T-Digest Functions

T-digest and quantile digest are two older algorithms for estimating rank-based metrics. T-digest generally has better performance than quantile digest and better accuracy at the tails (often dramatically better), but may have worse accuracy at the median depending on the compression factor used. In comparison, quantile digest supports more numeric types and provides a maximum rank error guarantee, which ensures relative uniformity of precision along the quantiles. Quantile digests are also formally proven to support lossless merges, while T-digest is not (though it does empirically demonstrate lossless merges).

T-digest was developed by Ted Dunning and is more restrictive in its type support, accepting only double type parameters. This contrasts with quantile digest, which supports a broader range of numeric types including bigint, double, and real, making quantile digest more versatile for different data types.

Velox uses the modern KLL sketch algorithm for the approx_percentile function, which provides stronger accuracy guarantees than both T-digest and quantile digest. The T-digest functions documented here exist primarily to support pre-existing workloads that have data stored using the T-digest format for backward compatibility.

Data Structures

A T-digest is a data sketch which stores approximate percentile information. The Velox type for this data structure is called tdigest, and it accepts a parameter of type double which represents the set of numbers to be ingested by the tdigest.

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

Functions

construct_tdigest(means: array<double>, counts: array<integer>, compression: double, min: double, max: double, sum: double, count: bigint) → tdigest<double>

Constructs a T-digest from the given parameters:

  • means - array of centroid means

  • counts - array of centroid counts (weights)

  • compression - compression factor

  • min - minimum value

  • max - maximum value

  • sum - sum of all values

  • count - total count of values

destructure_tdigest(digest: tdigest<double>) -> row(means array<double>, counts array<integer>, compression double, min double, max double, sum double, count bigint)

Destructures a T-digest into its component parts, returning a row containing:

  • means - array of centroid means

  • counts - array of centroid counts

  • compression - compression factor

  • min - minimum value

  • max - maximum value

  • sum - sum of all values

  • count - total count of values

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

Merges all input tdigests into a single tdigest.

merge_tdigest(digests: array<tdigest<double>>) → tdigest<double>

Merges an array of T-digests into a single T-digest.

quantile_at_value(digest: tdigest<double>, value: double) → double

Returns the approximate quantile (percentile) of the given value based on the T-digest digest. The result will be between zero and one (inclusive).

quantiles_at_values(digest: tdigest<double>, values: array<double>) → array<double>

Returns the approximate quantiles (percentiles) as an array for each of the given values based on the T-digest digest. All results will be between zero and one (inclusive).

scale_tdigest(digest: tdigest<double>, scale: double) → tdigest<double>

Scales the T-digest digest by the given scale factor. This multiplies all the centroid values in the T-digest by the scale factor.

tdigest_agg(x: double) → tdigest<double>

Returns the tdigest which summarizes the approximate distribution of all input values of x. The default compression factor is 100.

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

Returns the tdigest which summarizes the approximate distribution of all input values of x using per-item weight w. The default compression factor is 100.

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

Returns the tdigest which summarizes the approximate distribution of all input values of x using per-item weight w and the specified compression factor. compression must be a positive constant for all input rows. The default is 100, maximum is 1000, and values lower than 10 are rounded to 10. Higher compression means more accuracy at the cost of more memory.

trimmed_mean(digest: tdigest<double>, low_quantile: double, high_quantile: double) → double

Returns the mean of values between low_quantile and high_quantile (inclusive) from the T-digest digest. Both quantile values must be between zero and one (inclusive), and low_quantile must be less than or equal to high_quantile.

value_at_quantile(digest: tdigest<double>, quantile: double) → double

Returns the approximate percentile value from the T-digest digest at the given quantile. The quantile must be between zero and one (inclusive).

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

Returns the approximate percentile values as an array from the T-digest digest at each of the specified quantiles given in the quantiles array. All quantile values must be between zero and one (inclusive).