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Advances in large-scale, distributed software systems are changing the game for software engineering. As an industry, we are quick to adopt practices that increase flexibility of development and velocity of deployment.
An urgent question follows on the heels of these benefits: How much confidence we can have in the complex systems that we put into production? Even when all of the individual services in a distributed system are functioning properly, the interactions between those services can cause unpredictable outcomes.
Unpredictable outcomes, compounded by rare but disruptive real-world events that affect production environments, make these distributed systems inherently chaotic. We need to identify weaknesses before they manifest in system-wide, aberrant behaviors.
Systemic weaknesses could take the form of: We must address the most significant weaknesses proactively, before they affect our customers in production. We need a way to manage the chaos inherent in these systems, take advantage of increasing flexibility and velocity, and have confidence in our production deployments despite the complexity that they represent.
We learn about the behavior of a distributed system by observing it during a controlled experiment. We call this Chaos Engineering.
These experiments follow four steps: Hypothesize that this steady state will continue in both the control group and the experimental group. Introduce variables that reflect real world events like servers that crash, hard drives that malfunction, network connections that are severed, etc.
Try to disprove the hypothesis by looking for a difference in steady state between the control group and the experimental group. The harder it is to disrupt the steady state, the more confidence we have in the behavior of the system. If a weakness is uncovered, we now have a target for improvement before that behavior manifests in the system at large.
The degree to which these principles are pursued strongly correlates to the confidence we can have in a distributed system at scale. Vary Real-world Events Chaos variables reflect real-world events. Prioritize events either by potential impact or estimated frequency. Consider events that correspond to hardware failures like servers dying, software failures like malformed responses, and non-failure events like a spike in traffic or a scaling event.
Any event capable of disrupting steady state is a potential variable in a Chaos experiment.In practice, that means in the present tense (indicative and subjunctive) except in the nous and vous forms; In terms of pronunciation, most speakers nowadays pronounce the conditional and future forms (préférerais etc) as though they were written préfèrerais etc.
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