T-Digest Functions

T-digest was developed by Ted Dunning.

A T-digest is a data sketch which stores approximate percentile information. The Presto type for this data structure is called tdigest, and it accepts a parameter of type which represents the set of numbers to be ingested by the tdigest. Other numeric types may be added in a future release.

T-digests may be merged without losing precision, and for storage and retrieval they may be cast to/from VARBINARY.

Functions

merge(tdigest<double>) → tdigest<double>

Merges all input tdigests into a single tdigest.

value_at_quantile(tdigest<double>, quantile) → double

Returns the approximate percentile values from the T-digest given the number quantile between 0 and 1.

Returns the approximate quantile number between 0 and 1 from the T-digest given an input value. Null is returned if the T-digest is empty or the input value is outside of the range of the digest.

scale_tdigest(tdigest<double>, scale_factor) → tdigest<double>

Returns a whose distribution has been scaled by a factor specified by scale_factor.

values_at_quantiles(tdigest<double>, quantiles) → array<double>

Returns the approximate percentile values as an array given the input T-digest and array of values between 0 and 1 which represent the quantiles to return.

trimmed_mean(tdigest<double>, lower_quantile, upper_quantile) → double

Returns an estimate of the mean, excluding portions of the distribution outside the provided quantile bounds. Both lower_quantile and upper_quantile must be between 0 and 1.

Returns the tdigest which is composed of all input values of x.

tdigest_agg(x, w) → tdigest<double>

Returns the tdigest which is composed of all input values of using the per-item weight w.

tdigest_agg(x, w, compression) → tdigest<double>

Returns the tdigest which is composed of all input values of x using the per-item weight w and compression factor compression. compression must be a value greater than zero, and it must be constant for all input rows.

Compression factor of 500 is a good starting point that typically yields good accuracy and performance.

destructure_tdigest(tdigest<double>) → row<centroid_means array<double>, centroid_weights array<integer>, compression double, min double, max double, sum double, count bigint>