From 656e120470556721c9f62fb0bd7c32b54df2c7c0 Mon Sep 17 00:00:00 2001
From: qmhu <brillantroad@gmail.com>
Date: Sun, 10 Jul 2022 18:06:57 +0800
Subject: [PATCH 1/2] update docs for ehpa and prometheus-adapter

---
 ...ffective-hpa-with-prometheus-adapter.zh.md | 23 ++++++++-----------
 1 file changed, 10 insertions(+), 13 deletions(-)
 rename docs/{ => tutorials}/effective-hpa-with-prometheus-adapter.zh.md (92%)

diff --git a/docs/effective-hpa-with-prometheus-adapter.zh.md b/docs/tutorials/effective-hpa-with-prometheus-adapter.zh.md
similarity index 92%
rename from docs/effective-hpa-with-prometheus-adapter.zh.md
rename to docs/tutorials/effective-hpa-with-prometheus-adapter.zh.md
index 2546e4091..503dc47d5 100644
--- a/docs/effective-hpa-with-prometheus-adapter.zh.md
+++ b/docs/tutorials/effective-hpa-with-prometheus-adapter.zh.md
@@ -1,7 +1,7 @@
 # 基于 Effective HPA 实现自定义指标的智能弹性实践
 
 Kubernetes HPA 支持了丰富的弹性扩展能力，Kubernetes 平台开发者部署服务实现自定义 Metric 的服务，Kubernetes 用户配置多项内置的资源指标或者自定义 Metric 指标实现自定义水平弹性。
-Crane Effective HPA 兼容社区的 Kubernetes HPA 的能力，提供了更智能的弹性策略，比如基于预测的弹性和基于 Cron 周期的弹性等。
+Effective HPA 兼容社区的 Kubernetes HPA 的能力，提供了更智能的弹性策略，比如基于预测的弹性和基于 Cron 周期的弹性等。
 Prometheus 是当下流行的开源监控系统，通过 Prometheus 可以获取到用户的自定义指标配置。
 
 本文将通过一个例子介绍了如何基于 Effective HPA 实现自定义指标的智能弹性。
@@ -11,26 +11,23 @@ Prometheus 是当下流行的开源监控系统，通过 Prometheus 可以获取
 - Kubernetes 1.18+
 - Helm 3.1.0
 - Crane v0.6.0+
+- Prometheus
 
 ## 环境搭建
 
-### 集群版本
+### 安装 PrometheusAdapter
 
-版本：v1.22.9
-注：为了接入阿里云ECI以降低成本，需要指定节点进行副本扩缩，故采用该版本作为基础环境
+镜像及版本：registry.cn-hangzhou.aliyuncs.com/istios/prometheus-adapter-amd64:v0.9.0
+注：EHPA需要兼容目前已有的外部指标，该部分通过prometheus-adapter实现
 
