Skip to main content
RunBook Academy

ObservabilityXVIII · Alerting RulesAlertingRules

Rule Lint and Review

Intermediate⏱ ~20 minbash

What you'll learn

  • Explain rule lint and review in production terms
  • Configure and operate rule lint and review in a production observability stack
  • Recognise and diagnose the most common failure modes
  • Apply the discipline to a real Prometheus / Grafana / Loki / Tempo environment

Prerequisites

Verified against Prometheus 2.55.x · Alertmanager 0.28.x · node_exporter 1.8.x · blackbox_exporter 0.26.x · Grafana 11.x · Loki 3.x · Tempo current · OpenTelemetry Collector 0.110.x · Grafana Alloy current · Docker Engine 28.x · Ubuntu 24.04 LTS · Debian 12 (Bookworm) · RHEL / Rocky / AlmaLinux 9.x · 2026-08-13

Not yet marked complete on this device.

Rule changes pass through code review. The reviewer checks: does the rule have a runbook? does it have a dashboard? is the threshold derived from the SLO or a documented heuristic? is for: set appropriately?

A team that adopts a checklist for rule review catches bad rules before they become bad alerts.

What it is

A precise definition of rule lint and review, scoped to production operations.

Why a sysadmin cares

Production framing. The operational pain this concept addresses, or the incident class it prevents.

How it works

The mental model.

How to configure it

# Configuration snippet illustrating the lesson topic
example_setting: value

How to validate it

promtool check config /etc/prometheus/prometheus.yml

How it can fail

The high-frequency failure modes:

  1. Silent misconfiguration.
  2. Crash on load.
  3. Performance regression.
  4. Permissions failure.
  5. Schema / version drift.

How to troubleshoot it

The diagnostic order:

  1. Was it working before?
  2. What does the service’s view say?
  3. What does the platform’s view say?
  4. Form hypothesis, find evidence, test, validate.

Security implications

Rule Lint and Review has security implications wherever the relevant component exposes an HTTP endpoint, an authentication layer, or a credential.

Performance implications

Performance implications come from cardinality, scrape / push interval, rule size, retention, and query cost.

Production guidance

  • Validate before applying.
  • Test changes in a non-production environment.
  • Document operational defaults in the team’s instrumentation guide.

Verification

You should now be able to answer:

  • What is rule lint and review in production terms?
  • Why does a sysadmin care about it?
  • How does it fail and how do you diagnose the failure?

Quiz

Knowledge check · 8 questions

  1. Q1. What is the primary purpose of rule lint and review?

  2. Q2. Which failure mode of rule lint and review is most operationally costly?

  3. Q3. Production verification should run on production hosts.

  4. Q4. First response when rule lint and review misbehaves?

  5. Q5. Name one signal that confirms rule lint and review is healthy.

  6. Q6. Which of these are validation steps?

  7. Q7. Right discipline when changing in production?

  8. Q8. Telemetry usefulness requires:

Passing score: 75%. Answers are checked in this browser.