Time-Series Database Explained

Store and query time-stamped data efficiently with databases that handle millions of data points per second with built-in aggregation and retention.

Time-Series Database

A time-series database is optimized for storing and querying time-stamped data points, providing efficient compression, time-based aggregation, and retention policies for metrics, events, and sensor data.

Explanation

Time-series data is characterized by high write throughput, append-only patterns, and time-based queries. TSDBs optimize with columnar storage, time-based partitioning, built-in aggregation functions, and downsampling of old data. Popular TSDBs include InfluxDB, TimescaleDB (PostgreSQL extension), Prometheus, and ClickHouse. Common use cases include infrastructure monitoring, IoT sensor data, financial market data, and application metrics.

Bookuvai Implementation

Bookuvai uses TimescaleDB for time-series features because it extends PostgreSQL — teams use familiar SQL while getting time-series optimizations. For monitoring, we deploy Prometheus with Grafana dashboards. IoT projects use InfluxDB for high-throughput sensor data ingestion.

Key Facts

  • Optimized for time-stamped, append-only data with high write throughput
  • Columnar storage provides efficient compression for time-series data
  • Built-in time-based aggregation, downsampling, and retention policies
  • Popular TSDBs: InfluxDB, TimescaleDB, Prometheus, ClickHouse
  • Use cases: monitoring, IoT, financial data, and application metrics

Related Terms

Frequently Asked Questions

Can I use PostgreSQL for time-series data?
Yes, with TimescaleDB — a PostgreSQL extension that adds hypertables, continuous aggregates, and compression. You get time-series performance with full PostgreSQL capabilities including joins and foreign keys.
How do TSDBs handle data retention?
TSDBs provide built-in retention policies that automatically delete or downsample old data. You might keep full-resolution data for 30 days, 1-minute averages for 1 year, and 1-hour averages for 5 years.
What is the difference between InfluxDB and Prometheus?
InfluxDB accepts push-based data ingestion via API. Prometheus is pull-based — it scrapes metrics from targets at intervals. Prometheus excels at infrastructure monitoring with alerting; InfluxDB is more flexible for diverse time-series use cases.