Prometheus: Complete Definition and Guide

What is Prometheus?

Prometheus is an open-source monitoring and alerting system, originally developed at SoundCloud in 2012, which later became the second graduated project of the Cloud Native Computing Foundation (CNCF) after Kubernetes. It is designed to collect timestamped numerical metrics — CPU usage, memory, request latency, error counts — from systems and applications, store them in an optimized time-series database, and enable querying via PromQL, a dedicated query language.

Unlike traditional monitoring solutions that wait for monitored systems to push their metrics (push model), Prometheus uses a scraping model (pull model): it actively queries the HTTP endpoints of each target at regular intervals to retrieve exposed metrics. This architectural choice simplifies configuration, facilitates connectivity problem detection, and makes monitoring independent of the lifecycle of monitored applications.

Prometheus stores data in a local time-series database optimized for sequential writes and queries over time windows. Each metric is identified by a name and a set of labels (key-value pairs), creating an extremely flexible multidimensional data model. For example, http_requests_total{method="POST", handler="/api/orders", status="500"} precisely captures POST error requests on the orders endpoint.

Why Prometheus Matters

Monitoring is a pillar of any reliable production infrastructure. Prometheus has established itself as the reference solution for several fundamental reasons.

• Multidimensional data model: labels allow slicing metrics along any dimension (server, service, version, region) without having to predefine aggregations. A single metric can be analyzed from dozens of different angles.
• PromQL: the native query language is powerful and expressive. It enables complex calculations — rate of change, percentiles, trend predictions — that would be impossible or tedious with other monitoring systems.
• Integrated alerting: Alertmanager, the alerting component of Prometheus, handles grouping, deduplication, and routing of alerts to Slack, email, PagerDuty, or other channels, with silencing and inhibition mechanisms.
• Exporter ecosystem: hundreds of official and community exporters expose metrics from third-party systems — Node Exporter (Linux), PostgreSQL Exporter, Redis Exporter, Docker/cAdvisor — without modifying those systems.
• Cloud-native: Prometheus integrates natively with Kubernetes for automatic service discovery, pod and node monitoring, and containerized workload supervision.

How It Works

The Prometheus architecture relies on several components. The central Prometheus server scrapes metrics from configured targets via HTTP, stores them locally, and evaluates alerting rules. Alertmanager receives triggered alerts, groups them, and notifies via configured channels. Exporters expose metrics from systems that do not natively expose them (databases, web servers, operating systems).

Instrumented applications expose their metrics via an HTTP /metrics endpoint in Prometheus text format. For example, a Django application instrumented with the django-prometheus library automatically exposes request counts, latency per view, SQL queries, and migration status. Prometheus scrapes these endpoints at configurable intervals (typically 15 to 30 seconds).

PromQL queries these metrics with a syntax combining selection, aggregation, and functions. For example, rate(http_requests_total{status=~"5.."}[5m]) calculates the rate of 5xx error requests over the last 5 minutes. Results can be visualized directly in the Prometheus web interface or, more commonly, in Grafana dashboards that connect to Prometheus as a data source.

For long-term storage, Prometheus supports remote write to systems like Thanos, Cortex, or TimescaleDB (via Promscale), which aggregate and compress historical data beyond local retention.

Concrete Example

At KERN-IT, we build supervision platforms for our clients that require complete visibility into infrastructure and application status. Prometheus naturally integrates into our monitoring stack. For a network monitoring platform like the one developed for Venn Telecom, Prometheus collects SD-WAN equipment performance metrics — latency, packet loss, bandwidth — via SNMP exporters. Alertmanager triggers real-time notifications when a critical threshold is exceeded.

In our Docker deployments, Node Exporter and cAdvisor expose system metrics (CPU, memory, disk) and container metrics. Prometheus aggregates them and Grafana displays them in interactive dashboards that give operations teams a real-time overview. PromQL alerting rules automatically detect anomalies — a 5xx error rate exceeding 1%, memory usage above 90%, API latency crossing 500ms — before users are impacted.

Implementation

1. Installation: deploy Prometheus via Docker, Helm chart for Kubernetes, or native binary. Start with a single instance covering the entire development infrastructure.
2. Target configuration: define scrape targets in prometheus.yml. Use automatic discovery (service discovery) for dynamic environments like Kubernetes or Docker Swarm.
3. Application instrumentation: add the Prometheus client library to your applications (django-prometheus for Django, prometheus_client for pure Python) to expose custom business metrics.
4. Exporters: deploy necessary exporters — Node Exporter for system metrics, PostgreSQL Exporter for the database, Redis Exporter for the cache.
5. Alerting rules: define PromQL rules in configuration files that trigger alerts when conditions are met. Configure Alertmanager for notification routing.
6. Visualization: connect Grafana to Prometheus as a data source and create dashboards tailored to your supervision needs.

Related Technologies and Tools

• Grafana: visualization platform that integrates natively with Prometheus for creating rich, interactive monitoring dashboards.
• Docker: containerization facilitating the deployment of Prometheus, exporters, and Alertmanager in a reproducible environment.
• Kubernetes: container orchestrator with which Prometheus integrates natively for monitoring cloud-native workloads.
• TimescaleDB: time-series database that can serve as long-term storage for Prometheus metrics via Promscale.
• Alertmanager: Prometheus component dedicated to alert routing, grouping, and notification.
• Node Exporter: Linux system metrics exporter (CPU, memory, disk, network) for Prometheus.

Conclusion

Prometheus has become the de facto standard for infrastructure and application monitoring in the cloud-native ecosystem. Its multidimensional data model, PromQL query language, and Grafana integration provide unprecedented visibility into system status and performance. At KERN-IT, we integrate Prometheus into our supervision architectures to provide our Belgian clients with proactive monitoring and intelligent alerts, whether supervising IoT platforms, Django applications, or network infrastructure. Combined with Grafana, Prometheus transforms raw data into actionable insights.