Book Image

PySpark Cookbook

By : Denny Lee, Tomasz Drabas
Book Image

PySpark Cookbook

By: Denny Lee, Tomasz Drabas

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.
Table of Contents (13 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

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 to CPU registers
  • Virtual function dispatches elimination: This eliminates the necessity of multiple CPU calls

Note

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