High Performance Spark
O'Reilly Media (Verlag)
978-1-4919-4320-5 (ISBN)
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
Holden Karau is a Canadian, Apache Spark committer, and an active open source contributor. When not in San Francisco working as a software development engineer at IBM's Spark Technology Center, Holden talks internationally on Spark and holds office hours at coffee shops at home and abroad. She makes frequent contributions to Spark, specializing in PySpark and Machine Learning. Prior to IBM she worked on a variety of distributed, search, and classification problems at Alpine, Databricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a Bachelor of Mathematics in Computer Science. Outside of software she enjoys playing with fire, welding, scooters, poutine, and dancing.
Chapter 1 Introduction to High Performance Spark
Chapter 2 How Spark Works
Chapter 3 DataFrames, Datasets & Spark SQL
Chapter 4 Joins (SQL & Core)
Chapter 5 Effective Transformations
Chapter 6 Working with Key/Value Data
Chapter 7 Going Beyond Scala
Chapter 8 Testing & Validation
Chapter 9 Spark MLlib and ML
Chapter 10 Spark Components and Packages
Appendix Spark Tuning and Cluster Sizing
| Erscheinungsdatum | 08.06.2017 |
|---|---|
| Verlagsort | Sebastopol |
| Sprache | englisch |
| Maße | 178 x 233 mm |
| Gewicht | 618 g |
| Einbandart | kartoniert |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Schlagworte | Apache • Big Data • High Performance • Spark • SQL |
| ISBN-10 | 1-4919-4320-3 / 1491943203 |
| ISBN-13 | 978-1-4919-4320-5 / 9781491943205 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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