一、基于Prometheus的HPA自动伸缩

1、背景

Kubernetes集群规模大、动态变化快，而且容器化应用部署和服务治理机制的普及，传统的基础设施监控方式已经无法满足Kubernetes集群的监控需求。
需要使用专门针对Kubernetes集群设计的监控工具来监控集群的状态和服务质量。

Prometheus则是目前Kubernetes集群中最常用的监控工具之一，它可以通过Kubernetes API中的 metrics-server 获取 Kubernetes 集群的指标数据，从而实现对Kubernetes集群的应用层面监控，以及基于它们的水平自动伸缩对象HorizontalPodAutoscaler 。

2、Metrics-server

官网：资源指标管道 | Kubernetes

Metrics Server 是一个专门用来收集 Kubernetes 核心资源指标（metrics）的工具，它定时从所有节点的 kubelet 里采集信息，但是对集群的整体性能影响极小，每个节点只大约会占用 1m 的 CPU 和 2MB 的内存，所以性价比非常高。

Metrics Server工作原理

图中从右到左的架构组件包括：

cAdvisor: 用于收集、聚合和公开 Kubelet 中包含的容器指标的守护程序。
kubelet: 用于管理容器资源的节点代理。可以使用 /metrics/resource 和/stats kubelet API 端点访问资源指标。
Summary API: kubelet 提供的 API，用于发现和检索可通过 /stats 端点获得的每个节点的汇总统计信息。
metrics-server: 集群插件组件，用于收集和聚合从每个 kubelet 中提取的资源指标。 API 服务器提供 Metrics API 以供 HPA、VPA 和 kubectl top 命令使用。
Metrics Server 是 Metrics API 的参考实现。
Metrics API: Kubernetes API 支持访问用于工作负载自动缩放的 CPU 和内存。要在你的集群中进行这项工作，你需要一个提供 Metrics API 的 API 扩展服务器。

2.1 Metrics-server 部署配置

Metrics Server 的项目网址（https://github.com/kubernetes-sigs/metrics-server）

https://github.com/kubernetes-sigs/metrics-server/releases

wget https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.6.4/components.yaml && mv components.yaml metrics-server.yaml

修改yam文件

# cat metrics-server.yaml

 [root@master-1-230 7.3]# cat metrics-server.yaml 
apiVersion: v1
kind: ServiceAccount
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  labels:
    k8s-app: metrics-server
    rbac.authorization.k8s.io/aggregate-to-admin: "true"
    rbac.authorization.k8s.io/aggregate-to-edit: "true"
    rbac.authorization.k8s.io/aggregate-to-view: "true"
  name: system:aggregated-metrics-reader
rules:
- apiGroups:
  - metrics.k8s.io
  resources:
  - pods
  - nodes
  verbs:
  - get
  - list
  - watch
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  labels:
    k8s-app: metrics-server
  name: system:metrics-server
rules:
- apiGroups:
  - ""
  resources:
  - nodes/metrics
  verbs:
  - get
- apiGroups:
  - ""
  resources:
  - pods
  - nodes
  verbs:
  - get
  - list
  - watch
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server-auth-reader
  namespace: kube-system
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: extension-apiserver-authentication-reader
subjects:
- kind: ServiceAccount
  name: metrics-server
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server:system:auth-delegator
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: system:auth-delegator
subjects:
- kind: ServiceAccount
  name: metrics-server
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  labels:
    k8s-app: metrics-server
  name: system:metrics-server
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: system:metrics-server
subjects:
- kind: ServiceAccount
  name: metrics-server
  namespace: kube-system
---
apiVersion: v1
kind: Service
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server
  namespace: kube-system
spec:
  ports:
  - name: https
    port: 443
    protocol: TCP
    targetPort: https
  selector:
    k8s-app: metrics-server
---
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    k8s-app: metrics-server
  name: metrics-server
  namespace: kube-system
spec:
  selector:
    matchLabels:
      k8s-app: metrics-server
  strategy:
    rollingUpdate:
      maxUnavailable: 0
  template:
    metadata:
      labels:
        k8s-app: metrics-server
    spec:
      containers:
      - args:
        - --kubelet-insecure-tls
        - --cert-dir=/tmp
        - --secure-port=4443
        - --kubelet-preferred-address-types=InternalIP,ExternalIP,Hostname
        - --kubelet-use-node-status-port
        - --metric-resolution=15s
        image: registry.k8s.io/metrics-server/metrics-server:v0.6.4
        imagePullPolicy: IfNotPresent
        livenessProbe:
          failureThreshold: 3
          httpGet:
            path: /livez
            port: https
            scheme: HTTPS
          periodSeconds: 10
        name: metrics-server
        ports:
        - containerPort: 4443
          name: https
          protocol: TCP
        readinessProbe:
          failureThreshold: 3
          httpGet:
            path: /readyz
            port: https
            scheme: HTTPS
          initialDelaySeconds: 20
          periodSeconds: 10
        resources:
          requests:
            cpu: 100m
            memory: 200Mi
        securityContext:
          allowPrivilegeEscalation: false
          readOnlyRootFilesystem: true
          runAsNonRoot: true
          runAsUser: 1000
        volumeMounts:
        - mountPath: /tmp
          name: tmp-dir
      nodeSelector:
        kubernetes.io/os: linux
      priorityClassName: system-cluster-critical
      serviceAccountName: metrics-server
      volumes:
      - emptyDir: {}
        name: tmp-dir
---
apiVersion: apiregistration.k8s.io/v1
kind: APIService
metadata:
  labels:
    k8s-app: metrics-server
  name: v1beta1.metrics.k8s.io
spec:
  group: metrics.k8s.io
  groupPriorityMinimum: 100
  insecureSkipTLSVerify: true
  service:
    name: metrics-server
    namespace: kube-system
  version: v1beta1
  versionPriority: 100

