Video Lesson
Prometheus Architecture
Prometheus is a pull-based metrics system. It scrapes HTTP endpoints at regular intervals, stores time-series data, and provides PromQL for querying. Key components: the Prometheus server, exporters (node_exporter, application exporters), Alertmanager, and service discovery.
We will deploy Prometheus using Docker Compose and configure it to scrape a sample application’s metrics endpoint. AI tools can help generate prometheus.yml configurations from natural language descriptions of your infrastructure.
Grafana Dashboard Creation with AI
Grafana transforms raw metrics into visual dashboards. AI-powered features in Grafana Cloud (and tools like ChatGPT) can generate PromQL queries and dashboard JSON from natural language descriptions.
We will build a production-ready dashboard covering: request rate, error rate, latency percentiles (p50, p95, p99), resource utilization, and saturation metrics. AI will help us write the PromQL queries for complex aggregations.
Alert Rules and Routing
Good alerting follows key principles: alert on symptoms (high error rate) not causes (high CPU), include runbook links, route to the right team, and avoid alert fatigue.
AI can analyze historical alert data to suggest threshold values, identify noisy alerts, and even generate runbook drafts. We will create a tiered alerting system: critical (page), warning (ticket), and informational (log).
Hands-On Exercises
- Deploy Prometheus and Grafana locally with Docker Compose — scrape at least 2 targets
- Use AI to generate PromQL queries for: average request latency over 5 minutes, error rate by endpoint, and 95th percentile latency
- Create 3 alert rules with proper severity labels, annotations, and a mock runbook link