docs/source/user-guide/sql/aggregate_functions.md

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# Aggregate Functions

Aggregate functions operate on a set of values to compute a single result. Please refer to [PostgreSQL](https://www.postgresql.org/docs/current/functions-aggregate.html) for usage of standard SQL functions.

## General

- min
- max
- count
- avg
- sum
- array_agg

## Statistical

- var / var_samp / var_pop
- stddev / stddev_samp / stddev_pop
- covar / covar_samp / covar_pop
- corr

## Approximate

### approx_distinct

`approx_distinct(x) -> uint64` returns the approximate number (HyperLogLog) of distinct input values

### approx_median

`approx_median(x) -> x` returns the approximate median of input values. it is an alias of `approx_percentile_cont(x, 0.5)`.

### approx_percentile_cont

`approx_percentile_cont(x, p) -> x` return the approximate percentile (TDigest) of input values, where `p` is a float64 between 0 and 1 (inclusive).

It supports raw data as input and build Tdigest sketches during query time, and is approximately equal to `approx_percentile_cont_with_weight(x, 1, p)`.

`approx_percentile_cont(x, p, n) -> x` return the approximate percentile (TDigest) of input values, where `p` is a float64 between 0 and 1 (inclusive),

and `n` (default 100) is the number of centroids in Tdigest which means that if there are `n` or fewer unique values in `x`, you can expect an exact result.

A higher value of `n` results in a more accurate approximation and the cost of higher memory usage.

### approx_percentile_cont_with_weight

`approx_percentile_cont_with_weight(x, w, p) -> x` returns the approximate percentile (TDigest) of input values with weight, where `w` is weight column expression and `p` is a float64 between 0 and 1 (inclusive).

It supports raw data as input or pre-aggregated TDigest sketches, then builds or merges Tdigest sketches during query time. TDigest sketches are a list of centroid `(x, w)`, where `x` stands for mean and `w` stands for weight.

It is suitable for low latency OLAP system where a streaming compute engine (e.g. Spark Streaming/Flink) pre-aggregates data to a data store, then queries using Datafusion.