# Aggregate Functions¶

Aggregate functions operate on a set of values to compute a single result. Please refer to PostgreSQL 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_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.