-```bash
-# kubectl version
+通过 Helm 安装 PrometheusAdapter
 
-Client Version: version.Info{Major:"1", Minor:"22", GitVersion:"v1.22.9", GitCommit:"6df4433e288edc9c40c2e344eb336f63fad45cd2", GitTreeState:"clean", BuildDate:"2022-04-13T19:57:43Z", GoVersion:"go1.16.15", Compiler:"gc", Platform:"linux/amd64"}
-Server Version: version.Info{Major:"1", Minor:"22", GitVersion:"v1.22.9", GitCommit:"6df4433e288edc9c40c2e344eb336f63fad45cd2", GitTreeState:"clean", BuildDate:"2022-04-13T19:52:02Z", GoVersion:"go1.16.15", Compiler:"gc", Platform:"linux/amd64"}
+```bash
+helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
+helm repo update
+helm install my-release prometheus-community/prometheus-adapter
 ```
 
-### Prometheus
-
-镜像及版本：registry.cn-hangzhou.aliyuncs.com/istios/prometheus-adapter-amd64:v0.9.0
-注：EHPA需要兼容目前已有的外部指标，该部分通过prometheus-adapter实现
-
 #### 指标采集
 
 服务设置

From 82d292ae360ee5163466786a8dba9c8b024464f4 Mon Sep 17 00:00:00 2001
From: qmhu <brillantroad@gmail.com>
Date: Tue, 12 Jul 2022 12:12:00 +0800
Subject: [PATCH 2/2] fix bug: missing custom metric info

---
 docs/images/remote-adapter.png                | Bin 0 -> 45180 bytes
 ...ffective-hpa-with-prometheus-adapter.zh.md | 535 ++++++++++--------
 mkdocs.yml                                    |   3 +
 pkg/metricprovider/custom_metric_provider.go  |  30 +-
 .../external_metric_provider.go               |  18 +-
 5 files changed, 317 insertions(+), 269 deletions(-)
 create mode 100644 docs/images/remote-adapter.png

diff --git a/docs/images/remote-adapter.png b/docs/images/remote-adapter.png
new file mode 100644
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diff --git a/docs/tutorials/effective-hpa-with-prometheus-adapter.zh.md b/docs/tutorials/effective-hpa-with-prometheus-adapter.zh.md
index 503dc47d5..3ed7a2b80 100644
--- a/docs/tutorials/effective-hpa-with-prometheus-adapter.zh.md
+++ b/docs/tutorials/effective-hpa-with-prometheus-adapter.zh.md
@@ -4,7 +4,7 @@ Kubernetes HPA 支持了丰富的弹性扩展能力，Kubernetes 平台开发者
 Effective HPA 兼容社区的 Kubernetes HPA 的能力，提供了更智能的弹性策略，比如基于预测的弹性和基于 Cron 周期的弹性等。
 Prometheus 是当下流行的开源监控系统，通过 Prometheus 可以获取到用户的自定义指标配置。
 
-本文将通过一个例子介绍了如何基于 Effective HPA 实现自定义指标的智能弹性。
+本文将通过一个例子介绍了如何基于 Effective HPA 实现自定义指标的智能弹性。部分配置来自于 [官方文档](https://github.com/kubernetes-sigs/prometheus-adapter/blob/master/docs/walkthrough.md)
 
 ## 部署环境要求
 
@@ -13,104 +13,66 @@ Prometheus 是当下流行的开源监控系统，通过 Prometheus 可以获取
 - Crane v0.6.0+
 - Prometheus
 
+参考 [安裝文档](https://docs.gocrane.io/dev/installation/) 在集群中安装 Crane，Prometheus 可以使用安装文档中的也可以是已部署的 Prometheus。
+
 ## 环境搭建
 
 ### 安装 PrometheusAdapter
 
-镜像及版本：registry.cn-hangzhou.aliyuncs.com/istios/prometheus-adapter-amd64:v0.9.0
-注：EHPA需要兼容目前已有的外部指标，该部分通过prometheus-adapter实现
-
-通过 Helm 安装 PrometheusAdapter
+Crane 组件 Metric-Adapter 和 PrometheusAdapter 都基于 [custom-metric-apiserver](https://github.com/kubernetes-sigs/custom-metrics-apiserver) 实现了 Custom Metric 和 External Metric 的 ApiService。在安装 Crane 时会将对应的 ApiService 安装为 Crane 的 Metric-Adapter，因此安装 PrometheusAdapter 前需要删除 ApiService 以确保 Helm 安装成功。
 
 ```bash
-helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
-helm repo update
-helm install my-release prometheus-community/prometheus-adapter
+# 查看当前集群 ApiService
+kubectl get apiservice 
 ```
 
-#### 指标采集
-
-服务设置
+因为安装了 Crane， 结果如下：
 
 ```bash
-# kubectl get deployment metric-external-service -o yaml
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: metric-external-service
-  namespace: default
-spec:
-  template:
-    metadata:
-      annotations:
-        prometheus.aispeech.com/metric_path: /metrics
-        prometheus.aispeech.com/scrape_port: "28002"
-        prometheus.aispeech.com/scrape_scheme: http
-        prometheus/should_be_scraped: "true"
+NAME                                   SERVICE                           AVAILABLE   AGE
+v1beta1.batch                          Local                             True        35d
+v1beta1.custom.metrics.k8s.io          crane-system/metric-adapter       True        18d
+v1beta1.discovery.k8s.io               Local                             True        35d
+v1beta1.events.k8s.io                  Local                             True        35d
+v1beta1.external.metrics.k8s.io        crane-system/metric-adapter       True        18d
+v1beta1.flowcontrol.apiserver.k8s.io   Local                             True        35d
+v1beta1.metrics.k8s.io                 kube-system/metrics-service       True        35d
 ```
 
-接口验证
+删除 crane 安装的 ApiService
 
 ```bash
-# kubectl get pods -owide
-NAME READY STATUS RESTARTS AGE
-metric-external-service-6c6b4b4648-n7bmc 1/1 Running 0 14d
-# curl 10.244.0.59:28002/metrics
-mock_traffic{} 13820
+kubectl delete apiservice v1beta1.custom.metrics.k8s.io
+kubectl delete apiservice v1beta1.external.metrics.k8s.io
 ```
 
-#### 外部指标创建
-
-注：指标来源与需要应用该指标实现扩缩容的服务跨namespace，设置为false
+通过 Helm 安装 PrometheusAdapter
 
