forked from weibohe/fortio
-
Notifications
You must be signed in to change notification settings - Fork 0
/
stats.go
530 lines (485 loc) · 15.1 KB
/
stats.go
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
// Copyright 2017 Istio Authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package stats // import "fortio.org/fortio/stats"
import (
"bufio"
"bytes"
"errors"
"fmt"
"io"
"math"
"strconv"
"strings"
"fortio.org/fortio/log"
)
// Counter is a type whose instances record values
// and calculate stats (count,average,min,max,stddev).
type Counter struct {
Count int64
Min float64
Max float64
Sum float64
sumOfSquares float64
}
// Record records a data point.
func (c *Counter) Record(v float64) {
c.RecordN(v, 1)
}
// RecordN efficiently records the same value N times
func (c *Counter) RecordN(v float64, n int) {
isFirst := (c.Count == 0)
c.Count += int64(n)
if isFirst {
c.Min = v
c.Max = v
} else if v < c.Min {
c.Min = v
} else if v > c.Max {
c.Max = v
}
s := v * float64(n)
c.Sum += s
c.sumOfSquares += (s * s)
}
// Avg returns the average.
func (c *Counter) Avg() float64 {
return c.Sum / float64(c.Count)
}
// StdDev returns the standard deviation.
func (c *Counter) StdDev() float64 {
fC := float64(c.Count)
sigma := (c.sumOfSquares - c.Sum*c.Sum/fC) / fC
// should never happen but it does
if sigma < 0 {
log.Warnf("Unexpected negative sigma for %+v: %g", c, sigma)
return 0
}
return math.Sqrt(sigma)
}
// Print prints stats.
func (c *Counter) Print(out io.Writer, msg string) {
_, _ = fmt.Fprintf(out, "%s : count %d avg %.8g +/- %.4g min %g max %g sum %.9g\n", // nolint(errorcheck)
msg, c.Count, c.Avg(), c.StdDev(), c.Min, c.Max, c.Sum)
}
// Log outputs the stats to the logger.
func (c *Counter) Log(msg string) {
log.Infof("%s : count %d avg %.8g +/- %.4g min %g max %g sum %.9g",
msg, c.Count, c.Avg(), c.StdDev(), c.Min, c.Max, c.Sum)
}
// Reset clears the counter to reset it to original 'no data' state.
func (c *Counter) Reset() {
var empty Counter
*c = empty
}
// Transfer merges the data from src into this Counter and clears src.
func (c *Counter) Transfer(src *Counter) {
if src.Count == 0 {
return // nothing to do
}
if c.Count == 0 {
*c = *src // copy everything at once
src.Reset()
return
}
c.Count += src.Count
if src.Min < c.Min {
c.Min = src.Min
}
if src.Max > c.Max {
c.Max = src.Max
}
c.Sum += src.Sum
c.sumOfSquares += src.sumOfSquares
src.Reset()
}
// Histogram - written in go with inspiration from https://github.com/facebook/wdt/blob/master/util/Stats.h
// The intervals are ]prev,current] so for "90" (previous is 80) the values in that bucket are >80 and <=90
// that way a cumulative % up to that bucket means X% of the data <= 90 (or 100-X% > 90), works well for max too
// There are 2 special buckets - the first one is from min to and including 0,
// one after the last for value > last and up to max
var (
histogramBucketValues = []int32{
0, 1, 2, 3, 4, 5, 6,
7, 8, 9, 10, 11, // initially increment buckets by 1, my amp goes to 11 !
12, 14, 16, 18, 20, // then by 2
25, 30, 35, 40, 45, 50, // then by 5
60, 70, 80, 90, 100, // then by 10
120, 140, 160, 180, 200, // line3 *10
250, 300, 350, 400, 450, 500, // line4 *10
600, 700, 800, 900, 1000, // line5 *10
2000, 3000, 4000, 5000, 7500, 10000, // another order of magnitude coarsly covered
20000, 30000, 40000, 50000, 75000, 100000, // ditto, the end
}
numValues = len(histogramBucketValues)
numBuckets = numValues + 1 // 1 special first bucket is <= 0; and 1 extra last bucket is > 100000
firstValue = float64(histogramBucketValues[0])
lastValue = float64(histogramBucketValues[numValues-1])
val2Bucket []int // ends at 1000. Remaining values will not be received in constant time.
maxArrayValue = int32(1000) // Last value looked up as O(1) array, the rest is linear search
maxArrayValueIndex = -1 // Index of maxArrayValue
)
// Histogram extends Counter and adds an histogram.
