements for a Big Data environment, using Impalathe massively parallel processing SQL query engine for Apache Hadoop. The best practices in this practical guide help you design database schemas that not only interoperate with other Hadoop components, and are convenient for administers to manage and monitor, but also accommodate future expansion in data size and evolution of software capabilities.
Ideal for database developers and business analysts, Getting Started with Impala includes advice from Clouderas development team, as well as insights from its consulting engagements with customers.
- Learn how Impala integrates with a wide range of Hadoop components
- Attain high performance and scalability for huge data sets on production clusters
- Explore common developer tasks, such as porting code to Impala and optimizing performance
- Use tutorials for working with billion-row tables, date- and time-based values, and other techniques
- Learn how to transition from rigid schemas to a flexible model that evolves as needs change
- Take a deep dive into joins and the roles of statistics
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Cloudera Impala is an open source project that opens up the Apache Hadoop software stack to a wide audience of database analysts, users, and developers. The Impala massively parallel processing (MPP) engine makes SQL queries of Hadoop data simple enough to be accessible to analysts familiar with SQL and to users of business intelligence tools, and its fast enough to be used for interactive exploration and experimentation.
From the ground up, the Impala software is written for high performance of SQL queries distributed across clusters of connected machines.
This book is intended for a broad audience of users from a variety of database, data warehousing, or Big Data backgrounds.It assumes that youre experienced enough with SQL not to need explanations for familiar statements such as CREATE TABLE , SELECT , INSERT , and their major clauses.Linux experience is a plus.Experience with the Apache Hadoop software stack is useful but not required.
This book points out instances where some aspect of Impalaarchitecture or usage might be new to people who are experiencedwith databases but not the Apache Hadoop software stack.
The SQL examples in this book start from a simple base for easy comprehension, thenbuild toward best practices that demonstrate high performance and scalability.
The following typographical conventions are used in this book:
Constant widthConstant width boldConstant width italicThis element signifies a tip or suggestion.
This element signifies a general note.
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Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/oreillymedia/get-started-impala.
This book is here to help you get your job done. In general, if example code is offered with this book, you may use it 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: Getting Started with Impala by John Russell (OReilly). Copyright 2015 Cloudera, Inc., 978-1-491-90577-7.
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I have to start by acknowledging the vision and execution of the Impala development team, led by Marcel Kornacker. I have learned a lot from them especially Justin Erickson, Alex Behm, Lenni Kuff, Alan Choi, and Nong Li that has made it into this book. Thanks to all the Impala team members and to Gwen Shapira, Mark Grover, Kate Ting, and Uri Laserson for their feedback and insights on my drafts.
Going a little further back, Ive been lucky to be able to consult and collaborate with really good individuals and teams at each stage and transition in my career. Thanks to James Hamilton who convinced me to switch from programming languages to the database track all those years ago at IBM. Thanks to the late Mark Townsend at Oracle for many insights about the database industry. Thanks to Ken Jacobs who helped me switch into the open source group at Oracle, and the InnoDB team under Calvin Sun and later Sunny Bains for being great to work with and teaching me database internals. Thanks to Mike Olson and Justin Kestelyn at Cloudera for showing me the right way for a small company to tackle the enterprise software market, and to do developer and community outreach. Thanks to Paul Battaglia, Jolly Chen, and Frank Liva for building and supporting the Cloudera technical publications department.
Last but not least, this book would not be possible if not for my wonderful and supportive wife, Lotus Goldstein.
The Apache Hadoop ecosystem is very data-centric, making it a natural fit for database developers with SQL experience. Much application development work for Hadoop consists of writing programs to copy, convert or reorganize, and analyze data files. A lot of effort goes into finding ways to do these things reliably, on a large scale, and in parallel across clusters of networked machines. Impala focuses on making these activities fast and easy, without requiring you to have a PhD in distributed computing, learn a lot of new APIs, or write a complete program when your intent can be conveyed with a single SQL statement.
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