func calculateConfidenceInterval(s scoreResult) confInterval {
var t0s []float64
var t1s []float64
// Partition the data into treatment 0 and treatment 1
// and save the score for evaluation
for _, each := range s.t0 {
t0s = append(t0s, each.y)
}
for _, each := range s.t1 {
t1s = append(t1s, each.y)
}
var ci confInterval
//var z = 1.96 // http://www.dummies.com/how-to/content/creating-a-confidence-interval-for-the-difference-.html
//var z = 1.645 // http://www.dummies.com/how-to/content/creating-a-confidence-interval-for-the-difference-.html
//var z = 2.58
var m0, _ = stats.Mean(t0s)
var n0 = float64(len(t0s))
var sd0, _ = stats.StandardDeviation(t0s)
var m1, _ = stats.Mean(t1s)
var n1 = float64(len(t1s))
var sd1, _ = stats.StandardDeviation(t1s)
var mDiff = m0 - m1
var sd0s = sd0 * sd0
var sd1s = sd1 * sd1
ci.min = mDiff - zScore*math.Sqrt(sd1s/n1+sd0s/n0)
ci.max = mDiff + zScore*math.Sqrt(sd1s/n1+sd0s/n0)
ci.diff = ci.min - ci.max
ci.t1Max = m1 + ci.max
ci.t1Min = m1 + ci.min
ci.diffSd = ci.diff / sd1
// how close is the score to the middle of the confidence interval
ci.middle = (ci.min + ci.max) / 2
//ci.closeness = math.Abs(s.score - ci.middle)
//ci.closeness = math.Abs(ci.diffSd - s.score)
// Difference in sample means +- confidence interval
//fmt.Printf("conf interval: %f to %f, conf diff: %f, t1: %f, t1max: %f, t1min: %f, diffSd: %f\n", ci.min, ci.max, ci.diff, m1, ci.t1Max, ci.t1Min, ci.diffSd)
return ci
}
// https://github.com/hermanschaaf/stats/blob/master/stats.go
func NormalConfidenceInterval(nums []float64) (lower float64, upper float64) {
conf := 1.95996 // 95% confidence for the mean, http://bit.ly/Mm05eZ
mean, _ := stats.Mean(nums)
dev, _ := stats.StandardDeviation(nums)
dev = dev / math.Sqrt(float64(len(nums)))
return mean - dev*conf, mean + dev*conf
}
func bench(requests, concurrency int, images []string, args []string) {
start := time.Now()
timings := make([]float64, requests)
// Create a buffered channel so our display goroutine can't slow down the workers.
completeCh := make(chan time.Duration, requests)
doneCh := make(chan struct{})
current := 0
go func() {
for timing := range completeCh {
timings = append(timings, timing.Seconds())
current++
percent := float64(current) / float64(requests) * 100
fmt.Printf("[%3.f%%] %d/%d containers started\n", percent, current, requests)
}
doneCh <- struct{}{}
}()
session(requests, concurrency, images, args, completeCh)
close(completeCh)
<-doneCh
total := time.Since(start)
mean, _ := stats.Mean(timings)
p90th, _ := stats.Percentile(timings, 90)
p99th, _ := stats.Percentile(timings, 99)
meanMillis := mean * MILLIS_IN_SECOND
p90thMillis := p90th * MILLIS_IN_SECOND
p99thMillis := p99th * MILLIS_IN_SECOND
fmt.Printf("\n")
fmt.Printf("Time taken for tests: %.3fs\n", total.Seconds())
fmt.Printf("Time per container: %.3fms [mean] | %.3fms [90th] | %.3fms [99th]\n", meanMillis, p90thMillis, p99thMillis)
}
func updateComplexity(v *Web, pop neat.Population) {
// Build complexity slice
x := make([]float64, len(pop.Genomes))
for i, g := range pop.Genomes {
x[i] = float64(g.Complexity())
}
var b neat.Genome
max := -1.0
for _, g := range pop.Genomes {
if g.Fitness > max {
b = g
max = g.Fitness
}
}
// Append the record
min, _ := stats.Min(x)
max, _ = stats.Max(x)
mean, _ := stats.Mean(x)
v.complexity = append(v.complexity, [4]float64{
min,
mean,
max,
float64(b.Complexity()),
})
}
// startStats blocks and periodically logs transaction statistics (throughput,
// success rates, durations, ...).
