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這篇文章主要講解了“Kubernetes HPA Controller怎么使用”，文中的講解內容簡單清晰，易于學習與理解，下面請大家跟著小編的思路慢慢深入，一起來研究和學習“Kubernetes HPA Controller怎么使用”吧！
HorizontalPodAutoscaler(以下簡稱HPA)的主要代碼如下，主要涉及的文件不多。
cmd/kube-controller-manager/app/autoscaling.go // HPA Controller的啟動代碼 /pkg/controller/podautoscaler . ├── BUILD ├── OWNERS ├── doc.go ├── horizontal.go // podautoscaler的核心代碼，包括其創建和運行的代碼 ├── horizontal_test.go ├── metrics │ ├── BUILD │ ├── metrics_client.go │ ├── metrics_client_test.go │ ├── metrics_client_test.go.orig │ ├── metrics_client_test.go.rej │ └── utilization.go ├── replica_calculator.go // ReplicaCaculator的創建，以及根據cpu/metrics計算replicas的方法 └── replica_calculator_test.go
其中，horizontal.go和replica_calculator.go是最核心的文件，他們對應的Structure如下：
horizontal.go
replica_calculator.go
HPA Controller同其他Controller一樣，都是在kube-controller-manager啟動時完成初始化并啟動的，如下代碼所示。
cmd/kube-controller-manager/app/controllermanager.go:224
func newControllerInitializers() map[string]InitFunc {
controllers := map[string]InitFunc{}
...
controllers["horizontalpodautoscaling"] = startHPAController
...
return controllers
}kube-controller-manager啟動時會initial一堆的controllers，對于HPA controller，它的啟動就交給startHPAController了。
cmd/kube-controller-manager/app/autoscaling.go:29
func startHPAController(ctx ControllerContext) (bool, error) {
...
// HPA Controller需要集群已經部署Heapster，由Heapster提供監控數據，來進行replicas的計算。
metricsClient := metrics.NewHeapsterMetricsClient(
hpaClient,
metrics.DefaultHeapsterNamespace,
metrics.DefaultHeapsterScheme,
metrics.DefaultHeapsterService,
metrics.DefaultHeapsterPort,
)
// 創建ReplicaCaculator，后面會用它來計算desired replicas。
replicaCalc := podautoscaler.NewReplicaCalculator(metricsClient, hpaClient.Core())
// 創建HPA Controller，并啟動goroutine執行其Run方法，開始工作。
go podautoscaler.NewHorizontalController(
hpaClient.Core(),
hpaClient.Extensions(),
hpaClient.Autoscaling(),
replicaCalc,
ctx.Options.HorizontalPodAutoscalerSyncPeriod.Duration,
).Run(ctx.Stop)
return true, nil
}首先我們來看看NewHorizontalController創建HPA Controller的代碼。
pkg/controller/podautoscaler/horizontal.go:112
func NewHorizontalController(evtNamespacer v1core.EventsGetter, scaleNamespacer unversionedextensions.ScalesGetter, hpaNamespacer unversionedautoscaling.HorizontalPodAutoscalersGetter, replicaCalc *ReplicaCalculator, resyncPeriod time.Duration) *HorizontalController {
...
// 構建HPA Controller
controller := &HorizontalController{
replicaCalc: replicaCalc,
eventRecorder: recorder,
scaleNamespacer: scaleNamespacer,
hpaNamespacer: hpaNamespacer,
}
// 創建Informer，配置對應的ListWatch Func，及其對應的EventHandler，用來監控HPA Resource的Add和Update事件。newInformer是HPA的核心代碼入口。
store, frameworkController := newInformer(controller, resyncPeriod)
controller.store = store
controller.controller = frameworkController
return controller
}我們有必要來看看HPA Controller struct的定義：
pkg/controller/podautoscaler/horizontal.go:59
type HorizontalController struct {
scaleNamespacer unversionedextensions.ScalesGetter
hpaNamespacer unversionedautoscaling.HorizontalPodAutoscalersGetter
replicaCalc *ReplicaCalculator
eventRecorder record.EventRecorder
// A store of HPA objects, populated by the controller.
store cache.Store
// Watches changes to all HPA objects.
