Book Image

Implementing Event-Driven Microservices Architecture in .NET 7

By : Joshua Garverick, Omar Dean McIver
4 (1)
Book Image

Implementing Event-Driven Microservices Architecture in .NET 7

4 (1)
By: Joshua Garverick, Omar Dean McIver

Overview of this book

This book will guide you through various hands-on practical examples for implementing event-driven microservices architecture using C# 11 and .NET 7. It has been divided into three distinct sections, each focusing on different aspects of this implementation. The first section will cover the new features of .NET 7 that will make developing applications using EDA patterns easier, the sample application that will be used throughout the book, and how the core tenets of domain-driven design (DDD) are implemented in .NET 7. The second section will review the various components of a local environment setup, the containerization of code, testing, deployment, and the observability of microservices using an EDA approach. The third section will guide you through the need for scalability and service resilience within the application, along with implementation details related to elastic and autoscale components. You’ll also cover how proper telemetry helps to automatically drive scaling events. In addition, the topic of observability is revisited using examples of service discovery and microservice inventories. By the end of this book, you’ll be able to identify and catalog domains, events, and bounded contexts to be used for the design and development of a resilient microservices architecture.
Table of Contents (21 chapters)
1
Part 1:Event-Driven Architecture and .NET 7
6
Part 2:Testing and Deploying Microservices
12
Part 3:Testing and Deploying Microservices

Mechanisms for autoscaling

There are four primary indicators to watch during the operation of an application that can indicate potential issues with maintaining uptime or responsiveness SLAs. These include CPU load, I/O load (often seen as disk pressure in Kubernetes), request and network load (often seen as network pressure in Kubernetes), and memory load. Understanding these indicators is essential in helping to prepare you to adjust configuration settings, thus leading to scalable supporting infrastructure. Understanding how your application components affect each of these indicators is important as well.

Compute and CPU load

The amount of CPU that’s utilized will vary heavily across physical machines, virtual machines, and even hosted cloud services. With cloud services, even greater amounts of fine-grained adjustments can be made. For example, with Azure App Service, the amount of compute initially available to an App Service is tied directly to the App Service Plan...