 ```bash
-# kubectl -n ${prometheus-adapter-namespace} get configmap prometheus-adapter-config -o yaml
-apiVersion: v1
-data:
-  config.yaml: |
-    externalRules:
-    - seriesQuery:'{__name__="mock_traffic",pod_name!=""}'
-      metricsQuery: max(<<.Series>>{<<.LabelMatchers>>}) by (pod_name)
-      resources:
-        namespaced: false
+helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
+helm repo update
+helm install prometheus-adapter -n crane-system prometheus-community/prometheus-adapter
+```
 
+再将 ApiService 改回 Crane 的 Metric-Adapter
+
+```bash
+kubectl apply -f https://raw.githubusercontent.com/gocrane/crane/main/deploy/metric-adapter/apiservice.yaml
 ```
 
-配置添加后重启prometheus-adapter
-查询外部指标状态
+### 配置 Metric-Adapter 开启 RemoteAdapter 功能
 
-external-apiservice
+在安装 PrometheusAdapter 时没有将 ApiService 指向 PrometheusAdapter，因此为了让 PrometheusAdapter 也可以提供自定义 Metric，通过 Crane Metric Adapter 的 `RemoteAdapter` 功能将请求转发给 PrometheusAdapter。
 
-```bash
-# kubectl get apiservice v1beta1.external.metrics.k8s.io
-NAME SERVICE AVAILABLE AGE
-v1beta1.external.metrics.k8s.io monitoring/prometheus-adapter True 36d
-# kubectl get --raw /apis/external.metrics.k8s.io/v1beta1|tee |python -m json.tool
-{
-  "apiVersion": "v1",
-  "groupVersion": "external.metrics.k8s.io/v1beta1",
-  "kind":"APIResourceList",
-  "resources": [
-    {
-        "kind": "ExternalMetricValueList",
-        "name": "mock_traffic",
-        "namespaced": true,
-        "singularName":"",
-        "verbs": [
-          "get"
-        ]
-      }
-    ]
-}
+修改 Metric-Adapter 的配置，将 PrometheusAdapter 的 Service 配置成 Crane Metric Adapter 的 RemoteAdapter
 
+```bash
+# 查看当前集群 ApiService
+kubectl edit deploy metric-adapter -n crane-system
 ```
 
-### Metric-Adapter
-
-镜像及版本：docker.io/gocrane/metric-adapter:v0.5.0-tke.1-7-g10ddeb6
-注：时序预测模型功能需要通过metric-adapter获取，因此需要对prometheus-adapter与metric-adapter进行指标集成
+根据 PrometheusAdapter 的配置做以下修改：
 
-#### 配置remote-adapter
 ```yaml
 apiVersion: apps/v1
 kind: Deployment
@@ -122,96 +84,195 @@ spec:
     spec:
       containers:
       - args:
-#添加外部Adapter
+          #添加外部 Adapter 配置
         - --remote-adapter=true
-        - --remote-adapter-service-namespace=monitoring
+        - --remote-adapter-service-namespace=crane-system
         - --remote-adapter-service-name=prometheus-adapter
         - --remote-adapter-service-port=443
 ```
 
-#### 修改apiservice
+#### RemoteAdapter 能力
+
+![](../images/remote-adapter.png)
+
+Kubernetes 限制一个 ApiService 只能配置一个后端服务，因此，为了在一个集群内使用 Crane 提供的 Metric 和 PrometheusAdapter 提供的 Metric，Crane 支持了 RemoteAdapter 解决此问题
+
+- Crane Metric-Adapter 支持配置一个 Kubernetes Service 作为一个远程 Adapter
+- Crane Metric-Adapter 处理请求时会先检查是否是 Crane 提供的 Local Metric，如果不是，则转发给远程 Adapter
 
