Grafana Dashboards

Grafana is an open-source visualisation and analytics platform that turns your metrics, logs, and traces into interactive dashboards. It connects to dozens of data sources — Prometheus, Loki, Elasticsearch, InfluxDB, PostgreSQL, and many more — making it the most popular observability frontend.

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A Brief History

• 2014 — Torkel Odegaard creates Grafana as a fork of Kibana
• 2015 — Grafana Labs is founded
• 2019 — Grafana 6.0 introduces Explore mode for ad-hoc investigation
• 2020 — Grafana 7.0 adds a new panel editor and transformations
• 2023 — Grafana 10.0 introduces Scenes for complex, dynamic dashboards
• Today — Grafana is the standard visualisation layer for observability

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Core Concepts

Concept	Description
Dashboard	A collection of panels arranged on a canvas
Panel	A single visualisation (graph, stat, table, heatmap, etc.)
Data source	A connection to a backend (Prometheus, Loki, etc.)
Query	A request for data from a data source (e.g., a PromQL expression)
Variable	A dynamic value (dropdown) that parameterises dashboard queries
Alert	A condition on a panel that triggers notifications
Annotation	A marker on a graph (e.g., deployment events)

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Setting Up Grafana

Docker Compose

version: '3.8'
services:
  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    volumes:
      - grafana-data:/var/lib/grafana
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=secret

  prometheus:
    image: prom/prometheus:latest
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml

volumes:
  grafana-data:

After starting, navigate to http://localhost:3000 and log in with admin / secret.

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Adding a Data Source

1. Go to Configuration → Data Sources → Add data source
2. Select Prometheus
3. Set the URL to http://prometheus:9090
4. Click Save & Test

Grafana supports many data sources out of the box:

Data Source	Type
Prometheus	Metrics
Loki	Logs
Tempo	Traces
Elasticsearch	Logs, Metrics
InfluxDB	Metrics
PostgreSQL	SQL
CloudWatch	AWS Metrics

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Building a Dashboard

Creating Panels

Each panel runs a query against a data source and renders the result:

# Request rate
rate(http_requests_total{job="api"}[5m])

# Error rate percentage
100 * sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m]))

# 95th percentile latency
histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket{job="api"}[5m])) by (le))

Panel Types