// TODO(tschottdorf): Use a proper metrics subsystem for this (+the store-level
// stats).
// TODO(mrtracy): Add this to TimeSeries.
func (tc *TxnCoordSender) startStats() {
res := time.Millisecond // for duration logging resolution
lastNow := tc.clock.PhysicalNow()
for {
select {
case <-time.After(statusLogInterval):
if !log.V(1) {
continue
}
tc.Lock()
curStats := tc.txnStats
tc.txnStats = txnCoordStats{}
tc.Unlock()
now := tc.clock.PhysicalNow()
// Tests have weird clocks.
if now-lastNow <= 0 {
continue
}
num := len(curStats.durations)
dMax := time.Duration(stats.Max(curStats.durations))
dMean := time.Duration(stats.Mean(curStats.durations))
dDev := time.Duration(stats.StdDevP(curStats.durations))
rMax := stats.Max(curStats.restarts)
rMean := stats.Mean(curStats.restarts)
rDev := stats.StdDevP(curStats.restarts)
rate := float64(int64(num)*int64(time.Second)) / float64(now-lastNow)
var pCommitted, pAbandoned, pAborted float32
if num > 0 {
pCommitted = 100 * float32(curStats.committed) / float32(num)
pAbandoned = 100 * float32(curStats.abandoned) / float32(num)
pAborted = 100 * float32(curStats.aborted) / float32(num)
}
log.Infof("txn coordinator: %.2f txn/sec, %.2f/%.2f/%.2f %%cmmt/abrt/abnd, %s/%s/%s avg/σ/max duration, %.1f/%.1f/%.1f avg/σ/max restarts (%d samples)",
rate, pCommitted, pAborted, pAbandoned, util.TruncateDuration(dMean, res),
util.TruncateDuration(dDev, res), util.TruncateDuration(dMax, res),
rMean, rDev, rMax, num)
lastNow = now
case <-tc.stopper.ShouldStop():
return
}
}
}
// Finalize calculation of the risk using available datapoints
func riskFinalize(op opContext, rs *slib.RRAServiceRisk) error {
var (
rvals []float64
err error
)
for _, x := range rs.Scenarios {
// If the scenario had no data, don't include it in the
// final scoring
if x.NoData {
continue
}
rvals = append(rvals, x.Score)
}
// Note the highest business impact value that was determined from
// the RRA. This can be used as an indication of the business impact
// for the service.
rs.Risk.Impact = rs.UsedRRAAttrib.Impact
rs.Risk.ImpactLabel, err = slib.ImpactLabelFromValue(rs.Risk.Impact)
if err != nil {
return err
}
if len(rvals) == 0 {
// This can occur if we have no metric data, including no valid
// information in the RRA
logf("error in risk calculation: %q has no valid scenarios", rs.RRA.Name)
rs.Risk.Median = 0.0
rs.Risk.Average = 0.0
rs.Risk.WorstCase = 0.0
rs.Risk.MedianLabel = "unknown"
rs.Risk.AverageLabel = "unknown"
rs.Risk.WorstCaseLabel = "unknown"
rs.Risk.DataClass, err = slib.DataValueFromLabel(rs.RRA.DefData)
return nil
}
rs.Risk.Median, err = stats.Median(rvals)
if err != nil {
return err
}
rs.Risk.MedianLabel = slib.NormalLabelFromValue(rs.Risk.Median)
rs.Risk.Average, err = stats.Mean(rvals)
if err != nil {
return err
}
rs.Risk.AverageLabel = slib.NormalLabelFromValue(rs.Risk.Average)
rs.Risk.WorstCase, err = stats.Max(rvals)
if err != nil {
return err
}
rs.Risk.WorstCaseLabel = slib.NormalLabelFromValue(rs.Risk.WorstCase)
rs.Risk.DataClass, err = slib.DataValueFromLabel(rs.RRA.DefData)
if err != nil {
return err
}
return nil
}
func (s *statistics) report() {
for range time.Tick(time.Second) {
s.Lock()
writeTimes := s.writeTimes
s.writeTimes = nil
s.Unlock()
// The stats functions return an error only when the input is empty.
