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PySpark Cookbook

You're reading from   PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788835367
Length 330 pages
Edition 1st Edition
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Concepts
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Authors (2):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
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Toc

Table of Contents (9) Chapters Close

Preface 1. Installing and Configuring Spark FREE CHAPTER 2. Abstracting Data with RDDs 3. Abstracting Data with DataFrames 4. Preparing Data for Modeling 5. Machine Learning with MLlib 6. Machine Learning with the ML Module 7. Structured Streaming with PySpark 8. GraphFrames – Graph Theory with PySpark

Introduction

In this chapter, we will explore the current fundamental data structure—DataFrames. DataFrames take advantage of the developments in the tungsten project and the Catalyst Optimizer. These two improvements bring the performance of PySpark on par with that of either Scala or Java.

Project tungsten is a set of improvements to Spark Engine aimed at bringing its execution process closer to the bare metal. The main deliverables include:

  • Code generation at runtime: This aims at leveraging the optimizations implemented in modern compilers
  • Taking advantage of the memory hierarchy: The algorithms and data structures exploit memory hierarchy for fast execution
  • Direct-memory management: Removes the overhead associated with Java garbage collection and JVM object creation and management
  • Low-level programming: Speeds up memory access by loading immediate data...
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