A single failure in a distributed system can trigger dozens of alerts across every layer it touches. Correlation groups the symptoms back into one cause — so the on-call engineer sees a problem, not a storm.

Availability and latency are the obvious SLIs. But they don't fit every service type. This guide provides SLI selection frameworks for APIs, data pipelines, batch jobs, storage systems, event-driven services, and more.

A page is just the starting gun. What happens between the alert firing and the postmortem closing determines whether your team gets better or just gets tired.

An SLI is a quantitative measure of service behaviour from the user's perspective. Choose the metric that, when it degrades, users notice — not server-side proxies that feel measurable but don't map to user experience.

A runbook that's hard to follow under pressure isn't a runbook. It's a liability. Here's the anatomy of one that actually shortens incident response, and how to keep it true.

The P0-P4 framework was built for a world of static thresholds. Here's how to reconnect it to SLO burn rates — so severity reflects actual user impact, not arbitrary lines.

The alert fires. The on-call is up. Now what? If the answer is 'check the dashboard', the alert isn't finished. The alert body is where the fix starts, or where you lose an hour chasing context.

Service Level Objectives and error budgets give reliability a quantitative shape — a target, a budget for deviation, and burn rate signals that tell you when to stop shipping and start fixing.

Synthetic monitoring runs scripted tests on a schedule. RUM captures what real users actually experience. They answer different questions, and you need both.

A synchronous log write makes the request thread wait on disk or network I/O. Async logging hands that work to a background thread, and quietly adds a queue that can fill up, drop records and lose them at shutdown. How to size it, watch it and flush it, with Serilog, the OpenTelemetry SDK and Python.

At hundreds of thousands of requests per second, the logging call itself becomes the bottleneck. Async channels, pooling, batching, and circuit breakers keep log I/O off the request thread.