telegraf/plugins/inputs/statsd/running_stats.go

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package statsd
import (
"math"
"math/rand"
"sort"
)
const defaultPercentileLimit = 1000
// RunningStats calculates a running mean, variance, standard deviation,
// lower bound, upper bound, count, and can calculate estimated percentiles.
// It is based on the incremental algorithm described here:
// https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
type RunningStats struct {
k float64
n int64
ex float64
ex2 float64
// Array used to calculate estimated percentiles
// We will store a maximum of PercLimit values, at which point we will start
// randomly replacing old values, hence it is an estimated percentile.
perc []float64
PercLimit int
upper float64
lower float64
// cache if we have sorted the list so that we never re-sort a sorted list,
// which can have very bad performance.
sorted bool
}
func (rs *RunningStats) AddValue(v float64) {
// Whenever a value is added, the list is no longer sorted.
rs.sorted = false
if rs.n == 0 {
rs.k = v
rs.upper = v
rs.lower = v
if rs.PercLimit == 0 {
rs.PercLimit = defaultPercentileLimit
}
rs.perc = make([]float64, 0, rs.PercLimit)
}
// These are used for the running mean and variance
rs.n += 1
rs.ex += v - rs.k
rs.ex2 += (v - rs.k) * (v - rs.k)
// track upper and lower bounds
if v > rs.upper {
rs.upper = v
} else if v < rs.lower {
rs.lower = v
}
if len(rs.perc) < rs.PercLimit {
rs.perc = append(rs.perc, v)
} else {
// Reached limit, choose random index to overwrite in the percentile array
rs.perc[rand.Intn(len(rs.perc))] = v
}
}
func (rs *RunningStats) Mean() float64 {
return rs.k + rs.ex/float64(rs.n)
}
func (rs *RunningStats) Variance() float64 {
return (rs.ex2 - (rs.ex*rs.ex)/float64(rs.n)) / float64(rs.n)
}
func (rs *RunningStats) Stddev() float64 {
return math.Sqrt(rs.Variance())
}
func (rs *RunningStats) Upper() float64 {
return rs.upper
}
func (rs *RunningStats) Lower() float64 {
return rs.lower
}
func (rs *RunningStats) Count() int64 {
return rs.n
}
func (rs *RunningStats) Percentile(n int) float64 {
if n > 100 {
n = 100
}
if !rs.sorted {
sort.Float64s(rs.perc)
rs.sorted = true
}
i := int(float64(len(rs.perc)) * float64(n) / float64(100))
if i < 0 {
i = 0
}
return rs.perc[i]
}