Book Image

Apache Spark 2.x Cookbook

By : Rishi Yadav
Book Image

Apache Spark 2.x Cookbook

By: Rishi Yadav

Overview of this book

While Apache Spark 1.x gained a lot of traction and adoption in the early years, Spark 2.x delivers notable improvements in the areas of API, schema awareness, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and more accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Further on, you will be introduced to working with RDDs, DataFrames and Datasets to operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning & recommendation engines in Spark. Last but not least, the final few chapters delve deeper into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Streaming using Kafka


Kafka is a distributed, partitioned, and replicated commit log service. In simple words, it is a distributed messaging server. Kafka maintains the message feed in categories called topics. An example of a topic can be the ticker symbol of a company you would like to get news about, for example, CSCO for Cisco.

Processes that produce messages are called producers and those that consume messages are called consumers. In traditional messaging, the messaging service has one central messaging server, also called the broker. Since Kafka is a distributed messaging service, it has a cluster of brokers, which functionally acts as one Kafka broker, as shown here:

For each topic, Kafka maintains the partitioned log. This partitioned log consists of one or more partitions spread across the cluster, as shown in the following figure:

Kafka borrows a lot of concepts from Hadoop and other big data frameworks. The concept of partition is very similar to the concept of InputSplit in Hadoop...