-指定外部指标源为metric-adapter，prometheus-adapter指标通过metric-adapter代理
+## 运行例子
+
+### 准备应用
+
+将以下应用部署到集群中，应用暴露了 Metric 展示每秒收到的 http 请求数量。
+
+<summary>sample-app.deploy.yaml</summary>
+
+```yaml
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: sample-app
+  labels:
+    app: sample-app
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: sample-app
+  template:
+    metadata:
+      labels:
+        app: sample-app
+    spec:
+      containers:
+      - image: luxas/autoscale-demo:v0.1.2
+        name: metrics-provider
+        ports:
+        - name: http
+          containerPort: 8080
+```
+
+<summary>sample-app.service.yaml</summary>
+
+```yaml
+apiVersion: v1
+kind: Service
+metadata:
+  labels:
+    app: sample-app
+  name: sample-app
+spec:
+  ports:
+  - name: http
+    port: 80
+    protocol: TCP
+    targetPort: 8080
+  selector:
+    app: sample-app
+  type: ClusterIP
+```
 
 ```bash
-# kubectl edit apiservice v1beta1.external.metrics.k8s.io
-# kubectl get apiservice v1beta1.external.metrics.k8s.io -o yaml
+kubectl create -f sample-app.deploy.yaml
+kubectl create -f sample-app.service.yaml
+```
+
+当应用部署完成后，您可以通过命令检查 `http_requests_total` Metric：
+
+```bash
+curl http://$(kubectl get service sample-app -o jsonpath='{ .spec.clusterIP }')/metrics
+```
+
+### 配置采集规则
+
+配置 Prometheus 的 ScrapeConfig，收集应用的 Metric: http_requests_total
 
-#外部指标正常
-# kubectl get --raw /apis/external.metrics.k8s.io/v1beta1|tee |python -m json.tool
+```bash
+kubectl edit configmap -n crane-system prometheus-server
+```
+
+添加以下配置
+
+```yaml
+    - job_name: sample-app
+      kubernetes_sd_configs:
+      - role: pod
+      relabel_configs:
+      - action: keep
+        regex: default;sample-app-(.+)
+        source_labels:
+        - __meta_kubernetes_namespace
+        - __meta_kubernetes_pod_name
+      - action: labelmap
+        regex: __meta_kubernetes_pod_label_(.+)
+      - action: replace
+        source_labels:
+        - __meta_kubernetes_namespace
+        target_label: namespace
+      - source_labels: [__meta_kubernetes_pod_name]
+        action: replace
+        target_label: pod
+```
+
+此时，您可以在 Prometheus 查询 psql：sum(rate(http_requests_total[5m])) by (pod)
+
+### 验证 PrometheusAdapter 
+
+PrometheusAdapter 默认的 Rule 配置支持将 http_requests_total 转换成 Pods 类型的 Custom Metric，通过命令验证：
+
+```bash
+kubectl get --raw /apis/custom.metrics.k8s.io/v1beta1 | jq . 
+```
+
+结果应包括 `pods/http_requests`:
+
+```bash
 {
-  "apiVersion":"v1",
-  "groupVersion":"external.metrics.k8s.io/v1beta1",
-  "kind":"APIResourceList",
-  "resources": [
-  {
-    "kind":"ExternalMetricValueList",
-    "name":"mock_traffic",
-    "namespaced":true,
-    "singularName":"",
-    "verbs": [
-      "get"
-    ]
-  }
-]
+  "name": "pods/http_requests",
+  "singularName": "",
+  "namespaced": true,
+  "kind": "MetricValueList",
+  "verbs": [
+    "get"
+  ]
 }
 ```
 
-## 配置弹性
+这表明现在可以通过 Pod Metric 配置 HPA。
+
+### 配置弹性
+
+现在我们可以创建 Effective HPA。此时 Effective HPA 可以通过 Pod Metric `http_requests` 进行弹性：
+
+#### 如何定义一个自定义指标开启预测功能
 
-### EHPA
+在 Effective HPA 的 Annotation 按以下规则添加配置：
+
+```yaml
+annotations:
+  # metric-query.autoscaling.crane.io 是固定的前缀，后面是 Metric 名字，需跟 spec.metrics 中的 Metric.name 相同，支持 Pods 类型和 External 类型
+  metric-query.autoscaling.crane.io/http_requests: "sum(rate(http_requests_total[5m])) by (pod)"
+```
 