mean, _ := stats.Mean(writeTimes)
stddev, _ := stats.StandardDeviation(writeTimes)
log.Infof("wrote %d messages, latency mean=%s, stddev=%s",
len(writeTimes), time.Duration(mean), time.Duration(stddev))
}
}
func updateFitness(v *Web, pop neat.Population) {
// Build fitness slice
x := make([]float64, len(pop.Genomes))
for i, g := range pop.Genomes {
x[i] = g.Fitness
}
// Append the record
min, _ := stats.Min(x)
max, _ := stats.Max(x)
mean, _ := stats.Mean(x)
v.fitness = append(v.fitness, [3]float64{
min,
mean,
max,
})
}
// for a partition in the set of data, calculate the effective treatement
// score using (mean t1 - mean t0) / population standard deviation
func evalScore(d []coreData, rc []rowCriteria, dataSetId int) scoreResult {
var s scoreResult
s.rc = rc
s.d = d
s.score = 0
s.dataSetId = dataSetId
// check for minimum row threshhold
if len(d) <= rowThreshhold {
return s
}
var t0 []coreData
var t1 []coreData
var t0s []float64
var t1s []float64
var allTs []float64
// Partition the data into treatment 0 and treatment 1
// and save the score for evaluation
for _, each := range d {
// Save all responses for later SD calculation
allTs = append(allTs, each.y)
if each.treatment == 0 {
t0 = append(t0, each)
t0s = append(t0s, each.y)
} else {
t1 = append(t1, each)
t1s = append(t1s, each.y)
}
}
// Must have minimum threshhold of records
if len(t0)+len(t1) < rowThreshhold {
return s
}
// Must have at least one in each group
if len(t0) == 0 || len(t1) == 0 {
return s
}
// then calculate the median, also experiment with average
var mean0, _ = stats.Mean(t0s)
var mean1, _ = stats.Mean(t1s)
//var meanAll, _ = stats.Mean(allTs)
//var sd, _ = stats.StandardDeviationPopulation(allTs)
// subtract the two t0-t1, we want t1 to be smaller
// Note: use spooled
// square root of ((Nt-1)St^2 + (Nc-1)Sc^2)/(Nt+Nc))
var St, _ = stats.StandardDeviation(t1s)
var Sc, _ = stats.StandardDeviation(t0s)
var Nt = float64(len(t1s))
var Nc = float64(len(t0s))
//func calculateConfidenceInterval2(nt, nc, mt, mc, sdt, sdc float64) confInterval2
var ci = calculateConfidenceInterval2(Nt, Nc, mean1, mean0, St, Sc)
// If the confidence intervals overlap then not valid range
if ci.overlap {
return s
}
var St2 = St * St
var Sc2 = Sc * Sc
//var Ntm1 = float64(Nt - 1)
//var Ncm1 = float64(Nc - 1)
//var kt = Ntm1 * St2
//var kc = Ncm1 * Sc2
//var ksum = kt + kc
//var Nsum = Nt + Nc
//var sPooled = math.Sqrt(ksum / Nsum)
//http://www.uccs.edu/~lbecker/
var sPooled = math.Sqrt((St2 + Sc2) / 2)
s.t0 = t0
s.t1 = t1
//sPooled = math.Sqrt((St2 * Sc2) / 2)
//var _, t1confh = NormalConfidenceInterval(t1s)
//var _, t0confh = NormalConfidenceInterval(t0s)
//var meanValue = mean1 - mean0
//var meanValue = (mean1/St - mean0/Sc) / sPooled
// Score Type 1
var meanDifference = mean1 - mean0
//s.score = meanDifference / meanAll
//var max, _ = stats.Max(allTs)
//s.score = meanDifference / sPooled
var cohensd = meanDifference / sPooled
var a = ((Nt + Nc) * (Nt + Nc)) / (Nt + Nc)
