ceph-csi/vendor/k8s.io/component-base/metrics/testutil/metrics.go

/*
Copyright 2019 The Kubernetes 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 testutil

import (
	"fmt"
	"io"
	"math"
	"reflect"
	"sort"
	"strings"

	dto "github.com/prometheus/client_model/go"
	"github.com/prometheus/common/expfmt"
	"github.com/prometheus/common/model"

	"k8s.io/component-base/metrics"
)

var (
	// MetricNameLabel is label under which model.Sample stores metric name
	MetricNameLabel model.LabelName = model.MetricNameLabel
	// QuantileLabel is label under which model.Sample stores latency quantile value
	QuantileLabel model.LabelName = model.QuantileLabel
)

// Metrics is generic metrics for other specific metrics
type Metrics map[string]model.Samples

// Equal returns true if all metrics are the same as the arguments.
func (m *Metrics) Equal(o Metrics) bool {
	var leftKeySet []string
	var rightKeySet []string
	for k := range *m {
		leftKeySet = append(leftKeySet, k)
	}
	for k := range o {
		rightKeySet = append(rightKeySet, k)
	}
	if !reflect.DeepEqual(leftKeySet, rightKeySet) {
		return false
	}
	for _, k := range leftKeySet {
		if !(*m)[k].Equal(o[k]) {
			return false
		}
	}
	return true
}

// NewMetrics returns new metrics which are initialized.
func NewMetrics() Metrics {
	result := make(Metrics)
	return result
}

// ParseMetrics parses Metrics from data returned from prometheus endpoint
func ParseMetrics(data string, output *Metrics) error {
	dec := expfmt.NewDecoder(strings.NewReader(data), expfmt.NewFormat(expfmt.TypeTextPlain))
	decoder := expfmt.SampleDecoder{
		Dec:  dec,
		Opts: &expfmt.DecodeOptions{},
	}

	for {
		var v model.Vector
		if err := decoder.Decode(&v); err != nil {
			if err == io.EOF {
				// Expected loop termination condition.
				return nil
			}
			continue
		}
		for _, metric := range v {
			name := string(metric.Metric[MetricNameLabel])
			(*output)[name] = append((*output)[name], metric)
		}
	}
}

// TextToMetricFamilies reads 'in' as the simple and flat text-based exchange
// format and creates MetricFamily proto messages. It returns the MetricFamily
// proto messages in a map where the metric names are the keys, along with any
// error encountered.
func TextToMetricFamilies(in io.Reader) (map[string]*dto.MetricFamily, error) {
	var textParser expfmt.TextParser
	return textParser.TextToMetricFamilies(in)
}

// PrintSample returns formatted representation of metric Sample
func PrintSample(sample *model.Sample) string {
	buf := make([]string, 0)
	// Id is a VERY special label. For 'normal' container it's useless, but it's necessary
	// for 'system' containers (e.g. /docker-daemon, /kubelet, etc.). We know if that's the
	// case by checking if there's a label "kubernetes_container_name" present. It's hacky
	// but it works...
	_, normalContainer := sample.Metric["kubernetes_container_name"]
	for k, v := range sample.Metric {
		if strings.HasPrefix(string(k), "__") {
			continue
		}

		if string(k) == "id" && normalContainer {
			continue
		}
		buf = append(buf, fmt.Sprintf("%v=%v", string(k), v))
	}
	return fmt.Sprintf("[%v] = %v", strings.Join(buf, ","), sample.Value)
}

// ComputeHistogramDelta computes the change in histogram metric for a selected label.
// Results are stored in after samples
func ComputeHistogramDelta(before, after model.Samples, label model.LabelName) {
	beforeSamplesMap := make(map[string]*model.Sample)
	for _, bSample := range before {
		beforeSamplesMap[makeKey(bSample.Metric[label], bSample.Metric["le"])] = bSample
	}
	for _, aSample := range after {
		if bSample, found := beforeSamplesMap[makeKey(aSample.Metric[label], aSample.Metric["le"])]; found {
			aSample.Value = aSample.Value - bSample.Value
		}
	}
}

func makeKey(a, b model.LabelValue) string {
	return string(a) + "___" + string(b)
}

// GetMetricValuesForLabel returns value of metric for a given dimension
func GetMetricValuesForLabel(ms Metrics, metricName, label string) map[string]int64 {
	samples, found := ms[metricName]
	result := make(map[string]int64, len(samples))
	if !found {
		return result
	}
	for _, sample := range samples {
		count := int64(sample.Value)
		dimensionName := string(sample.Metric[model.LabelName(label)])
		result[dimensionName] = count
	}
	return result
}

