Time-Series Database

Definition

A time-series database (TSDB) is a database optimized for storing and querying data points indexed by time, such as sensor readings, metrics, or logs, prioritizing high-throughput writes and fast time-range aggregation.

Overview

Time-series data has distinct characteristics that general-purpose databases handle poorly at scale: it arrives as a continuous, append-heavy stream (rarely updated after being written), it is almost always queried by time range, and it is frequently aggregated (averages, sums, percentiles over minutes, hours, or days). Time-series databases are built around these patterns, using storage engines optimized for sequential writes and compression techniques — like delta and delta-of-delta encoding — that exploit the fact that consecutive readings are often similar. Most TSDBs also provide built-in functions for downsampling (reducing granularity of older data to save space), retention policies (automatically expiring data past a certain age), and continuous aggregation (pre-computing rollups so dashboards querying months of data stay fast). Many are structured as a columnar database internally, since column-oriented storage compresses and scans repetitive numeric time-series data very efficiently. TSDBs are the backbone of observability stacks (metrics, monitoring, alerting), IoT sensor pipelines, and financial tick data systems, where the volume of incoming data points can reach millions per second and dashboards need to render aggregates over long time windows in milliseconds. Popular options include InfluxDB, TimescaleDB (a PostgreSQL extension), Prometheus (metrics-focused), and cloud-native services like Amazon Timestream, each balancing write throughput, query flexibility, and operational simplicity differently.

Key Features

• Optimized for high-throughput, append-only writes of timestamped data points
• Built-in downsampling and retention policies to manage data volume over time
• Compression techniques exploiting similarity between consecutive readings
• Fast time-range queries and continuous aggregation for dashboards
• Often built on columnar storage internally for efficient numeric compression
• Native support for common time-series functions like rate-of-change and rolling averages

Use Cases

Frequently Asked Questions

Related in Database