-#### 设置EHPA
+<summary>sample-app-hpa.yaml</summary>
 
 ```yaml
 apiVersion: autoscaling.crane.io/v1alpha1
 kind: EffectiveHorizontalPodAutoscaler
 metadata:
-  name: metric-source-service
+  name: php-apache
   annotations:
-    metric-name.autoscaling.crane.io/mock_traffic: |
-#添加注解，当前版本需要配置查询语句
-#metric-name.autoscaling.crane.io/${需要获取时序模型的指标名}:
-      mock_traffic{job="metrics-service1-lyg-test", pod_namespace="cloud", pod_project="metric-external-service"}
+    # metric-query.autoscaling.crane.io 是固定的前缀，后面是 Metric 名字，需跟 spec.metrics 中的 Metric.name 相同，支持 Pods 类型和 External 类型
+    metric-query.autoscaling.crane.io/http_requests: "sum(rate(http_requests_total[5m])) by (pod)"
 spec:
-  behavior:
-    scaleDown:
-      stabilizationWindowSeconds: 6
-      policies:
-      - type: Percent
-        value: 100
-        periodSeconds: 15
-    scaleUp:
-      stabilizationWindowSeconds: 0
-      policies:
-      - type: Percent
-        value: 100
-        periodSeconds: 15
-      - type: Pods
-        value: 2
-        periodSeconds: 15
-      selectPolicy: Max
+  # ScaleTargetRef is the reference to the workload that should be scaled.
   scaleTargetRef:
-#指定需要实现扩缩容的deployment
     apiVersion: apps/v1
     kind: Deployment
-    name: metric-source-service
-  minReplicas: 2
-  maxReplicas: 20
-#Auto为应用，Previoew为DryRun模式
-  scaleStrategy: Auto
+    name: sample-app
+  minReplicas: 1        # MinReplicas is the lower limit replicas to the scale target which the autoscaler can scale down to.
+  maxReplicas: 10       # MaxReplicas is the upper limit replicas to the scale target which the autoscaler can scale up to.
+  scaleStrategy: Auto   # ScaleStrategy indicate the strategy to scaling target, value can be "Auto" and "Manual".
+  # Metrics contains the specifications for which to use to calculate the desired replica count.
   metrics:
-#控制扩缩容的外部指标
-  - type: External
-    external:
+  - type: Resource
+    resource:
+      name: cpu
+      target:
+        type: Utilization
+        averageUtilization: 50
+  - type: Pods
+    pods:
       metric:
-        name: mock_traffic
+        name: http_requests
       target:
-        averageValue: 2000
         type: AverageValue
-#设置时序预测模型
+        averageValue: 500m
+  # Prediction defines configurations for predict resources.
+  # If unspecified, defaults don't enable prediction.
   prediction:
-    predictionWindowSeconds: 3600
+    predictionWindowSeconds: 3600   # PredictionWindowSeconds is the time window to predict metrics in the future.
     predictionAlgorithm:
       algorithmType: dsp
       dsp:
@@ -219,141 +280,123 @@ spec:
         historyLength: "7d"
 ```
 
-应用并查看EHPA状态
-
 ```bash
-# kubectl apply -f ehpa.yaml
-#查询ehpa状态
-# kubectl get ehpa
-NAME                   STRATEGY   MINPODS   MAXPODS   SPECIFICPODS   REPLICAS   AGE
-metric-source-service  Auto       2         20                       10         1m
+kubectl create -f sample-app-hpa.yaml
 ```
 
-#### 时序预测模型
-注：craned定义TimeSeriesPrediction资源作为时序预测模型，命名定义ehpa-${ehpa-name}
-增加时序预测指标，命名定义crane-${extrenal-metric-name}
+查看 TimeSeriesPrediction 状态，如果应用运行时间较短，可能会无法预测：
 