// Score type 5
s.score = cohensd / math.Sqrt((cohensd*cohensd)+4)
// Score type 6
s.score = cohensd / math.Sqrt((cohensd*cohensd)+a)
//s.score = (mean1/St - mean0/Sc) / St
return s
}
func main() {
d := stats.LoadRawData([]interface{}{1.1, "2", 3.0, 4, "5"})
a, _ := stats.Min(d)
fmt.Println(a) // 1.1
a, _ = stats.Max(d)
fmt.Println(a) // 5
a, _ = stats.Sum([]float64{1.1, 2.2, 3.3})
fmt.Println(a) // 6.6
a, _ = stats.Mean([]float64{1, 2, 3, 4, 5})
fmt.Println(a) // 3
a, _ = stats.Median([]float64{1, 2, 3, 4, 5, 6, 7})
fmt.Println(a) // 4
m, _ := stats.Mode([]float64{5, 5, 3, 3, 4, 2, 1})
fmt.Println(m) // [5 3]
a, _ = stats.PopulationVariance([]float64{1, 2, 3, 4, 5})
fmt.Println(a) // 2
a, _ = stats.SampleVariance([]float64{1, 2, 3, 4, 5})
fmt.Println(a) // 2.5
a, _ = stats.MedianAbsoluteDeviationPopulation([]float64{1, 2, 3})
fmt.Println(a) // 1
a, _ = stats.StandardDeviationPopulation([]float64{1, 2, 3})
fmt.Println(a) // 0.816496580927726
a, _ = stats.StandardDeviationSample([]float64{1, 2, 3})
fmt.Println(a) // 1
a, _ = stats.Percentile([]float64{1, 2, 3, 4, 5}, 75)
fmt.Println(a) // 4
a, _ = stats.PercentileNearestRank([]float64{35, 20, 15, 40, 50}, 75)
fmt.Println(a) // 40
c := []stats.Coordinate{
{1, 2.3},
{2, 3.3},
{3, 3.7},
{4, 4.3},
{5, 5.3},
}
r, _ := stats.LinearRegression(c)
fmt.Println(r) // [{1 2.3800000000000026} {2 3.0800000000000014} {3 3.7800000000000002} {4 4.479999999999999} {5 5.179999999999998}]
r, _ = stats.ExponentialRegression(c)
fmt.Println(r) // [{1 2.5150181024736638} {2 3.032084111136781} {3 3.6554544271334493} {4 4.406984298281804} {5 5.313022222665875}]
r, _ = stats.LogarithmicRegression(c)
fmt.Println(r) // [{1 2.1520822363811702} {2 3.3305559222492214} {3 4.019918836568674} {4 4.509029608117273} {5 4.888413396683663}]
s, _ := stats.Sample([]float64{0.1, 0.2, 0.3, 0.4}, 3, false)
fmt.Println(s) // [0.2,0.4,0.3]
s, _ = stats.Sample([]float64{0.1, 0.2, 0.3, 0.4}, 10, true)
fmt.Println(s) // [0.2,0.2,0.4,0.1,0.2,0.4,0.3,0.2,0.2,0.1]
q, _ := stats.Quartile([]float64{7, 15, 36, 39, 40, 41})
fmt.Println(q) // {15 37.5 40}
iqr, _ := stats.InterQuartileRange([]float64{102, 104, 105, 107, 108, 109, 110, 112, 115, 116, 118})
fmt.Println(iqr) // 10
mh, _ := stats.Midhinge([]float64{1, 3, 4, 4, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9, 9, 10, 11, 12, 13})
fmt.Println(mh) // 7.5
tr, _ := stats.Trimean([]float64{1, 3, 4, 4, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9, 9, 10, 11, 12, 13})
fmt.Println(tr) // 7.25
o, _ := stats.QuartileOutliers([]float64{-1000, 1, 3, 4, 4, 6, 6, 6, 6, 7, 8, 15, 18, 100})
fmt.Printf("%+v\n", o) // {Mild:[15 18] Extreme:[-1000 100]}
gm, _ := stats.GeometricMean([]float64{10, 51.2, 8})
fmt.Println(gm) // 15.999999999999991
hm, _ := stats.HarmonicMean([]float64{1, 2, 3, 4, 5})
fmt.Println(hm) // 2.18978102189781
a, _ = stats.Round(2.18978102189781, 3)
fmt.Println(a) // 2.189
}
func main() {
verbose := flag.Bool("v", false, "verbose output")
