Book Image

Architecting Cloud Native Applications

By : Kamal Arora, Erik Farr, John Gilbert, Piyum Zonooz
Book Image

Architecting Cloud Native Applications

By: Kamal Arora, Erik Farr, John Gilbert, Piyum Zonooz

Overview of this book

Cloud computing has proven to be the most revolutionary IT development since virtualization. Cloud native architectures give you the benefit of more flexibility over legacy systems. This Learning Path teaches you everything you need to know for designing industry-grade cloud applications and efficiently migrating your business to the cloud. It begins by exploring the basic patterns that turn your database inside out to achieve massive scalability. You’ll learn how to develop cloud native architectures using microservices and serverless computing as your design principles. Then, you’ll explore ways to continuously deliver production code by implementing continuous observability in production. In the concluding chapters, you’ll learn about various public cloud architectures ranging from AWS and Azure to the Google Cloud Platform, and understand the future trends and expectations of cloud providers. By the end of this Learning Path, you’ll have learned the techniques to adopt cloud native architectures that meet your business requirements. This Learning Path includes content from the following Packt products: • Cloud Native Development Patterns and Best Practices by John Gilbert • Cloud Native Architectures by Erik Farr et al.
Table of Contents (24 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Alerting


Alerting is a double-edged sword that we have to learn to use productively. The teams have created synthetic transactions to exercise their components on a regular basis to help assert the success of their deployments and the health of their components. Each component has been instrumented to sufficiently increase the observability of its internal operation. As a result, the teams are now awash in a sea of metrics. Categorizing this data into work metrics, resource metrics, and events helps to make sense of the different signals emitted by the components. Some teams have honed in on their key performance indicators, while others are still waiting for the dust to settle. Regardless, there is too much information to consume manually. Monitors need to be defined to watch the data and alert the team accordingly.

The classic problem with monitoring is alert fatigue. Teams will receive far too many alerts when monitors are not well crafted. Receiving too many alerts is not much better...