Implementation of Chaos Engineering Practices for a Web Application

To enhance the resilience of our web application, I plan to introduce chaos engineering experiments. Our web application is a three-tier architecture using a PostgreSQL database and RESTful APIs built with Python (Flask framework). We are deploying to AWS using Docker containers orchestrated with Kubernetes. Could you assist in:

Designing experiments to simulate failures, specifically focusing on:

• Database connection failures to the PostgreSQL database

• API endpoint unavailability for RESTful APIs

• High CPU usage on application serversoccurring in a controlled staging environment that mirrors the production environment.

• Establishing safety measures and rollback procedures to prevent unintended consequences (e.g., data corruption, service outages) during testing. We have a low risk tolerance; any potential data corruption is unacceptable, and service outages should be limited to a maximum of 5 minutes. These measures should include automated checks and manual approval gates.

• Creating a framework for analyzing experiment outcomes and implementing improvements. This framework should integrate with our existing monitoring and alerting tools, specifically Datadog and PagerDuty, to provide real-time insights and notifications.

• Considering compliance requirements, specifically GDPR, when designing and executing experiments to ensure no sensitive data is exposed or compromised. Our main GDPR concern is ensuring that PII (Personally Identifiable Information) stored in the database or passed through APIs is not logged or inadvertently accessed during chaos experiments.

Please provide an in-depth technical manual-style, step-by-step guide, including example configuration instructions and code snippets (Python, Bash, and Kubernetes YAML where applicable), demonstrating successful chaos engineering practices for a web application.