Book Image

Learning PySpark

By : Tomasz Drabas, Denny Lee
Book Image

Learning PySpark

By: Tomasz Drabas, Denny Lee

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. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.
Table of Contents (20 chapters)
Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Summary


RDDs are the backbone of Spark; these schema-less data structures are the most fundamental data structures that we will deal with within Spark.

In this chapter, we presented ways to create RDDs from text files, by means of the .parallelize(...) method as well as by reading data from text files. Also, some ways of processing unstructured data were shown.

Transformations in Spark are lazy - they are only applied when an action is called. In this chapter, we discussed and presented the most commonly used transformations and actions; the PySpark documentation contains many more http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.

One major distinction between Scala and Python RDDs is speed: Python RDDs can be much slower than their Scala counterparts.

In the next chapter we will walk you through a data structure that made PySpark applications perform on par with those written in Scala - the DataFrames.