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使用文档/Pod水平自动缩放.md
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使用文档/Pod水平自动缩放.md
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> 本文作者：丁辉
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# Pod水平自动缩放
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[官方文档](https://kubernetes.io/zh-cn/docs/concepts/workloads/controllers/deployment/#scaling-a-deployment)
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## 基础配置准备
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1. 确保 Metrics Server 已安装
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[Github](https://github.com/kubernetes-sigs/metrics-server/)
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检查 metrics-server 是否运行
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```bash
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kubectl get pods -n kube-system | grep metrics-server
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```
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如果没有，可以安装它（如果是标准的 Kubernetes 集群）
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```bash
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kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml
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```
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2. 确保 Pod 已设置资源请求
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[Pod配置资源请求](https://gitee.com/offends/Kubernetes/blob/main/%E4%BD%BF%E7%94%A8%E6%96%87%E6%A1%A3/Pod%E9%85%8D%E7%BD%AE%E8%B5%84%E6%BA%90%E8%AF%B7%E6%B1%82.md)
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## 介绍
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> 自动伸缩（HPA）的计算是基于 `requests` 的，而不是 `limits`
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**详细解释**
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HPA 使用的计算公式是：
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- 使用率(Utilization)= Pod 当前实际使用量 ÷ Pod 的 requests 值
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- 绝对值(AverageValue) = 所有 Pod 的实际 CPU 使用量总和 ÷ Pod 数量
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## 使用条件介绍
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- ✅ **类型**：`AverageValue`（绝对值）
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- ✅ **目标**：所有 Pod 平均使用 **80m CPU**
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- ✅ **特点**：不依赖 `requests` 设置
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- ⚠️ **注意**：如果 Pod 的 `requests` 设置变化，可能需要调整这个值
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- 🔄 **对比**：如果改为 `Utilization: 80%`，则目标是 CPU 使用量达到 `requests.cpu` 的 80%
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**选择建议**：
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- 如果 Pod 的 `requests.cpu` 稳定且合理 → 使用 `Utilization`
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- 如果 `requests` 经常变化或设置不合理 → 使用 `AverageValue`
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- 两者可以互相转换：`averageValue = requests.cpu × (utilization/100)`
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## 创建 HPA
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**基础命令格式对比表**
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| 参数/命令部分 | 基础命令 | 说明 |
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| :------------: | :---------------------------: | :----------------------: |
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| **命令结构** | `kubectl autoscale` | 创建或更新HPA |
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| **目标资源** | `deployment/nginx-deployment` | 要自动扩缩容的Deployment |
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| **最小副本数** | `--min=3` | 最少保持的Pod数量 |
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| **最大副本数** | `--max=6` | 最多允许的Pod数量 |
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| **CPU阈值** | `--cpu=80` | CPU使用率目标值 |
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| **内存阈值** | `--memory=75` | 内存使用率目标值 |
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### 命令创建(默认使用AverageValue算计)
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- 绝对值 50m
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```bash
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kubectl autoscale deployment/nginx-deployment --min=1 --max=3 --cpu=50 --memory=50
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```
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- 绝对值50Mi
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```bash
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kubectl autoscale deployment/nginx-deployment --min=1 --max=3 --memory=50
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```
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### Yaml 创建(使用Utilization)
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- CPU 80%
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- 内存 75%
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1. 编辑 Yaml
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```bash
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vi nginx-deployment-hpa-utilization.yaml
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```
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内容如下
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```yaml
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apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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name: nginx-deployment
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spec:
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scaleTargetRef:
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apiVersion: apps/v1
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kind: Deployment
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name: nginx-deployment
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minReplicas: 1
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maxReplicas: 3
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metrics:
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- type: Resource
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resource:
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name: cpu
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target:
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type: Utilization
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averageUtilization: 80
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- type: Resource
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resource:
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name: memory
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target:
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type: Utilization
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averageUtilization: 75
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```
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2. 部署
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```bash
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kubectl apply -f nginx-deployment-hpa-utilization.yaml
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```
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### Yaml 创建(使用AverageValue)
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- CPU 绝对值 50m
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- 内存 绝对值50Mi
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1. 编辑 Yaml
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```bash
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vi nginx-deployment-hpa-averagevalue.yaml
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```
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内容如下
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```yaml
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apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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name: nginx-deployment
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spec:
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scaleTargetRef:
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apiVersion: apps/v1
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kind: Deployment
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name: nginx-deployment
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minReplicas: 1
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maxReplicas: 3
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metrics:
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- type: Resource
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resource:
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name: cpu
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target:
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type: AverageValue
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averageValue: 50m
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- type: Resource
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resource:
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name: memory
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target:
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type: AverageValue
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averageValue: 50Mi
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```
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2. 部署
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```bash
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kubectl apply -f nginx-deployment-hpa-averagevalue.yaml
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```
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## 验证 HPA
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创建后，可以检查 HPA 状态：
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- 查看 HPA
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```bash
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kubectl get hpa
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```
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- 详细查看
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```bash
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kubectl describe hpa nginx-deployment
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```
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- 查看当前 CPU 使用率
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```bash
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kubectl top pods
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```
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## 增加负载验证
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1. 模拟负载
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```bash
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kubectl run -i --tty load-generator --rm --image=busybox:1.28 --restart=Never -- /bin/sh -c "while sleep 0.01; do wget -q -O- http://nginx-deployment; done"
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```
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2. 观察
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```bash
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kubectl get hpa nginx-deployment --watch
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```
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## 其他类型
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### 根据每个 Pod 每秒处理的数据包数量来扩缩容
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具体解释：
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- **监控指标**：`packets-per-second`（每秒数据包数）
