What are the common mistakes in chaos engineering?
An example of an AI chatbot conversation about common mistakes in chaos engineering.
What are the common mistakes in chaos engineering?
Some common mistakes include not defining clear objectives, running tests in production without proper safeguards, and failing to analyze results.
Can you elaborate on why not defining clear objectives is a mistake?
Not defining clear objectives can lead to ambiguous results, making it difficult to determine if the chaos experiment was successful or if it provided valuable insights.
What safeguards should be in place when running tests in production?
Safeguards include using feature flags, limiting the blast radius, and ensuring proper monitoring and alerting systems are in place.
How can I effectively analyze the results of a chaos engineering experiment?
Effective analysis involves comparing system performance before and after the experiment, reviewing logs, and gathering feedback from the team.
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