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:
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');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 can be installed via a variety of ways:
We recommend following our detailed installation instructions.
Prerequisites:
- A standard PostgreSQL 9.6 or 10 installation with development
environment (header files) (e.g.,
postgresql-server-dev-9.6package 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 installPlease see our additional configuration instructions.
Prerequisites:
- A standard PostgreSQL 9.6 or 10 64-bit installation
- Microsoft Visual Studio 2017 with CMake and Git components
- OR Visual Studio 2015/2016 with CMake version 3.4 or greater and Git
- Make sure all relevant binaries are in your PATH:
pg_config,cmake,MSBuild
If using Visual Studio 2017 with the CMake and Git components, you should be able to simply clone the repo and open the folder in Visual Studio which will take care of the rest.
If you are using an earlier version of Visual Studio, then it can be built in the following way:
git clone [email protected]:timescale/timescaledb.git
cd timescaledb
# Bootstrap the build system
./bootstrap.bat
# To build the extension from command line
cd build
MSBuild.exe timescaledb.sln
# To install
MSBuild.exe /p:Configuration=Release INSTALL.vcxproj
# Alternatively, open build/timescaledb.sln in Visual Studio and build
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