controller *cache.Controller
}scaleNamespacer其實是一個ScaleInterface，包括Scale subresource的Get和Update接口。
hpaNamespacer是HorizontalPodAutoscalerInterface，包括HorizontalPodAutoscaler的Create, Update, UpdateStatus, Delete, Get, List, Watch等接口。
replicaCalc根據Heapster提供的監控數據，計算對應desired replicas。
pkg/controller/podautoscaler/replica_calculator.go:31
type ReplicaCalculator struct {
metricsClient metricsclient.MetricsClient
podsGetter v1core.PodsGetter
}store和controller：controller用來watch HPA objects，并更新到store這個cache中。
上面提到了Scale subresource，那是個什么東西？好吧，我們得看看Scale的定義。
pkg/apis/extensions/v1beta1/types.go:56
// represents a scaling request for a resource.
type Scale struct {
metav1.TypeMeta `json:",inline"`
// Standard object metadata; More info: http://releases.k8s.io/HEAD/docs/devel/api-conventions.md#metadata.
// +optional
v1.ObjectMeta `json:"metadata,omitempty" protobuf:"bytes,1,opt,name=metadata"`
// defines the behavior of the scale. More info: http://releases.k8s.io/HEAD/docs/devel/api-conventions.md#spec-and-status.
// +optional
Spec ScaleSpec `json:"spec,omitempty" protobuf:"bytes,2,opt,name=spec"`
// current status of the scale. More info: http://releases.k8s.io/HEAD/docs/devel/api-conventions.md#spec-and-status. Read-only.
// +optional
Status ScaleStatus `json:"status,omitempty" protobuf:"bytes,3,opt,name=status"`
}
// describes the attributes of a scale subresource
type ScaleSpec struct {
// desired number of instances for the scaled object.
Replicas int `json:"replicas,omitempty"`
}
// represents the current status of a scale subresource.
type ScaleStatus struct {
// actual number of observed instances of the scaled object.
Replicas int `json:"replicas"`
// label query over pods that should match the replicas count.
Selector map[string]string `json:"selector,omitempty"`
}Scale struct作為一次scale動作的請求數據。
其中Spec定義的是desired replicas number。
ScaleStatus定義了current replicas number。
看完了HorizontalController的結構后，接著看看NewHorizontalController中調用的newInformer。在上面的注釋中，我提到newInformer是整個HPA的核心代碼入口。
pkg/controller/podautoscaler/horizontal.go:75
func newInformer(controller *HorizontalController, resyncPeriod time.Duration) (cache.Store, *cache.Controller) {
return cache.NewInformer(
// 配置ListFucn和WatchFunc，用來定期List和watch HPA resource。
&cache.ListWatch{
ListFunc: func(options v1.ListOptions) (runtime.Object, error) {
return controller.hpaNamespacer.HorizontalPodAutoscalers(v1.NamespaceAll).List(options)
},
WatchFunc: func(options v1.ListOptions) (watch.Interface, error) {
return controller.hpaNamespacer.HorizontalPodAutoscalers(v1.NamespaceAll).Watch(options)
},
},
// 定義期望收到的object為HorizontalPodAutoscaler
&autoscaling.HorizontalPodAutoscaler{},
// 定義定期List的周期
resyncPeriod,
// 配置HPA resource event的Handler（AddFunc, UpdateFunc）
cache.ResourceEventHandlerFuncs{
AddFunc: func(obj interface{}) {
hpa := obj.(*autoscaling.HorizontalPodAutoscaler)
hasCPUPolicy := hpa.Spec.TargetCPUUtilizationPercentage != nil
_, hasCustomMetricsPolicy := hpa.Annotations[HpaCustomMetricsTargetAnnotationName]
if !hasCPUPolicy && !hasCustomMetricsPolicy {
controller.eventRecorder.Event(hpa, v1.EventTypeNormal, "DefaultPolicy", "No scaling policy specified - will use default one. See documentation for details")
}
// 根據監控調整hpa的數據
err := controller.reconcileAutoscaler(hpa)
if err != nil {
glog.Warningf("Failed to reconcile %s: %v", hpa.Name, err)
}
},
UpdateFunc: func(old, cur interface{}) {
hpa := cur.(*autoscaling.HorizontalPodAutoscaler)
// 根據監控調整hpa的數據
err := controller.reconcileAutoscaler(hpa)
if err != nil {
glog.Warningf("Failed to reconcile %s: %v", hpa.Name, err)
}
},
// We are not interested in deletions.