-```bash
-# kubectl get tsp
-NAME                               TARGETREFNAME   TARGETREFKIND   PREDICTIONWINDOWSECONDS   AGE
-ehpa-metric-source-service         metric-source-service   Deployment      3600                      1m
-# kubectl get tsp ehpa-metric-source-service -o yaml
+```yaml
 apiVersion: prediction.crane.io/v1alpha1
 kind: TimeSeriesPrediction
 metadata:
-  name: ehpa-metric-source-service
+  creationTimestamp: "2022-07-11T16:10:09Z"
+  generation: 1
+  labels:
+    app.kubernetes.io/managed-by: effective-hpa-controller
+    app.kubernetes.io/name: ehpa-php-apache
+    app.kubernetes.io/part-of: php-apache
+    autoscaling.crane.io/effective-hpa-uid: 1322c5ac-a1c6-4c71-98d6-e85d07b22da0
+  name: ehpa-php-apache
   namespace: default
 spec:
   predictionMetrics:
-  - algorithm:
-      algorithmType: dsp
-      dsp:
-        estimators: {}
-        historyLength: 7d
-        sampleInterval: 60s
-    expressionQuery:
-      expression: |
-        mock_traffic{job="metrics-service1-lyg-test", pod_namespace="cloud", pod_project="metric-external-service"}
-    resourceIdentifier: crane-mock_traffic
-    type: ExpressionQuery
+    - algorithm:
+        algorithmType: dsp
+        dsp:
+          estimators: {}
+          historyLength: 7d
+          sampleInterval: 60s
+      resourceIdentifier: crane_pod_cpu_usage
+      resourceQuery: cpu
+      type: ResourceQuery
+    - algorithm:
+        algorithmType: dsp
+        dsp:
+          estimators: {}
+          historyLength: 7d
+          sampleInterval: 60s
+      expressionQuery:
+        expression: sum(rate(http_requests_total[5m])) by (pod)
+      resourceIdentifier: crane_custom.pods_http_requests
+      type: ExpressionQuery
+  predictionWindowSeconds: 3600
   targetRef:
     apiVersion: apps/v1
     kind: Deployment
-    name: metric-source-service
+    name: sample-app
     namespace: default
 status:
+  conditions:
+    - lastTransitionTime: "2022-07-12T06:54:42Z"
+      message: not all metric predicted
+      reason: PredictPartial
+      status: "False"
+      type: Ready
   predictionMetrics:
-  - prediction:
-    - labels:
-      samples:
-      - timestamp: 1656402060
-        value: "15767.77871"
-        ...
-        ...
-      - timestamp: 1656409200
-        value: "22202.37755"
-    resourceIdentifier: crane-mock_traffic
-```
-
-#### HPA
-
-HPA通过相关指标实现扩缩
-
-```bash
-# kubectl get hpa
-NAME                  REFERENCE                  TARGETS                                MINPODS   MAXPODS   REPLICAS   AGE
-metric-source-service Deployment/metric-source-service   1480200m/2k (avg), 2020300m/2k (avg)   2         20        10         1m
-# kubectl describe hpa ehpa-metric-source-service
-Name:                                           ehpa-metric-source-service
-Namespace:                                      default
-Labels:                                         app.kubernetes.io/managed-by=effective-hpa-controller
-                                                app.kubernetes.io/name=ehpa-metric-source-service
-                                                app.kubernetes.io/part-of=metric-source-service
-                                                autoscaling.crane.io/effective-hpa-uid=b2cb76db-61c9-4d00-b333-af67d36bbd65
-Annotations:                                    <none>
-CreationTimestamp:                              Tue, 28 Jun 2022 13:38:06 +0800
-Reference:                                      Deployment/metric-source-service
-Metrics:                                        ( current / target )
-  "mock_traffic" (target average value):        1470600m / 2k
-  "crane-mock_traffic" (target average value):  2032500m / 2k
-Min replicas:                                   2
-Max replicas:                                   20
-Behavior:
-  Scale Up:
-    Stabilization Window: 0 seconds
-    Select Policy: Max
-    Policies:
-      - Type: Percent  Value: 100  Period: 15 seconds
-      - Type: Pods     Value: 2    Period: 15 seconds
-  Scale Down:
-    Stabilization Window: 6 seconds
-    Select Policy: Max
-    Policies:
-      - Type: Percent  Value: 100  Period: 15 seconds
-Deployment pods:       10 current / 10 desired
+    - ready: false
+      resourceIdentifier: crane_pod_cpu_usage
+    - prediction:
+        - labels:
+            - name: pod
+              value: sample-app-7cfb596f98-8h5vv
+          samples:
+            - timestamp: 1657608900
+              value: "0.01683"
+            - timestamp: 1657608960
+              value: "0.01683"
+            ......
+      ready: true
+      resourceIdentifier: crane_custom.pods_http_requests  
 ```
 
-### craned指标采集
+查看 Effective HPA 创建的 HPA 对象，可以观测到已经创建出基于自定义指标预测的 Metric: `crane_custom.pods_http_requests`
 
-#### 副本指标
-
-```bash
-# kubectl -n crane-system get deployment craned -o yaml
-apiVersion: apps/v1
-kind: Deployment
+```yaml
+apiVersion: autoscaling/v2beta2
+kind: HorizontalPodAutoscaler
 metadata:
-  name: craned
-  namespace: crane-system
+  creationTimestamp: "2022-07-11T16:10:10Z"
+  labels:
+    app.kubernetes.io/managed-by: effective-hpa-controller
+    app.kubernetes.io/name: ehpa-php-apache
+    app.kubernetes.io/part-of: php-apache
+    autoscaling.crane.io/effective-hpa-uid: 1322c5ac-a1c6-4c71-98d6-e85d07b22da0
+  name: ehpa-php-apache
+  namespace: default
 spec:
-  template:
-    metadata:
-      annotations:
-        prometheus.aispeech.com/metric_path: /metrics
-        prometheus.aispeech.com/scrape_port: "8080"
-        prometheus.aispeech.com/scrape_scheme: http
-        prometheus.aispeech.com/should_be_scraped: "true"
- 
-# kubectl -n crane-system get pods craned-854bcdb88b-d5fgx -o wide
-NAME                      READY   STATUS    RESTARTS   AGE   IP             NODE          NOMINATED NODE   READINESS GATES
-craned-854bcdb88b-d5fgx   2/2     Running   0          96m   10.244.0.177   d2-node-012   <none>           <none>
-#指标查询
-# curl -sL 10.244.0.177:8080/metrics | grep ehpa
-crane_autoscaling_effective_hpa_replicas{name="metric-source-service",namespace="default"} 10
-crane_autoscaling_hpa_replicas{name="ehpa-metric-source-service",namespace="default"} 10
-crane_autoscaling_hpa_scale_count{name="ehpa-metric-source-service",namespace="default",type="hpa"} 3
-```
-
-#### TSP指标
-
-```bash
-# curl -sL 10.244.0.177:8080/metrics | grep ^crane_prediction_time_series_prediction
-crane_prediction_time_series_prediction_external_by_window{algorithm="dsp",resourceIdentifier="crane-mock_traffic",targetKind="Deployment",targetName="metric-source-service",targetNamespace="default",type="ExpressionQuery"} 23011 1657270905000
-crane_prediction_time_series_prediction_resource{algorithm="dsp",resourceIdentifier="crane_pod_cpu_usage",targetKind="Deployment",targetName="metric-source-service",namespace="default"} 10
+  maxReplicas: 10
+  metrics:
+  - pods:
+      metric:
+        name: http_requests
+      target:
+        averageValue: 500m
+        type: AverageValue
+    type: Pods
+  - pods:
+      metric:
+        name: crane_custom.pods_http_requests
+        selector:
+          matchLabels:
+            autoscaling.crane.io/effective-hpa-uid: 1322c5ac-a1c6-4c71-98d6-e85d07b22da0
+      target:
+        averageValue: 500m
+        type: AverageValue
+    type: Pods
+  - resource:
+      name: cpu
+      target:
+        averageUtilization: 50
+        type: Utilization
+    type: Resource
+  minReplicas: 1
+  scaleTargetRef:
+    apiVersion: apps/v1
+    kind: Deployment
+    name: sample-app
 ```
 
