Detailed Introduction to Prometheus: Architecture, Quick Deployment, Advantages and Drawbacks

Prometheus, originally developed by SoundCloud and later donated to the CNCF in 2016, has become the de‑facto monitoring solution for Kubernetes and cloud‑native environments.

The system’s architecture consists of a Prometheus Server that pulls metrics from exporters or Pushgateway, stores data in an internal TSDB, evaluates alerting rules, and forwards alerts to Alertmanager, while users can query data via the built‑in Web UI or Grafana.

Quick deployment :

wget https://github.com/prometheus/prometheus/releases/download/v2.16.0/prometheus-2.16.0.darwin-amd64.tar.gz
tar xvzf prometheus-2.16.0.darwin-amd64.tar.gz
cd prometheus-2.16.0.darwin-amd64

Configure Prometheus with a YAML file, for example:

global:
  scrape_interval:     15s
  evaluation_interval: 15s

rule_files:
  # - "first.rules"
  # - "second.rules"

scrape_configs:
  - job_name: prometheus
    static_configs:
      - targets: ['localhost:9090']

Start the server: ./prometheus --config.file=prometheus.yml After launching, the Web UI is accessible at http://localhost:9090 , where users can query metrics, view active alerts, and inspect the current configuration and status.

Advantages include a multi‑dimensional data model, powerful PromQL query language, support for both local and remote storage, flexible static and service‑discovery configurations, seamless integration with Grafana, and tight coupling with Kubernetes.

Drawbacks are that Prometheus only collects individual metric values (no full trace), its local storage is intended for short‑term data (requiring external systems for long‑term retention), and metrics lack built‑in unit definitions.

In summary, Prometheus stands out for its performance, extensibility, active community, and especially its first‑class support for cloud‑native workloads, making it a compelling choice for modern observability stacks.