# Presto 316 Documentation

## 7.21. Quantile Digest Functions

Presto implements the `approx_percentile` function with the quantile digest data structure. The underlying data structure, qdigest, is exposed as a data type in Presto, and can be created, queried and stored separately from `approx_percentile`.

## Data Structures

A quantile digest is a data sketch which stores approximate percentile information. The presto type for this data structure is called `qdigest`, and it takes a parameter which must be one of `bigint`, `double` or `real` which represent the set of numbers that may be ingested by the `qdigest`. They may be merged without losing precision, and for storage and retrieval they may be cast to/from `VARBINARY`.

## Functions

`merge`(qdigest) → qdigest

  Merges all input `qdigest`s into a single `qdigest`.

`value_at_quantile`(qdigest(T), quantile) → T

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

`values_at_quantiles`(qdigest(T), quantiles) → T

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

`qdigest_agg`(x) → qdigest<[same as x]>

  Returns the `qdigest` which is composed of all input values of `x`.

`qdigest_agg`(x, w) → qdigest<[same as x]>

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

`qdigest_agg`(x, w, accuracy) → qdigest<[same as x]>

  Returns the `qdigest` which is composed of all input values of `x` using the per-item weight `w` and maximum error of `accuracy`. `accuracy` must be a value greater than zero and less than one, and it must be constant for all input rows.