Metrics Server 默认使用TLS协议，验证证书才能与kubelet 通信，测试使用内网环境先取消配置

应用yaml

[root@master-1-230 7.3]# kubectl  apply -f metrics-server.yaml 
serviceaccount/metrics-server created
clusterrole.rbac.authorization.k8s.io/system:aggregated-metrics-reader created
clusterrole.rbac.authorization.k8s.io/system:metrics-server created
rolebinding.rbac.authorization.k8s.io/metrics-server-auth-reader created
clusterrolebinding.rbac.authorization.k8s.io/metrics-server:system:auth-delegator created
clusterrolebinding.rbac.authorization.k8s.io/system:metrics-server created
service/metrics-server created
deployment.apps/metrics-server created
apiservice.apiregistration.k8s.io/v1beta1.metrics.k8s.io created

验证：

[root@master-1-230 7.3]# kubectl   top node
NAME           CPU(cores)   CPU%   MEMORY(bytes)   MEMORY%   
master-1-230   465m         11%    1905Mi          69%       
node-1-231     427m         7%     1645Mi          16%       
node-1-232     358m         5%     1168Mi          15%       
node-1-233     270m         4%     1129Mi          14%       
[root@master-1-230 7.3]# 
[root@master-1-230 7.3]# kubectl   top node -n kube-system
NAME           CPU(cores)   CPU%   MEMORY(bytes)   MEMORY%   
master-1-230   465m         11%    1905Mi          69%       
node-1-231     427m         7%     1645Mi          16%       
node-1-232     358m         5%     1168Mi          15%       
node-1-233     270m         4%     1129Mi          14%

3、HorizontalPodAutoscaler

HorizontalPodAutoscaler (HPA)是Kubernetes中的一个控制器，用于动态地调整Pod副本的数量。HPA可以根据Metrics-server提供的指标（如CPU使用率、内存使用率等）或内部指标（如每秒的请求数）来自动调整Pod的副本数量，以确保应用程序具有足够的资源，并且不会浪费资源。

HPA是Kubernetes扩展程序中非常常用的部分，特别是在负载高峰期自动扩展应用程序时。

3.1 使用HorizontalPodAutoscaler

创建一个Nginx 应用，定义Deployment 和Service ，作为自动伸缩的目标对象

apiVersion: apps/v1
kind: Deployment
metadata:
  name: ngx-hpa-dep
spec:
  replicas: 1
  selector:
    matchLabels:
      app: ngx-hpa-dep
  template:
    metadata:
      labels:
        app: ngx-hpa-dep
    spec:
      containers:
      - image: nginx:alpine
        name: nginx
        ports:
        - containerPort: 80
        resources:
          requests:
            cpu: 50m
            memory: 10Mi
          limits:
            cpu: 100m
            memory: 20Mi
---
apiVersion: v1
kind: Service
metadata:
  name: ngx-hpa-svc
spec:
  ports:
  - port: 80
    protocol: TCP
    targetPort: 80
  selector:
    app: ngx-hpa-dep

[root@master-1-230 7.3]# kubectl  apply -f nginx-demo-hpa.yaml 
deployment.apps/ngx-hpa-dep created
service/ngx-hpa-svc created

注意他在spec一定使用resources字段写清楚资源配额，否则HorizontalPodAutoscaler 会无法获取Pod的指标，也就无法实现自动化扩容。

接下来使用kubectl autoscale 创建HorizontalPodAutoscaler的样本YAML文件，他有三个参数：

min，Pod数量的最小值，缩容的下限
max，Pod数量的最大值，扩容的上线
cpu-percent，CPU使用率指标，当大于这个值时扩容，小于这个值时缩容

现在我们使用Nginx应用创建 HorizontalPodAutoscaler，指定Pod数量最少为2个，最多为8个，CPU使用率设置小一点，5%，方便观察扩容现象

kubectl autoscale deploy ngx-hpa-dep --min=2 --max=8 --cpu-percent=5 --dry-run=client -o yaml > nginx-demo-hpa.yaml

查看yaml文件

[root@master-1-230 7.3]# cat nginx-demo-hpa1.yaml 
apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  creationTimestamp: null
  name: ngx-hpa-dep
spec:
  maxReplicas: 8
  minReplicas: 2
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: ngx-hpa-dep
  targetCPUUtilizationPercentage: 5
status:
  currentReplicas: 0
  desiredReplicas: 0

通过应用yaml 创建HorizontalPodAutoscaler 后，它会发现
Deployment 里的实例只有 1 个，不符合 min 定义的下限的要求，就先扩容到 2 个：

[root@master-1-230 7.3]# kubectl  apply -f nginx-demo-hpa1.yaml 
horizontalpodautoscaler.autoscaling/ngx-hpa-dep created

