Merge pull request #85861 from ingvagabund/scheduler-perf-collect-data-items-from-metrics

Collect some of scheduling metrics and scheduling throughput

test/integration/scheduler_perf/BUILD

@@ -20,7 +20,10 @@ go_library(
         "//staging/src/k8s.io/client-go/informers/core/v1:go_default_library",
         "//staging/src/k8s.io/client-go/kubernetes:go_default_library",
         "//staging/src/k8s.io/client-go/rest:go_default_library",
+        "//staging/src/k8s.io/component-base/metrics/legacyregistry:go_default_library",
         "//test/integration/util:go_default_library",
+        "//vendor/github.com/prometheus/client_model/go:go_default_library",
+        "//vendor/k8s.io/klog:go_default_library",
     ],
 )
 

test/integration/scheduler_perf/scheduler_perf_test.go

@@ -38,6 +38,15 @@ const (
 	configFile = "config/performance-config.yaml"
 )
 
+var (
+	defaultMetrics = []string{
+		"scheduler_scheduling_algorithm_predicate_evaluation_seconds",
+		"scheduler_scheduling_algorithm_priority_evaluation_seconds",
+		"scheduler_binding_duration_seconds",
+		"scheduler_e2e_scheduling_duration_seconds",
+	}
+)
+
 // testCase configures a test case to run the scheduler performance test. Users should be able to
 // provide this via a YAML file.
 //
@@ -92,6 +101,7 @@ type testParams struct {
 }
 
 func BenchmarkPerfScheduling(b *testing.B) {
+	dataItems := DataItems{Version: "v1"}
 	tests := getSimpleTestCases(configFile)
 
 	for _, test := range tests {
@@ -100,12 +110,15 @@ func BenchmarkPerfScheduling(b *testing.B) {
 			for feature, flag := range test.FeatureGates {
 				defer featuregatetesting.SetFeatureGateDuringTest(b, utilfeature.DefaultFeatureGate, feature, flag)()
 			}
-			perfScheduling(test, b)
+			dataItems.DataItems = append(dataItems.DataItems, perfScheduling(test, b)...)
 		})
 	}
+	if err := dataItems2JSONFile(dataItems, b.Name()); err != nil {
+		klog.Fatalf("%v: unable to write measured data: %v", b.Name(), err)
+	}
 }
 
-func perfScheduling(test testCase, b *testing.B) {
+func perfScheduling(test testCase, b *testing.B) []DataItem {
 	var nodeStrategy testutils.PrepareNodeStrategy = &testutils.TrivialNodePrepareStrategy{}
 	if test.Nodes.NodeAllocatableStrategy != nil {
 		nodeStrategy = test.Nodes.NodeAllocatableStrategy
@@ -180,15 +193,45 @@ func perfScheduling(test testCase, b *testing.B) {
 
 	// start benchmark
 	b.ResetTimer()
+
+	// Start measuring throughput
+	stopCh := make(chan struct{})
+	throughputCollector := newThroughputCollector(podInformer)
+	go throughputCollector.run(stopCh)
+
+	// Scheduling the main workload
 	config = testutils.NewTestPodCreatorConfig()
 	config.AddStrategy(testNamespace, test.PodsToSchedule.Num, testPodStrategy)
 	podCreator = testutils.NewTestPodCreator(clientset, config)
 	podCreator.CreatePods()
 
 	<-completedCh
+	close(stopCh)
 
 	// Note: without this line we're taking the overhead of defer() into account.
 	b.StopTimer()
+
+	setNameLabel := func(dataItem *DataItem) DataItem {
+		if dataItem.Labels == nil {
+			dataItem.Labels = map[string]string{}
+		}
+		dataItem.Labels["Name"] = b.Name()
+		return *dataItem
+	}
+
+	dataItems := []DataItem{
+		setNameLabel(throughputCollector.collect()),
+	}
+
+	for _, metric := range defaultMetrics {
+		dataItem := newPrometheusCollector(metric).collect()
+		if dataItem == nil {
+			continue
+		}
+		dataItems = append(dataItems, setNameLabel(dataItem))
+	}
+
+	return dataItems
 }
 
