1. Introduction: “It Was Only Meant as a Backup” The fallback path was added in a hurry. It was supposed to: Months later, nothing looks obviously broken.Latency averages look acceptable.Error rates are “within range”. And yet: The uncomfortable truth is this:your “temporary” fallback may now be handling more traffic than the primary route — quietly,…

1. Introduction: “The Payment Succeeded, So Why Can’t We Refund?” The refund ticket looks simple. The user paid.The order shows as completed.A refund request comes in. But the system says: From the outside, it feels random.From the inside, it’s almost always the same root cause: Your payment service and order service do not mean the…

1. Introduction: The Same Service Fails on the Same Pattern Traffic grows on weekends.Most services stay fine. But one microservice struggles every single time: It’s tempting to conclude: “We need more capacity.” Sometimes you do. But if the same service fails in the same way every weekend, the bigger question is this:Is it truly overall…

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…