TimeSeriesDatabases

Time series databases (TSDBs) are databases optimized for storing and querying time-stamped data. They are designed to ingest large volumes of measurements quickly, store data efficiently, and answer queries over time ranges with minimal latency.

The data model typically centers on a time series identified by a measurement name, a set of tags or labels, and a timestamp. This structure supports fast filtering by time and by attributes such as source or sensor. Writes are often append-only, enabling high write throughput and sequential I/O. Many TSDBs use compression tailored to temporally correlated data and organize storage into chunks or shards for scalable querying.

Typical capabilities include time-bounded aggregations (min, max, avg, sum), downsampling and rollups, handling of irregular sampling, and gap-aware querying. Retention policies and data downsampling strategies manage storage costs, keeping recent data at high resolution while older data is summarized or discarded.

Use cases span infrastructure and application monitoring, IoT telemetry, financial tick data, and scientific sensor networks. Related systems often provide specialized ingestion and querying interfaces, such as domain-specific query languages or SQL extensions, and integrate with dashboards or alerting tools.

Common examples of TSDBs include InfluxDB, TimescaleDB, OpenTSDB, KairosDB, and Prometheus, each with trade-offs in schema design, query language, and persistence model. In general, TSDBs complement broader data platforms by offering optimized performance for time-centric workloads and retention-aware storage.