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This release adds new experimental features since the 2.3.1 release.

The experimental features in this release are:
* APIs for chunk manipulation across data nodes in a distributed
hypertable setup. This includes the ability to add a data node and move
chunks to the new data node for cluster rebalancing.
* The `time_bucket_ng` function, a newer version of `time_bucket`. This
function supports years, months, days, hours, minutes, and seconds.

We’re committed to developing these experiments, giving the community
 a chance to provide early feedback and influence the direction of
TimescaleDB’s development. We’ll travel faster with your input!

Please create your feedback as a GitHub issue (using the
experimental-schema label), describe what you found, and tell us the
steps or share the code snip to recreate it.

This release also includes several bug fixes.

PostgreSQL 11 deprecation announcement
Timescale is working hard on our next exciting features. To make that
possible, we require functionality that is available in Postgres 12 and
above. Postgres 11 is not supported with TimescaleDB 2.4.

**Experimental Features**
* #3293 Add timescaledb_experimental schema
* #3302 Add block_new_chunks and allow_new_chunks API to experimental
schema. Add chunk based refresh_continuous_aggregate.
* #3211 Introduce experimental time_bucket_ng function
* #3366 Allow use of experimental time_bucket_ng function in continuous aggregates
* #3408 Support for seconds, minutes and hours in time_bucket_ng
* #3446 Implement cleanup for chunk copy/move.

* #3401 Fix segfault for RelOptInfo without fdw_private
* #3411 Verify compressed chunk validity for compressed path
* #3416 Fix targetlist names for continuous aggregate views
* #3434 Remove extension check from relcache invalidation callback
* #3440 Fix remote_tx_heal_data_node to work with only current database

* @fvannee for reporting an issue with hypertable expansion in functions
* @amalek215 for reporting an issue with cache invalidation during pg_class vacuum full
* @hardikm10 for reporting an issue with inserting into compressed chunks
* @dberardo-com and @iancmcc for reporting an issue with extension updates after renaming columns of continuous aggregates.

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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.

Timescale Cloud is our fully managed, hosted version of TimescaleDB, available in the cloud of your choice (pay-as-you-go, with free trial credits 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 Apache License, Version 2.0 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 (database-as-a-service) 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.


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