[root@master-1-230 7.3]# kubectl  get deploy
NAME                  READY   UP-TO-DATE   AVAILABLE   AGE
harbor10-core         1/1     1            1           13d
harbor10-jobservice   1/1     1            1           13d
harbor10-nginx        1/1     1            1           13d
harbor10-portal       1/1     1            1           13d
harbor10-registry     1/1     1            1           13d
nginx                 1/1     1            1           6d
ngx-hpa-dep           2/2     2            2           2m59s

3.2 测试验证：

我们给Nginx加上压力流量，运行一个测试pod，使用httpd:alpine 它里面有HTTP性能测试工具ab （Apache Bench）

[root@master-1-230 ~]# kubectl  run test it --image=httpd:alpine -- sh
pod/test created

然后向Nginx 发送请求，持续一分钟，再用kubectl get hpa观察HorizontalPodAutoscaler 的运行情况

ab -c 10 -t 60 -n 1000000 'http://ngx-hpa-svc/'

[root@master-1-230 ~]# kubectl  run test -it --image=httpd:alpine -- sh
If you don't see a command prompt, try pressing enter.
/usr/local/apache2 # ab -c 10 -t 60 -n 1000000 'http://ngx-hpa-svc/'
This is ApacheBench, Version 2.3 <$Revision: 1903618 $>
Copyright 1996 Adam Twiss, Zeus Technology Ltd, http://www.zeustech.net/
Licensed to The Apache Software Foundation, http://www.apache.org/

Benchmarking ngx-hpa-svc (be patient)
apr_socket_recv: Connection refused (111)
Total of 14209 requests completed
/usr/local/apache2 #

Metrics Server 大约15秒采集一次数据，所以HorizontalPodAutoscaler 的自动化扩容和缩容也按照这个时间点逐步处理

当他发现目标的CPU使用率超过预定的5%后，就会以2的倍数开始扩容，一直到数量上线，然后持续监控一段时间。

如果CPU使用率回落，就会再缩容到最小值（默认会等待五分钟如果负载没有上去，就会缩容到最低水平，防止抖动）

^C[root@master-1-230 ~]# kubectl  get po -w |grep ngx
ngx-hpa-dep-5df685854d-9g5d4           1/1     Running   0                6m6s
ngx-hpa-dep-5df685854d-k6792           1/1     Running   0                6m21s
ngx-hpa-dep-5df685854d-fw8wq           0/1     Pending   0                0s
ngx-hpa-dep-5df685854d-tjmjg           0/1     Pending   0                0s
ngx-hpa-dep-5df685854d-fw8wq           0/1     Pending   0                0s
ngx-hpa-dep-5df685854d-tjmjg           0/1     Pending   0                0s
ngx-hpa-dep-5df685854d-tjmjg           0/1     ContainerCreating   0                0s
ngx-hpa-dep-5df685854d-fw8wq           0/1     ContainerCreating   0                0s
ngx-hpa-dep-5df685854d-fw8wq           0/1     ContainerCreating   0                1s
ngx-hpa-dep-5df685854d-tjmjg           0/1     ContainerCreating   0                1s
ngx-hpa-dep-5df685854d-fw8wq           1/1     Running             0                2s
ngx-hpa-dep-5df685854d-tjmjg           1/1     Running             0                3s
ngx-hpa-dep-5df685854d-fdzzx           0/1     Pending             0                0s
ngx-hpa-dep-5df685854d-sz5jl           0/1     Pending             0                0s
ngx-hpa-dep-5df685854d-px99l           0/1     Pending             0                0s
ngx-hpa-dep-5df685854d-fdzzx           0/1     Pending             0                0s
ngx-hpa-dep-5df685854d-sz5jl           0/1     Pending             0                0s
ngx-hpa-dep-5df685854d-px99l           0/1     Pending             0                0s
ngx-hpa-dep-5df685854d-fdzzx           0/1     ContainerCreating   0                0s
ngx-hpa-dep-5df685854d-d5k94           0/1     Pending             0                0s
ngx-hpa-dep-5df685854d-d5k94           0/1     Pending             0                0s
ngx-hpa-dep-5df685854d-px99l           0/1     ContainerCreating   0                0s
ngx-hpa-dep-5df685854d-sz5jl           0/1     ContainerCreating   0                0s
ngx-hpa-dep-5df685854d-d5k94           0/1     ContainerCreating   0                0s
ngx-hpa-dep-5df685854d-fdzzx           0/1     ContainerCreating   0                1s
ngx-hpa-dep-5df685854d-sz5jl           0/1     ContainerCreating   0                1s
ngx-hpa-dep-5df685854d-px99l           0/1     ContainerCreating   0                1s
ngx-hpa-dep-5df685854d-d5k94           0/1     ContainerCreating   0                1s
ngx-hpa-dep-5df685854d-fdzzx           1/1     Running             0                3s
ngx-hpa-dep-5df685854d-px99l           1/1     Running             0                3s
ngx-hpa-dep-5df685854d-d5k94           1/1     Running             0                3s
ngx-hpa-dep-5df685854d-sz5jl           1/1     Running             0                3s

