Book Image

Building Data Streaming Applications with Apache Kafka

By : Chanchal Singh, Manish Kumar
Book Image

Building Data Streaming Applications with Apache Kafka

By: Chanchal Singh, Manish Kumar

Overview of this book

Apache Kafka is a popular distributed streaming platform that acts as a messaging queue or an enterprise messaging system. It lets you publish and subscribe to a stream of records, and process them in a fault-tolerant way as they occur. This book is a comprehensive guide to designing and architecting enterprise-grade streaming applications using Apache Kafka and other big data tools. It includes best practices for building such applications, and tackles some common challenges such as how to use Kafka efficiently and handle high data volumes with ease. This book first takes you through understanding the type messaging system and then provides a thorough introduction to Apache Kafka and its internal details. The second part of the book takes you through designing streaming application using various frameworks and tools such as Apache Spark, Apache Storm, and more. Once you grasp the basics, we will take you through more advanced concepts in Apache Kafka such as capacity planning and security. By the end of this book, you will have all the information you need to be comfortable with using Apache Kafka, and to design efficient streaming data applications with it.
Table of Contents (14 chapters)

Best practices

After going through the chapter, it is important to note a few of the best practices. They are listed as follows:

  • Exception handling: Just like producers, it is the sole responsibility of consumer programs to decide on program flows with respect to exceptions. A consumer application should define different exception classes and, as per your business requirements, decide on the actions that need to be taken.
  • Handling rebalances: Whenever any new consumer joins consumer groups or any old consumer shuts down, a partition rebalance is triggered. Whenever a consumer is losing its partition ownership, it is imperative that they should commit the offsets of the last event that they have received from Kafka. For example, they should process and commit any in-memory buffered datasets before losing the ownership of a partition. Similarly, they should close any open file...