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

Chapter 10, Modern Design Patterns for Scalability

  1. No; virtual and physical networks are created with a finite amount of resources and cannot be autoscaled. Monitoring conditions related to network traffic can be a means of triggering an autoscaling event to better handle an influx of traffic.
  2. CPU and memory tend to be the easiest resources to adjust and will generally be seen as the targets for monitoring usage or over-usage.
  3. Out of the box, HPAs support evaluating thresholds on CPU and memory usage relative to the pod(s) assigned to the application. While other types of metrics can be leveraged, from monitoring other cluster components or even outside hosted systems, the recommendation is to stick with usage patterns with HPAs that are supported by default.
  4. In the short term, yes. Longer term, even increasing the number of resources for the cluster nodes can become less maintainable and take away from your ability to separate workloads efficiently. Having a separation...