slo

The loop is easy; the statistics are not. This question finds engineers who can size a traffic slice against sample noise and name the false rollback that kills trust in automation.

The arithmetic takes ten seconds and most candidates stall on it. The stall is the signal: it means error budgets are something you have read about rather than used.

Alert fatigue is a design failure, not a discipline failure. The fix is a rule about what may page at all, and burn-rate alerting is how you implement it.

Anyone can divide bad requests by total. This answer shows the aggregation mistakes that quietly corrupt SLO dashboards, the rate-versus-increase traps that make budgets lie, and when subqueries cost too much.

Spending the budget is arithmetic; enforcing consequences is politics plus automation. The answer that lands names trigger thresholds, the deploy gate that blocks releases, and the dated escape valve.

Request-shaped SLIs quietly fail on data platforms. The strong answer defines freshness, completeness and deadline SLIs on consumer-visible artifacts, not job success rates.

You have added a dependency with a latency distribution an order of magnitude wider than anything else in your stack, whose failure mode is a plausible wrong answer rather than an error.

There is no status code and no caller waiting, so latency and error rate stop meaning what they meant. The signals that replace them are age, lag and completion, and they answer a question the request-response set never had to ask.

Scale to zero is correct for some workloads and an outage generator for others, and the deciding number is what the first request after idle experiences. Cold start turns an availability promise into a queue.

A request that streams for ninety seconds is not slow, it is long. Total latency mixes the part users wait for with the part they read, and an SLO built on it will page you for success.