-## 总结：
+## 总结
 
-基于历史指标预测功能实现原理:
-● EHPA开启预测，建立相应指标的时序预测模型【TimeSeriesPrediction】
-● 创建HPA，在原有指标基础上，增加时序预测模型指标
-● HPA基于metric-adapter服务获取时序预测模型指标，实现服务提前扩容
+由于生产环境的复杂性，基于多指标的弹性（CPU/Memory/自定义指标）往往是生产应用的常见选择，因此 Effective HPA 通过预测算法覆盖了多指标的弹性，达到了帮助更多业务在生产环境落地水平弹性的成效。
diff --git a/mkdocs.yml b/mkdocs.yml
index d80e64ab7..314504085 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -56,6 +56,7 @@ plugins:
           Roadmap: 路线图
           Overview: 概述
           Load-aware Scheduling: 负载感知调度
+          Custom Metric Prediction With Prometheus: 基于自定义指标的弹性预测
         zh_TW:
           Getting Started: 入門
           Introduction: 介紹
@@ -111,6 +112,8 @@ nav:
       - Crane-scheduler:
         - Overview: tutorials/scheduling-pods-based-on-actual-node-load.md
         - Load-aware Scheduling: tutorials/dynamic-scheduler-plugin.md
+  - Best Practices:
+      - Custom Metric Prediction With Prometheus: tutorials/effective-hpa-with-prometheus-adapter.zh.md
   - Proposals:
       - Advanced CpuSet Manager: proposals/20220228-advanced-cpuset-manger.md
       - Pod Sorting And Precise Execution For Crane Agent: proposals/Pod-Sorting-And-Precise-Execution-For-Crane-Agent.md
diff --git a/pkg/metricprovider/custom_metric_provider.go b/pkg/metricprovider/custom_metric_provider.go
index 896683a22..54c827e67 100644
--- a/pkg/metricprovider/custom_metric_provider.go
+++ b/pkg/metricprovider/custom_metric_provider.go
@@ -22,7 +22,6 @@ import (
 	"sigs.k8s.io/controller-runtime/pkg/client"
 	"sigs.k8s.io/custom-metrics-apiserver/pkg/provider"
 
-	autoscalingapi "github.com/gocrane/api/autoscaling/v1alpha1"
 	predictionapi "github.com/gocrane/api/prediction/v1alpha1"
 