flag.Parse()
file, err := os.Open("delta_data.bin")
check(err)
defer file.Close()
buffer := bufio.NewReader(file)
sizes := make([]float64, 0)
speeds := make([]float64, 0)
encode := qpc.NewHistory("encode")
decode := qpc.NewHistory("decode")
server := physics.NewState(901)
client := physics.NewState(901)
// initialize the base state
for i := 0; i < 6; i += 1 {
server.ReadNext(buffer)
client.IncFrame()
client.Current().Assign(server.Current())
}
frame := 6
for {
err = server.ReadNext(buffer)
if err == io.EOF {
break
}
check(err)
frame += 1
runtime.GC()
// Server side
encode.Start()
snapshot := server.Encode()
encode.Stop()
// ===
runtime.GC()
// Client side
decode.Start()
client.IncFrame()
client.Decode(snapshot)
decode.Stop()
// ===
size := float64(len(snapshot)*8) / 1000.0
sizes = append(sizes, size)
speed := size * 60.0
speeds = append(speeds, speed)
equal := server.Current().Equals(client.Current())
if *verbose {
if !equal {
fmt.Print("! ")
}
fmt.Printf("%04d %8.3fkbps %10s %10s\n", frame, speed, encode.Last(), decode.Last())
} else {
if equal {
fmt.Print(".")
} else {
fmt.Print("X")
}
}
}
fmt.Println()
fmt.Printf("#%d %.3fkbps ±%.3fkbps\n", len(sizes), stats.Mean(speeds), stats.StdDevS(speeds))
fmt.Println()
fmt.Printf("MIN %10.3f kbps\n", stats.Min(speeds))
for _, p := range []float64{5, 10, 25, 50, 75, 90, 95} {
fmt.Printf("P%02.f %10.3f kbps\n", p, stats.Percentile(speeds, p))
}
fmt.Printf("MAX %10.3f kbps\n", stats.Max(speeds))
fmt.Println()
fmt.Printf("TOTAL %10.3f kb\n", stats.Sum(sizes))
fmt.Printf(" AVG %10.3f kb per frame\n", stats.Mean(sizes))
fmt.Printf(" AVG %10.3f bits per cube\n", stats.Mean(sizes)*1000/float64(len(sizes)))
fmt.Println()
fmt.Println("TIMING:")
qpc.PrintSummary(encode, decode)
}
//apply transforms an array of data
func apply(data []string, transformation templates.Transformation) ([]string, []Mapping) {
p := transformation.Parameters
var wg sync.WaitGroup
var mapping []Mapping
switch transformation.Operation {
case "toDate":
if len(p) != 2 {
log.Fatal("toDate transformation requires 2 parameters: current format, new format")
}
oldFormat := p[0]
newFormat := p[1]
for i, x := range data {
y, err := time.Parse(oldFormat, x)
if err != nil {
log.Print("Error parsing date with index ", i, " with format: ", oldFormat)
} else {
data[i] = y.Format(newFormat)
}
}
case "setNull":
for i, x := range data {
if arrayPos(x, p) != -1 {
data[i] = ""
}
}
case "standardize":
if len(p) != 1 {
log.Fatal("standardize transformation requires 1 parameter: type (min-max|z-score)")
}
stype := p[0]
switch stype {
case "min-max":
newData := strArrToFloatArr(data)
min, err := stats.Min(newData)
if err != nil {
log.Fatal("Error finding minimum of data: ", err)
}
max, err := stats.Max(newData)
if err != nil {
log.Fatal("Error finding maximum of data: ", err)
}
srange := max - min
for i, x := range newData {
data[i] = floatToString((x - min) / srange)
}
case "z-score":
newData := strArrToFloatArr(data)
