221437e8ef
Add support for continuous aggregates for distributed hypertables by allowing a continuous aggregate to read from a distributed hypertable so that the continuous aggregate is on the access node while the hypertable data is on the data nodes. For distributed hypertables, both the hypertable and continuous aggregate invalidation log are kept on the data nodes and the refresh window is computed at refresh time on each data node. Since the continuous aggregate materialization hypertable is not present on the data nodes, the invalidation log was extended to allow using a non-local hypertable id on the data nodes. This means that you cannot create continuous aggregates on the data nodes since those could clash with continuous aggregates on the access node. Some utility statements added entries to the invalidation logs directly (truncating chunks and hypertables, as well as dropping individual chunks), so to handle this case, internal functions were added to allow logging invalidation on the data nodes from the access node. The commit also includes some fixes to memory context usage that caused crashes for invalidation triggers and also disable per data node queries during refresh since that would otherwise generate an exception. Fixes #3435 Co-authored-by: Mats Kindahl <mats@timescale.com>
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TimescaleDB
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.
(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
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 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.)
Installation
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.
Resources
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
COPYacross multiple workers.
Additional documentation
- Why use TimescaleDB?
- Migrating from PostgreSQL
- Writing data
- Querying and data analytics
- Tutorials and sample data
Community & help
- Slack Channel
- Github Issues
- Timescale Support: see support options (community & subscription)
Releases & updates
- Timescale Release Notes & Future Plans: see planned and in-progress updates and detailed information about current and past releases.
- Subscribe to Timescale Release Notes to get notified about new releases, fixes, and early access/beta programs.