HomeOperationsChaos engineering: A step-by-step guide

Chaos engineering: A step-by-step guide

Chaos engineering is a discipline that helps to improve system resilience through controlled experiments that simulate real-world failures. By intentionally causing disruptions, chaos engineering allows organizations to identify weaknesses in their systems and address them before they lead to actual outages. 

Learn more about how to design and conduct chaos experiments effectively.

The how and the why

In 2011, a crucial database corruption issue triggered Netflix’s operational resilience plan.

To fortify its infrastructure, Netflix migrated to the AWS cloud eight years ago, aiming for scalability and efficiency. Despite this move, a disruptive 2015 AWS outage left Netflix offline for an extended period.

In response, Netflix conceptualized Chaos Engineering to stress-test and fortify systems against unforeseen disruptions. This led to the development of “Chaos Monkey,” which helped identify and address weaknesses in their infrastructure design, ultimately building a more fault-tolerant system.

The what

Chaos engineering might sound complex, but it’s about making tech systems stronger by intentionally causing chaos. Imagine it like testing a car by driving it on bumpy roads to see if anything breaks before you hit the highway. That’s what chaos engineering does for tech.

Chaos engineering is about finding weaknesses in tech systems before they cause significant problems. Think of it as a safety net for your favorite apps and websites. By intentionally shaking things up, tech companies can spot trouble spots and fix them before they become major headaches for users.

Even big companies like Salesforce, known for their customer relationship management platform, use chaos engineering to keep their systems running smoothly. They intentionally mess with their systems, like slowing down how different parts talk to each other or pretending a crucial piece isn’t working.

By causing controlled chaos, Salesforce and other companies like Netflix find and fix problems before they affect customers. This proactive approach helps keep tech systems strong and reliable so you can trust them even when things get hectic.

Chaos engineering vs testing

When we create an application, we test it in different ways, like Unit Tests, Integration Tests, and System Tests.

Unit testing checks how individual parts of the application work independently without relying on anything else. Integration testing checks how these parts work together.

But even with all these tests, we can’t be sure our system is completely error-free. These tests only cover specific scenarios we already know about. They don’t tell us what might happen in new situations or how our system performs in the real world. This uncertainty becomes even bigger when we use microservices, where the system gets more complicated over time.

Chaos engineering is different. Instead of following specific scenarios, it creates unexpected problems in our system to see how it reacts. This helps us understand how our system handles difficult situations and what problems might arise. Chaos testing is a way to find and fix issues before they cause major problems for our application and business.

A step-by-step guide to designing and conducting chaos experiments

Below, we’ll walk through a step-by-step guide on how to design and conduct chaos experiments effectively:

1. Define your objectives

Before diving into chaos experiments, clarifying what you aim to achieve is essential. Determine the specific aspects of your system you want to test, such as resilience to network failures, database outages, or high traffic loads.

2. Identify hypotheses

Formulate hypotheses based on your objectives. These hypotheses should articulate assumptions about how your system behaves under stress. For example, you might hypothesize that your system gracefully degrades when a service is unavailable.

3. Select target systems

Choose the components or services within your system that you’ll subject to chaos experiments. Start with non-critical systems or environments to minimize potential impacts on production.

4. Design experiments

Design experiments that align with your hypotheses. Decide on the variables you’ll manipulate, such as introducing latency, simulating network partitions, or shutting down services. Ensure that your experiments are safe, controlled, and reversible.

5. Establish baselines

Before doing chaos experiments, set baseline metrics to see how your system works. Measure key performance indicators (KPIs) like response times, error rates, and throughput under normal conditions.

6. Implement safeguards

Implement safeguards to mitigate risks associated with chaos experiments. Establish surveillance and alert mechanisms to detect anomalies during experiments. Create ways to return the system to its original state if needed quickly.

7. Execute experiments

Execute the chaos experiments according to your predefined plan. Monitor the system closely throughout the experiments to observe how it responds to the injected faults. Document any unexpected behaviors or failures for analysis.

8. Analyze results

After conducting chaos experiments, it’s crucial to thoroughly analyze the outcomes to validate or refute the hypotheses formulated beforehand. This analysis involves comparing the observed outcomes with the expected behaviors outlined in the hypotheses, pinpointing weaknesses, and identifying areas for improvement in the system’s resilience.

9. Iterate and refine

Utilize the knowledge acquired from chaos experiments to enhance your system’s design and architecture iteratively. Introduce adjustments aimed at rectifying vulnerabilities and elevating resilience levels. Continuously cycle through the stages of designing, executing, and evaluating chaos experiments to uphold a steadfast system.

10. Document learnings

Document the learnings from each chaos experiment, including both successes and failures. Share what you learned with your team and organization to help them be more resilient and keep getting better.

Conclusion

In conclusion, chaos engineering offers a proactive approach to building resilient systems in an unpredictable technological landscape. By systematically designing and conducting chaos experiments, engineering teams can uncover weaknesses, validate assumptions, and fortify their systems against potential failures.

Through the steps outlined in this guide, from defining objectives to documenting learnings, organizations can cultivate a culture of resilience and continuous improvement. Chaos experiments reveal vulnerabilities and provide valuable insights that drive iterative refinement and innovation.

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