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

Format:

Book

Author/Creator:

Karau, Holden, author.

Warren, Rachel, author.

Language:

English

Subjects (All):

Spark (Electronic resource : Apache Software Foundation).

Big data.

Data mining--Computer programs.

Data mining.

Physical Description:

1 online resource (340 pages) : illustrations (some color)

Edition:

First edition.

Place of Publication:

Beijing, [China] : O'Reilly, 2017.

System Details:

text file

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

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.

Notes:

Includes index.

Includes bibliographical references at the end of each chapters and index.

Description based on online resource; title from PDF title page (ebrary, viewed June 22, 2017).

ISBN:

9781491943151

1491943157

9781491943199

149194319X

9781491943175

1491943173

OCLC:

990085799