timescaledb: Time-Series data

The timescaledb extension is deprecated in projects using Postgres 17. It continues to be supported in projects using Postgres 15, but will need to dropped before those projects are upgraded to Postgres 17. See the Upgrading to Postgres 17 notes for more information.

If you are using hypertables, follow the migration guide to convert to native partitioning managed by pg_partman.

For additional support, contact our Success team by creating a support ticket in the Supabase Dashboard.

timescaledb is a Postgres extension designed for improved handling of time-series data. It provides a scalable, high-performance solution for storing and querying time-series data on top of a standard Postgres database.

timescaledb uses a time-series-aware storage model and indexing techniques to improve performance of Postgres in working with time-series data. The extension divides data into chunks based on time intervals, allowing it to scale efficiently, especially for large data sets. The data is then compressed, optimized for write-heavy workloads, and partitioned for parallel processing. timescaledb also includes a set of functions, operators, and indexes that work with time-series data to reduce query times, and make data easier to work with.

Supabase projects come with TimescaleDB Apache 2 Edition. Functionality only available under the Community Edition is not available.

Enable the extension

<Tabs scrollable size="small" type="underlined" defaultActiveId="dashboard" queryGroup="database-method"

1. Go to the Database page in the Dashboard.
2. Click on Extensions in the sidebar.
3. Search for timescaledb and enable the extension.

-- Disable the "timescaledb" extension drop extension if exists timescaledb;

</TabPanel>
</Tabs>

Even though the SQL code is `create extension`, this is the equivalent of "enabling the extension". To disable an extension you can call `drop extension`.

It's good practice to create the extension within a separate schema (like `extensions`) to keep your `public` schema clean.

## Usage

To demonstrate how `timescaledb` works, let's consider a simple example where we have a table that stores temperature data from different sensors. We will create a table named "temperatures" and store data for two sensors.

First we create a hypertable, which is a virtual table that is partitioned into chunks based on time intervals. The hypertable acts as a proxy for the actual table and makes it easy to query and manage time-series data.
```sql
create table temperatures (
  time timestamptz not null,
  sensor_id int not null,
  temperature double precision not null
);

select create_hypertable('temperatures', 'time');

Next, we can populate some values

insert into temperatures (time, sensor_id, temperature)
values
    ('2023-02-14 09:00:00', 1, 23.5),
    ('2023-02-14 09:00:00', 2, 21.2),
    ('2023-02-14 09:05:00', 1, 24.5),
    ('2023-02-14 09:05:00', 2, 22.3),
    ('2023-02-14 09:10:00', 1, 25.1),
    ('2023-02-14 09:10:00', 2, 23.9),
    ('2023-02-14 09:15:00', 1, 24.9),
    ('2023-02-14 09:15:00', 2, 22.7),
    ('2023-02-14 09:20:00', 1, 24.7),
    ('2023-02-14 09:20:00', 2, 23.5);

And finally we can query the table using timescaledb's time_bucket function to divide the time-series into intervals of the specified size (in this case, 1 hour) averaging the temperature reading within each group.

select
    time_bucket('1 hour', time) AS hour,
    avg(temperature) AS average_temperature
from
    temperatures
where
    sensor_id = 1
    and time > NOW() - interval '1 hour'
group by
    hour;

Resources

• Official timescaledb documentation