Core Principles of Chaos Engineering
Chaos Engineering is guided by a set of core principles that ensure experiments are conducted safely, ethically, and effectively. These principles help differentiate Chaos Engineering from simply breaking things in production and are fundamental to building confidence in system resilience.
1. Define a Hypothesis Around Steady State Behavior
Before injecting any chaos, it's crucial to have a clear understanding of your system's normal, healthy behavior – its "steady state." This involves defining measurable metrics that represent system stability (e.g., request latency, error rates, throughput). Your hypothesis will be that the system will maintain this steady state even when subjected to a specific type of failure.
Example: "If we inject 100ms of latency into the authentication service, the overall login success rate will remain above 99.9%, and P99 login latency will not exceed 500ms."
2. Vary Real-World Events
Chaos experiments should reflect realistic failure scenarios. This includes events like server outages, network latency, disk failures, resource exhaustion, and even failures in dependent services. The more closely experiments mimic potential real-world problems, the more valuable the insights gained. Consider exploring Cloud Computing Fundamentals to understand common failure points in cloud environments.
3. Run Experiments in Production (Carefully)
The most accurate way to understand how a system behaves under stress is to experiment in the production environment. Staging or testing environments, while useful, often differ from production in subtle ways (data volume, traffic patterns, configurations). However, production experiments must be approached with extreme caution. Start with a small blast radius and gradually increase scope as confidence grows. If production is too risky initially, start with a pre-production environment that closely mirrors it.
4. Automate Experiments to Run Continuously
Systems are constantly changing due to new deployments, configuration updates, and shifting traffic patterns. A weakness that doesn't exist today might emerge tomorrow. Automating chaos experiments and running them continuously helps ensure ongoing resilience and provides early detection of new vulnerabilities.
5. Minimize the Blast Radius
This is a critical safety principle. Experiments should be designed to minimize potential negative impact on users and the business. Start with experiments that affect a small, contained portion of the system or a limited set of users. Have a clear rollback plan and be prepared to stop the experiment immediately if it causes unintended harm. The goal is to learn, not to cause outages.
By adhering to these principles, teams can confidently and safely explore their system's weaknesses, leading to more robust and resilient applications. This structured approach to identifying and mitigating risks is vital for maintaining service availability and user trust.