Uses of Class
dev.frostlake.functions.AggregateFunction

Packages that use AggregateFunction

Package	Description
dev.frostlake.functions
dev.frostlake.functions.aggregate

Uses of AggregateFunction in dev.frostlake.functions

Methods in dev.frostlake.functions that return AggregateFunction

Modifier and Type	Method	Description
AggregateFunction	FunctionRegistry.getAggregateFunction(String name)

Methods in dev.frostlake.functions that return types with arguments of type AggregateFunction

Modifier and Type	Method	Description
Collection<AggregateFunction>	FunctionRegistry.getAllAggregateFunctions()

Methods in dev.frostlake.functions with parameters of type AggregateFunction

Modifier and Type	Method	Description
void	FunctionRegistry.registerAggregate(AggregateFunction function)

Uses of AggregateFunction in dev.frostlake.functions.aggregate

Subclasses of AggregateFunction in dev.frostlake.functions.aggregate

Modifier and Type	Class	Description
class	AnyValue	ANY_VALUE(expr) — an arbitrary non-NULL value from the group (this implementation keeps the first one it sees), or NULL when the group holds none.
class	ApproxCountDistinct	APPROX_COUNT_DISTINCT(expr) — an approximate count of the distinct non-NULL values in the group, estimated with the HllAccumulator HyperLogLog sketch (~0.8% standard error, near-exact at low cardinality).
class	ApproxPercentile	APPROX_PERCENTILE(expr, percentile) — the approximate value at the given percentile (a constant in [0, 1]) of expr.
class	ApproxPercentileAccumulate	APPROX_PERCENTILE_ACCUMULATE(expr): the PercentileDigest of a numeric input as an OBJECT state, for APPROX_PERCENTILE_COMBINE to merge and APPROX_PERCENTILE_ESTIMATE to read.
class	ApproxPercentileCombine	APPROX_PERCENTILE_COMBINE(state): the APPROX_PERCENTILE_ACCUMULATE states of its input merged into one digest — every centroid kept with its weight, sorted by mean.
class	ApproxTopK	APPROX_TOP_K(expr [, k [, counters]]): the k most frequent values of the input as an ARRAY of [value, count] pairs, count descending, computed by the Space-Saving summary in TopKSummary over counters counters (default 100000; k defaults to 1).
class	ApproxTopKAccumulate	APPROX_TOP_K_ACCUMULATE(expr, counters): the whole TopKSummary of the input as an OBJECT state — {"counters":C,"datatype":T,"precision":P,"scale":S,"state":[[value, count], …], "type":"approx_top_k"} — for APPROX_TOP_K_COMBINE to merge and APPROX_TOP_K_ESTIMATE to rank.
class	ApproxTopKCombine	APPROX_TOP_K_COMBINE(state [, counters]): the APPROX_TOP_K_ACCUMULATE states of its input merged into one — equal values add their counts, nothing is inherited — and trimmed to counters (a constant between 1 and 100000; by default the inputs' own limit, which every input must share: differing limits refuse with Invalid parameter value: ApproxTopK state. Reason: combining states with different numbers of counters).
class	ArrayAgg
class	ArrayUnionAgg	ARRAY_UNION_AGG(array) — an aggregate whose every input row is itself an ARRAY.
class	ArrayUniqueAgg	ARRAY_UNIQUE_AGG(expr) — an aggregate that collects the DISTINCT non-NULL scalar input values (each input row contributes a single element, not an array) into a VARIANT ARRAY.
class	Avg	AVG(expr) — see AvgAccumulator / AggregateNumerics.avg(Iterable) for the live-verified Snowflake result typing (fixed-point inputs → BigDecimal scale = max input scale + 6, HALF_UP; any double/VARIANT input → double; empty → NULL).
class	BitAndAgg
class	BitOrAgg
class	BitXorAgg
class	BoolAndAgg
class	BooleanAggregate	The three boolean aggregates' shared argument rule.
class	BoolOrAgg
class	BoolXorAgg
class	Corr
class	Count
class	CountIf
class	CovarPop
class	CovarSamp
class	HashAgg	HASH_AGG(expr) — a single, order-INDEPENDENT signed 64-bit hash over the whole (multi)set of input rows.
class	Kurtosis
class	ListAgg	LISTAGG(<expr> [, <delimiter>]) — concatenate values into a single string.
class	Max
class	MaxBy	MAX_BY(value, sort_key) — a two-argument aggregate returning the value from the row whose sort_key is the maximum.
class	Median
class	Min
class	MinBy	MIN_BY(value, sort_key) — a two-argument aggregate returning the value from the row whose sort_key is the minimum.
class	Mode
class	ObjectAgg	OBJECT_AGG(key, value) — aggregates key/value pairs into a single OBJECT.
class	PercentileCont
class	PercentileDisc
class	RegrAvgx	REGR_AVGX(y, x) — average of the independent variable x over non-null (y, x) pairs (y dependent, x independent).
class	RegrAvgy	REGR_AVGY(y, x) — average of the dependent variable y over non-null (y, x) pairs (y dependent, x independent).
class	RegrCount	REGR_COUNT(y, x) — count over non-null (y, x) pairs (y dependent, x independent).
class	RegrIntercept	REGR_INTERCEPT(y, x) — y-intercept of the least-squares regression line over non-null (y, x) pairs (y dependent, x independent).
class	RegrR2	REGR_R2(y, x) — coefficient of determination (R squared) over non-null (y, x) pairs (y dependent, x independent).
class	RegrSlope	REGR_SLOPE(y, x) — slope of the least-squares regression line over non-null (y, x) pairs (y dependent, x independent).
class	RegrSxx	REGR_SXX(y, x) — sum of squares of x deviations over non-null (y, x) pairs (y dependent, x independent).
class	RegrSxy	REGR_SXY(y, x) — sum of products of x and y deviations over non-null (y, x) pairs (y dependent, x independent).
class	RegrSyy	REGR_SYY(y, x) — sum of squares of y deviations over non-null (y, x) pairs (y dependent, x independent).
class	Skew
class	StdDev
class	StdDevPop
class	StdDevSamp
class	Sum
class	Variance
class	VarPop
class	VarSamp
class	VectorAvg	VECTOR_AVG(v) — the element-wise MEAN of a group's vectors, always a VECTOR(FLOAT, n): over the rows [1,2,3] and [4,5,6] it is [2.5,3.5,4.5] (live-verified).
class	VectorMax	VECTOR_MAX(v) — the element-wise MAXIMUM of a group's vectors, keeping the element type: over the rows [1,9,3], [4,5,-6] and [7,2,0] it is [7.0,9.0,3.0] (live-verified), i.e. each dimension is maximised on its own.
class	VectorMin	VECTOR_MIN(v) — the element-wise MINIMUM of a group's vectors, keeping the element type: over the rows [1,9,3], [4,5,-6] and [7,2,0] it is [1.0,2.0,-6.0] (live-verified), i.e. each dimension is minimised on its own.
class	VectorSum	VECTOR_SUM(v) — the element-wise SUM of a group's vectors, keeping the element type: over the rows [1,2,3] and [4,5,6] it is [5.0,7.0,9.0] (live-verified).