This release contains performance improvements for compressed hypertables and continuous aggregates and bug fixes since the 2.11.2 release. We recommend that you upgrade at the next available opportunity. This release moves all internal functions from the _timescaleb_internal schema into the _timescaledb_functions schema. This separates code from internal data objects and improves security by allowing more restrictive permissions for the code schema. If you are calling any of those internal functions you should adjust your code as soon as possible. This version also includes a compatibility layer that allows calling them in the old location but that layer will be removed in 2.14.0. **PostgreSQL 12 support removal announcement** Following the deprecation announcement for PostgreSQL 12 in TimescaleDB 2.10, PostgreSQL 12 is not supported starting with TimescaleDB 2.12. Currently supported PostgreSQL major versions are 13, 14 and 15. PostgreSQL 16 support will be added with a following TimescaleDB release. **Features** * #5137 Insert into index during chunk compression * #5150 MERGE support on hypertables * #5515 Make hypertables support replica identity * #5586 Index scan support during UPDATE/DELETE on compressed hypertables * #5596 Support for partial aggregations at chunk level * #5599 Enable ChunkAppend for partially compressed chunks * #5655 Improve the number of parallel workers for decompression * #5758 Enable altering job schedule type through `alter_job` * #5805 Make logrepl markers for (partial) decompressions * #5809 Relax invalidation threshold table-level lock to row-level when refreshing a Continuous Aggregate * #5839 Support CAgg names in chunk_detailed_size * #5852 Make set_chunk_time_interval CAggs aware * #5868 Allow ALTER TABLE ... REPLICA IDENTITY (FULL|INDEX) on materialized hypertables (continuous aggregates) * #5875 Add job exit status and runtime to log * #5909 CREATE INDEX ONLY ON hypertable creates index on chunks **Bugfixes** * #5860 Fix interval calculation for hierarchical CAggs * #5894 Check unique indexes when enabling compression * #5951 _timescaledb_internal.create_compressed_chunk doesn't account for existing uncompressed rows * #5988 Move functions to _timescaledb_functions schema * #5788 Chunk_create must add an existing table or fail * #5872 Fix duplicates on partially compressed chunk reads * #5918 Fix crash in COPY from program returning error * #5990 Place data in first/last function in correct mctx * #5991 Call eq_func correctly in time_bucket_gapfill * #6015 Correct row count in EXPLAIN ANALYZE INSERT .. ON CONFLICT output * #6035 Fix server crash on UPDATE of compressed chunk * #6044 Fix server crash when using duplicate segmentby column * #6045 Fix segfault in set_integer_now_func * #6053 Fix approximate_row_count for CAggs * #6081 Improve compressed DML datatype handling * #6084 Propagate parameter changes to decompress child nodes **Thanks** * @ajcanterbury for reporting a problem with lateral joins on compressed chunks * @alexanderlaw for reporting multiple server crashes * @lukaskirner for reporting a bug with monthly continuous aggregates * @mrksngl for reporting a bug with unusual user names * @willsbit for reporting a crash in time_bucket_gapfill
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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.