1. Introduction: “We Only Changed One Parameter” The A/B test looked harmless. You changed one API parameter.No new endpoints.No schema changes.No traffic increase. Yet after rollout: This feels irrational. How can a single parameter cause so much damage? The answer is simple but uncomfortable:you didn’t break the API — you broke downstream assumptions that were…
1. Introduction: When Only One Region Breaks, the Rule Is Telling You Something You deploy a new traffic routing rule.Globally, things look fine. But one region starts to misbehave: Everywhere else stays stable. This is frustrating because the rule is “global,” yet the failure is local. Here is the simple truth.When only one region becomes…
1. Introduction: Nothing Looks Wrong at First Two microservices use the same config field.Everyone assumes it means the same thing. In testing, everything looks fine.In early production, nothing obvious breaks. Then strange things start to happen: This is confusing because nothing changed suddenly. Here is the simple truth.The problem starts immediately, but you only notice…
1. Introduction: Speed Saves Incidents, Then Quietly Breaks Consistency Hotfix culture starts with good intentions. An outage hits, someone pushes a quick patch, the system recovers, and the team moves on. Everyone celebrates speed. Then months later, the system feels “haunted.” Two endpoints behave differently for no clear reason. A retry rule exists in three…
1. Introduction: More Dashboards, Less Understanding As infrastructure grows, dashboards multiply. New services ship with their own metrics. Alerts increase. Logs get longer. Traces get sampled less. On paper, observability improves. In practice, teams often understand the system less than they did before. This is the core problem: scaling infrastructure does not automatically scale observability….
1. Introduction: “We Fixed the Incident” Isn’t the Same as “We Fixed the System” The incident is over. Services are back. Dashboards are green again. A postmortem document is written, action items assigned, and everyone moves on. Then, weeks later, a very similar failure happens again. From the outside, it looks like bad execution. From…
1. Introduction: The System Didn’t Get Worse Overnight Every release feels reasonable on its own. One more feature, one more exception, one more workaround to meet a deadline. Nothing breaks immediately. Metrics stay acceptable. Users don’t complain—yet. Then one day, a small change triggers a disproportionate failure. Something unrelated slows down. A rollback doesn’t fully…
1. Introduction: “Bad Luck” Is Usually a Pattern You Haven’t Measured Yet A workflow fails once, you shrug. It fails twice, you blame the target. It fails in bursts, you blame proxies. And when it keeps happening across different tasks—timeouts here, bans there, random login friction—you start calling it “bad luck.” But the failures aren’t…
1. Introduction: Systems Don’t “Suddenly” Break At 10 tasks, everything feels under control. Dashboards are green, proxy success rates look stable, and automation workflows finish without drama. Then you scale to 100 tasks and the system starts behaving irrationally: latency spikes, retries explode, IP bans cluster, and once-reliable jobs collapse. The uncomfortable question is not…
You promise 24/7 uptime, then reality tests it. A region gets unstable. One carrier has packet loss. A “healthy” pool starts timing out under load. You fail over—and suddenly costs spike because traffic floods premium routes. Next incident, you cut redundancy to save money, and the blast radius gets worse. This is the real pain…
