這篇blog名字起的搞得和我寫論文一樣，xxxx的設計與實現。其實這個東西原理很簡單，kubernetes的hpa使用的是heapster，heapster是k8s那幫傢伙在搞的，所以k8s還是喜歡自己搞的東西，所以k8s的hpa預設使用的heapster，但在業內，還有一個比heapster更好的監控方案，那就是prometheus。如果按照寫論文的方式，我這邊應該分別介紹一下k8s和prometheus，但真的沒有那個閒功夫，在此略過，我之前blog也做過它們的原始碼分析。

上面的圖片展示了整個體系結構
下面就分析一下adapter的具體實現，這裡需要結合上一篇blog關於api聚合的功能，這個adapter就是通過api聚合的方式註冊到apiserver上面。
先看一個hpa的例子

kind: HorizontalPodAutoscaler
apiVersion: autoscaling/v2alpha1
metadata:
  name: wordpress
  namespace: default
spec:
  scaleTargetRef:
    apiVersion: apps/v1beta1
    kind: Deployment
    name: wordpress
  minReplicas: 1
  maxReplicas: 3
  metrics:
  - type: Pods
    pods:
      metricName: memory_usage_bytes
      targetAverageValue: 100000
 

上面的程式碼新增一個記憶體使用量的hpa例子，通過targetAverageValue去設定閾值還有最大最小副本數，以及管理的deployment。在此額外說一下，hpa支援三種指標，分別是Object：描述k8s物件的某種指標，譬如ingress的hits-per-second。Pods：pod的平均指標，譬如transactions-processed-per-second，描述每個pod的事務每秒事務數，Resource是描述pod資源用量，譬如CPU或者記憶體。下面舉一個object的例子,pod的http請求數。

  - type: Object
    object:
      target:
        kind: Service
        name: sample-metrics-app
      metricName: http_requests
      targetValue: 100
 

先從hpa程式碼開始pkg/controller/podautoscaler/horizontal.go，裡面的computeReplicasForMetrics方法，它是負責獲取監控指標並且計算副本數的方法。針對不同資源呼叫不同的方法：

case autoscalingv2.ObjectMetricSourceType:
GetObjectMetricReplicas
...

case autoscalingv2.PodsMetricSourceType:
GetMetricReplicas

case autoscalingv2.ResourceMetricSourceType:
GetRawResourceReplicas

先看看GetObjectMetricReplicas這個方法
pkg/controller/podautoscaler/replica_calculator.go

func (c *ReplicaCalculator) GetObjectMetricReplicas(currentReplicas int32, targetUtilization int64, metricName string, namespace string, objectRef *autoscaling.CrossVersionObjectReference) (replicaCount int32, utilization int64, timestamp time.Time, err error) {
    utilization, timestamp, err = c.metricsClient.GetObjectMetric(metricName, namespace, objectRef)
    if err != nil {
        return 0, 0, time.Time{}, fmt.Errorf("unable to get metric %s: %v on %s %s/%s", metricName, objectRef.Kind, namespace, objectRef.Name, err)
    }

    usageRatio := float64(utilization) / float64(targetUtilization)
    if math.Abs(1.0-usageRatio) <= c.tolerance {
        // return the current replicas if the change would be too small
        return currentReplicas, utilization, timestamp, nil
    }

    return int32(math.Ceil(usageRatio * float64(currentReplicas))), utilization, timestamp, nil
}

GetObjectMetric是一個介面，有兩個方法，就是上面圖所示的heapster和自定義custom介面。heapster這個就是呼叫heapster介面去獲取效能指標，本blog著重介紹自定義效能指標，在啟動controller-manager時候指定–horizontal-pod-autoscaler-use-rest-clients就可以使用自定義的效能指標了
pkg/controller/podautoscaler/metrics/rest_metrics_client.go

func (c *customMetricsClient) GetObjectMetric(metricName string, namespace string, objectRef *autoscaling.CrossVersionObjectReference) (int64, time.Time, error) {
    gvk := schema.FromAPIVersionAndKind(objectRef.APIVersion, objectRef.Kind)
    var metricValue *customapi.MetricValue
    var err error
    if gvk.Kind == "Namespace" && gvk.Group == "" {
        // handle namespace separately
        // NB: we ignore namespace name here, since CrossVersionObjectReference isn't
        // supposed to allow you to escape your namespace
        metricValue, err = c.client.RootScopedMetrics().GetForObject(gvk.GroupKind(), namespace, metricName)
    } else {
        metricValue, err = c.client.NamespacedMetrics(namespace).GetForObject(gvk.GroupKind(), objectRef.Name, metricName)
    }

    if err != nil {
        return 0, time.Time{}, fmt.Errorf("unable to fetch metrics from API: %v", err)
    }

    return metricValue.Value.MilliValue(), metricValue.Timestamp.Time, nil
}

上面的objectRef針對本blog只為
{Kind:Service,Name:wordpress,APIVersion:,}，就是我們在yaml檔案裡面metrics裡面定義。

上面通過vendor/k8s.io/metrics/pkg/client/custom_metrics/client.go

func (m *rootScopedMetrics) GetForObject(groupKind schema.GroupKind, name string, metricName string) (*v1alpha1.MetricValue, error) {
    // handle namespace separately
    if groupKind.Kind == "Namespace" && groupKind.Group == "" {
        return m.getForNamespace(name, metricName)
    }

    resourceName, err := m.client.qualResourceForKind(groupKind)
    if err != nil {
        return nil, err
    }

    res := &v1alpha1.MetricValueList{}
    err = m.client.client.Get().
        Resource(resourceName).
        Name(name).
        SubResource(metricName).
        Do().
        Into(res)

    if err != nil {
        return nil, err
    }

    if len(res.Items) != 1 {
        return nil, fmt.Errorf("the custom metrics API server returned %v results when we asked for exactly one", len(res.Items))
    }

    return &res.Items[0], nil
}

通過client傳送https請求獲取metrics。具體傳送如下所示object：

https://localhost:6443/apis/custom-metrics.metrics.k8s.io/v1alpha1/namespaces/default/services/wordpress/requests-per-second

如果是pod則傳送的請求是

https://localhost:6443/apis/custom-metrics.metrics.k8s.io/v1alpha1/namespaces/default/pods/%2A/memory_usage_bytes?labelSelector=app%3Dwordpress%2Ctier%3Dfrontend

至於group為啥是custom-metrics.metrics.k8s.io這個不是別的，是程式碼裡面寫死的，vendor/k8s.io/metrics/pkg/apis/custom_metrics/v1alpha1/register.go

// GroupName is the group name use in this package
const GroupName = "custom-metrics.metrics.k8s.io"

// SchemeGroupVersion is group version used to register these objects
var SchemeGroupVersion = schema.GroupVersion{Group: GroupName, Version: "v1alpha1"}

這樣k8s的部分已經講解完畢。下面就是adapter的部分了。