Book Image

Practical Site Reliability Engineering

By : Pethuru Raj Chelliah, Shreyash Naithani, Shailender Singh
Book Image

Practical Site Reliability Engineering

By: Pethuru Raj Chelliah, Shreyash Naithani, Shailender Singh

Overview of this book

Site reliability engineering (SRE) is being touted as the most competent paradigm in establishing and ensuring next-generation high-quality software solutions. This book starts by introducing you to the SRE paradigm and covers the need for highly reliable IT platforms and infrastructures. As you make your way through the next set of chapters, you will learn to develop microservices using Spring Boot and make use of RESTful frameworks. You will also learn about GitHub for deployment, containerization, and Docker containers. Practical Site Reliability Engineering teaches you to set up and sustain containerized cloud environments, and also covers architectural and design patterns and reliability implementation techniques such as reactive programming, and languages such as Ballerina and Rust. In the concluding chapters, you will get well-versed with service mesh solutions such as Istio and Linkerd, and understand service resilience test practices, API gateways, and edge/fog computing. By the end of this book, you will have gained experience on working with SRE concepts and be able to deliver highly reliable apps and services.
Table of Contents (19 chapters)
Title Page
Dedication
About Packt
Contributors
Preface
10
Containers, Kubernetes, and Istio Monitoring
Index

Summary


There are several activities being strategically planned and executed to enhance the resiliency, robustness, and versatility of enterprise, edge, and embedded IT. It is overwhelmingly accepted that the domains of data analytics and machine learning are going to be the key differentiators for corporations in fulfilling the varying expectations of their customers, clients, and consumers. This chapter has described the various post-production data analytics to allow you to gain a deeper understanding of applications, middleware solutions, databases, and IT infrastructures to manage them effectively and efficiently. Machine-learning algorithms enable the formation of self-learning models to predict problems and prescribe the viable solutions to surmount them. Thus, data analytics methods and ML algorithms come in handy in realizing resilient IT. The other important facets include static and dynamic code analyzes to proactively identify bugs in software code to enhance application reliability...