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

Introducing Structured Streaming


With Spark 2.0, the Apache Spark community is working on simplifying streaming by introducing the concept of structured streaming which bridges the concepts of streaming with Datasets/DataFrames (as noted in the following diagram):

As noted in earlier chapters on DataFrames, the execution of SQL and/or DataFrame queries within the Spark SQL Engine (and Catalyst Optimizer) revolves around building a logical plan, building numerous physical plans, the engine choosing the correct physical plan based on its cost optimizer, and then generating the code (i.e. code gen) that will deliver the results in a performant manner. What Structured Streaming introduces is the concept of an Incremental Execution Plan. When working with blocks of data, structured streaming repeatedly applies the execution plan for every new set of blocks it receives. By running in this manner, the engine can take advantage of the optimizations included within Spark DataFrames/Datasets and apply...