Monitoring Kubernetes with Prometheus
summary
Prometheus-on-Kubernetes setup is described end-to-end: use kube-state-metrics, node-exporter and cAdvisor plus Grafana for dashboards, or deploy via Prometheus Operator or Ksonnet Mixin. Examples show concrete PromQL (rate(), aggregation, label-joins) to compute percent-error by path, CPU-by-service, and alerts for pod restarts, missing replicas, and cluster overcommit that can detect certificate failures. Cortex is mentioned as a horizontally scalable Prometheus backend. Live queries and recording rules are used in alerts and dashboards that are already production-proven.
AI-written summary · about
chapters (20)
- 0:00Introduction
- 1:15What is Prometheus
- 4:18What Prometheus looks like
- 9:15Metrics format
- 9:48Prometheus data model
- 10:26Timeseries
- 12:00Everything to Counter
- 13:04Time Series Window
- 13:47Error Rates
- 14:25Subdivide by Path
- 15:31What is Kubernetes
- 19:11Kubernetes metrics
- 21:09Overview
- 21:33Read Method
- 23:17Use Method
- 23:58CPU Usage
- 24:50Container Usage
- 29:57Prometheus Metrics
- 34:07Cortex
- 34:39Resources
related talks
19mKeynote: Monitoring Kubernetes Clusters with Prometheus
24mPractical Kubernetes Monitoring with Prometheus
32mDemocratizing Application Monitoring with Prometheus
02h 43mPrometheus from A to Z
26mEnd-to-end monitoring with the Prometheus Operator
14mFrom Metrics to Mastery: Monitoring vital signs of Kubernetes
08mPrometheus in 5 minutes
31mEvolution of Metric Monitoring and Alerting: Upgrade Your Prometheus Today