This release contains performance improvements and bug fixes since the 2.14.2 release. We recommend that you upgrade at the next available opportunity. In addition, it includes these noteworthy features: * Support `time_bucket` with `origin` and/or `offset` on Continuous Aggregate * Compression improvements: - Improve expression pushdown - Add minmax sparse indexes when compressing columns with btree indexes - Make compression use the defaults functions - Vectorize filters in WHERE clause that contain text equality operators and LIKE expressions **Deprecation warning** * Starting on this release will not be possible to create Continuous Aggregate using `time_bucket_ng` anymore and it will be completely removed on the upcoming releases. * Recommend users to [migrate their old Continuous Aggregate format to the new one](https://docs.timescale.com/use-timescale/latest/continuous-aggregates/migrate/) because it support will be completely removed in next releases prevent them to migrate. * This is the last release supporting PostgreSQL 13. **For on-premise users and this release only**, you will need to run [this SQL script](https://github.com/timescale/timescaledb-extras/blob/master/utils/2.15.X-fix_hypertable_foreign_keys.sql) after running `ALTER EXTENSION`. More details can be found in the pull request [#6786](https://github.com/timescale/timescaledb/pull/6797). **Features** * #6382 Support for time_bucket with origin and offset in CAggs * #6696 Improve defaults for compression segment_by and order_by * #6705 Add sparse minmax indexes for compressed columns that have uncompressed btree indexes * #6754 Allow DROP CONSTRAINT on compressed hypertables * #6767 Add metadata table `_timestaledb_internal.bgw_job_stat_history` for tracking job execution history * #6798 Prevent usage of deprecated time_bucket_ng in CAgg definition * #6810 Add telemetry for access methods * #6811 Remove no longer relevant timescaledb.allow_install_without_preload GUC * #6837 Add migration path for CAggs using time_bucket_ng * #6865 Update the watermark when truncating a CAgg **Bugfixes** * #6617 Fix error in show_chunks * #6621 Remove metadata when dropping chunks * #6677 Fix snapshot usage in CAgg invalidation scanner * #6698 Define meaning of 0 retries for jobs as no retries * #6717 Fix handling of compressed tables with primary or unique index in COPY path * #6726 Fix constify cagg_watermark using window function when querying a CAgg * #6729 Fix NULL start value handling in CAgg refresh * #6732 Fix CAgg migration with custom timezone / date format settings * #6752 Remove custom autovacuum setting from compressed chunks * #6770 Fix plantime chunk exclusion for OSM chunk * #6789 Fix deletes with subqueries and compression * #6796 Fix a crash involving a view on a hypertable * #6797 Fix foreign key constraint handling on compressed hypertables * #6816 Fix handling of chunks with no contraints * #6820 Fix a crash when the ts_hypertable_insert_blocker was called directly * #6849 Use non-orderby compressed metadata in compressed DML * #6867 Clean up compression settings when deleting compressed cagg * #6869 Fix compressed DML with constraints of form value OP column * #6870 Fix bool expression pushdown for queries on compressed chunks **Thanks** * @brasic for reporting a crash when the ts_hypertable_insert_blocker was called directly * @bvanelli for reporting an issue with the jobs retry count * @djzurawsk For reporting error when dropping chunks * @Dzuzepppe for reporting an issue with DELETEs using subquery on compressed chunk working incorrectly. * @hongquan For reporting a 'timestamp out of range' error during CAgg migrations * @kevcenteno for reporting an issue with the show_chunks API showing incorrect output when 'created_before/created_after' was used with time-partitioned columns. * @mahipv For starting working on the job history PR * @rovo89 For reporting constify cagg_watermark not working using window function when querying a CAgg
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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, try Timescale, our fully managed cloud offering (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:
- Developer Documentation
- Slack Channel
- Timescale Community Forum
- Timescale Release Notes & Future Plans
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 (Linux, Docker, MacOS, Windows). More information can be found in our documentation.
To build from source, see instructions here.
Timescale, a fully managed TimescaleDB in the cloud, is available via a free trial. Create a PostgreSQL database in the cloud with TimescaleDB pre-installed so you can power your application with TimescaleDB without the management overhead.
Resources
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
- 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.