/usr/share/gocode/src/github.com/retailnext/hllpp/hllpp.go is in golang-github-retailnext-hllpp-dev 1.0.0+git20170901.6e8b6d3-3.
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// All rights reserved.
// hllpp implements the HyperLogLog++ cardinality estimator as specified
// in the HyperLogLog++ paper http://goo.gl/Z5Sqgu. hllpp uses a built-in
// non-streaming implementation of murmur3 to hash data as you add it to
// the estimator.
package hllpp
import (
"errors"
"fmt"
"math"
)
// HLLPP represents a single HyperLogLog++ estimator. Create one via New().
// It is not safe to interact with an HLLPP object from multiple goroutines
// at once.
type HLLPP struct {
// raw data be it sparse or dense (this makes serialization easier)
data []byte
// accumulates unsorted values in sparse mode
tmpSet uint32Slice
sparse bool
sparseLength uint32
// how many bits we are using to store each register value
bitsPerRegister uint32
p uint8
m uint32
// p' and m'
pp uint8
mp uint32
}
// Approximate size in bytes of h (used for testing).
func (h *HLLPP) memSize() int {
return cap(h.data) + 4*cap(h.tmpSet) + 20
}
// New creates a HyperLogLog++ estimator with p=14, p'=20.
func New() *HLLPP {
h, err := NewWithConfig(Config{})
if err != nil {
panic(err)
}
return h
}
// Config is used to set configurable fields on a HyperLogLog++ via
// NewWithConfig.
type Config struct {
// Precision (p). Must be in the range [4..16]. This value can be used
// to adjust the typical relative error of the estimate. Space requirements
// grow exponentially as this value is increased. Defaults to 14, the
// recommended value, which gives an expected error of about 0.8%
Precision uint8
// Precision in sparse mode (p'). Must be in the range [p..25] for this
// implementation. This value can be used to adjust the typical relative
// error of the estimate when using the sparse representation (typically
// for cardinalities below 8000 at p'=20). Lowering p' will allow the
// estimator to remain in sparse mode longer, but will increase the relative
// error. The HyperLogLog++ paper recommends 20 or 25. Defaults to 20 since
// that still gives you a much lower error vs. p=14, but saves a significant
// amount of space vs. p'=25 (20-25% for cardinalities less than 5000).
SparsePrecision uint8
}
// NewWithConfig creates a HyperLogLog++ estimator with the given Config.
func NewWithConfig(c Config) (*HLLPP, error) {
if c.Precision == 0 {
c.Precision = 14
}
if c.SparsePrecision == 0 {
c.SparsePrecision = 20
}
p, pp := c.Precision, c.SparsePrecision
if p < 4 || p > 16 || pp < p || pp > 25 {
return nil, fmt.Errorf("invalid precision (p: %d, p': %d)", p, pp)
}
return &HLLPP{
p: p,
pp: pp,
m: 1 << p,
mp: 1 << pp,
sparse: true,
}, nil
}
// Add will hash v and add the result to the HyperLogLog++ estimator h. hllpp
// uses a built-in non-streaming implementation of murmur3.
func (h *HLLPP) Add(v []byte) {
x := murmurSum64(v)
if h.sparse {
h.tmpSet = append(h.tmpSet, h.encodeHash(x))
// is tmpSet >= 1/4 of memory limit?
if 4*uint32(len(h.tmpSet))*8 >= 6*h.m/4 {
h.flushTmpSet()
}
} else {
idx := uint32(sliceBits64(x, 63, 64-h.p))
rho := rho(x<<h.p | 1<<(h.p-1))
h.updateRegisterIfBigger(idx, rho)
}
}
func (h *HLLPP) updateRegisterIfBigger(idx uint32, rho uint8) {
if rho > 31 && h.bitsPerRegister == 5 {
h.bitsPerRegister = 6
newData := make([]byte, h.m*h.bitsPerRegister/8)
for i := uint32(0); i < h.m; i++ {
setRegister(newData, 6, i, getRegister(h.data, 5, i))
}
h.data = newData
}
if rho > getRegister(h.data, h.bitsPerRegister, idx) {
setRegister(h.data, h.bitsPerRegister, idx, rho)
}
}
// Count returns the current cardinality estimate for h.
func (h *HLLPP) Count() uint64 {
if h.sparse {
h.flushTmpSet()
return linearCounting(h.mp, h.mp-h.sparseLength)
}
var (
est float64
numZeros uint32
)
for i := uint32(0); i < h.m; i++ {
reg := getRegister(h.data, h.bitsPerRegister, i)
est += 1.0 / float64(uint64(1)<<reg)
if reg == 0 {
numZeros++
}
}
if numZeros > 0 {
lc := linearCounting(h.m, numZeros)
if lc < threshold[h.p-4] {
return lc
}
}
est = alpha(h.m) * float64(h.m) * float64(h.m) / est
if est <= float64(h.m*5) {
est -= h.estimateBias(est)
}
return uint64(est + 0.5)
}
// Merge turns h into the union of h and other. h and other must have the same
// p and p' values.
func (h *HLLPP) Merge(other *HLLPP) error {
if h.p != other.p || h.pp != other.pp {
return errors.New("HLLPPs have different parameters")
}
if h.sparse && !other.sparse {
h.toNormal()
}
if other.sparse {
other.flushTmpSet()
}
if h.sparse && other.sparse {
tmpSet := make([]uint32, other.sparseLength)
reader := newSparseReader(other.data)
for index := 0; !reader.Done(); index++ {
tmpSet[index] = reader.Next()
}
h.mergeSparse(tmpSet)
} else if !h.sparse && !other.sparse {
for i := uint32(0); i < h.m; i++ {
rho := getRegister(other.data, other.bitsPerRegister, i)
h.updateRegisterIfBigger(i, rho)
}
} else {
reader := newSparseReader(other.data)
for !reader.Done() {
idx, rho := other.decodeHash(reader.Next(), other.p)
h.updateRegisterIfBigger(idx, rho)
}
}
return nil
}
func (h *HLLPP) toNormal() {
if !h.sparse {
return
}
if h.bitsPerRegister == 0 {
h.bitsPerRegister = 5
}
newData := make([]byte, h.m*h.bitsPerRegister/8)
reader := newSparseReader(h.data)
for !reader.Done() {
idx, rho := h.decodeHash(reader.Next(), h.p)
if rho > 31 && h.bitsPerRegister == 5 {
h.bitsPerRegister = 6
h.toNormal()
return
}
if rho > getRegister(newData, h.bitsPerRegister, idx) {
setRegister(newData, h.bitsPerRegister, idx, rho)
}
}
h.data = newData
h.tmpSet = nil
h.sparse = false
}
func linearCounting(m, v uint32) uint64 {
return uint64(float64(m)*math.Log(float64(m)/float64(v)) + 0.5)
}
// slice out inclusive bit section [x.high..x.low]
func sliceBits64(x uint64, high, low uint8) uint64 {
return (x << (63 - high)) >> (low + (63 - high))
}
// slice out inclusive bit section [x.high..x.low]
func sliceBits32(x uint32, high, low uint8) uint32 {
return (x << (31 - high)) >> (low + (31 - high))
}
// number of leading zeros plus 1 (rho as in "ϱ" in paper)
func rho(x uint64) (z uint8) {
for bit := uint64(1 << 63); bit&x == 0 && bit > 0; bit >>= 1 {
z++
}
return z + 1
}
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