timescaledb
A time-series database for high-performance real-time analytics packaged as a Postgres extension
A PostgreSQL extension that makes storing and querying large amounts of time-stamped data, sensor readings, metrics, logs, fast and efficient using standard SQL you already know.
TimescaleDB is a database extension for PostgreSQL — the popular open-source database — that makes it fast and efficient to store and query time-series data. Time-series data is any information recorded with a timestamp, like sensor readings from IoT devices, stock prices, server performance metrics, or application logs. Regular databases can handle this kind of data, but they tend to slow down significantly as the data grows because they were not designed for it.
TimescaleDB solves this by transparently dividing your time-stamped data into time-based chunks behind the scenes — you write normal SQL queries and it handles the partitioning automatically. It also includes a columnstore feature that compresses data and reorganizes it to speed up analytical queries that scan large amounts of historical data. A special function called time_bucket lets you easily group data into intervals like "every hour" or "every day" with a single SQL command.
Since it runs as an extension inside PostgreSQL, you do not need to learn a new database system or query language. Your existing PostgreSQL tools, clients, and knowledge carry over. You just get time-series-specific capabilities added on top.
You would use TimescaleDB when you need to store and analyze large volumes of data that has a time dimension — IoT sensor streams, financial tick data, infrastructure monitoring, or any application that keeps growing a timestamped log. It is written in C.
Where it fits
- Store and query IoT sensor readings that grow by millions of rows per day without query slowdown.
- Build a monitoring dashboard that aggregates server metrics by hour or day using SQL time_bucket queries.
- Migrate an existing PostgreSQL app to handle time-series data without changing your query code.
- Run analytics on years of application log data efficiently using automatic compression.