13.深入k8s：Pod 水平自动扩缩HPA及其源码分析 (3)

这一段代码是reconcileAutoscaler里面的核心代码，在这里会确定一个区间，首先根据当前的scale对象和当前hpa里面配置的对应的参数的值，决策当前的副本数量，其中针对于超过设定的maxReplicas和小于minReplicas两种情况，只需要简单的修正为对应的值，直接更新对应的scale对象即可，而scale副本为0的对象，则hpa不会在进行任何操作。

对于当前副本数在maxReplicas和minReplicas之间的时候，则需要计算是否需要扩缩容，计算则是调用computeReplicasForMetrics方法来实现。

最后如果设置了Behavior则调用normalizeDesiredReplicasWithBehaviors函数来修正最后的结果，Behavior相关可以看文档：

下面我们一步步分析。

computeReplicasForMetrics：遍历度量目标 func (a *HorizontalController) computeReplicasForMetrics(hpa *autoscalingv2.HorizontalPodAutoscaler, scale *autoscalingv1.Scale, metricSpecs []autoscalingv2.MetricSpec) (replicas int32, metric string, statuses []autoscalingv2.MetricStatus, timestamp time.Time, err error) { ... //这里的度量目标可以是一个列表，所以遍历之后取最大的需要扩缩容的数量 for i, metricSpec := range metricSpecs { //根据type类型计算需要扩缩容的数量 replicaCountProposal, metricNameProposal, timestampProposal, condition, err := a.computeReplicasForMetric(hpa, metricSpec, specReplicas, statusReplicas, selector, &statuses[i]) if err != nil { if invalidMetricsCount <= 0 { invalidMetricCondition = condition invalidMetricError = err } invalidMetricsCount++ } //记录最大的需要扩缩容的数量 if err == nil && (replicas == 0 || replicaCountProposal > replicas) { timestamp = timestampProposal replicas = replicaCountProposal metric = metricNameProposal } } ... return replicas, metric, statuses, timestamp, nil }

因为我们在设置metrics的时候实际上是一个数组，如下：

apiVersion: autoscaling/v2beta2 kind: HorizontalPodAutoscaler metadata: name: php-apache spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: php-apache minReplicas: 1 maxReplicas: 10 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 50 - type: Pods pods: metric: name: packets-per-second target: type: AverageValue averageValue: 1k - type: Object object: metric: name: requests-per-second describedObject: apiVersion: networking.k8s.io/v1beta1 kind: Ingress name: main-route target: type: Value value: 10k

例如这个官方的例子中，设置了三个metric，所以我们在上面的代码中遍历所有的metrics，然后选取返回副本数最大的那个。主要计算逻辑都在computeReplicasForMetric中，下面我们看看这个方法。

computeReplicasForMetric：根据type计算副本数 func (a *HorizontalController) computeReplicasForMetric(hpa *autoscalingv2.HorizontalPodAutoscaler, spec autoscalingv2.MetricSpec, specReplicas, statusReplicas int32, selector labels.Selector, status *autoscalingv2.MetricStatus) (replicaCountProposal int32, metricNameProposal string, timestampProposal time.Time, condition autoscalingv2.HorizontalPodAutoscalerCondition, err error) { //根据不同的类型来进行计量 switch spec.Type { //表示如果是一个k8s对象，如Ingress对象 case autoscalingv2.ObjectMetricSourceType: ... // 表示pod度量类型 case autoscalingv2.PodsMetricSourceType: metricSelector, err := metav1.LabelSelectorAsSelector(spec.Pods.Metric.Selector) if err != nil { condition := a.getUnableComputeReplicaCountCondition(hpa, "FailedGetPodsMetric", err) return 0, "", time.Time{}, condition, fmt.Errorf("failed to get pods metric value: %v", err) } //仅支持AverageValue度量目标,计算需要扩缩容的数量 replicaCountProposal, timestampProposal, metricNameProposal, condition, err = a.computeStatusForPodsMetric(specReplicas, spec, hpa, selector, status, metricSelector) if err != nil { return 0, "", time.Time{}, condition, fmt.Errorf("failed to get pods metric value: %v", err) } // 表示Resource度量类型 case autoscalingv2.ResourceMetricSourceType: ... case autoscalingv2.ExternalMetricSourceType: ... default: errMsg := fmt.Sprintf("unknown metric source type %q", string(spec.Type)) err = fmt.Errorf(errMsg) condition := a.getUnableComputeReplicaCountCondition(hpa, "InvalidMetricSourceType", err) return 0, "", time.Time{}, condition, err } return replicaCountProposal, metricNameProposal, timestampProposal, autoscalingv2.HorizontalPodAutoscalerCondition{}, nil }