SQL made scalable for time-series data.

An open-source time-series database optimized for fast ingest and complex queries. Fully compatible with Postgres.


How we scaled SQL

Time-series workloads are different. TimescaleDB introduces special partitioning and distributed query optimizations to unlock new possibilities for SQL.

Learn more

Ease of use

Query with standard SQL. Connect to tools that speak standard database connections. Your team and infra already knows how to use it.

Fast queries

TimescaleDB incorporates data and query optimizations so you can query more data, faster. Support for secondary indexes and rich query predicates.

High ingest rates

In a world where data is king, your business can’t afford bottlenecks. Our database allows you to take in more data, faster.

Real time

Write and observe individual data records as soon as they’re available. Monitor and query live data. Don’t be limited by a database that requires large batch loads.

An open-source database for time-series data

Scaling SQL for high ingest rates and complex queries

Engineered up from PostgreSQL

Rock-solid reliability

Trust in PostgreSQL’s 20-year open-source record and strong developer community

Mature ecosystem

Connect via standard ODBC, JDBC, or postgres for 3rd-party viz tools, BI tools, web platforms and ORMs

Standard interface

Leverage your team’s existing comfort with SQL and Postgres

Operational ease

Reuse known and trusted methods for backups, snapshots, replication, and other operational tasks

Connect third-party tools

via standard ODBC, JDBC, Postgres connectors

“We’re now using the data from Timescale heavily for our customer support operations. We’re really happy with it and are really benefitting from the service. Thank you!”

- Dan P., Kuna Systems

Business stories

  • Industrial Machine Learning

  • Smart Home

  • Transportation and Logistics

  • IoT Platforms

Industrial Machine Learning

Data scientists developing a predictive maintenance service at one industrial sensing company were faced with two problems: how to interactively sift through their raw data; and how to measure their model effectiveness over time. In particular, they wanted to compare the performance of various model iterations to measure improvement.

With TimescaleDB, not only are they now able to store their raw data, but also capture metadata related to the model’s performance: accuracy, latency, etc. Because TimescaleDB supports pure SQL, they use their existing tools to query the data. Our complex query support means they can monitor model effectiveness by cohort, providing a real-time machine learning dashboard.

Past & upcoming presentations

Building a scalable time-series database using PostgreSQL

March 30, 2017, PGConf US, Jersey City, NJ

Designing a time series database to support IoT workloads

March 15, 2017, Strata - Hadoop World, San Jose, CA