You're reading from Microservices with Spring Boot 3 and Spring Cloud, Third Edition Build resilient and scalable microservices using Spring Cloud, Istio, and Kubernetes

Product type Paperback

Published in Aug 2023

Publisher Packt

ISBN-13 9781805128694

Length 706 pages

Edition 3rd Edition

Languages

Java

Tools

Istio

Concepts

Microservices

Author (1):

Magnus Larsson AB

View More author details

Table of Contents (26) Chapters

Preface

1. Introduction to Microservices

2. Introduction to Spring Boot FREE CHAPTER

3. Creating a Set of Cooperating Microservices

4. Deploying Our Microservices Using Docker

5. Adding an API Description Using OpenAPI

6. Adding Persistence

7. Developing Reactive Microservices

8. Introduction to Spring Cloud

9. Adding Service Discovery Using Netflix Eureka

10. Using Spring Cloud Gateway to Hide Microservices behind an Edge Server

11. Securing Access to APIs

12. Centralized Configuration

13. Improving Resilience Using Resilience4j

14. Understanding Distributed Tracing

15. Introduction to Kubernetes

16. Deploying Our Microservices to Kubernetes

17. Implementing Kubernetes Features to Simplify the System Landscape

18. Using a Service Mesh to Improve Observability and Management

19. Centralized Logging with the EFK Stack

20. Monitoring Microservices

21. Installation Instructions for macOS

22. Installation Instructions for Microsoft Windows with WSL 2 and Ubuntu

23. Native-Complied Java Microservices

24. Other Books You May Enjoy

25. Index

Centralized Logging with the EFK Stack

In this chapter, we will learn how to collect and store log records from microservice instances, as well as how to search and analyze log records. As we mentioned in Chapter 1, Introduction to Microservices, it is difficult to get an overview of what is going on in a system landscape of microservices when each microservice instance writes log records to its local filesystem. We need a component that can collect the log records from the microservice’s local filesystem and store them in a central database for analysis, search, and visualization. A popular open-source-based solution for this is based on the following tools:

Elasticsearch, a distributed database with great capabilities for searching and analyzing large datasets
Fluentd, a data collector that can be used to collect log records from various sources, filter and transform the collected information, and finally send it to various consumers, for example, Elasticsearch...