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mirror of https://github.com/timescale/timescaledb.git synced 2025-05-18 11:45:11 +08:00
Sven Klemm e4eb666ca3 Release 2.16.0
This release contains significant performance improvements when working with compressed data, extended join
support in continuous aggregates, and the ability to define foreign keys from regular tables towards hypertables.
We recommend that you upgrade at the next available opportunity.

In TimescaleDB v2.16.0 we:

* Introduce multiple performance focused optimizations for data manipulation operations (DML) over compressed chunks.

  Improved upsert performance by more than 100x in some cases and more than 1000x in some update/delete scenarios.

* Add the ability to define chunk skipping indexes on non-partitioning columns of compressed hypertables

  TimescaleDB v2.16.0 extends chunk exclusion to use those skipping (sparse) indexes when queries filter on the relevant columns,
  and prune chunks that do not include any relevant data for calculating the query response.

* Offer new options for use cases that require foreign keys defined.

  You can now add foreign keys from regular tables towards hypertables. We have also removed
  some really annoying locks in the reverse direction that blocked access to referenced tables
  while compression was running.

* Extend Continuous Aggregates to support more types of analytical queries.

  More types of joins are supported, additional equality operators on join clauses, and
  support for joins between multiple regular tables.

**Highlighted features in this release**

* Improved query performance through chunk exclusion on compressed hypertables.

  You can now define chunk skipping indexes on compressed chunks for any column with one of the following
  integer data types: `smallint`, `int`, `bigint`, `serial`, `bigserial`, `date`, `timestamp`, `timestamptz`.

  After you call `enable_chunk_skipping` on a column, TimescaleDB tracks the min and max values for
  that column. TimescaleDB uses that information to exclude chunks for queries that filter on that
  column, and would not find any data in those chunks.

* Improved upsert performance on compressed hypertables.

  By using index scans to verify constraints during inserts on compressed chunks, TimescaleDB speeds
  up some ON CONFLICT clauses by more than 100x.

* Improved performance of updates, deletes, and inserts on compressed hypertables.

  By filtering data while accessing the compressed data and before decompressing, TimescaleDB has
  improved performance for updates and deletes on all types of compressed chunks, as well as inserts
  into compressed chunks with unique constraints.

  By signaling constraint violations without decompressing, or decompressing only when matching
  records are found in the case of updates, deletes and upserts, TimescaleDB v2.16.0 speeds
  up those operations more than 1000x in some update/delete scenarios, and 10x for upserts.

* You can add foreign keys from regular tables to hypertables, with support for all types of cascading options.
  This is useful for hypertables that partition using sequential IDs, and need to reference those IDs from other tables.

* Lower locking requirements during compression for hypertables with foreign keys

  Advanced foreign key handling removes the need for locking referenced tables when new chunks are compressed.
  DML is no longer blocked on referenced tables while compression runs on a hypertable.

* Improved support for queries on Continuous Aggregates

  `INNER/LEFT` and `LATERAL` joins are now supported. Plus, you can now join with multiple regular tables,
  and you can have more than one equality operator on join clauses.

**PostgreSQL 13 support removal announcement**

Following the deprecation announcement for PostgreSQL 13 in TimescaleDB v2.13,
PostgreSQL 13 is no longer supported in TimescaleDB v2.16.

The Currently supported PostgreSQL major versions are 14, 15 and 16.
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Build Status Linux/macOS Build Status Linux i386 Windows build status Coverity Scan Build Status Code Coverage OpenSSF Best Practices

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 the 30 day free trial of Timescale, our fully managed cloud offering. Timescale is pay-as-you-go. We don't charge for storage you dont use, backups, snapshots, ingress or egress.

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.

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.

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

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.

TimescaleDB is also available pre-packaged for several platforms such as Linux, Windows, MacOS, Docker, and Kubernetes. For more information, see Install TimescaleDB.

To build from source, see Building from source.

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

Community & help

Releases & updates

Contributing

Description
An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
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