// Quantiles expects a regex whose first capture group can be parsed as a
// float64. It will dump the WriterTo and parse each line, expecting to find a
// match. It observes all captured floats into a generic.Histogram with the
// given number of buckets, and returns the 50th, 90th, 95th, and 99th quantiles
// from that histogram.
func Quantiles(w io.WriterTo, regex string, buckets int) func() (float64, float64, float64, float64) {
return func() (float64, float64, float64, float64) {
h := generic.NewHistogram("quantile-test", buckets)
stats(w, regex, h)
return h.Quantile(0.50), h.Quantile(0.90), h.Quantile(0.95), h.Quantile(0.99)
}
}
func TestHistogram(t *testing.T) {
in := New(map[string]string{"foo": "alpha"}, influxdb.BatchPointsConfig{}, log.NewNopLogger())
re := regexp.MustCompile(`influx_histogram,foo=alpha bar="beta",value=([0-9\.]+) [0-9]+`)
histogram := in.NewHistogram("influx_histogram").With("bar", "beta")
quantiles := func() (float64, float64, float64, float64) {
w := &bufWriter{}
in.WriteTo(w)
h := generic.NewHistogram("h", 50)
matches := re.FindAllStringSubmatch(w.buf.String(), -1)
for _, match := range matches {
f, _ := strconv.ParseFloat(match[1], 64)
h.Observe(f)
}
return h.Quantile(0.50), h.Quantile(0.90), h.Quantile(0.95), h.Quantile(0.99)
}
if err := teststat.TestHistogram(histogram, quantiles, 0.01); err != nil {
t.Fatal(err)
}
}
func TestHistogram(t *testing.T) {
const name = "ghi"
// Circonus just emits bucketed counts. We'll dump them into a generic
// histogram (losing some precision) and take statistics from there. Note
// this does assume that the generic histogram computes statistics properly,
// but we have another test for that :)
re := regexp.MustCompile(`^H\[([0-9\.e\+]+)\]=([0-9]+)$`) // H[1.2e+03]=456
var p50, p90, p95, p99 float64
s := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
var res map[string]struct {
Values []string `json:"_value"` // reverse-engineered :\
}
json.NewDecoder(r.Body).Decode(&res)
h := generic.NewHistogram("dummy", len(res[name].Values)) // match tbe bucket counts
for _, v := range res[name].Values {
match := re.FindStringSubmatch(v)
f, _ := strconv.ParseFloat(match[1], 64)
n, _ := strconv.ParseInt(match[2], 10, 64)
for i := int64(0); i < n; i++ {
h.Observe(f)
}
}
p50 = h.Quantile(0.50)
p90 = h.Quantile(0.90)
p95 = h.Quantile(0.95)
p99 = h.Quantile(0.99)
}))
defer s.Close()
m := newCirconusMetrics(s.URL)
histogram := New(m).NewHistogram(name).With("label values", "not supported")
quantiles := func() (float64, float64, float64, float64) { m.Flush(); return p50, p90, p95, p99 }
// Circonus metrics, because they do their own bucketing, are less precise
// than other systems. So, we bump the tolerance to 5 percent.
if err := teststat.TestHistogram(histogram, quantiles, 0.05); err != nil {
t.Fatal(err)
}
}
// NewHistogram returns a new usable Histogram metric.
func NewHistogram(name string, buckets int) *Histogram {
return &Histogram{generic.NewHistogram(name, buckets)}
}