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Add a function to decompress a compressed batch entirely in one go, and
use it in some query plans. As a result of decompression, produce
ArrowArrays. They will be the base for the subsequent vectorized
computation of aggregates.

As a side effect, some heavy queries to compressed hypertables speed up
by about 15%. Point queries with LIMIT 1 can regress by up to 1 ms. If
the absolute highest performace is desired for such queries, bulk
decompression can be disabled by a GUC.

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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.

If you prefer not to install or administer your instance of TimescaleDB, hosted versions of TimescaleDB are available in the cloud of your choice (pay-as-you-go, with a free trial to start).

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:

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.

Contributors welcome.

(To build TimescaleDB from source, see instructions in Building from source.)

Using TimescaleDB

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. (Architecture discussion)

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.

Before you start

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.

Creating a hypertable

-- Do not forget to create timescaledb extension

-- We start by creating a regular SQL table
CREATE TABLE conditions (
  time        TIMESTAMPTZ       NOT NULL,
  location    TEXT              NOT NULL,

-- Then we convert it into a hypertable that is partitioned by time
SELECT create_hypertable('conditions', 'time');

Inserting and querying data

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.)


TimescaleDB is available pre-packaged for several platforms:

Timescale Cloud (cloud-hosted and managed TimescaleDB) is available via free trial. You create database instances in the cloud of your choice and use TimescaleDB to power your queries, automating common operational tasks and reducing management overhead.

We recommend following our detailed installation instructions.

To build from source, see instructions here.


Architecture documents

Useful tools

  • 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 COPY across multiple workers.

Additional documentation

Community & help

Releases & updates