TimescaleDB is an open-source database designed to make SQL scalable for time-series data. It is engineered up from PostgreSQL, providing automatic partitioning across time and space (partitioning key), as well as full SQL support.

TimescaleDB is packaged as a PostgreSQL extension and released under the Apache 2 open-source license. Contributors welcome.

Below is an introduction to TimescaleDB. For more information, please check out these other resources:

• Developer Documentation
• Slack Channel
• Support Email

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.

-- 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');

• Quick start: Creating hypertables
• Reference examples

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.)

• Quick start: Basic operations
• Reference examples
• TimescaleDB API

TimescaleDB can be installed via a variety of ways:

• Linux: yum, apt, Docker
• MacOS: brew, Docker
• Windows: Docker

We recommend following our detailed installation instructions.

Prerequisites:

• A standard PostgreSQL 9.6 installation with development environment (header files) (e.g., postgresql-server-dev-9.6 package for Linux, Postgres.app for MacOS)
• C compiler (e.g., gcc or clang)
• CMake version 3.4 or greater

git clone [email protected]:timescale/timescaledb.git
cd timescaledb

# Bootstrap the build system
./bootstrap

# To build the extension
cd build && make

# To install
make install

Please see our additional configuration instructions.

• Why use TimescaleDB?
• Migrating from PostgreSQL
• Writing data
• Querying and data analytics
• Tutorials and sample data

• Github Issues
• Slack Channel
• Support Email