Chaos Engineering
summary
Defines Chaos Engineering as live experiments that inject failures into production to learn real failure modes, names Netflix tools Chaos Monkey and Chaos Kong and the whole Simian Army, and shows concrete practices: shut down individual VMs, inject latency, automate recurring experiments, run game days, and escalate scope while you minimize blast radius. Prerequisites are fixing known faults and having detailed monitoring; adoption follows a maturity model from isolated pilots to org-wide automated testing.
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chapters (27)
- 0:00Intro
- 0:34Chaos Engineering: Definition!
- 2:40Testing in your Environment
- 3:26The world is naturally chaotic!
- 4:10Environment matters
- 5:388 Fallacies of distributed computing
- 6:14Cascading Failures
- 7:15AWS Outage: Feb 28, 2017
- 8:24Chaos Engineering is ...
- 10:15But How is it Different?
- 11:49Common Misconception
- 12:34History of Chaos Engineering
- 15:33Prerequisites for Chaos Engineering
- 16:56Principles of Chaos Engineering
- 20:21Designing Experiments
- 21:55The Experimentation Cycle
- 22:56Chaos Monkey
- 23:31Examples of Experiments
- 24:38Simian Army
- 25:08Adoption in Organization
- 27:47Game Days
- 29:22Chaos Maturity Model (CMM)
- 29:49CMM: Sophistication
- 32:05CMM: Adoption
- 33:28Organizations that use it
- 33:59Resources
- 34:52Conclusion
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