Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By : Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei
Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By: Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei

Overview of this book

Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: • Mastering Apache Spark 2.x by Romeo Kienzler • Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla • Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook
Table of Contents (23 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

Streaming data and debugging with queueStream


In this recipe, we explore the concept of queueStream(), which is a valuable tool while trying to get a streaming program to work during the development cycle. We found the queueStream() API very useful and felt that other developers can benefit from a recipe that fully demonstrates its usage.

We start by simulating a user browsing various URLs associated with different web pages using the program ClickGenerator.scala and then proceed to consume and tabulate the data (user behavior/visits) using the ClickStream.scala program:

We use Spark's streaming API with Dstream(), which will require the use of a streaming context. We are calling this out explicitly to highlight one of the differences between Spark streaming and the Spark structured streaming programming model.

Note

There are two distinct programs (ClickGenerator.scala and ClickStream.scala) that make up this recipe.

How to do it...

  1. Start a new project in IntelliJ or in an IDE of your choice....