4 、总结

Metrics Server 是Kubernetes中的一个组件，它可以将集群中的散布的资源使用情况数据收集并聚合起来。收集的数据包括节点的CPU和内存使用情况等。
通过API提供给Kubernetes 中的其它组件（如HPA）使用。Metrics Server可以帮助集群管理员和应用程序开发者更好的了解集群中资源的使用情况，并根据
这些数据做出合理的决策，例如调整Pod副本数、扩展集群等。
Metrics Server 对于Kubernetes中的资源管理和应用程序扩展非常重要。

二、基于Prometheus的全方位监控平台-黑盒监控(Blackbox)

Prometheus监控分为两种

黑盒监控
白盒监控

白盒监控：指我们日常监控主机的资源使用量、容器的运行状态的运行数据

黑盒监控：常见的黑盒监控包括：HTTP探针、TCP探针、DNS、ICMP等用于检测站点、服务的可访问性、服务的联通性，以及访问效率等

两者比较：

黑盒监控是以故障为导向当故障发生时，黑盒监控能快速发现故障
白盒监控侧重于主动发现或者预测潜在的问题

一个完善的监控目标是要能从白盒的角度发现潜在问题，能够在黑盒的角度快速发现已经发生的问题

目前支持的应用场景：

ICMP 测试：主机探活机制
TCP测试：
业务组件端口状态监听
应用层协议定义与监听
HTTP测试:
定义Request Header信息
判断Http status/http Response Header/http body内容
POST 测试：接口连通性
SSL证书过期时间

1、Blackbox Exporter部署

Exporter Configmap 定义，可以参考下面两个链接

blackbox_exporter/CONFIGURATION.md at master · prometheus/blackbox_exporter · GitHub

blackbox_exporter/example.yml at master · prometheus/blackbox_exporter · GitHub

首先声明一个Blackbox的Deployment 并利用Configmap为Blackbox提供配置文件

Configmap：

参考BlackBox Exporter的github示例配置文件：blackbox_exporter/example.yml at master · prometheus/blackbox_exporter · GitHub

[root@master-1-230 7.4]# cat blackbox-configmap.yaml 
apiVersion: v1
kind: ConfigMap
metadata:
  name: blackbox-exporter
  namespace: monitor
  labels:
    app: blackbox-exporter
data:
  blackbox.yml: |-
    modules:
      ## ----------- DNS 检测配置 -----------
      dns_tcp:  
        prober: dns
        dns:
          transport_protocol: "tcp"
          preferred_ip_protocol: "ip4"
          query_name: "kubernetes.default.svc.cluster.local" # 用于检测域名可用的网址
          query_type: "A" 
      ## ----------- TCP 检测模块配置 -----------
      tcp_connect:
        prober: tcp
        timeout: 5s
      ## ----------- ICMP 检测配置 -----------
      icmp:
        prober: icmp
        timeout: 5s
        icmp:
          preferred_ip_protocol: "ip4"
      ## ----------- HTTP GET 2xx 检测模块配置 -----------
      http_get_2xx:  
        prober: http
        timeout: 10s
        http:
          method: GET
          preferred_ip_protocol: "ip4"
          valid_http_versions: ["HTTP/1.1","HTTP/2"]
          valid_status_codes: [200]           # 验证的HTTP状态码,默认为2xx
          no_follow_redirects: false          # 是否不跟随重定向
      ## ----------- HTTP GET 3xx 检测模块配置 -----------
      http_get_3xx:  
        prober: http
        timeout: 10s
        http:
          method: GET
          preferred_ip_protocol: "ip4"
          valid_http_versions: ["HTTP/1.1","HTTP/2"]
          valid_status_codes: [301,302,304,305,306,307]  # 验证的HTTP状态码,默认为2xx
          no_follow_redirects: false                     # 是否不跟随重定向
      ## ----------- HTTP POST 监测模块 -----------
      http_post_2xx: 
        prober: http
        timeout: 10s
        http:
          method: POST
          preferred_ip_protocol: "ip4"
          valid_http_versions: ["HTTP/1.1", "HTTP/2"]
          #headers:                             # HTTP头设置
          #  Content-Type: application/json
          #body: '{}'                           # 请求体设置

Deployment：

[root@master-1-230 7.4]# cat blackbox-exporter.yaml 
apiVersion: v1
kind: Service
metadata:
  name: blackbox-exporter
  namespace: monitor
  labels:
    k8s-app: blackbox-exporter
spec:
  type: ClusterIP
  ports:
  - name: http
    port: 9115
    targetPort: 9115
  selector:
    k8s-app: blackbox-exporter
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: blackbox-exporter
  namespace: monitor
  labels:
    k8s-app: blackbox-exporter
spec:
  replicas: 1
  selector:
    matchLabels:
      k8s-app: blackbox-exporter
  template:
    metadata:
      labels:
        k8s-app: blackbox-exporter
    spec:
      containers:
      - name: blackbox-exporter
        image: prom/blackbox-exporter:v0.21.0
        imagePullPolicy: IfNotPresent
        args:
        - --config.file=/etc/blackbox_exporter/blackbox.yml
        - --web.listen-address=:9115
        - --log.level=info
        ports:
        - name: http
          containerPort: 9115
        resources:
          limits:
            cpu: 200m
            memory: 256Mi
          requests:
            cpu: 100m
            memory: 50Mi
        livenessProbe:
          tcpSocket:
            port: 9115
          initialDelaySeconds: 5
          timeoutSeconds: 5
          periodSeconds: 10
          successThreshold: 1
          failureThreshold: 3
        readinessProbe:
          tcpSocket:
            port: 9115
          initialDelaySeconds: 5
          timeoutSeconds: 5
          periodSeconds: 10
          successThreshold: 1
          failureThreshold: 3
        volumeMounts:
        - name: config
          mountPath: /etc/blackbox_exporter
      volumes:
      - name: config
        configMap:
          name: blackbox-exporter
          defaultMode: 420

