Getting Started with Chaos Engineering Experiments
Starting your Chaos Engineering journey can seem daunting, but by following a structured approach, you can begin to uncover weaknesses and build resilience in your systems effectively. This guide outlines the initial steps to plan and execute your first chaos experiments.
1. Understand the Fundamentals
Before diving in, ensure you and your team have a solid grasp of What is Chaos Engineering? and its Core Principles. This foundational knowledge is crucial for designing meaningful and safe experiments.
2. Pick a System and Define its Steady State
Start with a non-critical but representative system or service. Identify its key performance indicators (KPIs) and metrics that define its normal, healthy operation (steady state). This could include metrics like:
- Request success rates
- Response latencies (average, P90, P95, P99)
- Resource utilization (CPU, memory, network)
- Queue lengths
- Error counts
Having robust monitoring and observability in place is paramount before you begin. Exploring Explainable AI (XAI) can offer parallels in understanding system behavior through data.
3. Formulate a Hypothesis
Based on the system's steady state, formulate a hypothesis for your first experiment. What do you expect to happen when a specific fault is injected? For example:
"If one instance of our three-instance web server fleet is terminated, the overall P95 response time for user logins will remain below 300ms, and the login success rate will stay above 99.9%."
4. Choose Your First Experiment (Start Small)
Select a simple, common failure mode for your initial experiment. Good starting points include:
- Resource Exhaustion: Simulate high CPU or memory usage on a single host or container.
- Network Latency: Introduce a small amount of latency between two services.
- Instance Termination: Shut down a single, redundant instance of a stateless service.
Refer to our page on Tools and Platforms to select a tool that fits your environment and experiment type.
5. Define the Blast Radius
Critically important: clearly define and limit the scope of your experiment. Who or what could be affected? Start with the smallest possible blast radius. For example, target only internal test accounts, a specific availability zone, or a small percentage of traffic. Ensure you have immediate ways to halt the experiment if things go unexpectedly wrong.
6. Run the Experiment (and Observe!)
Execute the experiment, preferably during a planned GameDay or a low-traffic period initially. Closely monitor your steady-state metrics and system dashboards. Document all observations, both expected and unexpected.
7. Analyze the Results and Learn
Once the experiment is complete (or halted):
- Was your hypothesis confirmed or refuted?
- Did the system behave as expected? Were alerts triggered? Did auto-scaling or failover mechanisms work?
- What weaknesses were uncovered?
- What improvements can be made to the system, monitoring, or incident response procedures?
Document these findings and create action items to address any identified issues. This iterative learning process is at the heart of Chaos Engineering.
8. Iterate and Expand
Once you're comfortable with simple experiments and have made initial improvements, gradually increase the complexity and scope of your chaos tests. Explore different failure modes and target different parts of your system. The goal is to build a continuous practice of Chaos Engineering.
Getting started is about taking that first controlled step. By planning carefully, starting small, and focusing on learning, you can begin to build significant confidence in your system's resilience. Consider looking into AI & Machine Learning Basics as many modern systems incorporate these technologies, which can also be subjects of chaos experiments.
Review Chaos Principles