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Data structures

A quantile digest is a data sketch which stores approximate percentile information. The Trino 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.

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Functions

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merge

merge(qdigest) -> qdigest
Merges all input qdigests into a single qdigest.

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value_at_quantile

value_at_quantile(qdigest(T), quantile) -> T
Returns the approximate percentile value from the quantile digest given the number quantile between 0 and 1.

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quantile_at_value

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

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values_at_quantiles

values_at_quantiles(qdigest(T), quantiles) -> array(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.

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qdigest_agg

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.