The best technical standard ever created came from one of the worst codebases in Unix history.

Syslog. Written by Eric Allman in the early 1980s as part of Sendmail. If you’ve ever been exposed to sendmail.cf and M4 configuration, that name should strike fear into your heart. But Allman got one thing exactly right: he made Syslog simple. So simple it became the de facto standard across Unix-like systems and network equipment for 45 years. RFC 3164 didn’t formalize it until nearly 2003. RFC 5424 wasn’t ratified until 2009.

A metric is not reality. It’s a lossy measurement with assumptions baked in.

I said it. I’ll own it. But when we talk about observability, what really is a metric?

The Observability Vendor Subscription Model

The site is down. Customers are complaining. Your on-call engineer is in hour three of spelunking through dashboards that look like a Jackson Pollock painting. You’re paying six+ figures annually for an observability platform, and the most useful alert so far has been “Something is wrong. Probably.”

The observability vendors charge by the byte. They want you to send everything. The industry tells you to measure everything. So you instrument everything, send it all to your vendor, and wait for clarity.

Instead, you get an Observability bill that’s higher than your AWS or GCP compute costs. And you still can’t answer basic questions: Is my application healthy? Are customers experiencing problems right now? Should I be paging someone?

Even with a top-tier vendor and unlimited budget, more data doesn’t equal more clarity. You’re drowning in metrics, dashboards, and alerts — but you still don’t know what actually matters.

Quick reference for Prometheus Query Language (PromQL) with practical examples for monitoring, alerting, and SLO calculations. Covers essential functions, aggregations, and common patterns for effective Observability.

Every alert should have a Runbook. (Sometimes called Playbook.) A Runbook is a guide for SREs, DevOps, On-Call engineers, and Software Developers that prescribes potential remediations for specific alerts. The goal is to reduce MTTR and improve incident response with structured troubleshooting, verification steps, and escalation paths for SRE and DevOps teams. A place to build and share knowledge about a potential event.

Traditional infrastructure alerts page you when CPU hits 80%, but your users are fine. Meanwhile, degraded API performance goes unnoticed because no arbitrary threshold was crossed. An SLO (Service Level Objective) changes this - it’s a target reliability goal that measures what users actually experience, like “99.9% of requests succeed over 30 days.” Born from Google’s Site Reliability Engineering (SRE) practices, SLO-based alerting only pages when user experience is genuinely at risk, eliminating alert fatigue while catching real issues early.

Traditional threshold alerts fire on every spike, creating alert fatigue. Burn rate alerting is different - it tracks how quickly you’re consuming your error budget and only alerts when errors are sustained enough to threaten your reliability target. This gives you early warnings before user experience degrades, while dramatically reducing noise.

Calculating error budget over 30 days should be simple, but naive Prometheus queries time out on high-cardinality metrics. This tool uses a Riemann Sum-inspired technique that pre-computes error ratios at 5-minute intervals, turning an expensive range query into a single fast aggregation. The result: accurate error budget calculations that scale.