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

Understanding global aggregations

In the previous section, our recipe provided a snapshot count of events. That is, it provided the count of events at the point in time. But what if you want to understand a sum of events for some time window? This is the concept of global aggregations:

If we wanted global aggregations, the same example as before (Time 1: 5 blue, 3 green, Time 2: 1 gohawks, Time 4: 2 greens) would be calculated as:

  • Time 1: 5 blue, 3 green
  • Time 2: 5 blue, 3 green, 1 gohawks
  • Time 4: 5 blue, 5 green, 1 gohawks

Within the traditional batch calculations, this would be similar to a groupbykey or GROUP BY statement. But in the case of streaming applications, this calculation needs to be done within milliseconds, which is typically too short of a time window to perform a GROUP BY calculation. However, with Spark Streaming global aggregations, this calculation can be completed quickly by performing a stateful streaming calculation. That is, using the Spark Streaming framework, all of the...