Time-series database
A time-series database stores data points indexed by time.
The core idea is that time is the primary dimension. The database is optimized for appending new measurements and querying ranges of time.
Example
| timestamp | metric | value |
|---|---|---|
| 2026-05-14T10:00:00Z | cpu_usage | 0.72 |
| 2026-05-14T10:01:00Z | cpu_usage | 0.69 |
| 2026-05-14T10:02:00Z | cpu_usage | 0.81 |
Typical queries ask for aggregates over time windows.
What it is good for
Time-series databases are useful for:
- metrics,
- logs,
- IoT measurements,
- financial ticks,
- monitoring dashboards,
- event streams ordered by time.
Tradeoff
They are strong for append-heavy, time-windowed data, but they are not general purpose stores for arbitrary relational queries.
Observability systems like Prometheus are built around this time-series shape.