Tools: Why your system can be 100% up and still completely broken

The illusion of “up” ## The real failure class: state drift ## Why uptime is the wrong mental model ## What you should actually measure ## 1. Correctness SLIs ## 2. End-to-end invariants ## 3. User-journey success rate ## 4. Lag and staleness metrics ## The mindset shift ## The bottom line Your monitoring is green. 99.99% uptime. Health checks passing. No alerts. Then support starts forwarding screenshots from users: “I paid, but my order says cancelled.” “The price changed after checkout.” “It said in stock. Then refund.” Welcome to a harsh truth engineers eventually learn: Uptime measures server liveness. Users care about state correctness. And those are very different things. Most systems monitor process health: But a distributed system can respond perfectly while being completely wrong. The system is alive. The truth inside it is dead. Most “it's up but broken” incidents are not crashes. They’re state divergence problems. Systems look healthy because: Your monitoring says “system operational”. Reality says “state is no longer trustworthy.” That’s not downtime. That’s silent correctness failure — much worse. “Is the machine alive?” “Did the system do the correct thing?” Those are different layers: Most outages today are not infrastructure failures. They are correctness failures in distributed state. Not just response success — result validity. If the side-effect didn’t happen, the request was a failure — even if it returned 200. Every system has truths that must always hold: These invariants breaking is worse than downtime. Not “endpoint success”. Journey success: If this drops from 98% to 85%, you're broken. Even if uptime is 100%. Distributed systems rot from delay: Lag is future inconsistency waiting to explode. “Is the system still telling the truth?” Because modern outages look like this: The worst failures are quiet. A system that responds but lies is worse than a system that’s down. Downtime is visible. Incorrect state is invisible — until money, trust, or data integrity is gone. Uptime is liveness. Users care about correctness. Those are not the same metric. What’s the worst “everything green, everything wrong” incident you’ve seen? Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: HTTP 200 OK Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: HTTP 200 OK CODE_BLOCK: HTTP 200 OK CODE_BLOCK: Login → Browse → Add to cart → Pay → Order confirmed Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Login → Browse → Add to cart → Pay → Order confirmed CODE_BLOCK: Login → Browse → Add to cart → Pay → Order confirmed - API returns 200 with stale data - Writes succeed but never reach downstream systems - Auth works, but data permissions are wrong - Checkout returns success, but payment never captured - Stock shows available, but orders already consumed it elsewhere - DB reachable - Services responding - caches out of sync - queues lagging - partial writes - retries overwriting newer state - external APIs delayed - eventual consistency biting you - Did the order actually get created? - Did payment get captured? - Did inventory decrement once, not twice? - Is user data consistent across services? - Stock never negative - Order cannot be paid twice - One user = one identity - Total debits = total credits - queue depth - replication lag - sync delay between services - data mismatches - user confusion - support chaos