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High performance Spark : best practices for scaling and optimizing Apache Spark / Hoolden Karau & Rachel Warren.

By: Karau, Holden [author.].
Contributor(s): Warren, Rachel [author.].
Publisher: Sebastopol, CA : O'Reilly Media, Inc., 2017Copyright date: 2017Edition: First edition : June 2017.Description: xiv, 341 pages : black and white illustrations, graphs, charts ; 24 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781491943205; 1491943203.Subject(s): Spark (Electronic resource : Apache Software Foundation) | Big data | Data mining -- Computer programs
Contents:
Table of Contents : Preface -- 1. Introduction high performance Spark -- 2. How Spark works -- 3. Dataframes, datasets, and Spark SQL -- 4. Joins (SQL and Core) -- 5. Effective transformations -- 6. Working with Key/Value Data -- 7. Going beyond Scala -- 8. Testing and validation -- 9. Spark MLlib and ML -- 10. Spark components and packages -- A. Tuning, debugging and other things developers like to pretend don't exist -- Index.
Summary: "Apache Spark is amazing when everything clicks. But if you haven't seen the performance improvements you expected, or still don't feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you'll also learn how to make it sing. With this book, you'll explore : How Spark SQL's new interfaces improve performance over SQL's RDD data structure ; The choice between data joins in Core Spark and Spark SQL ; Techniques for getting the most out of standard RDD transformations ; How to work around performance issues in Spark's key/value pair paradigm ; Writing high-performance Spark code without Scala or the JVM ; How to test for functionality and performance when applying suggested improvements ; Using Spark MLlib and Spark ML machine learning libraries ; Spark's Streaming components and external community packages." -- back cover.
List(s) this item appears in: Recent Additions to the NCAR Library Collection
Item type Current location Call number Status Date due Item holds
BOOK BOOK Mesa Lab QA76.9.D343 K37 2017 (Browse shelf) Available
Total holds: 0

Includes index.

Table of Contents : Preface -- 1. Introduction high performance Spark -- 2. How Spark works -- 3. Dataframes, datasets, and Spark SQL -- 4. Joins (SQL and Core) -- 5. Effective transformations -- 6. Working with Key/Value Data -- 7. Going beyond Scala -- 8. Testing and validation -- 9. Spark MLlib and ML -- 10. Spark components and packages -- A. Tuning, debugging and other things developers like to pretend don't exist -- Index.

"Apache Spark is amazing when everything clicks. But if you haven't seen the performance improvements you expected, or still don't feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you'll also learn how to make it sing. With this book, you'll explore : How Spark SQL's new interfaces improve performance over SQL's RDD data structure ; The choice between data joins in Core Spark and Spark SQL ; Techniques for getting the most out of standard RDD transformations ; How to work around performance issues in Spark's key/value pair paradigm ; Writing high-performance Spark code without Scala or the JVM ; How to test for functionality and performance when applying suggested improvements ; Using Spark MLlib and Spark ML machine learning libraries ; Spark's Streaming components and external community packages." -- back cover.

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