},
)
}newInformer的代碼也不長嘛，簡單說來，就是配置了HPA resource的ListWatch的Func，注冊HPA resource 的Add和Update Event的handler Func。
最終通過調用reconcileAutoscaler來矯正hpa的數據。
上面代碼中，將HPA resource的ListWatch Func注冊為HorizontalPodAutoscaler Interface定義的List和Watch接口。
等等，說了這么多，怎么還沒看到HorizontalPodAutoscaler struct的定義呢！好吧，下面就來看看，正好HorizontalPodAutoscaler Interface中出現了。
pkg/apis/autoscaling/v1/types.go:76
// configuration of a horizontal pod autoscaler.
type HorizontalPodAutoscaler struct {
metav1.TypeMeta `json:",inline"`
// Standard object metadata. More info: http://releases.k8s.io/HEAD/docs/devel/api-conventions.md#metadata
// +optional
v1.ObjectMeta `json:"metadata,omitempty" protobuf:"bytes,1,opt,name=metadata"`
// behaviour of autoscaler. More info: http://releases.k8s.io/HEAD/docs/devel/api-conventions.md#spec-and-status.
// +optional
Spec HorizontalPodAutoscalerSpec `json:"spec,omitempty" protobuf:"bytes,2,opt,name=spec"`
// current information about the autoscaler.
// +optional
Status HorizontalPodAutoscalerStatus `json:"status,omitempty" protobuf:"bytes,3,opt,name=status"`
}Spec HorizontalPodAutoscalerSpec存的是hpa的描述信息，是可以通過kube-controller-manager配置對應flag的信息。包括最小副本數MinReplicas，最大副本數MaxReplicas，hpa對應的所有pods的平均的百分比形式的目標CPU利用率TargetCPUUtilizationPercentage。
pkg/apis/autoscaling/v1/types.go:36
// specification of a horizontal pod autoscaler.
type HorizontalPodAutoscalerSpec struct {
// reference to scaled resource; horizontal pod autoscaler will learn the current resource consumption
// and will set the desired number of pods by using its Scale subresource.
ScaleTargetRef CrossVersionObjectReference `json:"scaleTargetRef" protobuf:"bytes,1,opt,name=scaleTargetRef"`
// lower limit for the number of pods that can be set by the autoscaler, default 1.
// +optional
MinReplicas *int32 `json:"minReplicas,omitempty" protobuf:"varint,2,opt,name=minReplicas"`
// upper limit for the number of pods that can be set by the autoscaler; cannot be smaller than MinReplicas.
MaxReplicas int32 `json:"maxReplicas" protobuf:"varint,3,opt,name=maxReplicas"`
// target average CPU utilization (represented as a percentage of requested CPU) over all the pods;
// if not specified the default autoscaling policy will be used.
// +optional
TargetCPUUtilizationPercentage *int32 `json:"targetCPUUtilizationPercentage,omitempty" protobuf:"varint,4,opt,name=targetCPUUtilizationPercentage"`
}Status HorizontalPodAutoscalerStatu存的是HPA的當前狀態數據，包括前后兩次scale的時間間隔ObservedGeneration，上一次scale的時間戳LastScaleTime，當前副本數CurrentReplicas，期望副本數DesiredReplicas，hpa對應的所有pods的平均的百分比形式的當前CPU利用率。
pkg/apis/autoscaling/v1/types.go:52
// current status of a horizontal pod autoscaler
type HorizontalPodAutoscalerStatus struct {
// most recent generation observed by this autoscaler.
// +optional
ObservedGeneration *int64 `json:"observedGeneration,omitempty" protobuf:"varint,1,opt,name=observedGeneration"`
// last time the HorizontalPodAutoscaler scaled the number of pods;
// used by the autoscaler to control how often the number of pods is changed.
// +optional
LastScaleTime *metav1.Time `json:"lastScaleTime,omitempty" protobuf:"bytes,2,opt,name=lastScaleTime"`
// current number of replicas of pods managed by this autoscaler.
CurrentReplicas int32 `json:"currentReplicas" protobuf:"varint,3,opt,name=currentReplicas"`
// desired number of replicas of pods managed by this autoscaler.