// Must be created using NewHistogram or anotherHistogram.Clone()
// and not directly.
type Histogram struct {
Counter
Offset float64 // offset applied to data before fitting into buckets
Divider float64 // divider applied to data before fitting into buckets
// Don't access directly (outside of this package):
Hdata []int32 // numValues buckets (one more than values, for last one)
}
// For export of the data:
// Interval is a range from start to end.
// Interval are left closed, open right expect the last one which includes Max.
// ie [Start, End[ with the next one being [PrevEnd, NextEnd[.
type Interval struct {
Start float64
End float64
}
// Bucket is the data for 1 bucket: an Interval and the occurrence Count for
// that interval.
type Bucket struct {
Interval
Percent float64 // Cumulative percentile
Count int64 // How many in this bucket
}
// Percentile value for the percentile
type Percentile struct {
Percentile float64 // For this Percentile
Value float64 // value at that Percentile
}
// HistogramData is the exported Histogram data, a sorted list of intervals
// covering [Min, Max]. Pure data, so Counter for instance is flattened
type HistogramData struct {
Count int64
Min float64
Max float64
Sum float64
Avg float64
StdDev float64
Data []Bucket
Percentiles []Percentile
}
// NewHistogram creates a new histogram (sets up the buckets).
// Divider value can not be zero, otherwise returns zero
func NewHistogram(Offset float64, Divider float64) *Histogram {
h := new(Histogram)
h.Offset = Offset
if Divider == 0 {
return nil
}
h.Divider = Divider
h.Hdata = make([]int32, numBuckets)
return h
}
// Val2Bucket values are kept in two different structure
// val2Bucket allows you reach between 0 and 1000 in constant time
func init() {
val2Bucket = make([]int, maxArrayValue)
maxArrayValueIndex = -1
for i, value := range histogramBucketValues {
if value == maxArrayValue {
maxArrayValueIndex = i
break
}
}
if maxArrayValueIndex == -1 {
log.Fatalf("Bug boundary maxArrayValue=%d not found in bucket list %v", maxArrayValue, histogramBucketValues)
}
idx := 0
for i := int32(0); i < maxArrayValue; i++ {
if i >= histogramBucketValues[idx] {
idx++
}
val2Bucket[i] = idx
}
// coding bug detection (aka impossible if it works once) until 1000
if idx != maxArrayValueIndex {
log.Fatalf("Bug in creating histogram index idx %d vs index %d up to %d", idx, int(maxArrayValue), maxArrayValue)
}
}
// lookUpIdx looks for scaledValue's index in histogramBucketValues
// TODO: change linear time to O(log(N)) with binary search
func lookUpIdx(scaledValue int) int {
scaledValue32 := int32(scaledValue)
if scaledValue32 < maxArrayValue { //constant
return val2Bucket[scaledValue]
}
for i := maxArrayValueIndex; i < numValues; i++ {
if histogramBucketValues[i] > scaledValue32 {
return i
}
}
log.Fatalf("never reached/bug")
return 0
}
// Record records a data point.
func (h *Histogram) Record(v float64) {
h.RecordN(v, 1)
}
// RecordN efficiently records a data point N times.
func (h *Histogram) RecordN(v float64, n int) {
h.Counter.RecordN(v, n)
h.record(v, n)
}
// Records v value to count times
func (h *Histogram) record(v float64, count int) {
// Scaled value to bucketize - we subtract epsilon because the interval
// is open to the left ] start, end ] so when exactly on start it has
// to fall on the previous bucket. TODO add boundary tests
scaledVal := (v-h.Offset)/h.Divider - 0.0001
var idx int
if scaledVal <= firstValue {
idx = 0
} else if scaledVal > lastValue {
idx = numBuckets - 1 // last bucket is for > last value
} else {
// else we look it up
idx = lookUpIdx(int(scaledVal))
}
h.Hdata[idx] += int32(count)
}
// CalcPercentile returns the value for an input percentile
// e.g. for 90. as input returns an estimate of the original value threshold
// where 90.0% of the data is below said threshold.
