TimescaleDB is PostgreSQL for time-series data. TimescaleDB provides all the benefits of PostgreSQL, including:
- Ability to coexist with other TimescaleDB databases and PostgreSQL databases on a PostgreSQL server
- Full SQL as its primary interface language
- All the standard database objects (like tables, indexes, triggers, and more)
- Ability to use the entire PostgreSQL ecosystem of third-party tools
The way the database accomplishes this synchronicity is through its packaging as a PostgreSQL extension, whereby a standard PostgreSQL database is transformed into a TimescaleDB database.
But TimescaleDB improves upon PostgreSQL for handling time-series data. These advantages are most easily seen when interacting with hypertables, which behave like normal tables yet maintain high performance even while scaling storage to normally prohibitive amounts of data. Hypertables can engage in normal table operations, including JOINs with standard tables.
If you know PostgreSQL, you are 90% of the way to knowing TimescaleDB. If you want to learn more, here are some additional resources:
- Learn more about time-series data and how you can best use it for your applications.
- Learn more about the TimescaleDB architecture.
- Learn more about the unique features of TimescaleDB.
The best way to get TimescaleDB is through our hosted offering. You can try TimecaleDB for free and get started in seconds. Hosted TimescaleDB lets you focus on your workloads while we handle the operations and management of your critical time-series data. TimescaleDB is available in the three top cloud providers (Amazon Web Services, Microsoft Azure, and Google Cloud Platform) across 75+ regions and over 2000 different configurations.
You can also install TimescaleDB on your desktop or self-managed in your own infrastructure for free.
There are a lot of things TimescaleDB can do for you and your time-series data. Here are some of our favorite features, along with links to learn more:
- Migrating your data to a hypertable (optional)
- Analyze your data using advanced time-series analytical functions (e.g., gap filling, LOCF, interpolation)
- Native compression can reduce storage by up to 90%, saving you a significant amount of money on your time-series deployment
- Continuous aggregates automatically calculate the results of a query in the background and materialize the results
- Data retention policies allow you to decide how long raw data is kept, separately from data rollups stored in Continuous Aggregates
- Achieve petabyte scale with Multi-node (distributed hypertables)
- Support for high cardinality datasets
- Support for all PostgreSQL extensions, such as PostGIS
- Compatibility with Grafana, Tableau, and most visualization tools
- Support for VPC Peering (in Timescale Cloud)
- SSL Support for database connections and better security
The best way to gain familiarity with TimescaleDB is to use it. The following tutorials (complete with sample data) will help you learn how to harness the power of your time-series data and give you a guided tour of TimescaleDB.
- Start with Hello Timescale, our 20-minute guided tour of TimescaleDB
- Many people use visualization tools with their time-series data, and our Grafana tutorials will walk you through these steps
- We’ve also built other tutorials for language-specific developers, data migration, and more
Our world-class support team is here to support you through multiple channels:
- Join our Community Slack and get to know your fellow time-series developers
- Consider paid support options for a deeper relationship with Timescale engineers
- Join our worldwide TimescaleDB community and stay on top of the latest developments in time-series data