DesiredReplicas int32 `json:"desiredReplicas" protobuf:"varint,4,opt,name=desiredReplicas"`
// current average CPU utilization over all pods, represented as a percentage of requested CPU,
// e.g. 70 means that an average pod is using now 70% of its requested CPU.
// +optional
CurrentCPUUtilizationPercentage *int32 `json:"currentCPUUtilizationPercentage,omitempty" protobuf:"varint,5,opt,name=currentCPUUtilizationPercentage"`
}newInformer的代碼可見，不管hpa resource的event為Add或者update，最終都是調用reconcileAutoscaler來觸發HorizontalPodAutoscaler數據的更新。
pkg/controller/podautoscaler/horizontal.go:272
func (a *HorizontalController) reconcileAutoscaler(hpa *autoscaling.HorizontalPodAutoscaler) error {
...
// 獲取對應resource的scale subresource數據。
scale, err := a.scaleNamespacer.Scales(hpa.Namespace).Get(hpa.Spec.ScaleTargetRef.Kind, hpa.Spec.ScaleTargetRef.Name)
...
// 得到當前副本數
currentReplicas := scale.Status.Replicas
cpuDesiredReplicas := int32(0)
cpuCurrentUtilization := new(int32)
cpuTimestamp := time.Time{}
cmDesiredReplicas := int32(0)
cmMetric := ""
cmStatus := ""
cmTimestamp := time.Time{}
desiredReplicas := int32(0)
rescaleReason := ""
timestamp := time.Now()
rescale := true
// 如果期望副本數為0，這不進行scale操作。
if scale.Spec.Replicas == 0 {
// Autoscaling is disabled for this resource
desiredReplicas = 0
rescale = false
}
// 期望副本數不能超過hpa中配置的最大副本數
else if currentReplicas > hpa.Spec.MaxReplicas {
rescaleReason = "Current number of replicas above Spec.MaxReplicas"
desiredReplicas = hpa.Spec.MaxReplicas
}
// 期望副本數不能低于配置的最小副本數
else if hpa.Spec.MinReplicas != nil && currentReplicas < *hpa.Spec.MinReplicas {
rescaleReason = "Current number of replicas below Spec.MinReplicas"
desiredReplicas = *hpa.Spec.MinReplicas
}
// 期望副本數最少為1
else if currentReplicas == 0 {
rescaleReason = "Current number of replicas must be greater than 0"
desiredReplicas = 1
}
// 如果當前副本數在Min和Max之間，則需要根據cpu或者custom metrics（如果加了對應的Annotation）數據進行算法計算得到期望副本數。
else {
// All basic scenarios covered, the state should be sane, lets use metrics.
cmAnnotation, cmAnnotationFound := hpa.Annotations[HpaCustomMetricsTargetAnnotationName]
if hpa.Spec.TargetCPUUtilizationPercentage != nil || !cmAnnotationFound {
// 根據cpu利用率計算期望副本數
cpuDesiredReplicas, cpuCurrentUtilization, cpuTimestamp, err = a.computeReplicasForCPUUtilization(hpa, scale)
if err != nil {
// 更新hpa的當前副本數
a.updateCurrentReplicasInStatus(hpa, currentReplicas)
return fmt.Errorf("failed to compute desired number of replicas based on CPU utilization for %s: %v", reference, err)
}
}
if cmAnnotationFound {
// 根據custom metrics數據計算期望副本數
cmDesiredReplicas, cmMetric, cmStatus, cmTimestamp, err = a.computeReplicasForCustomMetrics(hpa, scale, cmAnnotation)
if err != nil {
// 更新hpa的當前副本數
a.updateCurrentReplicasInStatus(hpa, currentReplicas)
return fmt.Errorf("failed to compute desired number of replicas based on Custom Metrics for %s: %v", reference, err)
}
}
// 取cpu和custom metric得到的期望副本數的最大值作為最終的desired replicas，并且要在min和max范圍內。
rescaleMetric := ""
if cpuDesiredReplicas > desiredReplicas {
desiredReplicas = cpuDesiredReplicas
timestamp = cpuTimestamp
rescaleMetric = "CPU utilization"
}
if cmDesiredReplicas > desiredReplicas {
desiredReplicas = cmDesiredReplicas
timestamp = cmTimestamp
rescaleMetric = cmMetric
}
if desiredReplicas > currentReplicas {
rescaleReason = fmt.Sprintf("%s above target", rescaleMetric)
}
if desiredReplicas < currentReplicas {
rescaleReason = "All metrics below target"
}
if hpa.Spec.MinReplicas != nil && desiredReplicas < *hpa.Spec.MinReplicas {
desiredReplicas = *hpa.Spec.MinReplicas
}
// never scale down to 0, reserved for disabling autoscaling
if desiredReplicas == 0 {
desiredReplicas = 1
}
if desiredReplicas > hpa.Spec.MaxReplicas {
desiredReplicas = hpa.Spec.MaxReplicas
}
// Do not upscale too much to prevent incorrect rapid increase of the number of master replicas caused by
// bogus CPU usage report from heapster/kubelet (like in issue #32304).