// with 3 data points 10, 20, 30; p0-p33.33 == 10, p 66.666 = 20, p100 = 30
// p33.333 - p66.666 = linear between 10 and 20; so p50 = 15
// TODO: consider spreading the count of the bucket evenly from start to end
// so the % grows by at least to 1/N on start of range, and for last range
// when start == end we should get to that % faster
func (e *HistogramData) CalcPercentile(percentile float64) float64 {
if len(e.Data) == 0 {
log.Errf("Unexpected call to CalcPercentile(%g) with no data", percentile)
return 0
}
if percentile >= 100 {
return e.Max
}
// We assume Min is at least a single point so at least covers 1/Count %
pp := 100. / float64(e.Count) // previous percentile
if percentile <= pp {
return e.Min
}
for _, cur := range e.Data {
if percentile <= cur.Percent {
return cur.Start + (percentile-pp)/(cur.Percent-pp)*(cur.End-cur.Start)
}
pp = cur.Percent
}
return e.Max // not reached
}
// Export translate the internal representation of the histogram data in
// an externally usable one. Calculates the request Percentiles.
func (h *Histogram) Export() *HistogramData {
var res HistogramData
res.Count = h.Counter.Count
res.Min = h.Counter.Min
res.Max = h.Counter.Max
res.Sum = h.Counter.Sum
res.Avg = h.Counter.Avg()
res.StdDev = h.Counter.StdDev()
multiplier := h.Divider
offset := h.Offset
// calculate the last bucket index
lastIdx := -1
for i := numBuckets - 1; i >= 0; i-- {
if h.Hdata[i] > 0 {
lastIdx = i
break
}
}
if lastIdx == -1 {
return &res
}
// previous bucket value:
prev := histogramBucketValues[0]
var total int64
ctrTotal := float64(h.Count)
// export the data of each bucket of the histogram
for i := 0; i <= lastIdx; i++ {
if h.Hdata[i] == 0 {
// empty bucket: skip it but update prev which is needed for next iter
if i < numValues {
prev = histogramBucketValues[i]
}
continue
}
var b Bucket
total += int64(h.Hdata[i])
if len(res.Data) == 0 {
// First entry, start is min
b.Start = h.Min
} else {
b.Start = multiplier*float64(prev) + offset
}
b.Percent = 100. * float64(total) / ctrTotal
if i < numValues {
cur := histogramBucketValues[i]
b.End = multiplier*float64(cur) + offset
prev = cur
} else {
// Last Entry
b.Start = multiplier*float64(prev) + offset
b.End = h.Max
}
b.Count = int64(h.Hdata[i])
res.Data = append(res.Data, b)
}
res.Data[len(res.Data)-1].End = h.Max
return &res
}
// CalcPercentiles calculates the requested percentile and add them to the
// HistogramData. Potential TODO: sort or assume sorting and calculate all
// the percentiles in 1 pass (greater and greater values).
func (e *HistogramData) CalcPercentiles(percentiles []float64) *HistogramData {
if e.Count == 0 {
return e
}
for _, p := range percentiles {
e.Percentiles = append(e.Percentiles, Percentile{p, e.CalcPercentile(p)})
}
return e
}
// Print dumps the histogram (and counter) to the provided writer.
// Also calculates the percentile.
func (e *HistogramData) Print(out io.Writer, msg string) {
if len(e.Data) == 0 {
_, _ = fmt.Fprintf(out, "%s : no data\n", msg) // nolint: gas
return
}
// the base counter part:
_, _ = fmt.Fprintf(out, "%s : count %d avg %.8g +/- %.4g min %g max %g sum %.9g\n",
msg, e.Count, e.Avg, e.StdDev, e.Min, e.Max, e.Sum)
_, _ = fmt.Fprintln(out, "# range, mid point, percentile, count")
sep := ">="
for i, b := range e.Data {
if i > 0 {
sep = ">" // last interval is inclusive (of max value)
}
_, _ = fmt.Fprintf(out, "%s %.6g <= %.6g , %.6g , %.2f, %d\n", sep, b.Start, b.End, (b.Start+b.End)/2., b.Percent, b.Count)
}
// print the information of target percentiles
for _, p := range e.Percentiles {
_, _ = fmt.Fprintf(out, "# target %g%% %.6g\n", p.Percentile, p.Value) // nolint: gas
}
}
// Print dumps the histogram (and counter) to the provided writer.
