Principles of Chaos Engineering

Author(s): Peace Aisosa

Originally published on Towards AI.

The cost of system failures can be astronomical, not just in monetary terms but also in brand reputation and customer trust. As systems grow more complex, ensuring their reliability becomes critical. Chaos Engineering, which began with Netflix’s “Chaos Monkey” randomly disrupting services, offers a solution. This proactive method intentionally introduces system failures to uncover vulnerabilities. In this article, we’ll delve into its core principles and their importance for modern businesses.

Why Chaos Engineering?

Modern software systems have, over time, evolved to such complexity that the traditional means of ensuring reliability no longer suffice. While meticulous design, rigorous testing, and vigilant monitoring play pivotal roles, they alone cannot guarantee a fault-free experience in production. This realization brings us to the quintessential question: Why do we need Chaos Engineering?

• The complexity of Modern Systems: As applications shift from monolithic structures to microservices architectures, often managed by platforms like Kubernetes, the resulting system becomes a web of interdependent services. Each service, whether it stores data or processes it, communicates through various methods such as API calls or message queues. This setup, while offering development flexibility, also introduces the risk of chain-reaction failures. Chaos engineering proactively tests these connections, ensuring that if one part fails, it doesn’t lead to a system-wide collapse.
• The unpredictability of Distributed Systems: Systems distributed across various data centers or hybrid-cloud environments face inherent challenges. Factors like network interruptions or differing data update speeds can cause hiccups. Traditional quality assurance might catch standard issues, but chaos engineering goes a step further. It tests for scenarios unique to distributed environments, ensuring, for instance, that a delay in one region doesn’t cripple the entire system.
• Cost of System Failures: Beyond the immediate financial hit, system outages can lead to deployment setbacks and extensive troubleshooting. In a world where we deploy updates frequently, an unnoticed issue can rapidly become a live problem. By incorporating chaos engineering into regular processes, we can catch these potential disruptors early, ensuring not just functionality but also robustness.

Core Principles of Chaos Engineering

The fundamental concepts that underlie chaos engineering are built on a set of principles. These principles guide practitioners in conducting chaos experiments thoughtfully and effectively.

1. Build Hypotheses Around Steady State Behavior

A system’s ‘steady state’ is its standard operating behavior — the norm. It’s imperative to understand this before introducing chaos, as it serves as our baseline. If we don’t know how our system behaves under typical conditions, how can we measure the impact of a simulated failure? By framing our chaos experiments with hypotheses based on this steady state, we can make pointed observations about what changes and what remains resilient.

2. Vary Real-world Events

Real-world systems are subjected to a myriad of unpredictable events. These can range from spikes in traffic to the sudden loss of a database. Begin by listing out possible real-world disruptions your system could encounter. Once identified, simulate them. For instance, if you’re an e-commerce platform, what happens if your payment gateway fails? Intentionally disconnect it and observe.

3. Run Experiments in Production

While staging environments have their merits, the unpredictable nature of production offers the rawest, most genuine insights into how systems behave. This principle often raises eyebrows, but it’s where the true value of chaos engineering shines. Of course, this doesn’t mean we dive in recklessly. Every experiment in production is carried out with meticulous planning and a well-charted rollback plan. This isn’t about being reckless — it’s about being realistic but prepared. Set clear boundaries for your experiments and monitor them in real-time to understand the ripples of your introduced chaos.

4. Automate Experiments to Run Continuously

Systems aren’t static. They evolve, scale, and adapt. To ensure our systems remain resilient amidst this flux, our chaos experiments must be a recurring event. Modern tools, from Gremlin to Chaos Monkey, have made it feasible to automate these experiments. By embedding chaos into the regular cadence of our operations, we ensure that our systems are consistently validated against potential disruptions.

5. Minimize the Blast Radius

But let’s be clear: Chaos Engineering isn’t about wreaking havoc. It’s about controlled disruption. As we begin, our experiments should be small, affecting a limited scope of our user base or infrastructure. This way, we learn, iterate, and scale our experiments up with minimal risk. For a cloud-based application, you could start by shutting down a single instance in a cluster. Observe the impact, then consider simulating a failure of an entire availability zone.

The Importance of Game Days

Game days are planned, controlled simulations or exercises where engineering teams practice their response to various scenarios, especially failure scenarios, to test systems and processes. These exercises are integral to the discipline of chaos engineering and carry several benefits:

• Real-time Response Training: Game Days equip teams to react efficiently and effectively in real-time situations. It’s one thing to know the protocol; it’s another to execute it under pressure.
• Strengthening Inter-team Communication: Often, during outages or incidents, multiple teams must collaborate rapidly. Game Days foster better inter-team communication, highlighting areas for improvement.
• Discovering Unknown Weaknesses: Even with the best chaos engineering practices, some vulnerabilities might be overlooked. Game Days often bring these to light, allowing teams to proactively address them.
• Improving Documentation: Post-game day reviews frequently result in the refinement of documentation, ensuring clarity and ease of access to critical information.

To orchestrate an effective Game Day, the following elements should be in place:

• Setting Clear Objectives: Clearly outline the services, resources, or components you will target. Avoid critical production services initially, especially if you’re new to chaos engineering. Begin with experiments that have minimal potential impact and gradually increase the scope as you gain confidence and experience.
• Implement Monitoring and Observability: Ensure you have real-time monitoring tools in place to detect any anomalies quickly. Visualize key metrics and system health, so that any adverse effects can be observed instantaneously. Set up alerts to notify relevant teams if something goes beyond expected behavior.
• Have a Rollback Plan: Before conducting an experiment, know exactly how to reverse any changes or interventions. This might involve restarting services, rolling back deployments, or rerouting traffic. Ensure that there are backups of critical data and systems, so you can restore to a known good state if necessary.
• Involving All Stakeholders: Before running an experiment, ensure all relevant parties (from engineering teams to customer support) are informed and prepared. This inclusivity not only prepares the entire team but also fosters a culture of collective ownership of system reliability. Foster a culture where everyone is aware of and can contribute to the experiment’s objectives and potential outcomes.
• Automate with Caution: Even if your chaos experiments are automated, ensure there’s always human oversight, especially during initial tests. Implement sanity checks in automated scripts to stop experiments if certain critical thresholds are breached.
• Post-mortem Analysis: After every chaos experiment, conduct a review. Understand what went right, what went wrong, and how the system responded. Use these learnings to refine your future chaos experiments and also to enhance your actual systems based on observed behaviors. This iterative process is crucial for continuous enhancement.

Conclusion

The transformative value of chaos engineering isn’t solely about strengthening systems but also about fostering a culture of continuous learning and adaptability. It galvanizes teams to collaboratively interrogate and enhance system behaviors, ensuring that when real-world disruptions arise, the system’s robustness and the team’s preparedness synergize to minimize adverse impacts.

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Published via Towards AI