Prometheus Basics

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What is Prometheus?

• Metric collection by scraping HTTP endpoints that expose plaintext metrics (typically a /metrics endpoint).
• A time-series database that stores scraped data efficiently.
• A powerful query language, PromQL, for aggregations, filtering, and creating dashboards.
• Alerting rules that produce alerts which can be routed to Alertmanager for notifications and deduplication.

• System-level metrics (CPU, memory, disk).
• Service-level metrics (request counts, error rates, service uptime).
• Application-specific metrics (latency histograms, exception counters, queue lengths).

# curl http://my-app:8080/metrics
# HELP http_requests_total Total number of HTTP requests
# TYPE http_requests_total counter
http_requests_total{method="GET",handler="/"} 1027

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How Prometheus integrates with systems

• node_exporter — host and OS metrics (CPU, memory, disk).
• blackbox_exporter — probing endpoints (HTTP, TCP, ICMP).
• Database exporters — PostgreSQL, MySQL, MongoDB, etc.
• Cloud/service-specific exporters for third-party systems and devices.

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Metric types and what to monitor

• Counter — monotonically increasing value (e.g., request count).
• Gauge — point-in-time value that can go up or down (e.g., memory usage).
• Histogram — samples and buckets for latency distributions.
• Summary — quantiles over a sliding time window.

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Quick examples

scrape_configs:
  - job_name: 'my-app'
    static_configs:
      - targets: ['my-app:8080']

# Instant vector: current requests per second (rate of counter)
rate(http_requests_total[1m])

# Aggregate by HTTP method
sum by (method) (rate(http_requests_total[5m]))

# 95th percentile latency from a histogram
histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))

groups:
- name: example-alerts
  rules:
  - alert: HighErrorRate
    expr: rate(http_requests_total{code=~"5.."}[5m]) > 0.05
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "High 5xx error rate for {{ $labels.job }}"
      description: "5xx rate is > 5% over the last 5 minutes."

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Component overview

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Historical and implementation notes

• Originally developed at SoundCloud; joined the Cloud Native Computing Foundation (CNCF) as a project in 2016 and later graduated.
• Implemented primarily in Go.
• Official docs and getting started guides: https://prometheus.io/docs/

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Next steps

• Prometheus data model in depth (labels, metric types).
• Configuring scrape targets, relabeling, and exporters.
• Writing effective PromQL queries and visualizing with Grafana.
• Creating robust alerting rules and integrating Alertmanager.

• Prometheus docs: https://prometheus.io/docs/
• Prometheus best practices and push model: https://prometheus.io/docs/practices/pushing/
• node_exporter: https://github.com/prometheus/node_exporter
• blackbox_exporter: https://github.com/prometheus/blackbox_exporter