Chaos Engineering
Deliberately break things in production to find weaknesses before real failures do.
Chaos engineering deliberately introduces failures into a production system : kill a server, slow a database, corrupt a network packet, simulate a cloud region going down : then watches what happens. The philosophy: if your system can’t survive a random server dying on a Tuesday afternoon, it definitely can’t survive a real outage at 3 AM on Black Friday. Netflix pioneered this with Chaos Monkey, which randomly terminates production instances during business hours. Every chaos experiment follows a scientific method: define steady state, form a hypothesis, inject failure, observe divergence, learn and harden.
How It Works
Define steady state (latency < 200ms, error rate < 0.1%) → hypothesize behavior under failure → inject fault → compare to steady state → fix weaknesses. Start small: one instance, then a whole zone. Kill switches required.
Example
The LinkedIn engine’s 10-run progression is effectively chaos engineering performed by production itself : each run surfaced failures that deliberate fault injection would have caught earlier: scroll-loading, shadow DOM, hydration timing. The progressive deployment philosophy of “seek failures rather than wait for them” mirrors chaos engineering. Project context at Jobs Apply.