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TimescaleDB is an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL and packaged as a PostgreSQL extension, providing automatic partitioning across time and space (partitioning key), as well as full SQL support.
Warning
The latest Postgres minor releases (17.1, 16.5, 15.9, 14.14, 13.17, 12.21), released 2024-11-14, have an unexpected breaking ABI change that may crash existing deployments of TimescaleDB, unless used with a TimescaleDB binary explicitly built against those new minor PG versions.
Status and recommendations:
- Users of Timescale Cloud are unaffected. We are currently not upgrading cloud databases to these latest minor PG releases. But regardless, Timescale Cloud recompiles TimescaleDB against each new minor Postgres version, which would prevent any such incompatibility.
- Users to Timescale's k8s docker image are unaffected. We are currently not building a new release against these latest minor PG releases. But regardless, our docker image build process recompiles TimescaleDB against each new minor Postgres version, which would prevent any such incompatibility.
- Users of other managed clouds (using TimescaleDB Apache-2 Edition) are recommended to not upgrade to these latest minor PG releases at this time, or discuss with their cloud provider how they build TimescaleDB with new minor releases.
- Users who self-manage TimescaleDB are recommended to not upgrade to these latest minor PG releases at this time.
We are working with the PG community about how best to address this issue. See this thread on pgsql-hackers for more info.
Thanks for your understanding! 🙏
If you prefer not to install or administer your instance of TimescaleDB, try the 30 day free trial of Timescale Cloud, our fully managed cloud offering. Timescale is pay-as-you-go. We don't charge for storage you dont use, backups, snapshots, ingress or egress.
To determine which option is best for you, see Timescale Products for more information about our Apache-2 version, TimescaleDB Community (self-hosted), and Timescale Cloud (hosted), including: feature comparisons, FAQ, documentation, and support.
Below is an introduction to TimescaleDB. For more information, please check out these other resources:
- Developer Documentation
- Slack Channel
- Timescale Community Forum
- Timescale Release Notes & Future Plans
For reference and clarity, all code files in this repository reference
licensing in their header (either the Apache-2-open-source license
or Timescale License (TSL)
). Apache-2 licensed binaries can be built by passing -DAPACHE_ONLY=1 to bootstrap.
(To build TimescaleDB from source, see instructions in Building from source.)
TimescaleDB scales PostgreSQL for time-series data via automatic partitioning across time and space (partitioning key), yet retains the standard PostgreSQL interface.
In other words, TimescaleDB exposes what look like regular tables, but are actually only an abstraction (or a virtual view) of many individual tables comprising the actual data. This single-table view, which we call a hypertable, is comprised of many chunks, which are created by partitioning the hypertable's data in either one or two dimensions: by a time interval, and by an (optional) "partition key" such as device id, location, user id, etc.
Virtually all user interactions with TimescaleDB are with hypertables. Creating tables and indexes, altering tables, inserting data, selecting data, etc., can (and should) all be executed on the hypertable.
From the perspective of both use and management, TimescaleDB just looks and feels like PostgreSQL, and can be managed and queried as such.
PostgreSQL's out-of-the-box settings are typically too conservative for modern
servers and TimescaleDB. You should make sure your postgresql.conf
settings are tuned, either by using timescaledb-tune
or doing it manually.
-- Do not forget to create timescaledb extension
CREATE EXTENSION timescaledb;
-- We start by creating a regular SQL table
CREATE TABLE conditions (
time TIMESTAMPTZ NOT NULL,
location TEXT NOT NULL,
temperature DOUBLE PRECISION NULL,
humidity DOUBLE PRECISION NULL
);
-- Then we convert it into a hypertable that is partitioned by time
SELECT create_hypertable('conditions', 'time');Inserting data into the hypertable is done via normal SQL commands:
INSERT INTO conditions(time, location, temperature, humidity)
VALUES (NOW(), 'office', 70.0, 50.0);
SELECT * FROM conditions ORDER BY time DESC LIMIT 100;
SELECT time_bucket('15 minutes', time) AS fifteen_min,
location, COUNT(*),
MAX(temperature) AS max_temp,
MAX(humidity) AS max_hum
FROM conditions
WHERE time > NOW() - interval '3 hours'
GROUP BY fifteen_min, location
ORDER BY fifteen_min DESC, max_temp DESC;In addition, TimescaleDB includes additional functions for time-series
analysis that are not present in vanilla PostgreSQL. (For example, the time_bucket function above.)
Timescale Cloud, a fully-managed TimescaleDB in the cloud, is available via a free trial. Create a PostgreSQL database in the cloud with TimescaleDB pre-installed so you can power your application with TimescaleDB without the management overhead.
TimescaleDB is also available pre-packaged for several platforms such as Linux, Windows, MacOS, Docker, and Kubernetes. For more information, see Install TimescaleDB.
To build from source, see Building from source.
- timescaledb-tune: Helps set your PostgreSQL configuration settings based on your system's resources.
- timescaledb-parallel-copy:
Parallelize your initial bulk loading by using PostgreSQL's
COPYacross multiple workers.
- Why use TimescaleDB?
- Migrating from PostgreSQL
- Writing data
- Querying and data analytics
- Tutorials and sample data
- Slack Channel
- Github Issues
- Timescale Support: see support options (community & subscription)
- Timescale Release Notes: see detailed information about current and past versions and subscribe to get notified about new releases, fixes, and early access/beta programs.