if desiredReplicas > calculateScaleUpLimit(currentReplicas) {
desiredReplicas = calculateScaleUpLimit(currentReplicas)
}
// 根據currentReplicas和desiredReplicas的對比，以及scale時間是否滿足配置間隔要求，決定是否此時需要rescale
rescale = shouldScale(hpa, currentReplicas, desiredReplicas, timestamp)
}
if rescale {
scale.Spec.Replicas = desiredReplicas
// 執行ScaleInterface的Update接口，觸發調用API Server的對應resource的scale subresource的數據更新。其實最終會去修改對應rc或者deployment的replicas，然后由rc或deployment Controller去最終擴容或者縮容，使得副本數達到新的期望值。
_, err = a.scaleNamespacer.Scales(hpa.Namespace).Update(hpa.Spec.ScaleTargetRef.Kind, scale)
if err != nil {
a.eventRecorder.Eventf(hpa, v1.EventTypeWarning, "FailedRescale", "New size: %d; reason: %s; error: %v", desiredReplicas, rescaleReason, err.Error())
return fmt.Errorf("failed to rescale %s: %v", reference, err)
}
a.eventRecorder.Eventf(hpa, v1.EventTypeNormal, "SuccessfulRescale", "New size: %d; reason: %s", desiredReplicas, rescaleReason)
glog.Infof("Successfull rescale of %s, old size: %d, new size: %d, reason: %s",
hpa.Name, currentReplicas, desiredReplicas, rescaleReason)
} else {
desiredReplicas = currentReplicas
}
// 更新hpa resource的status數據
return a.updateStatus(hpa, currentReplicas, desiredReplicas, cpuCurrentUtilization, cmStatus, rescale)
}上面reconcileAutoscaler的代碼很重要，把想說的都寫到對應的注釋了。其中computeReplicasForCPUUtilization 和computeReplicasForCustomMetrics 需要單獨提出來看看，因為這兩個方法是HPA算法的體現，實際上最終算法是在pkg/controller/podautoscaler/replica_calculator.go:45#GetResourceReplicas和pkg/controller/podautoscaler/replica_calculator.go:153#GetMetricReplicas實現的:
pkg/controller/podautoscaler/replica_calculator.go:45#GetResourceReplicas負責根據heapster提供的cpu利用率數據計算得到desired replicas number。
pkg/controller/podautoscaler/replica_calculator.go:153#GetMetricReplicas負責根據heapster提供的custom raw metric數據計算得到desired replicas number。
具體關于HPA算法的源碼分析，我后續會單獨寫一篇博客，有興趣的可以關注（對于絕大部分同學來說沒必要關注，除非需要定制HPA算法時，才會具體去分析）。
總而言之，根據cpu和custom metric數據分別計算得到desired replicas后，取兩者最大的值，但不能超過配置的Max Replicas。
稍等稍等，計算出了desired replicas還還夠，我們還要通過shouldScale看看現在距離上一次彈性伸縮的時間間隔是否滿足條件：
兩次縮容的間隔不得小于5min。
兩次擴容的間隔不得小于3min。
shouldScale的代碼如下：
pkg/controller/podautoscaler/horizontal.go:387
...
var downscaleForbiddenWindow = 5 * time.Minute
var upscaleForbiddenWindow = 3 * time.Minute
...
func shouldScale(hpa *autoscaling.HorizontalPodAutoscaler, currentReplicas, desiredReplicas int32, timestamp time.Time) bool {
if desiredReplicas == currentReplicas {
return false
}
if hpa.Status.LastScaleTime == nil {
return true
}
// Going down only if the usageRatio dropped significantly below the target
// and there was no rescaling in the last downscaleForbiddenWindow.
