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Hands-On Microservices with Spring Boot and Spring Cloud

Hands-On Microservices with Spring Boot and Spring Cloud

By : Magnus Larsson AB
2.9 (17)
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Hands-On Microservices with Spring Boot and Spring Cloud

Hands-On Microservices with Spring Boot and Spring Cloud

2.9 (17)
By: Magnus Larsson AB

Overview of this book

Microservices architecture allows developers to build and maintain applications with ease, and enterprises are rapidly adopting it to build software using Spring Boot as their default framework. With this book, you’ll learn how to efficiently build and deploy microservices using Spring Boot. This microservices book will take you through tried and tested approaches to building distributed systems and implementing microservices architecture in your organization. Starting with a set of simple cooperating microservices developed using Spring Boot, you’ll learn how you can add functionalities such as persistence, make your microservices reactive, and describe their APIs using Swagger/OpenAPI. As you advance, you’ll understand how to add different services from Spring Cloud to your microservice system. The book also demonstrates how to deploy your microservices using Kubernetes and manage them with Istio for improved security and traffic management. Finally, you’ll explore centralized log management using the EFK stack and monitor microservices using Prometheus and Grafana. By the end of this book, you’ll be able to build microservices that are scalable and robust using Spring Boot and Spring Cloud.
Table of Contents (25 chapters)
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Section 1: Getting Started with Microservice Development Using Spring Boot
9
Section 2: Leveraging Spring Cloud to Manage Microservices
17
Section 3: Developing Lightweight Microservices Using Kubernetes

Trying out distributed tracing

With the necessary changes to the source code in place, we can try out distributed tracing! We will do this by performing the following steps:

  1. Build, start, and verify the system landscape with RabbitMQ as the queue manager.
  2. Send a successful API request and see what trace information we can find in Zipkin related to this API request.
  3. Send an unsuccessful API request and see what the trace information in Zipkin looks like.
  4. Send a successful API request that triggers asynchronous processing and see how its trace information is represented in Zipkin.
  5. Investigate how we can monitor trace information that's passed to Zipkin in RabbitMQ.
  6. Switch the queue manager to Kafka and repeat the preceding steps.

We will discuss these steps in detail in the upcoming sections.

...

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