应用yaml文件

[root@master-1-230 7.4]# kubectl  apply -f blackbox-configmap.yaml 
configmap/blackbox-exporter created
[root@master-1-230 7.4]# 
[root@master-1-230 7.4]# kubectl  apply -f blackbox-exporter.yaml 
service/blackbox-exporter created
deployment.apps/blackbox-exporter created

查看部署后的资源

[root@master-1-230 7.4]# kubectl  get all -n monitor |grep blackbox
pod/blackbox-exporter-5b4f75cf4c-rzx2h    1/1     Running   0          34s
service/blackbox-exporter    ClusterIP      10.100.39.179   <none>          9115/TCP            35s
deployment.apps/blackbox-exporter    1/1     1            1           35s
replicaset.apps/blackbox-exporter-5b4f75cf4c    1         1         1       34s

2、DNS监控

    - job_name: "kubernetes-dns"
      metrics_path: /probe		# 不是 metrics，是 probe
      params:
        module: [dns_tcp]		# 使用 DNS TCP 模块
      static_configs:
        - targets:
          - kube-dns.kube-system:53		# 不要省略端口号
          - 8.8.4.4:53
          - 8.8.8.8:53
          - 223.5.5.5:53
      relabel_configs:
        - source_labels: [__address__]
          target_label: __param_target
        - source_labels: [__param_target]
          target_label: instance
        - target_label: __address__
          replacement: blackbox-exporter.monitor:9115		# 服务地址，和上面的 Service 定义保持一致

参数解释：

################ DNS 服务器监控 ###################
- job_name: "kubernetes-dns"
  metrics_path: /probe
  params:
    ## 配置要使用的模块,要与blackbox exporter配置中的一致
    ## 这里使用DNS模块
    module: [dns_tcp]
  static_configs:
    ## 配置要检测的地址
    - targets:
      - kube-dns.kube-system:53
      - 8.8.4.4:53
      - 8.8.8.8:53
      - 223.5.5.5
  relabel_configs:
    ## 将上面配置的静态DNS服务器地址转换为临时变量 “__param_target”
    - source_labels: [__address__]
      target_label: __param_target
    ## 将 “__param_target” 内容设置为 instance 实例名称
    - source_labels: [__param_target]
      target_label: instance
    ## BlackBox Exporter 的 Service 地址
    - target_label: __address__
      replacement: blackbox-exporter.monitor:9115

更新prometheus-config.yaml配置

curl -XPOST http://prometheus.ikubernetes.cloud/-/reload

打开Prometheus的Target页面，可以看到上面定义的blackbox-k8s-service-dns任务

grafana页面，可以使用probe_success 和probe_duration_seconds等来检测历史结果

3、ICMP监控

    - job_name: icmp-status
      metrics_path: /probe
      params:
        module: [icmp]
      static_configs:
      - targets:
        - 192.168.1.232
        labels:
          group: icmp
      relabel_configs:
      - source_labels: [__address__]
        target_label: __param_target
      - source_labels: [__param_target]
        target_label: instance
      - target_label: __address__
        replacement: blackbox-exporter.monitor:9115

按上面方法加载Prometheus，打开Prometheus的target页面，可以看到上面定义的任务

curl -XPOST http://prometheus.ikubernetes.cloud/-/reload

4、HTTP监控(K8S内部发现方法)

4.1 自定义发现Service 监控端口和路径

可以如下设置

    - job_name: 'kubernetes-services'
      metrics_path: /probe
      params:
        module:		## 使用HTTP_GET_2xx与HTTP_GET_3XX模块
        - "http_get_2xx"
        - "http_get_3xx"
      kubernetes_sd_configs:		## 使用Kubernetes动态服务发现,且使用Service类型的发现
      - role: service
      relabel_configs:		## 设置只监测Kubernetes Service中Annotation里配置了注解prometheus.io/http_probe: true的service
      - action: keep
        source_labels: [__meta_kubernetes_service_annotation_prometheus_io_http_probe]
        regex: "true"
      - action: replace
        source_labels: 
        - "__meta_kubernetes_service_name"
        - "__meta_kubernetes_namespace"
        - "__meta_kubernetes_service_annotation_prometheus_io_http_probe_port"
        - "__meta_kubernetes_service_annotation_prometheus_io_http_probe_path"
        target_label: __param_target
        regex: (.+);(.+);(.+);(.+)
        replacement: $1.$2:$3$4
      - target_label: __address__
        replacement: blackbox-exporter.monitor:9115		## BlackBox Exporter 的 Service 地址
      - source_labels: [__param_target]
        target_label: instance
      - action: labelmap
        regex: __meta_kubernetes_service_label_(.+)
      - source_labels: [__meta_kubernetes_namespace]
        target_label: kubernetes_namespace
      - source_labels: [__meta_kubernetes_service_name]
        target_label: kubernetes_name

