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

Preventing data loss

Ensuring the complete integrity of data for most monolithic applications is a static and manageable concept. Most underlying data stores are atomic, consistent, isolated, and durable (ACID). Once data has been received in the form of a request, a single compiled application will process it through to completion. Threats to the integrity of this operation are few and, more importantly, highly observable and consistent.

For example, a failure in the data store may consistently fail the whole application – or part of the application – until fixed. A failure of code presents itself as a bug that will fail the same way repeatedly for the same given request. Data corruption and loss in monoliths tend to be an all-or-nothing game. If data is being lost – you will know through consistent failures to operate.

Moving application designs to the cloud bring along two new challenges:

  • Scalable architecture means multiple separate components...