Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Netdata | 65,279 | 7 hours ago | 371 | gpl-3.0 | C | |||||
Monitor your servers, containers, and applications, in high-resolution and in real-time! | ||||||||||
Metabase | 34,409 | 6 hours ago | 1 | June 08, 2022 | 3,258 | other | Clojure | |||
The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum: | ||||||||||
Prisma | 34,035 | 442 | 10 hours ago | 4,993 | September 24, 2022 | 2,922 | apache-2.0 | TypeScript | ||
Next-generation ORM for Node.js & TypeScript | PostgreSQL, MySQL, MariaDB, SQL Server, SQLite, MongoDB and CockroachDB | ||||||||||
Dbeaver | 33,952 | 6 hours ago | 1,811 | apache-2.0 | Java | |||||
Free universal database tool and SQL client | ||||||||||
Typeorm | 32,183 | 1,994 | 3,154 | 2 days ago | 741 | July 22, 2023 | 2,162 | mit | TypeScript | |
ORM for TypeScript and JavaScript. Supports MySQL, PostgreSQL, MariaDB, SQLite, MS SQL Server, Oracle, SAP Hana, WebSQL databases. Works in NodeJS, Browser, Ionic, Cordova and Electron platforms. | ||||||||||
Directus | 23,279 | 185 | 8 hours ago | 86 | July 25, 2023 | 291 | other | TypeScript | ||
The Modern Data Stack 🐰 — Directus is an instant REST+GraphQL API and intuitive no-code data collaboration app for any SQL database. | ||||||||||
Postgrest | 21,155 | 4 | 2 days ago | 37 | July 12, 2022 | 215 | mit | Haskell | ||
REST API for any Postgres database | ||||||||||
Shardingsphere | 18,822 | 7 | a day ago | 7 | June 04, 2020 | 1,177 | apache-2.0 | Java | ||
Distributed SQL transaction & query engine for data sharding, scaling, encryption, and more - on any database. | ||||||||||
Mindsdb | 17,986 | 6 hours ago | 434 | July 25, 2023 | 558 | gpl-3.0 | Python | |||
MindsDB connects AI models to databases. | ||||||||||
Timescaledb | 15,571 | 6 hours ago | 582 | other | C | |||||
An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension. |
Linux/macOS | Linux i386 | Windows | Coverity | Code Coverage |
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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.
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:
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.)
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.
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.
-- 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 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 (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.
COPY
across
multiple workers.