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A time-series database is a specialized type of database optimized for handling time-stamped data, also known as time-series data. This data consists of a sequence of measurements or events recorded over time, where each data point is associated with a timestamp. Time-series databases are designed to efficiently ingest, store, query, and analyze this type of data, which is common in various domains like IoT, financial markets, application performance monitoring, and industrial sensors.

The core functionality of a time-series database revolves around its ability to manage large volumes of data that are appended sequentially. They often employ specific data structures and indexing techniques that are tailored to temporal queries, such as retrieving data within a specific time range, aggregating data over intervals, or detecting trends and anomalies. This optimization contrasts with traditional relational databases, which might struggle with the performance demands of high-frequency time-series data.

Key features of time-series databases include high write throughput to handle the constant stream of incoming data, efficient storage due to data compression and specialized formats, and fast query performance for time-based aggregations and analysis. Many time-series databases also offer built-in functions for common time-series operations, like downsampling, interpolation, and anomaly detection. Examples of popular time-series databases include InfluxDB, Prometheus, TimescaleDB, and Graphite.