mean, err := stats.Mean(newData)
if err != nil {
log.Fatal("Error finding mean of data: ", err)
}
sd, err := stats.StandardDeviation(newData)
if err != nil {
log.Fatal("Error finding standard deviation of data: ", err)
}
for i, x := range newData {
data[i] = floatToString((x - mean) / sd)
}
case "decimal":
newData := strArrToFloatArr(data)
max, err := stats.Max(newData)
if err != nil {
log.Fatal("Error finding maximum of data: ", err)
}
min, err := stats.Min(newData)
if err != nil {
log.Fatal("Error finding minimum of data: ", err)
}
var maxAbs float64
if math.Abs(max) > math.Abs(min) {
maxAbs = math.Abs(max)
} else {
maxAbs = math.Abs(min)
}
c := math.Ceil(math.Log10(maxAbs))
for i, x := range newData {
data[i] = floatToString(x / math.Pow10(int(c)))
}
}
case "binPercent":
table := NewPivotTable(data)
intP := strArrToIntArr(p)
sort.Ints(intP)
ps := NewPercentileService(*table, intP)
mapping = ps.CreateMappings()
ps.Bin(mapping, data)
case "fuzzyMap":
if len(p) != 3 {
log.Fatal("fuzzyMap transformation requires 3 parameters: datasource GUID, match, put")
}
dsGUID := p[0]
ds := datasources.NewDatasourceService(database.GetDatabase())
dsObj, err := ds.GetDatasource(dsGUID)
if err != nil {
log.Fatal("Error finding Datasource: ", err)
}
distinctValues := getDistinctValues(data)
for i, datum := range distinctValues {
wg.Add(1)
go func(i int, datum string, dsObj datasources.Datasource) {
result := fuzzyMap(datum, dsObj.Settings)
fuzzyMapping := NewMapping(datum, result)
mapping = append(mapping, *fuzzyMapping)
defer wg.Done()
}(i, datum, dsObj)
}
wg.Wait()
data = applyMappings(mapping, data)
}
return data, mapping
}
func main() {
flag.Parse()
n := *concurrency
m := *total / n
fmt.Printf("concurrency: %d\nrequests per client: %d\n\n", n, m)
serviceMethodName := "Hello.Say"
args := prepareArgs()
b := make([]byte, 1024*1024)
i, _ := args.MarshalTo(b)
fmt.Printf("message size: %d bytes\n\n", i)
var wg sync.WaitGroup
wg.Add(n * m)
var trans uint64
var transOK uint64
d := make([][]int64, n, n)
//it contains warmup time but we can ignore it
totalT := time.Now().UnixNano()
for i := 0; i < n; i++ {
dt := make([]int64, 0, m)
d = append(d, dt)
go func(i int) {
s := &rpcx.DirectClientSelector{Network: "tcp", Address: *host}
client := rpcx.NewClient(s)
client.ClientCodecFunc = codec.NewProtobufClientCodec
var reply BenchmarkMessage
//warmup
for j := 0; j < 5; j++ {
client.Call(serviceMethodName, args, &reply)
}
for j := 0; j < m; j++ {
t := time.Now().UnixNano()
err := client.Call(serviceMethodName, args, &reply)
t = time.Now().UnixNano() - t
d[i] = append(d[i], t)
if err == nil && reply.Field1 == "OK" {
atomic.AddUint64(&transOK, 1)
}
atomic.AddUint64(&trans, 1)
wg.Done()
}
client.Close()
}(i)
}
wg.Wait()
totalT = time.Now().UnixNano() - totalT
totalT = totalT / 1000000
fmt.Printf("took %d ms for %d requests", totalT, n*m)
totalD := make([]int64, 0, n*m)
for _, k := range d {
totalD = append(totalD, k...)