然后在Service中配置annotation

annotations:
  prometheus.io/http-probe: "true"		## 开启 HTTP 探针
  prometheus.io/http-probe-port: "8080"		## HTTP 探针会使用 8080 端口来进行探测
  prometheus.io/http-probe-path: "/healthCheck"		## HTTP 探针会请求  /healthCheck  路径来进行探测，以检查应用程序是否正常运行

示例：java应用的svc

apiVersion: v1
kind: Service
metadata:
  name: springboot
  annotations:
    prometheus.io/http-probe: "true"
    prometheus.io/http-probe-port: "8080"
    prometheus.io/http-probe-path: "/apptwo"
spec:
  type: ClusterIP
  selector:
    app: springboot
  ports:
  - name: http
    port: 8080
    protocol: TCP
    targetPort: 8080

按上面方法加载Prometheus，打开Prometheus的target页面，可以看到上面定义的任务

curl -XPOST http://prometheus.ikubernetes.cloud/-/reload

4.2 TCP检测

    - job_name: "service-tcp-probe"
      scrape_interval: 1m
      metrics_path: /probe
      # 使用blackbox exporter配置文件的tcp_connect的探针
      params:
        module: [tcp_connect]
      kubernetes_sd_configs:
      - role: service
      relabel_configs:
      # 保留prometheus.io/scrape: "true"和prometheus.io/tcp-probe: "true"的service
      - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape, __meta_kubernetes_service_annotation_prometheus_io_tcp_probe]
        action: keep
        regex: true;true
      # 将原标签名__meta_kubernetes_service_name改成service_name
      - source_labels: [__meta_kubernetes_service_name]
        action: replace
        regex: (.*)
        target_label: service_name
      # 将原标签名__meta_kubernetes_service_name改成service_name
      - source_labels: [__meta_kubernetes_namespace]
        action: replace
        regex: (.*)
        target_label: namespace
      # 将instance改成 `clusterIP:port` 地址
      - source_labels: [__meta_kubernetes_service_cluster_ip, __meta_kubernetes_service_annotation_prometheus_io_http_probe_port]
        action: replace
        regex: (.*);(.*)
        target_label: __param_target
        replacement: $1:$2
      - source_labels: [__param_target]
        target_label: instance
      # 将__address__的值改成 `blackbox-exporter.monitor:9115`
      - target_label: __address__
        replacement: blackbox-exporter.monitor:9115

按上面方法加载Prometheus，打开Prometheus的target页面，可以看到上面定义的任务

curl -XPOST http://prometheus.ikubernetes.cloud/-/reload

则需要在service上添加注释必须有以下三行

  annotations:
    prometheus.io/scrape: "true"		## 这个服务是可以被采集指标的，Prometheus 可以对这个服务进行数据采集
    prometheus.io/tcp-probe: "true"		## 开启 TCP 探针
    prometheus.io/http-probe-port: "8080"		## HTTP 探针会使用 8080 端口来进行探测，以检查应用程序是否正常运行

示例：java应用svc

[root@master-1-230 7.4]# cat java-svc.yaml 
apiVersion: v1
kind: Service
metadata:
  name: springboot
  annotations:
    prometheus.io/http-probe: "true"
    prometheus.io/http-probe-port: "8080"
    prometheus.io/http-probe-path: "/apptwo"
    prometheus.io/scrape: "true"
    prometheus.io/tcp-probe: "true"
spec:
  type: ClusterIP
  selector:
    app: springboot
  ports:
  - name: http
    port: 8080
    protocol: TCP
    targetPort: 8080

按上面方法加载Prometheus，打开Prometheus的target页面，可以看到上面定义的任务

curl -XPOST http://prometheus.ikubernetes.cloud/-/reload

4.3 Ingress服务的探测

    - job_name: 'blackbox-k8s-ingresses'
      scrape_interval: 30s
      scrape_timeout: 10s
      metrics_path: /probe
      params:
        module: [http_get_2xx]  # 使用定义的http模块
      kubernetes_sd_configs:
      - role: ingress  # ingress 类型的服务发现
      relabel_configs:
      # 只有ingress的annotation中配置了 prometheus.io/http_probe=true 的才进行发现
      - source_labels: [__meta_kubernetes_ingress_annotation_prometheus_io_http_probe]
        action: keep
        regex: true
      - source_labels: [__meta_kubernetes_ingress_scheme,__address__,__meta_kubernetes_ingress_path]
        regex: (.+);(.+);(.+)
        replacement: ${1}://${2}${3}
        target_label: __param_target
      - target_label: __address__
        replacement: blackbox-exporter.monitor:9115
      - source_labels: [__param_target]
        target_label: instance
      - action: labelmap
        regex: __meta_kubernetes_ingress_label_(.+)
      - source_labels: [__meta_kubernetes_namespace]
        target_label: kubernetes_namespace
      - source_labels: [__meta_kubernetes_ingress_name]
        target_label: kubernetes_name

则需要在ingress上添加注释必须有以下三行

  annotations:
    prometheus.io/http_probe: "true"
    prometheus.io/http-probe-port: '8080'
    prometheus.io/http-probe-path: '/healthz'

