“Those who ignore the lessons of failure are doomed to repeat them.”
Chaos Engineering is one of the most powerful ways to build resilience in modern systems. By deliberately injecting controlled failures, we learn how systems behave under stress and build the confidence to handle real-world outages. But like any powerful tool, it comes with responsibilities — and mistakes can lead to disaster.
On Day 4 of our 10 Days of Christmas Chaos, we’re taking a look at the “7 Deadly Sins of Chaos Engineering” — the most common mistakes that can turn a well-intentioned chaos experiment into a full-blown outage.
If you’ve ever run a chaos experiment that didn’t go as planned (or hesitated to run one at all), this post is for you. Learn how to avoid these 7 sins, strengthen your chaos practice, and keep your system resilient this holiday season. 🎄
🎁 Sin #1: Running Chaos Without a Steady-State Hypothesis
The Mistake: Running chaos experiments just to “see what happens” without defining a hypothesis in advance.
Why It’s Dangerous: Without a hypothesis, you’re not “testing” anything — you’re just breaking things. If you don’t know what you expect the system to do, how can you tell if it’s behaving incorrectly?
How to Avoid It:
- Define your steady-state: Identify what “normal operation” looks like for key metrics (latency, error rates, throughput, etc.).
- Write a hypothesis: Example: “If service A is unavailable, service B should continue processing requests via failover route C.”
- Focus on measurable outcomes: Set clear, observable metrics to track.
🎉 Pro Tip: Treat chaos experiments like scientific experiments — they should have a clear hypothesis, a controlled environment, and measurable results.
🎁 Sin #2: Running Chaos During Peak Load Times
The Mistake: Running chaos experiments during periods of high traffic, like Black Friday or major product launches.
Why It’s Dangerous: During peak load, your system is already under pressure. Introducing chaos at this time is like testing a lifeboat while the ship is sinking. You risk outages, slowdowns, and frustrated users.
How to Avoid It:
- Avoid peak periods: Schedule chaos experiments during low-traffic windows. Use load forecasting tools to identify off-peak hours.
- Run experiments in staging first: Validate chaos experiments in a safe, non-production environment.
- Game Days: Schedule dedicated “Game Day” events to simulate chaos when traffic is low, and engineers are available.
🎉 Pro Tip: Use calendar scheduling tools to plan chaos experiments when usage is at its lowest. Avoid running them during high-stakes events like holiday sales or major releases.
🎁 Sin #3: Ignoring Blast Radius Controls
The Mistake: Running chaos experiments with no limits on how much of the system can be affected.
Why It’s Dangerous: Chaos without a blast radius is just… chaos. Without blast radius controls, an experiment intended to impact one service could cascade across the system, causing a major outage.
How to Avoid It:
- Start small: Limit initial experiments to specific nodes, pods, or containers.
- Use blast radius controls: Tools like Gremlin and Azure Chaos Studio have built-in blast radius options.
- Incremental chaos: Start with small, targeted experiments before scaling up.
🎉 Pro Tip: Adopt a “one-container-first” strategy. Before running an experiment on a service, target a single container to observe behavior.
🎁 Sin #4: No Post-Experiment Review
The Mistake: Running a chaos experiment, reviewing the dashboards, and… that’s it. No lessons learned, no retrospective, no action items.
Why It’s Dangerous: If you’re not documenting what happened, what went wrong, and how to improve, you’re missing the entire point of Chaos Engineering.
How to Avoid It:
- Run postmortems: Treat chaos experiments like real incidents. Review what happened, what was expected, and what to improve.
- Document lessons learned: Use a “chaos playbook” to store findings from each experiment.
- Track action items: Assign tasks to fix issues found during the experiment.
🎉 Pro Tip: Turn every chaos experiment into a “blameless learning session” where teams review what they’ve learned.
🎁 Sin #5: Not Alerting Teams in Advance
The Mistake: Running chaos experiments without telling on-call engineers or stakeholders.
Why It’s Dangerous: Surprise chaos experiments can trigger “false incidents,” forcing teams to drop everything to investigate. This damages trust.
How to Avoid It:
- Notify in advance: Send alerts to on-call engineers, stakeholders, and product teams.
- Use a chaos calendar: Schedule experiments and notify relevant teams 24 hours in advance.
- Avoid the surprise factor: Don’t make chaos “surprise drills.” Announce them like Game Days.
🎉 Pro Tip: Schedule chaos experiments like any other deployment. Use Slack announcements and incident tracking tools like PagerDuty to notify teams.
🎁 Sin #6: Focusing Only on Production
The Mistake: Only running chaos experiments in production environments.
Why It’s Dangerous: While production is the “real world,” it’s also the most risky place to start. Without prior testing, your production chaos experiment could cause a real outage.
How to Avoid It:
- Run in staging first: Start in non-production environments to validate your chaos logic.
- Move from staging to production: Once tests pass in staging, move to production.
- Use feature flags: Control which chaos experiments run in production vs. staging.
🎉 Pro Tip: Use “staging” as a “training ground” for engineers to practice incident response before running chaos in production.
🎁 Sin #7: Not Integrating Chaos with Observability Tools
The Mistake: Running chaos experiments without connecting them to observability tools like Datadog, New Relic, or Prometheus.
Why It’s Dangerous: If you can’t observe it, you can’t improve it. Observability tools help track impact, validate hypotheses, and reveal cascading failures.
How to Avoid It:
- Instrument key metrics: Latency, request errors, and resource utilization should be tracked in real-time.
- Use annotations: Tag dashboards with “chaos experiment” markers so teams can see what’s happening.
- Correlate data with chaos: Track which failures impacted system behavior.
🎉 Pro Tip: Use “event annotations” in your dashboards to mark when chaos events begin and end.
🎉 Which Sins Have You Committed?
Nobody’s perfect, especially when it comes to Chaos Engineering. Have you committed one of these “7 Deadly Sins”? Drop your story in the comments!
Did you run chaos at peak traffic? Forget to notify the on-call team? Or maybe you ran an experiment without defining a hypothesis. We’d love to hear how you fixed it.
The best stories will be featured in a future post! 🎉
Closing Thoughts
Chaos Engineering is about learning, not perfection. By avoiding these 7 deadly sins, you’ll build stronger systems, more confident engineers, and faster incident response times.
This holiday season, take the time to reflect on your chaos practices. Are you following best practices, or are you letting chaos take control? 🎄
