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

Continuous aggregation with structured streaming

As noted in earlier chapters, the execution of Spark SQL or DataFrame queries revolves around building a logical plan, choosing a physical plan (of the many generated physical plans) based on its cost optimizer, and then generating the code (that is, code gen) via the Spark SQL Engine Catalyst Optimizer. What structured streaming introduces is the concept of an incremental execution plan. That is, structured streaming repeatedly applies the execution plan for every new block of data it receives. This way, the Spark SQL engine can take advantage of the optimizations included within Spark DataFrames and apply them to an incoming data stream. Because structured streaming is built on top of Spark DataFrames, this means it will also be easier to integrate other DataFrame-optimized components, including MLlib, GraphFrames, TensorFrames, and so on:

Getting ready

For these Apache Spark Streaming examples, we will be creating and executing a console...