High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
Author:: Holden Karau (Author), Rachel Warren (Author)
Publisher finelybook 出版社: O’Reilly Media
Publication Date 出版日期: 2017-07-20
Edition 版次: 1st
Language 语言: English
Length: 356
ISBN-10: 9781491943205
ISBN-13: 9781491943205
Book Description
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
Rachel Warren is a data scientist and software engineer at Alpine Data Labs, where she uses Spark to address real world data processing challenges. She has experience working as an analyst both in industry and academia. She graduated with a degree in Computer Science from Wesleyan University in Connecticut.
不好意思麻烦了,这本书还能找到吗?
http://finelybook.com/high-performance-spark-best-practices-for-scaling-and-optimizing-apache-spark/