// ValidateMetrics verifies if every sample of metric has all expected labels
func ValidateMetrics(metrics Metrics, metricName string, expectedLabels ...string) error {
	samples, ok := metrics[metricName]
	if !ok {
		return fmt.Errorf("metric %q was not found in metrics", metricName)
	}
	for _, sample := range samples {
		for _, l := range expectedLabels {
			if _, ok := sample.Metric[model.LabelName(l)]; !ok {
				return fmt.Errorf("metric %q is missing label %q, sample: %q", metricName, l, sample.String())
			}
		}
	}
	return nil
}

// Histogram wraps prometheus histogram DTO (data transfer object)
type Histogram struct {
	*dto.Histogram
}

// HistogramVec wraps a slice of Histogram.
// Note that each Histogram must have the same number of buckets.
type HistogramVec []*Histogram

// GetAggregatedSampleCount aggregates the sample count of each inner Histogram.
func (vec HistogramVec) GetAggregatedSampleCount() uint64 {
	var count uint64
	for _, hist := range vec {
		count += hist.GetSampleCount()
	}
	return count
}

// GetAggregatedSampleSum aggregates the sample sum of each inner Histogram.
func (vec HistogramVec) GetAggregatedSampleSum() float64 {
	var sum float64
	for _, hist := range vec {
		sum += hist.GetSampleSum()
	}
	return sum
}

// Quantile first aggregates inner buckets of each Histogram, and then
// computes q-th quantile of a cumulative histogram.
func (vec HistogramVec) Quantile(q float64) float64 {
	var buckets []bucket

	for i, hist := range vec {
		for j, bckt := range hist.Bucket {
			if i == 0 {
				buckets = append(buckets, bucket{
					count:      float64(bckt.GetCumulativeCount()),
					upperBound: bckt.GetUpperBound(),
				})
			} else {
				buckets[j].count += float64(bckt.GetCumulativeCount())
			}
		}
	}

	if len(buckets) == 0 || buckets[len(buckets)-1].upperBound != math.Inf(+1) {
		// The list of buckets in dto.Histogram doesn't include the final +Inf bucket, so we
		// add it here for the rest of the samples.
		buckets = append(buckets, bucket{
			count:      float64(vec.GetAggregatedSampleCount()),
			upperBound: math.Inf(+1),
		})
	}

	return bucketQuantile(q, buckets)
}

// Average computes wrapped histograms' average value.
func (vec HistogramVec) Average() float64 {
	return vec.GetAggregatedSampleSum() / float64(vec.GetAggregatedSampleCount())
}

// Validate makes sure the wrapped histograms have all necessary fields set and with valid values.
func (vec HistogramVec) Validate() error {
	bucketSize := 0
	for i, hist := range vec {
		if err := hist.Validate(); err != nil {
			return err
		}
		if i == 0 {
			bucketSize = len(hist.GetBucket())
		} else if bucketSize != len(hist.GetBucket()) {
			return fmt.Errorf("found different bucket size: expect %v, but got %v at index %v", bucketSize, len(hist.GetBucket()), i)
		}
	}
	return nil
}

// GetHistogramVecFromGatherer collects a metric, that matches the input labelValue map,
// from a gatherer implementing k8s.io/component-base/metrics.Gatherer interface.
// Used only for testing purposes where we need to gather metrics directly from a running binary (without metrics endpoint).
func GetHistogramVecFromGatherer(gatherer metrics.Gatherer, metricName string, lvMap map[string]string) (HistogramVec, error) {
	var metricFamily *dto.MetricFamily
	m, err := gatherer.Gather()
	if err != nil {
		return nil, err
	}
	for _, mFamily := range m {
		if mFamily.GetName() == metricName {
			metricFamily = mFamily
			break
		}
	}

	if metricFamily == nil {
		return nil, fmt.Errorf("metric %q not found", metricName)
	}

	if len(metricFamily.GetMetric()) == 0 {
		return nil, fmt.Errorf("metric %q is empty", metricName)
	}

	vec := make(HistogramVec, 0)
	for _, metric := range metricFamily.GetMetric() {
		if LabelsMatch(metric, lvMap) {
			if hist := metric.GetHistogram(); hist != nil {
				vec = append(vec, &Histogram{hist})
			}
		}
	}
	return vec, nil
}

func uint64Ptr(u uint64) *uint64 {
	return &u
}

// Bucket of a histogram
type bucket struct {
	upperBound float64
	count      float64
}

func bucketQuantile(q float64, buckets []bucket) float64 {
	if q < 0 {
		return math.Inf(-1)
	}
	if q > 1 {
		return math.Inf(+1)
	}

	if len(buckets) < 2 {
		return math.NaN()
	}

	rank := q * buckets[len(buckets)-1].count
	b := sort.Search(len(buckets)-1, func(i int) bool { return buckets[i].count >= rank })

	if b == 0 {
		return buckets[0].upperBound * (rank / buckets[0].count)
	}

	if b == len(buckets)-1 && math.IsInf(buckets[b].upperBound, 1) {
		return buckets[len(buckets)-2].upperBound
	}

