Timescale Analytics

This repository is the home of the Timescale Analytics team. Our mission is to ease all things analytics when using TimescaleDB, with a particular focus on developer ergonomics and performance. Our issue tracker contains more on the features we're planning to work on and the problems we're trying to solve, and our Discussions forum contains ongoing conversation.

We provide an experimental docker image containing a nightly build of the main branch of the repo, along with TimescaleDB 2.0, at timescaledev/timescale-analytics:nightly. This image is perma-unstable and will break from night to night; only use it to try out new features or as a canary in a CI setup.

Documentation for this version of the Timescale Analytics extension can be found in this repository at extension/docs.

🖥 Try It Out

Run

docker run -d --name timescaledb -p 5432:5432 -e POSTGRES_PASSWORD=password timescaledev/timescale-analytics:nightly

The extension contains experimental features in the timecale_analytics_experimental, schema see our docs section on experimental features for more details.

✏️ Get Involved

The Timescale Analytics project is still in the initial planning stage as we decide our priorities and what to implement first. As such, now is a great time to help shape the project's direction! Have a look at the list of features we're thinking of working on and feel free to comment on the features, expand the list, or hop on the Discussions forum for more in-depth discussions.

🐯 About TimescaleDB

TimescaleDB is a distributed time-series database built on PostgreSQL that scales to over 10 million of metrics per second, supports native compression, handles high cardinality, and offers native time-series capabilities, such as data-retention policies, continuous aggregate views, downsampling, data gap-filling and interpolation.

TimescaleDB also supports full SQL, a variety of data types (numerics, text, arrays, JSON, booleans), and ACID semantics. Operationally mature capabilities include high availability, streaming backups, upgrades over time, roles and permissions, and security.

TimescaleDB has a large and active user community (tens of millions of downloads, hundreds of thousands of active deployments, Slack channel with thousands of members). Users include Comcast, Fujitsu, Schneider Electric, Siemens, Walmart, Warner Music, and thousands of others.

Developers and organizations around the world trust TimescaleDB with their time-series data. AppDynamics (now part of Cisco Systems and one of the largest application performance monitoring providers) relies on TimescaleDB as its main metrics database. TimescaleDB is also the preferred (recommended) backend datasource for Zabbix users and is natively supported in Grafana.