Book Image

Microservices Deployment Cookbook

By : Vikram Murugesan
Book Image

Microservices Deployment Cookbook

By: Vikram Murugesan

Overview of this book

This book will help any team or organization understand, deploy, and manage microservices at scale. It is driven by a sample application, helping you gradually build a complete microservice-based ecosystem. Rather than just focusing on writing a microservice, this book addresses various other microservice-related solutions: deployments, clustering, load balancing, logging, streaming, and monitoring. The initial chapters offer insights into how web and enterprise apps can be migrated to scalable microservices. Moving on, you’ll see how to Dockerize your application so that it is ready to be shipped and deployed. We will look at how to deploy microservices on Mesos and Marathon and will also deploy microservices on Kubernetes. Next, you will implement service discovery and load balancing for your microservices. We’ll also show you how to build asynchronous streaming systems using Kafka Streams and Apache Spark. Finally, we wind up by aggregating your logs in Kafka, creating your own metrics, and monitoring the metrics for the microservice.
Table of Contents (15 chapters)
Microservices Deployment Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Improving the performance of the Kafka Streams program


Kafka claims that it is so fast that each broker can handle hundreds of megabytes of data per second from several applications. That is a bold statement. In fact, Kafka has proved to be much faster than this in several success stories. So using Kafka gives you this awesome performance by default. What if that is not sufficient? The answer to this question is scaling. Kafka is built in such a way that Kafka consumers or Kafka Streams applications can be scaled in such a way that they work together as a group. That's where the term "consumer group" kicks in. A consumer group is a group of consumers that share the same ID. Consumers in a consumer group subscribe to the same topic(s); however, each consumer group gets only one copy of each message produced in a topic. This is how Kafka achieves point-to-point behavior using topics. Internally, each consumer in the consumer group will be consuming messages from one dedicated partition. This...