High performance Spark : best practices for scaling and optimizing Apache Spark
Karau, Holden
| 자료유형 | 단행본 |
|---|---|
| 서명/저자사항 | High performance Spark : best practices for scaling and optimizing Apache Spark / Holden Karau and Rachel Warren. |
| 개인저자 | Karau, Holden. Warren, Rachel. |
| 발행사항 | Sebastopol, CA : O'Reilly Media, 2017. |
| 형태사항 | xiv, 341 p. : ill., graphs, charts ; 24 cm. |
| ISBN | 9781491943205 (paperback) 1491943203 |
| 일반주기 |
Includes index.
|
| 내용주기 | 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. |
| 요약 | "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. |
| 주제명 (통일서명) | Spark (Electronic resource : Apache Software Foundation) |
| 일반주제명 | Big data. Data mining --Computer programs. |
| 분류기호 | 006.312 |
| 언어 | 영어 |
태그가 저장되었습니다. 
태그저장 중 오류가 발생하였습니다. 재시도 하십시오. 
QUICK MENU
서평 (0 건)
*주제와 무관한 내용의 서평은 삭제될 수 있습니다. 한글 기준 10자 이상 작성해 주세요.
서평추가