 	"github.com/gocrane/crane/pkg/known"
@@ -182,33 +181,24 @@ func ListAllLocalMetrics(client client.Client) []provider.CustomMetricInfo {
 		Metric:        known.MetricNamePodCpuUsage,
 	})
 
-	var ehpaList autoscalingapi.EffectiveHorizontalPodAutoscalerList
-	err := client.List(context.TODO(), &ehpaList)
+	var hpaList autoscalingv2.HorizontalPodAutoscalerList
+	err := client.List(context.TODO(), &hpaList)
 	if err != nil {
-		klog.Errorf("Failed to list ehpa: %v", err)
+		klog.Errorf("Failed to list hpa: %v", err)
 		return metricInfos
 	}
-	for _, ehpa := range ehpaList.Items {
-		for _, metric := range ehpa.Spec.Metrics {
+	for _, hpa := range hpaList.Items {
+		if !strings.HasPrefix(hpa.Name, "ehpa-") {
+			// filter hpa that not created by ehpa
+			continue
+		}
+		for _, metric := range hpa.Spec.Metrics {
 			if metric.Type == autoscalingv2.PodsMetricSourceType &&
 				metric.Pods != nil &&
 				metric.Pods.Metric.Selector != nil &&
 				metric.Pods.Metric.Selector.MatchLabels != nil {
 				if _, exist := metric.Pods.Metric.Selector.MatchLabels[known.EffectiveHorizontalPodAutoscalerUidLabel]; exist {
-					metricName := utils.GetGeneralPredictionMetricName(autoscalingv2.PodsMetricSourceType, false, metric.Pods.Metric.Name)
-					metricInfos = append(metricInfos, provider.CustomMetricInfo{Metric: metricName})
-				}
-			}
-		}
-
-		for _, metric := range ehpa.Spec.Metrics {
-			if metric.Type == autoscalingv2.ObjectMetricSourceType &&
-				metric.Object != nil &&
-				metric.Object.Metric.Selector != nil &&
-				metric.Object.Metric.Selector.MatchLabels != nil {
-				if _, exist := metric.Object.Metric.Selector.MatchLabels[known.EffectiveHorizontalPodAutoscalerUidLabel]; exist {
-					metricName := utils.GetGeneralPredictionMetricName(autoscalingv2.ObjectMetricSourceType, false, metric.Object.Metric.Name)
-					metricInfos = append(metricInfos, provider.CustomMetricInfo{Metric: metricName})
+					metricInfos = append(metricInfos, provider.CustomMetricInfo{Metric: metric.Pods.Metric.Name, Namespaced: true, GroupResource: schema.GroupResource{Group: "", Resource: "pods"}})
 				}
 			}
 		}
diff --git a/pkg/metricprovider/external_metric_provider.go b/pkg/metricprovider/external_metric_provider.go
index 751e75070..62a9c7335 100644
--- a/pkg/metricprovider/external_metric_provider.go
+++ b/pkg/metricprovider/external_metric_provider.go
@@ -229,14 +229,26 @@ func ListAllLocalExternalMetrics(client client.Client) []provider.ExternalMetric
 			metricName := utils.GetGeneralPredictionMetricName(autoscalingv2.PodsMetricSourceType, true, ehpa.Name)
 			metricInfos = append(metricInfos, provider.ExternalMetricInfo{Metric: metricName})
 		}
-		for _, metric := range ehpa.Spec.Metrics {
+	}
+
+	var hpaList autoscalingv2.HorizontalPodAutoscalerList
+	err = client.List(context.TODO(), &hpaList)
+	if err != nil {
+		klog.Errorf("Failed to list hpa: %v", err)
+		return metricInfos
+	}
+	for _, hpa := range hpaList.Items {
+		if !strings.HasPrefix(hpa.Name, "ehpa-") {
+			// filter hpa that not created by ehpa
+			continue
+		}
+		for _, metric := range hpa.Spec.Metrics {
 			if metric.Type == autoscalingv2.ExternalMetricSourceType &&
 				metric.External != nil &&
 				metric.External.Metric.Selector != nil &&
 				metric.External.Metric.Selector.MatchLabels != nil {
 				if _, exist := metric.External.Metric.Selector.MatchLabels[known.EffectiveHorizontalPodAutoscalerUidLabel]; exist {
-					metricName := utils.GetGeneralPredictionMetricName(autoscalingv2.ExternalMetricSourceType, false, ehpa.Name)
-					metricInfos = append(metricInfos, provider.ExternalMetricInfo{Metric: metricName})
+					metricInfos = append(metricInfos, provider.ExternalMetricInfo{Metric: metric.External.Metric.Name})
 				}
 			}
 		}