}
totalD2 := make([]float64, 0, n*m)
for _, k := range totalD {
totalD2 = append(totalD2, float64(k))
}
mean, _ := stats.Mean(totalD2)
median, _ := stats.Median(totalD2)
max, _ := stats.Max(totalD2)
min, _ := stats.Min(totalD2)
fmt.Printf("sent requests : %d\n", n*m)
fmt.Printf("received requests : %d\n", atomic.LoadUint64(&trans))
fmt.Printf("received requests_OK : %d\n", atomic.LoadUint64(&transOK))
fmt.Printf("throughput (TPS) : %d\n", int64(n*m)*1000/totalT)
fmt.Printf("mean: %.f ns, median: %.f ns, max: %.f ns, min: %.f ns\n", mean, median, max, min)
fmt.Printf("mean: %d ms, median: %d ms, max: %d ms, min: %d ms\n", int64(mean/1000000), int64(median/1000000), int64(max/1000000), int64(min/1000000))
}
func summarize(vs []float64) {
fmt.Printf("%d %.3f ±%.3f\n", len(vs), stats.Mean(vs), stats.StdDevS(vs))
}
func main() {
flag.Parse()
n := *concurrency
m := *total / n
fmt.Printf("concurrency: %d\nrequests per client: %d\n\n", n, m)
args := prepareArgs()
b, _ := proto.Marshal(args)
fmt.Printf("message size: %d bytes\n\n", len(b))
var wg sync.WaitGroup
wg.Add(n * m)
var trans uint64
var transOK uint64
d := make([][]int64, n, n)
//it contains warmup time but we can ignore it
totalT := time.Now().UnixNano()
for i := 0; i < n; i++ {
dt := make([]int64, 0, m)
d = append(d, dt)
go func(i int) {
conn, err := grpc.Dial(*host, grpc.WithInsecure())
if err != nil {
log.Fatalf("did not connect: %v", err)
}
c := NewHelloClient(conn)
//warmup
for j := 0; j < 5; j++ {
c.Say(context.Background(), args)
}
for j := 0; j < m; j++ {
t := time.Now().UnixNano()
reply, err := c.Say(context.Background(), args)
t = time.Now().UnixNano() - t
d[i] = append(d[i], t)
if err == nil && *(reply.Field1) == "OK" {
atomic.AddUint64(&transOK, 1)
}
atomic.AddUint64(&trans, 1)
wg.Done()
}
conn.Close()
}(i)
}
wg.Wait()
totalT = time.Now().UnixNano() - totalT
totalT = totalT / 1000000
fmt.Printf("took %d ms for %d requests", totalT, n*m)
totalD := make([]int64, 0, n*m)
for _, k := range d {
totalD = append(totalD, k...)
}
totalD2 := make([]float64, 0, n*m)
for _, k := range totalD {
totalD2 = append(totalD2, float64(k))
}
mean, _ := stats.Mean(totalD2)
median, _ := stats.Median(totalD2)
max, _ := stats.Max(totalD2)
min, _ := stats.Min(totalD2)
fmt.Printf("sent requests : %d\n", n*m)
fmt.Printf("received requests : %d\n", atomic.LoadUint64(&trans))
fmt.Printf("received requests_OK : %d\n", atomic.LoadUint64(&transOK))
fmt.Printf("throughput (TPS) : %d\n", int64(n*m)*1000/totalT)
fmt.Printf("mean: %.f ns, median: %.f ns, max: %.f ns, min: %.f ns\n", mean, median, max, min)
fmt.Printf("mean: %d ms, median: %d ms, max: %d ms, min: %d ms\n", int64(mean/1000000), int64(median/1000000), int64(max/1000000), int64(min/1000000))
}
// startStats blocks and periodically logs transaction statistics (throughput,
// success rates, durations, ...). Note that this only captures write txns,
// since read-only txns are stateless as far as TxnCoordSender is concerned.
