1. Introduction: “Nothing Failed… But Data Looks Wrong” Overlapping cron jobs rarely cause a loud outage. Instead, you notice slow, expensive symptoms: This is the real pain: the system keeps running, but correctness quietly collapses. When cron schedules overlap, the problem is not “two jobs ran.”The problem is that the system has no hard guarantees…
1. Introduction: “We Have Rate Limits, So Why Is the System Still Melting?” On paper, everything looks safe. Each tenant has a rate limit.The API gateway enforces it.Requests above the limit are rejected early. Yet in production, you still see: This usually leads to confusion:“If the gateway is enforcing tenant limits, how can a single…
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….