	// linear approximation of b-th bucket
	brank := rank - buckets[b-1].count
	bSize := buckets[b].upperBound - buckets[b-1].upperBound
	bCount := buckets[b].count - buckets[b-1].count

	return buckets[b-1].upperBound + bSize*(brank/bCount)
}

// Quantile computes q-th quantile of a cumulative histogram.
// It's expected the histogram is valid (by calling Validate)
func (hist *Histogram) Quantile(q float64) float64 {
	var buckets []bucket

	for _, bckt := range hist.Bucket {
		buckets = append(buckets, bucket{
			count:      float64(bckt.GetCumulativeCount()),
			upperBound: bckt.GetUpperBound(),
		})
	}

	if len(buckets) == 0 || buckets[len(buckets)-1].upperBound != math.Inf(+1) {
		// The list of buckets in dto.Histogram doesn't include the final +Inf bucket, so we
		// add it here for the rest of the samples.
		buckets = append(buckets, bucket{
			count:      float64(hist.GetSampleCount()),
			upperBound: math.Inf(+1),
		})
	}

	return bucketQuantile(q, buckets)
}

// Average computes histogram's average value
func (hist *Histogram) Average() float64 {
	return hist.GetSampleSum() / float64(hist.GetSampleCount())
}

// Validate makes sure the wrapped histogram has all necessary fields set and with valid values.
func (hist *Histogram) Validate() error {
	if hist.SampleCount == nil || hist.GetSampleCount() == 0 {
		return fmt.Errorf("nil or empty histogram SampleCount")
	}

	if hist.SampleSum == nil || hist.GetSampleSum() == 0 {
		return fmt.Errorf("nil or empty histogram SampleSum")
	}

	for _, bckt := range hist.Bucket {
		if bckt == nil {
			return fmt.Errorf("empty histogram bucket")
		}
		if bckt.UpperBound == nil || bckt.GetUpperBound() < 0 {
			return fmt.Errorf("nil or negative histogram bucket UpperBound")
		}
	}

	return nil
}

// GetGaugeMetricValue extracts metric value from GaugeMetric
func GetGaugeMetricValue(m metrics.GaugeMetric) (float64, error) {
	metricProto := &dto.Metric{}
	if err := m.Write(metricProto); err != nil {
		return 0, fmt.Errorf("error writing m: %v", err)
	}
	return metricProto.Gauge.GetValue(), nil
}

// GetCounterMetricValue extracts metric value from CounterMetric
func GetCounterMetricValue(m metrics.CounterMetric) (float64, error) {
	metricProto := &dto.Metric{}
	if err := m.(metrics.Metric).Write(metricProto); err != nil {
		return 0, fmt.Errorf("error writing m: %v", err)
	}
	return metricProto.Counter.GetValue(), nil
}

// GetHistogramMetricValue extracts sum of all samples from ObserverMetric
func GetHistogramMetricValue(m metrics.ObserverMetric) (float64, error) {
	metricProto := &dto.Metric{}
	if err := m.(metrics.Metric).Write(metricProto); err != nil {
		return 0, fmt.Errorf("error writing m: %v", err)
	}
	return metricProto.Histogram.GetSampleSum(), nil
}

// GetHistogramMetricCount extracts count of all samples from ObserverMetric
func GetHistogramMetricCount(m metrics.ObserverMetric) (uint64, error) {
	metricProto := &dto.Metric{}
	if err := m.(metrics.Metric).Write(metricProto); err != nil {
		return 0, fmt.Errorf("error writing m: %v", err)
	}
	return metricProto.Histogram.GetSampleCount(), nil
}

// LabelsMatch returns true if metric has all expected labels otherwise false
func LabelsMatch(metric *dto.Metric, labelFilter map[string]string) bool {
	metricLabels := map[string]string{}

	for _, labelPair := range metric.Label {
		metricLabels[labelPair.GetName()] = labelPair.GetValue()
	}

	// length comparison then match key to values in the maps
	if len(labelFilter) > len(metricLabels) {
		return false
	}

	for labelName, labelValue := range labelFilter {
		if value, ok := metricLabels[labelName]; !ok || value != labelValue {
			return false
		}
	}

	return true
}