 func getPodStrategy(pc podCase) testutils.TestPodCreateStrategy {

test/integration/scheduler_perf/util.go

@@ -17,15 +17,34 @@ limitations under the License.
 package benchmark
 
 import (
+	"encoding/json"
+	"flag"
+	"fmt"
+	"io/ioutil"
+	"math"
+	"path"
+	"sort"
+	"time"
+
+	dto "github.com/prometheus/client_model/go"
 	v1 "k8s.io/api/core/v1"
 	"k8s.io/apimachinery/pkg/labels"
 	"k8s.io/apimachinery/pkg/runtime/schema"
 	coreinformers "k8s.io/client-go/informers/core/v1"
 	clientset "k8s.io/client-go/kubernetes"
 	restclient "k8s.io/client-go/rest"
+	"k8s.io/component-base/metrics/legacyregistry"
+	"k8s.io/klog"
 	"k8s.io/kubernetes/test/integration/util"
 )
 
+const (
+	dateFormat                = "2006-01-02T15:04:05Z"
+	throughputSampleFrequency = time.Second
+)
+
+var dataItemsDir = flag.String("data-items-dir", "", "destination directory for storing generated data items for perf dashboard")
+
 // mustSetupScheduler starts the following components:
 // - k8s api server (a.k.a. master)
 // - scheduler
@@ -66,3 +85,250 @@ func getScheduledPods(podInformer coreinformers.PodInformer) ([]*v1.Pod, error)
 	}
 	return scheduled, nil
 }
+
+// DataItem is the data point.
+type DataItem struct {
+	// Data is a map from bucket to real data point (e.g. "Perc90" -> 23.5). Notice
+	// that all data items with the same label combination should have the same buckets.
+	Data map[string]float64 `json:"data"`
+	// Unit is the data unit. Notice that all data items with the same label combination
+	// should have the same unit.
+	Unit string `json:"unit"`
+	// Labels is the labels of the data item.
+	Labels map[string]string `json:"labels,omitempty"`
+}
+
+// DataItems is the data point set. It is the struct that perf dashboard expects.
+type DataItems struct {
+	Version   string     `json:"version"`
+	DataItems []DataItem `json:"dataItems"`
+}
+
+func dataItems2JSONFile(dataItems DataItems, namePrefix string) error {
+	b, err := json.Marshal(dataItems)
+	if err != nil {
+		return err
+	}
+
+	destFile := fmt.Sprintf("%v_%v.json", namePrefix, time.Now().Format(dateFormat))
+	if *dataItemsDir != "" {
+		destFile = path.Join(*dataItemsDir, destFile)
+	}
+
+	return ioutil.WriteFile(destFile, b, 0644)
+}
+
+// prometheusCollector collects metrics from legacyregistry.DefaultGatherer.Gather() endpoint.
+// Currently only Histrogram metrics are supported.
+type prometheusCollector struct {
+	metric string
+	cache  *dto.MetricFamily
+}
+
+func newPrometheusCollector(metric string) *prometheusCollector {
+	return &prometheusCollector{
+		metric: metric,
+	}
+}
+
+func (pc *prometheusCollector) collect() *DataItem {
+	var metricFamily *dto.MetricFamily
+	m, err := legacyregistry.DefaultGatherer.Gather()
+	if err != nil {
+		klog.Error(err)
+		return nil
+	}
+	for _, mFamily := range m {
+		if mFamily.Name != nil && *mFamily.Name == pc.metric {
+			metricFamily = mFamily
+			break
+		}
+	}
+
+	if metricFamily == nil {
+		klog.Infof("Metric %q not found", pc.metric)
+		return nil
+	}
+
+	if metricFamily.GetMetric() == nil {
+		klog.Infof("Metric %q is empty", pc.metric)
+		return nil
+	}
+
+	if len(metricFamily.GetMetric()) == 0 {
+		klog.Infof("Metric %q is empty", pc.metric)
+		return nil
+	}
+
+	// Histograms are stored under the first index (based on observation).
+	// Given there's only one histogram registered per each metric name, accessaing
+	// the first index is sufficient.
+	dataItem := pc.promHist2Summary(metricFamily.GetMetric()[0].GetHistogram())
+	if dataItem.Data == nil {
+		return nil
+	}
+
+	// clear the metrics so that next test always starts with empty prometheus
+	// metrics (since the metrics are shared among all tests run inside the same binary)