示例：java应用ingress

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  annotations:
    #konghq.com/https-redirect-status-code: "301"
    #konghq.com/protocols: http
    #konghq.com/regex-priority: "1000"
    prometheus.io/http_probe: "true"
    prometheus.io/http-probe-port: '8080'
    prometheus.io/http-probe-path: '/healthz'
    kubernetes.io/ingress.class: "nginx"
  name: springboot-ing
spec:
  ingressClassName: nginx
  rules:
  - host: api-test2.ikubernetes.cloud
    http:
      paths:
      - backend:
          service:
            name: springboot
            port:
              number: 8080
        path: /
        pathType: ImplementationSpecific

验证：

5、HTTP监控（监控外部域名）

    - job_name: "blackbox-external-website"
      scrape_interval: 30s
      scrape_timeout: 15s
      metrics_path: /probe
      params:
        module: [http_get_2xx]
      static_configs:
      - targets:
        - https://www.baidu.com # 改为公司对外服务的域名
        - https://www.jd.com
      relabel_configs:
      - source_labels: [__address__]
        target_label: __param_target
      - source_labels: [__param_target]
        target_label: instance
      - target_label: __address__
        replacement: blackbox-exporter.monitor:9115

按上面方法重载 Prometheus，打开 Prometheus 的 Target 页面，就会看到上面定义的 blackbox-external-website 任务

curl -XPOST http://prometheus.ikubernetes.cloud/-/reload

6、HTTPPost 监控(监控外部域名)

    - job_name: 'blackbox-http-post'
      metrics_path: /probe
      params:
        module: [http_post_2xx]
      static_configs:
        - targets:
          - https://www.baidu.com # 要检查的网址
      relabel_configs:
        - source_labels: [__address__]
          target_label: __param_target
        - source_labels: [__param_target]
          target_label: instance
        - target_label: __address__
          replacement: blackbox-exporter.monitor:9115

按上面方法重载 Prometheus，打开 Prometheus 的 Target 页面，就会看到上面定义的 blackbox-http-post 任务

curl -XPOST http://prometheus.ikubernetes.cloud/-/reload

三、自定义资源接入监控系统

基于Prometheus的全方位监控平台--自定义资源接入

Prometheus使用各种Exporter来监控资源。Exporter可以看成是监控agent端，它负责手机对应资源的指标，并提供给到Prometheus读取

1、虚拟数据抓取

1.1 配置安装node-exporter

docker run -d -p 9100:9100 \
-v "/proc:/host/proc" \
-v "/sys:/host/sys" \
-v "/:/rootfs" \
-v "/etc/localtime:/etc/localtime" \
prom/node-exporter \
--path.procfs /host/proc \
--path.sysfs /host/sys \
--collector.filesystem.ignored-mount-points "^/(sys|proc|dev|host|etc)($|/)"

验证数据收集

[root@master-1-230 7.4]# curl http://localhost:9100/metrics

1.2 配置prometheus-config.yaml

    - job_name: 'other-ECS'
      static_configs:
        - targets: ['192.168.1.230:9100']
          labels:
            hostname: 'test-node-exporter'

按上面方法重载 Prometheus，打开 Prometheus 的 Target 页面，就会看到上面定义的 other-ECS 任务

[root@master-1-230 7.5]# curl -XPOST http://prometheus.ikubernetes.cloud/-/reload

2、process-exporter进程监控

2.1 创建挂载目录

 mkdir -p /opt/process-exporter/config
 cat /opt/process-exporter/config/process-exporter.yml
 process_names:
  - name: "{{.Matches}}"
    cmdline:
    - 'sd-api'

2.2 配置安装process-exporter

docker run -itd --rm -p 9256:9256 --privileged -v /proc:/host/proc -v /opt/process-exporter/config:/config ncabatoff/process-exporter --procfs /host/proc -config.path config/process-exporter.yml

2.3 配置文件

匹配sd-api 的进程(ps -ef|grep "etcd --advertise-client-urls")
通过process-exporter的网页查看监控数据，包含：namedprocess_namegroup_num_procs{groupname="map[:etcd --advertise-client-urls]"} 即代表启动正确

## 指定过程进行监控
# cat process-exporter.yml
process_names:
  - name: "{{.Matches}}"
    cmdline:
    - 'etcd --advertise-client-urls'

#  - name: "{{.Matches}}"
#    cmdline:
#    - 'mysqld'

#  - name: "{{.Matches}}"
#    cmdline:
#    - 'org.apache.zookeeper.server.quorum.QuorumPeerMain'

2.4 测试验证：

展示当前主机层面的"etcd --advertise-client-urls"这个进程数量

[root@master-1-230 config]# curl localhost:9256/metrics |grep namedprocess_namegroup_num_procs
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 13899    0 13899    0     0   731k      0 --:--:-- --:--:-- --:--:--  754k
# HELP namedprocess_namegroup_num_procs number of processes in this group
# TYPE namedprocess_namegroup_num_procs gauge
namedprocess_namegroup_num_procs{groupname="map[:etcd --advertise-client-urls]"}

