About Timescale hyperfunctions

Timescale hyperfunctions are a specialized set of functions that power real-time analytics on time series and events. IoT devices, IT systems, marketing analytics, user behavior, financial metrics, cryptocurrency - these are only a few examples of domains where hyperfunctions can make a huge difference. Hyperfunctions provide you with meaningful, actionable insights in real time.

Timescale Cloud includes all hyperfunctions by default, while self-hosted TimescaleDB includes a subset of them. For additional hyperfunctions, install the Timescale Toolkit PostgreSQL extension.

Here is a list of all the hyperfunctions provided by Timescale. Hyperfunctions with a tick in the Toolkit column require an installation of Timescale Toolkit for self-hosted deployments. Hyperfunctions with a tick in the Experimental column are still under development.

Warning

Experimental features could have bugs. They might not be backwards compatible, and could be removed in future releases. Use these features at your own risk, and do not use any experimental features in production.

Important

When you upgrade the timescaledb extension, the experimental schema is removed by default. To use experimental features after an upgrade, you need to add the experimental schema again.

For more information about each of the API calls listed in this table, see the hyperfunction API documentation.

Function pipelines are an experimental feature, designed to radically improve the developer ergonomics of analyzing data in PostgreSQL and SQL, by applying principles from functional programming and popular tools like Python's Pandas, and PromQL.

SQL is the best language for data analysis, but it is not perfect, and at times can get quite unwieldy. For example, this query gets data from the last day from the measurements table, sorts the data by the time column, calculates the delta between the values, takes the absolute value of the delta, and then takes the sum of the result of the previous steps:

SELECT device id,
sum(abs_delta) as volatility
FROM (
 SELECT device_id,
abs(val - lag(val) OVER last_day) as abs_delta
FROM measurements
WHERE ts >= now()-'1 day'::interval) calc_delta
GROUP BY device_id;

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You can express the same query with a function pipeline like this:

SELECT device_id,
 timevector(ts, val) -> sort() -> delta() -> abs() -> sum() as volatility
FROM measurements
WHERE ts >= now()-'1 day'::interval
GROUP BY device_id;

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Function pipelines are completely SQL compliant, meaning that any tool that speaks SQL is able to support data analysis using function pipelines.

For more information about how function pipelines work, read our blog post.

Timescale Toolkit features are developed in the open. As features are developed they are categorized as experimental, beta, stable, or deprecated. This documentation covers the stable features, but more information on our experimental features in development can be found in the Toolkit repository.

We want and need your feedback! What are the frustrating parts of analyzing time-series data? What takes far more code than you feel it should? What runs slowly, or only runs quickly after many rewrites? We want to solve community-wide problems and incorporate as much feedback as possible.

• Join the discussion.
• Check out the proposed features.
• Explore the current feature requests.
• Add your own feature request.