tdigest()

Toolkit

TimescaleDB Toolkit functions are available under Timescale Community Edition. They are automatically included with Timescale Cloud. Click to learn more.

tdigest(
    buckets INTEGER,
    value DOUBLE PRECISION
) RETURNS TDigest

This constructs and returns a tdigest with the specified number of buckets over the given values.

TimescaleDB provides an implementation of the tdigest data structure for quantile approximations. A tdigest is a space efficient aggregation which provides increased resolution at the edges of the distribution. This allows for more accurate estimates of extreme quantiles than traditional methods.

Timescale's tdigest is implemented as an aggregate function in PostgreSQL. It does not support moving-aggregate mode and is not an ordered-set aggregate. It is parallelizable and is a good candidate for continuous aggregation. Aggregating with tdigest is currently restricted to float values.

The tdigest function is somewhat dependent on the order of inputs. The percentile approximations should be nearly equal for the same underlying data, especially at the extremes of the quantile range where the tdigest is inherently more accurate. They are unlikely to be identical if built in a different order. While this should have little effect on the accuracy of the estimates, it is worth noting that repeating the creation of the tdigest might have subtle differences if the call is being parallelized by PostgreSQL.

• For more information about percentile approximation functions, see the hyperfunctions documentation.
• For some more technical details and usage examples of this algorithm, see the developer documentation.

Required arguments

Returns

Sample usage

This example uses a table called samples, with a column called weights, that holds DOUBLE PRECISION values. This query returns a digest over that column:

SELECT tdigest(100, data) FROM samples;

This example builds a view from the aggregate that can be passed to other tdigest functions:

CREATE VIEW digest AS
    SELECT tdigest(100, data)
    FROM samples;