// stats).
// TODO(mrtracy): Add this to TimeSeries.
func (tc *TxnCoordSender) startStats() {
res := time.Millisecond // for duration logging resolution
lastNow := tc.clock.PhysicalNow()
for {
select {
case <-time.After(statusLogInterval):
if !log.V(1) {
continue
}
tc.Lock()
curStats := tc.txnStats
tc.txnStats = txnCoordStats{}
tc.Unlock()
now := tc.clock.PhysicalNow()
// Tests have weird clocks.
if now-lastNow <= 0 {
continue
}
num := len(curStats.durations)
// Only compute when non-empty input.
var dMax, dMean, dDev, rMax, rMean, rDev float64
var err error
if num > 0 {
// There should never be an error in the below
// computations.
dMax, err = stats.Max(curStats.durations)
if err != nil {
panic(err)
}
dMean, err = stats.Mean(curStats.durations)
if err != nil {
panic(err)
}
dDev, err = stats.StdDevP(curStats.durations)
if err != nil {
panic(err)
}
rMax, err = stats.Max(curStats.restarts)
if err != nil {
panic(err)
}
rMean, err = stats.Mean(curStats.restarts)
if err != nil {
panic(err)
}
rDev, err = stats.StdDevP(curStats.restarts)
if err != nil {
panic(err)
}
}
rate := float64(int64(num)*int64(time.Second)) / float64(now-lastNow)
var pCommitted, pAbandoned, pAborted float32
if fNum := float32(num); fNum > 0 {
pCommitted = 100 * float32(curStats.committed) / fNum
pAbandoned = 100 * float32(curStats.abandoned) / fNum
pAborted = 100 * float32(curStats.aborted) / fNum
}
log.Infof(
"txn coordinator: %.2f txn/sec, %.2f/%.2f/%.2f %%cmmt/abrt/abnd, %s/%s/%s avg/σ/max duration, %.1f/%.1f/%.1f avg/σ/max restarts (%d samples)",
rate, pCommitted, pAborted, pAbandoned,
util.TruncateDuration(time.Duration(dMean), res),
util.TruncateDuration(time.Duration(dDev), res),
util.TruncateDuration(time.Duration(dMax), res),
rMean, rDev, rMax, num,
)
lastNow = now
case <-tc.stopper.ShouldStop():
return
}
}
}
func (c *cmdReport2) getFeatures(geneSnpChan chan SNPArr) {
w, err := os.Create(c.prefix + ".detectable.gene.csv")
if err != nil {
log.Fatalln(err)
}
defer w.Close()
w.WriteString("patric_id,genome,figfam,sample,pi,depth\n")
fn := func(txn *lmdb.Txn) error {
dbi, err := txn.OpenDBI("feature", 0)
if err != nil {
return err
}
for gs := range geneSnpChan {
if len(gs.Arr) < 100 {
continue
}
k := gs.Key
v, err := txn.Get(dbi, k)
if err != nil {
return err
}
f := Feature{}
if err := msgpack.Unmarshal(v, &f); err != nil {
return err
}
seqLen := f.End - f.Start + 1
// calculate median of depth
depthArr := []float64{}
piArr := []float64{}
for _, snp := range gs.Arr {
pos := snp.Position - f.Start
if f.IsComplementaryStrand() {
pos = seqLen - 1 - pos
}
if (pos+1)%3 == 0 {
depthArr = append(depthArr, float64(len(snp.Bases)))
piArr = append(piArr, snp.Pi())
}
}
depthMedian, _ := stats.Median(depthArr)
sort.Float64s(piArr)
piMean, _ := stats.Mean(piArr[10 : len(piArr)-10])
w.WriteString(fmt.Sprintf("%s,%s,%s,%s,%g,%g\n",
f.PatricID,
f.TaxID,
f.FigfamID,
c.prefix,
piMean,
depthMedian))
}
return nil
}
err = c.featureDB.View(fn)
if err != nil {
log.Panicln(err)
}
}
