timescaledb.mdx

id	title	description
timescaledb	timescaledb: Time-Series data	Scalable time-series data storage and analysis

timescaledb is a PostgreSQL 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 PostgreSQL database.

timescaledb uses a time-series-aware storage model and indexing techniques to improve performance of PostgreSQL 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.

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.

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-- Enable the "timescaledb" extension
create extension timescaledb;

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

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.

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create table temperatures (
  time timestampz not null,
  sensor_id int not null,
  temperature double precision not null
);

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

Next, we can populate some values

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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.

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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