20 - CHAOS ENGINEERING questions and answers with solutions 2025
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Module
Chaos
Institution
Chaos
Distributed systems are challenging & pervasive - ANSWER - multiple participants
- unreliable communication channels
- may be allowed to crash
- may need to tolerate malicious participants
- must eventually agree an some set of decisions
In general
- failure is always an option
- ordering ...
20 - CHAOS ENGINEERING questions
and answers with solutions 2025
Distributed systems are challenging & pervasive - ANSWER - multiple participants
- unreliable communication channels
- may be allowed to crash
- may need to tolerate malicious participants
- must eventually agree an some set of decisions
In general
- failure is always an option
- ordering is hard
- agreement is hard
- the software and what you think it does may differ
chaos engineering - ANSWER experimenting on a system to build confidence in the system's capability to
withstand turbulent conditions in production
- about finding the latent chaos in the system
*goal: manage chaos
- can think about chaos engineering as A/B testing for distributed systems where tests focus on
pathologies of system reliability
instead of looking for improvements, you look for degradation - ANSWER
The 8 fallacies of distributed computing - ANSWER - network is reliable
- latency is 0
- bandwidth is infinity
, - network is secure
- topology doesn't change
- one administrator
- transport cost is 0
- network is homogeneous
originally, experiments targeted these, but others are inspired by fault injection, race conditions, ...
How failure is handled varies depending on a system (5) - ANSWER - logging + continuing
- rerouting
- approximation + quality of service degradation
- error reporting
- terminal failure
(poorly tested + understood in distributed systems)
What impact might fallback strategies have on business performance? - ANSWER - companies don't
make money that day
- customers dislike service --> stop using it
Four common steps for a chaos experiment - ANSWER - measure + define the baseline behaviour of the
system
- hypothesize that the baseline should continue under stress
- simulate pathological behaviours on the deployed system
- try to disprove the hypothesis (show that there is a difference)
* the harder it is to show a difference, the more confidence you have in the robustness of your system
Just as in sequential hypothesis testing, you might want an "early out"
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