Data.HyperLogLog

See the original paper for details: http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdf

HyperLogLog

data HyperLogLog p

Initialize a new counter:

>>> mempty :: HyperLogLog $(3)
HyperLogLog {runHyperLogLog = fromList [0,0,0,0,0,0,0,0]}

Please note how you specify a counter size with the $(n) invocation. Sizes of up to 16 are valid, with 7 being a likely good minimum for decent accuracy.

Let's count a list of unique items and get the latest estimate:

>>> size (foldr insert mempty [1..10] :: HyperLogLog $(4))
Approximate {_confidence = 0.9972, _lo = 2, _estimate = 11, _hi = 20}

Note how insert can be used to add new observations to the approximate counter.

Instances

HasHyperLogLog k (HyperLogLog k p0) p0

Eq (HyperLogLog k p)

Show (HyperLogLog k p)

Generic (HyperLogLog k p)

ReifiesConfig k p => Monoid (HyperLogLog k p)

Serialize (HyperLogLog k p)

Semigroup (HyperLogLog k p)

ReifiesConfig k p => HasConfig (HyperLogLog k p)

class HasHyperLogLog t p | t -> p where

Methods

hyperLogLog :: Lens' t (HyperLogLog p)

Instances

HasHyperLogLog k (HyperLogLog k p0) p0

size :: ReifiesConfig p => HyperLogLog p -> Approximate Int64

Approximate size of our set

intersectionSize :: ReifiesConfig p => [HyperLogLog p] -> Approximate Int64

insert :: (ReifiesConfig s, Hashable a) => a -> HyperLogLog s -> HyperLogLog s

cast :: forall p q. (ReifiesConfig p, ReifiesConfig q) => HyperLogLog p -> Maybe (HyperLogLog q)

Config

data Config

Constants required for a bucketing factor b

Instances

Eq Config

Show Config

Generic Config

Serialize Config

HasConfig Config

hll :: Int -> Config

Precalculate constants for a given bucketing factor b

ReifiesConfig

class ReifiesConfig o

Instances

SingRep Nat n Integer => ReifiesConfig Nat n

Reifies * n Int => ReifiesConfig * (D n)

Reifies * n Int => ReifiesConfig * (SD n)

reifyConfig :: Int -> (forall o. ReifiesConfig o => Proxy o -> r) -> r