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- **目标值**：每个 Pod 平均每秒处理 **1000 个数据包**（1k = 1000）
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- **扩缩逻辑**：
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- 如果所有 Pod 的平均值 **超过** 1000 个包/秒 → 增加 Pod 数量
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- 如果所有 Pod 的平均值 **低于** 1000 个包/秒 → 减少 Pod 数量
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1. 编辑 Yaml
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```bash
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vi nginx-deployment-hpa-pod.yaml
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```
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内容如下
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```yaml
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apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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name: nginx-deployment
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spec:
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scaleTargetRef:
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apiVersion: apps/v1
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kind: Deployment
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name: nginx-deployment
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minReplicas: 1
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maxReplicas: 3
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metrics:
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- type: Pods
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pods:
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metric:
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name: packets-per-second
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target:
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type: AverageValue
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averageValue: 1k
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```
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2. 部署
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```bash
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kubectl apply -f nginx-deployment-hpa-pod.yaml
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```
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### 根据 Ingress 的每秒请求数来扩缩容后端应用
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具体解释：
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- **监控对象**：名为 `main-route` 的 Ingress
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- **监控指标**：`requests-per-second`（Ingress 每秒处理的请求数）
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- **目标值**：整个 Ingress 每秒处理 **2000 个请求**（2k = 2000）
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- **扩缩逻辑**：
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- 如果这个 Ingress 的请求数 **超过** 2000 请求/秒 → 增加后端 Pod 数量
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- 如果这个 Ingress 的请求数 **低于** 2000 请求/秒 → 减少后端 Pod 数量
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1. 编辑 Yaml
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```bash
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vi nginx-deployment-hpa-object.yaml
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```
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内容如下
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```yaml
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apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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name: nginx-deployment
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spec:
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scaleTargetRef:
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apiVersion: apps/v1
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kind: Deployment
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name: nginx-deployment
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minReplicas: 1
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maxReplicas: 3
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metrics:
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- type: Object
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object:
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metric:
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name: requests-per-second
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describedObject:
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apiVersion: networking.k8s.io/v1
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kind: Ingress
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name: main-route # 但监控的是这个 Ingress 的流量
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target:
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type: Value
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value: 2k # 整个 Ingress 的目标：2000 请求/秒
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```
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2. 部署
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```bash
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kubectl apply -f nginx-deployment-hpa-object.yaml
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```
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### 根据带 GET 标签的 HTTP 请求指标来扩缩容
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具体解释：
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- **监控对象**：某个 Kubernetes 资源（如 Ingress、Service 等）
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- **监控指标**：`http_requests`（HTTP 请求总数或速率）
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- **指标筛选**：只选择带有 `verb: GET` 标签的指标
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- **目标值**：需要在其他地方指定（这里没有显示完整的 target 部分）
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1. 编辑 Yaml
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```bash
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vi nginx-deployment-hpa-object-api.yaml
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```
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内容如下
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```yaml
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apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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name: api-hpa
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spec:
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scaleTargetRef:
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apiVersion: apps/v1
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kind: Deployment
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name: api-server
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minReplicas: 2
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maxReplicas: 10
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metrics:
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- type: Object
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object:
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describedObject:
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apiVersion: v1
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kind: Service
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name: api-service
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metric:
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name: http_requests
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selector:
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matchLabels:
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verb: GET # 只监控 GET 请求
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path: "/api/v1/users" # 可以进一步指定路径
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status: "200" # 还可以按状态码筛选
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target:
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type: AverageValue
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averageValue: 500 # 目标：每秒 500 个 GET 请求
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```
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2. 部署
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```bash
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kubectl apply -f nginx-deployment-hpa-object-api.yaml
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```
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### 根据外部消息队列中的任务数量来扩缩容
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具体解释：
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- **监控来源**：外部系统（不是 Kubernetes 内部）
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- **监控指标**：`queue_messages_ready`（队列中待处理的消息数量）
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- **队列筛选**：只监控名为 `worker_tasks` 的队列
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- **目标值**：保持队列中大约有 **30 个待处理消息**
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- **扩缩逻辑**：
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- 如果队列消息 **超过 30 个** → 增加 Worker Pod 数量
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- 如果队列消息 **低于 30 个** → 减少 Worker Pod 数量
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1. 编辑 Yaml
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```bash
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vi nginx-deployment-hpa-object-worker.yaml
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```
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内容如下
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```yaml
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apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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name: worker-hpa
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spec:
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scaleTargetRef:
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apiVersion: apps/v1
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kind: Deployment
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name: task-worker # 处理队列任务的 Worker 应用
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minReplicas: 1
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maxReplicas: 20
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metrics:
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- type: External
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external:
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metric:
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name: queue_messages_ready # 队列中"就绪"的消息数
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selector:
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matchLabels:
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queue: "worker_tasks" # 只监控这个特定队列
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# 还可以按其他标签筛选，如：
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# vhost: "production"
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# exchange: "orders"
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target:
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type: AverageValue
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averageValue: 30 # 目标：保持队列中有30个待处理消息
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```
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2. 部署
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```bash
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kubectl apply -f nginx-deployment-hpa-object-worker.yaml
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```
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Reference in New Issue
Block a user