Prometheus — Open-Source Monitoring for Cloud-Native Systems
Prometheus has become the default choice for monitoring in Kubernetes and container-heavy environments. It started as a side project at SoundCloud, grew quickly, and now lives under the CNCF umbrella. The idea is straightforward: Prometheus doesn’t wait for agents to push data; it goes out and collects it. This pull model keeps things simple when dozens of services appear and disappear every minute.
Why It Matters
In big clusters, metrics are the first thing admins reach for. But old push-based systems often collapse under churn — targets change too fast. Prometheus avoids that. It scrapes endpoints exposed by apps or exporters, stores everything in its own time-series engine, and lets teams query it with PromQL. That’s why so many DevOps teams stick with it: flexible, fast, and tuned for cloud-native life.
How It Works
– Runs as a single server with a built-in database.
– Every few seconds, it calls endpoints like /metrics on apps or exporters.
– Exporters exist for almost anything: Linux nodes, MySQL, blackbox probes, message queues.
– Rules can trigger alerts, which Prometheus sends to Alertmanager for routing.
– Visuals are minimal out of the box, but Grafana usually takes over for dashboards.
Deployment / Installation Guide
– Distributed as a single binary — drop it on Linux and configure with YAML.
– In Kubernetes, the common pattern is running it as a StatefulSet, usually installed by Helm.
– By default, metrics stay local; retention is days or weeks. For long-term storage, remote-write extensions ship data to systems like Thanos, Cortex, or VictoriaMetrics.
Integrations
– Grafana for visualization.
– Alertmanager for notifications.
– Service discovery hooks into Kubernetes, Consul, cloud APIs.
– Exporters cover hardware, databases, web servers, and more.
Real-World Applications
– Tracking container health in Kubernetes with automatic service discovery.
– Watching Linux hosts with node_exporter.
– Using blackbox_exporter for HTTP and ICMP checks.
– Feeding SLO dashboards where teams mix infra metrics with app-level numbers.
Limitations
– Prometheus server is single-node; scaling means federation or external storage.
– High-cardinality metrics can burn CPU and disk quickly.
– No native multi-tenancy or RBAC — left to external layers.
– Only metrics: logs and traces require other tools.
Snapshot Comparison
| Tool | Role | Strengths | Best Fit |
| Prometheus | Metrics DB | Pull model, cloud-native | Kubernetes and dynamic infra |
| Zabbix | NMS + metrics | Auto-discovery, SNMP support | Enterprises with mixed setups |
| VictoriaMetrics | Time-series DB | Efficient long-term storage | Teams needing scalable retention |
| Nagios Core | Monitoring engine | Plugins, simple checks | Legacy systems, static infra |