主机层面测试：

[root@master-1-230 config]# ps aux|grep  "etcd --advertise-client-urls" |egrep -v grep
root      2017  6.3 11.4 11486720 333796 ?     Ssl  16:41  15:01 etcd --advertise-client-urls=https://192.168.1.230:2379 --cert-file=/etc/kubernetes/pki/etcd/server.crt --client-cert-auth=true --data-dir=/var/lib/etcd --experimental-initial-corrupt-check=true --experimental-watch-progress-notify-interval=5s --initial-advertise-peer-urls=https://192.168.1.230:2380 --initial-cluster=master-1-230=https://192.168.1.230:2380 --key-file=/etc/kubernetes/pki/etcd/server.key --listen-client-urls=https://127.0.0.1:2379,https://192.168.1.230:2379 --listen-metrics-urls=http://0.0.0.0:2381 --listen-peer-urls=https://192.168.1.230:2380 --name=master-1-230 --peer-cert-file=/etc/kubernetes/pki/etcd/peer.crt --peer-client-cert-auth=true --peer-key-file=/etc/kubernetes/pki/etcd/peer.key --peer-trusted-ca-file=/etc/kubernetes/pki/etcd/ca.crt --snapshot-count=10000 --trusted-ca-file=/etc/kubernetes/pki/etcd/ca.crt

修改prometheus文件

新增job

    - job_name: 'process-exporter'
      static_configs:
      - targets: ['192.168.1.230:9256']

按上面方法重载 Prometheus，打开 Prometheus 的 Target 页面，就会看到上面定义的 process-exporter 任务

curl -XPOST http://prometheus.ikubernetes.cloud/-/reload

3、自定义中间件监控

3.1 创建mysql监听用户并授权

[root@master-1-230 7.5]# helm  list |grep mysql
mysql   	default  	5       	2023-12-09 16:49:03.310995002 +0800 CST	deployed	mysql-9.14.4 	8.0.35  

[root@master-1-230 7.5]# MYSQL_ROOT_PASSWORD=$(kubectl get secret --namespace default mysql -o jsonpath="{.data.mysql-root-password}" | base64 -d)
[root@master-1-230 7.5]# echo $MYSQL_ROOT_PASSWORD
TQtH0tjCLt
[root@master-1-230 7.5]# kubectl  get svc|grep mysql
mysql                     NodePort       10.110.136.173   <none>          3306:32000/TCP               7d4h

[root@master-1-230 7.5]# mysql -uroot -p -h 10.110.136.173
Enter password: 
Welcome to the MariaDB monitor.  Commands end with ; or \g.
Your MySQL connection id is 9151
Server version: 8.0.35 Source distribution

Copyright (c) 2000, 2018, Oracle, MariaDB Corporation Ab and others.

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

MySQL [(none)]> CREATE USER 'exporter'@'%' IDENTIFIED BY '123asdZXC';
Query OK, 0 rows affected (0.02 sec)

MySQL [(none)]> GRANT PROCESS, REPLICATION CLIENT, SELECT ON *.* TO 'exporter'@'%';
Query OK, 0 rows affected (0.00 sec)

MySQL [(none)]> flush privileges;
Query OK, 0 rows affected (0.00 sec)

MySQL [(none)]> exit
Bye

3.2 启动容器：

[root@master-1-230 7.5]# cat /tmp/mysql.cnf 
[client]
host=10.110.136.173
port=3306
user=exporter
password=123asdZXC

docker run -d  --restart=always  -v /tmp/mysql.cnf:/tmp/mysql.cnf  --name mysqld-exporter -p 9104:9104  prom/mysqld-exporter --config.my-cnf=/tmp/mysql.cnf

3.3 测试验证：

curl localhost:9104/metrics

3.4 修改prometheus文件

    - job_name: 'mysql-exporter'
      static_configs:
      - targets: ['192.168.1.230:9104']

按上面方法重载 Prometheus，打开 Prometheus 的 Target 页面，就会看到上面定义的 mysql-exporter 任务

curl -XPOST http://prometheus.ikubernetes.cloud/-/reload

4、总结

安装部署自定义 Exporter 组件；
配置prometheus-config文件做数据抓取；
配置prometheus-rules做监控告警；
配置grafana；

监控系统prometheus exporter性能

普罗监控系统prometheus系统

监控系统prometheus greatsql性能

监控系统prometheus exporter grafana

监控系统prometheus grafana系统

节点集群prometheus系统

526互联

day17 基于Prometheus的HPA自动伸缩 -Prometheus黑盒监控-自定义资源接入监控系统 (7.3-7.5)

一、基于Prometheus的HPA自动伸缩

1、背景

2、Metrics-server

2.1 Metrics-server 部署配置

3、HorizontalPodAutoscaler

3.1 使用HorizontalPodAutoscaler

3.2 测试验证：

4 、总结

二、基于Prometheus的全方位监控平台-黑盒监控(Blackbox)

1、Blackbox Exporter部署

2、DNS监控

3、ICMP监控

4、HTTP监控(K8S内部发现方法)

4.1 自定义发现Service 监控端口和路径

4.2 TCP检测

4.3 Ingress服务的探测

5、HTTP监控（监控外部域名）

6、HTTPPost 监控(监控外部域名)

三、自定义资源接入监控系统

1、虚拟数据抓取

1.1 配置安装node-exporter

1.2 配置prometheus-config.yaml

2、process-exporter进程监控

2.1 创建挂载目录

2.2 配置安装process-exporter

2.3 配置文件

2.4 测试验证：

3、自定义中间件监控

3.1 创建mysql监听用户并授权

3.2 启动容器：

3.3 测试验证：

3.4 修改prometheus文件

4、总结