+	clearPromHistogram(metricFamily.GetMetric()[0].GetHistogram())
+
+	return dataItem
+}
+
+// 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)
+	}
+
+	// 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)
+}
+
+func (pc *prometheusCollector) promHist2Summary(hist *dto.Histogram) *DataItem {
+	buckets := []bucket{}
+
+	if hist.SampleCount == nil || *hist.SampleCount == 0 {
+		return &DataItem{}
+	}
+
+	if hist.SampleSum == nil || *hist.SampleSum == 0 {
+		return &DataItem{}
+	}
+
+	for _, bckt := range hist.Bucket {
+		if bckt == nil {
+			return &DataItem{}
+		}
+		if bckt.UpperBound == nil || *bckt.UpperBound < 0 {
+			return &DataItem{}
+		}
+		buckets = append(buckets, bucket{
+			count:      float64(*bckt.CumulativeCount),
+			upperBound: *bckt.UpperBound,
+		})
+	}
+
+	// bucketQuantile expects the upper bound of the last bucket to be +inf
+	buckets[len(buckets)-1].upperBound = math.Inf(+1)
+
+	q50 := bucketQuantile(0.50, buckets)
+	q90 := bucketQuantile(0.90, buckets)
+	q99 := bucketQuantile(0.95, buckets)
+
+	msFactor := float64(time.Second) / float64(time.Millisecond)
+
+	return &DataItem{
+		Labels: map[string]string{
+			"Metric": pc.metric,
+		},
+		Data: map[string]float64{
+			"Perc50":  q50 * msFactor,
+			"Perc90":  q90 * msFactor,
+			"Perc99":  q99 * msFactor,
+			"Average": (*hist.SampleSum / float64(*hist.SampleCount)) * msFactor,
+		},
+		Unit: "ms",
+	}
+}
+
+func clearPromHistogram(hist *dto.Histogram) {
+	if hist.SampleCount != nil {
+		*hist.SampleCount = 0
+	}
+	if hist.SampleSum != nil {
+		*hist.SampleSum = 0
+	}
+	for _, b := range hist.Bucket {
+		if b.CumulativeCount != nil {
+			*b.CumulativeCount = 0
+		}
+		if b.UpperBound != nil {
+			*b.UpperBound = 0
+		}
+	}
+}
+
+type throughputCollector struct {
+	podInformer           coreinformers.PodInformer
+	schedulingThroughputs []float64
+}
+
+func newThroughputCollector(podInformer coreinformers.PodInformer) *throughputCollector {
+	return &throughputCollector{
+		podInformer: podInformer,
+	}
+}
+
+func (tc *throughputCollector) run(stopCh chan struct{}) {
+	podsScheduled, err := getScheduledPods(tc.podInformer)
+	if err != nil {
+		klog.Fatalf("%v", err)
+	}
+	lastScheduledCount := len(podsScheduled)
+	for {
+		select {
+		case <-stopCh:
+			return
+		case <-time.After(throughputSampleFrequency):
+			podsScheduled, err := getScheduledPods(tc.podInformer)
+			if err != nil {
+				klog.Fatalf("%v", err)
+			}
+
+			scheduled := len(podsScheduled)
+			samplingRatioSeconds := float64(throughputSampleFrequency) / float64(time.Second)
+			throughput := float64(scheduled-lastScheduledCount) / samplingRatioSeconds
+			tc.schedulingThroughputs = append(tc.schedulingThroughputs, throughput)
+			lastScheduledCount = scheduled
+
+			klog.Infof("%d pods scheduled", lastScheduledCount)
+		}
+	}
+}
+
+func (tc *throughputCollector) collect() *DataItem {
+	throughputSummary := &DataItem{}
+	if length := len(tc.schedulingThroughputs); length > 0 {
+		sort.Float64s(tc.schedulingThroughputs)
+		sum := 0.0
+		for i := range tc.schedulingThroughputs {
+			sum += tc.schedulingThroughputs[i]
+		}
+
+		throughputSummary.Labels = map[string]string{
+			"Metric": "SchedulingThroughput",
+		}
+		throughputSummary.Data = map[string]float64{
+			"Average": sum / float64(length),
+			"Perc50":  tc.schedulingThroughputs[int(math.Ceil(float64(length*50)/100))-1],
+			"Perc90":  tc.schedulingThroughputs[int(math.Ceil(float64(length*90)/100))-1],
+			"Perc99":  tc.schedulingThroughputs[int(math.Ceil(float64(length*99)/100))-1],
+		}
+		throughputSummary.Unit = "pods/s"
+	}
+	return throughputSummary
+}