if desiredReplicas < currentReplicas && hpa.Status.LastScaleTime.Add(downscaleForbiddenWindow).Before(timestamp) {
return true
}
// Going up only if the usage ratio increased significantly above the target
// and there was no rescaling in the last upscaleForbiddenWindow.
if desiredReplicas > currentReplicas && hpa.Status.LastScaleTime.Add(upscaleForbiddenWindow).Before(timestamp) {
return true
}
return false
}只有滿足這個條件后，接著才會調用Scales.Update接口與API Server交互，完成Scale對應的RC的replicas的設置。以rc Controller為例（deployment Controller的雷同），API Server對應的Scales.Update接口的實現邏輯如下：
pkg/registry/core/rest/storage_core.go:91
func (c LegacyRESTStorageProvider) NewLegacyRESTStorage(restOptionsGetter generic.RESTOptionsGetter) (LegacyRESTStorage, genericapiserver.APIGroupInfo, error) {
...
if autoscalingGroupVersion := (schema.GroupVersion{Group: "autoscaling", Version: "v1"}); registered.IsEnabledVersion(autoscalingGroupVersion) {
apiGroupInfo.SubresourceGroupVersionKind["replicationcontrollers/scale"] = autoscalingGroupVersion.WithKind("Scale")
}
...
restStorageMap := map[string]rest.Storage{
...
"replicationControllers": controllerStorage.Controller,
"replicationControllers/status": controllerStorage.Status,
...
}
return restStorage, apiGroupInfo, nil
}
pkg/registry/core/controller/etcd/etcd.go:124
func (r *ScaleREST) Update(ctx api.Context, name string, objInfo rest.UpdatedObjectInfo) (runtime.Object, bool, error) {
rc, err := r.registry.GetController(ctx, name, &metav1.GetOptions{})
if err != nil {
return nil, false, errors.NewNotFound(autoscaling.Resource("replicationcontrollers/scale"), name)
}
oldScale := scaleFromRC(rc)
obj, err := objInfo.UpdatedObject(ctx, oldScale)
if err != nil {
return nil, false, err
}
if obj == nil {
return nil, false, errors.NewBadRequest("nil update passed to Scale")
}
scale, ok := obj.(*autoscaling.Scale)
if !ok {
return nil, false, errors.NewBadRequest(fmt.Sprintf("wrong object passed to Scale update: %v", obj))
}
if errs := validation.ValidateScale(scale); len(errs) > 0 {
return nil, false, errors.NewInvalid(autoscaling.Kind("Scale"), scale.Name, errs)
}
// 設置rc對應spec.replicas為Scale中的期望副本數
rc.Spec.Replicas = scale.Spec.Replicas
rc.ResourceVersion = scale.ResourceVersion
// 更新到etcd
rc, err = r.registry.UpdateController(ctx, rc)
if err != nil {
return nil, false, err
}
return scaleFromRC(rc), false, nil
}了解kubernetes rc Controller的同學很清楚，修改rc的replicas后，會被rc Controller watch到，然后觸發rc Controller去執行創建或者銷毀對應差額數量的replicas，最終使得其副本數達到HPA計算得到的期望值。也就是說，最終由rc controller去執行具體的擴容或縮容動作。
最后，來看看HorizontalController的Run方法：
pkg/controller/podautoscaler/horizontal.go:130
func (a *HorizontalController) Run(stopCh <-chan struct{}) {
defer utilruntime.HandleCrash()
glog.Infof("Starting HPA Controller")
go a.controller.Run(stopCh)
<-stopCh
glog.Infof("Shutting down HPA Controller")
}很簡單，就是負責 HPA Resource的ListWatch，將change更新到對應的store(cache)。
HPA Resource的同步周期通過
--horizontal-pod-autoscaler-sync-period設置，默認值為30s。
感謝各位的閱讀，以上就是“Kubernetes HPA Controller怎么使用”的內容了，經過本文的學習后，相信大家對Kubernetes HPA Controller怎么使用這一問題有了更深刻的體會，具體使用情況還需要大家實踐驗證。這里是億速云，小編將為大家推送更多相關知識點的文章，歡迎關注！
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