// Also calculates the percentiles. Use Export() once and Print if you
// are going to need the Export results too.
func (h *Histogram) Print(out io.Writer, msg string, percentiles []float64) {
h.Export().CalcPercentiles(percentiles).Print(out, msg)
}
// Log Logs the histogram to the counter.
func (h *Histogram) Log(msg string, percentiles []float64) {
var b bytes.Buffer
w := bufio.NewWriter(&b)
h.Print(w, msg, percentiles)
w.Flush() // nolint: gas,errcheck
log.Infof("%s", b.Bytes())
}
// Reset clears the data. Reset it to NewHistogram state.
func (h *Histogram) Reset() {
h.Counter.Reset()
// Leave Offset and Divider alone
for i := 0; i < len(h.Hdata); i++ {
h.Hdata[i] = 0
}
}
// Clone returns a copy of the histogram.
func (h *Histogram) Clone() *Histogram {
copy := NewHistogram(h.Offset, h.Divider)
copy.CopyFrom(h)
return copy
}
// CopyFrom sets the content of this object to a copy of the src.
func (h *Histogram) CopyFrom(src *Histogram) {
h.Counter = src.Counter
h.copyHDataFrom(src)
}
// copyHDataFrom appends histogram data values to this object from the src.
// Src histogram data values will be appended according to this object's
// offset and divider
func (h *Histogram) copyHDataFrom(src *Histogram) {
if h.Divider == src.Divider && h.Offset == src.Offset {
for i := 0; i < len(h.Hdata); i++ {
h.Hdata[i] += src.Hdata[i]
}
return
}
hData := src.Export()
for _, data := range hData.Data {
h.record((data.Start+data.End)/2, int(data.Count))
}
}
// Merge two different histogram with different scale parameters
// Lowest offset and highest divider value will be selected on new Histogram as scale parameters
func Merge(h1 *Histogram, h2 *Histogram) *Histogram {
divider := h1.Divider
offset := h1.Offset
if h2.Divider > h1.Divider {
divider = h2.Divider
}
if h2.Offset < h1.Offset {
offset = h2.Offset
}
newH := NewHistogram(offset, divider)
newH.Transfer(h1)
newH.Transfer(h2)
return newH
}
// Transfer merges the data from src into this Histogram and clears src.
func (h *Histogram) Transfer(src *Histogram) {
if src.Count == 0 {
return
}
if h.Count == 0 {
h.CopyFrom(src)
src.Reset()
return
}
h.copyHDataFrom(src)
h.Counter.Transfer(&src.Counter)
src.Reset()
}
// ParsePercentiles extracts the percentiles from string (flag).
func ParsePercentiles(percentiles string) ([]float64, error) {
percs := strings.Split(percentiles, ",") // will make a size 1 array for empty input!
res := make([]float64, 0, len(percs))
for _, pStr := range percs {
pStr = strings.TrimSpace(pStr)
if len(pStr) == 0 {
continue
}
p, err := strconv.ParseFloat(pStr, 64)
if err != nil {
return res, err
}
res = append(res, p)
}
if len(res) == 0 {
return res, errors.New("list can't be empty")
}
log.LogVf("Will use %v for percentiles", res)
return res, nil
}
// RoundToDigits rounds the input to digits number of digits after decimal point.
// Note this incorrectly rounds the last digit of negative numbers.
func RoundToDigits(v float64, digits int) float64 {
p := math.Pow(10, float64(digits))
return math.Floor(v*p+0.5) / p
}
// Round rounds to 4 digits after the decimal point.
func Round(v float64) float64 {
return RoundToDigits(v, 4)
}