Time-Series Data and Grafana
Time-series data captures measurements over time — CPU usage every minute, request counts every second, error rates every hour. This data powers operational monitoring, helping you understand system behavior and catch problems early. Grafana has become the standard tool for visualizing time-series data beautifully.
Understanding Time-Series Data
Time-series data consists of timestamps paired with values. Unlike regular analytics events, time-series focuses on continuous measurements:
- CPU usage: 45% at 10:00:00, 52% at 10:00:01, 48% at 10:00:02
- Request count: 150 requests in the last minute
- Response time: p95 latency of 230ms
This data reveals trends, patterns, and anomalies. A gradual increase in memory usage might indicate a leak. A sudden spike in errors signals a problem needing immediate attention.
Time-Series Databases
Regular databases aren't optimized for time-series workloads. Specialized databases handle the unique requirements:
Prometheus excels at metrics collection and alerting. It pulls metrics from your services and stores them efficiently. The query language (PromQL) is powerful but has a learning curve.
InfluxDB offers a more traditional database experience with SQL-like queries. It's easier to get started with if you're familiar with relational databases.
TimescaleDB extends PostgreSQL for time-series data. If you already use PostgreSQL, TimescaleDB lets you keep your existing tools and knowledge.
Grafana for Visualization
Grafana connects to these databases (and many others) to create interactive dashboards. It doesn't store data itself — it queries your data sources and renders visualizations.
A typical dashboard includes several panel types:
Single stats show current values — active users right now, current error rate, latest deployment time.
Time-series graphs display trends over time — request rate over the last hour, memory usage over the last day.
Tables present detailed data when you need specifics — top 10 slowest endpoints, recent errors with details.
Alerts trigger when metrics cross thresholds — notify the team when error rate exceeds 1%.
Dashboard Design
Effective dashboards tell a story at a glance. Organize panels logically:
Example: Application Health Dashboard
Row 1: Key metrics (single stats)
- Request rate | Error rate | p95 latency | Active users
Row 2: Traffic patterns (graphs)
- Requests over time | Errors over time
Row 3: Performance (graphs)
- Response time percentiles | Database query time
Row 4: Resources (graphs)
- CPU usage | Memory usage | Disk I/O
Start with high-level health indicators at the top. Drill down into details below. Someone glancing at the dashboard should immediately know if things are healthy or need attention.