Book Image

AWS Observability Handbook

By : Phani Kumar Lingamallu, Fabio Braga de Oliveira
Book Image

AWS Observability Handbook

By: Phani Kumar Lingamallu, Fabio Braga de Oliveira

Overview of this book

As modern application architecture grows increasingly complex, identifying potential points of failure and measuring end user satisfaction, in addition to monitoring application availability, is key. This book helps you explore AWS observability tools that provide end-to-end visibility, enabling quick identification of performance bottlenecks in distributed applications. You’ll gain a holistic view of monitoring and observability on AWS, starting from observability basics using Amazon CloudWatch and AWS X-Ray to advanced ML-powered tools such as AWS DevOps Guru. As you progress, you'll learn about AWS-managed open source services such as AWS Distro for OpenTelemetry (ADOT) and AWS managed Prometheus, Grafana, and the ELK Stack. You’ll implement observability in EC2 instances, containers, Kubernetes, and serverless apps and grasp UX monitoring. With a fair mix of concepts and examples, this book helps you gain hands-on experience in implementing end-to-end AWS observability in your applications and navigating and troubleshooting performance issues with the help of use cases. You'll also learn best practices and guidelines, such as how observability relates to the Well-Architected Framework. By the end of this AWS book, you’ll be able to implement observability and monitoring in your apps using AWS’ native and managed open source tools in real-world scenarios.
Table of Contents (22 chapters)
1
Part 1: Getting Started with Observability on AWS
6
Part 2: Automated and Machine Learning-Powered Observability on AWS
11
Part 3: Open Source Managed Services on AWS
15
Part 4: Scaled Observability and Beyond

Troubleshooting performance issues using X-Ray groups

It would be practically difficult to analyze all the X-Ray traces generated by a complex system and look at each trace to understand the issues. That’s where X-Ray groups will be helpful. X-Ray groups will help simplify the process by focusing on the filtered traces based on rule-based criteria when there is a breach in a specific parameter. For example, if you would like to focus on the traces where the response time is greater than 3 seconds, you can create an X-Ray group with the criteria of responseTime > 3. This way, you can quickly isolate and analyze only the traces that indicate a problem, making it easier to identify and resolve issues. Let’s create an X-Ray group and understand only the problematic traces from the generated traces:

  1. You can see from the following figure that there are three traces with different response times. You can filter and focus only on the traces with a response time greater...