k8s实战之部署Prometheus+Grafana可视化监控告警平台

920

八十年代的歌

写在前面

之前部署web网站的时候，架构图中有一环节是监控部分，并且搭建一套有效的监控平台对于运维来说非常之重要，只有这样才能更有效率的保证我们的服务器和服务的稳定运行，常见的开源监控软件有好几种，如zabbix、Nagios、open-flcon还有prometheus,每一种有着各自的优劣势，感兴趣的童鞋可以自行百度，但是与k8s集群监控，相对于而已更加友好的是Prometheus，今天我们就看看如何部署一套Prometheus全方位监控K8S

主要内容

  • 1.Prometheus架构

  • 2.K8S监控指标及实现思路

  • 3.在K8S平台部署Prometheus

  • 4.基于K8S服务发现的配置解析

  • 5.在K8S平台部署Grafana

  • 6.监控K8S集群中Pod、Node、资源对象

  • 7.使用Grafana可视化展示Prometheus监控数据

  • 8.告警规则与告警通知

1 Prometheus架构

Prometheus 是什么

Prometheus（普罗米修斯）是一个最初在SoundCloud上构建的监控系统。自2012年成为社区开源项目，拥有非常活跃的开发人员和用户社区。为强调开源及独立维护，Prometheus于2016年加入云原生云计算基金会（CNCF），成为继Kubernetes之后的第二个托管项目。
官网地址:
https://prometheus.io
https://github.com/prometheus

Prometheus 组成及架构

图片

  • Prometheus Server：收集指标和存储时间序列数据，并提供查询接口

  • ClientLibrary：客户端库

  • Push Gateway：短期存储指标数据。主要用于临时性的任务

  • Exporters：采集已有的第三方服务监控指标并暴露metrics

  • Alertmanager：告警

  • Web UI：简单的Web控制台

    数据模型

    Prometheus将所有数据存储为时间序列；具有相同度量名称以及标签属于同一个指标。
    每个时间序列都由度量标准名称和一组键值对（也成为标签）唯一标识。
    时间序列格式：

{

作业和实例

实例：可以抓取的目标称为实例（Instances）
作业：具有相同目标的实例集合称为作业（Job）
scrape_configs:
-job_name: ‘prometheus’
static_configs:
-targets: [‘localhost:9090’]
-job_name: ‘node’
static_configs:
-targets: [‘192.168.1.10:9090’]

2 K8S监控指标及实现思路

k8S监控指标

Kubernetes本身监控

  • Node资源利用率

  • Node数量

  • Pods数量（Node）

  • 资源对象状态

Pod监控

  • Pod数量（项目）

  • 容器资源利用率

  • 应用程序

    Prometheus监控K8S实现的架构

    图片

监控指标 具体实现 举例
Pod性能 cAdvisor 容器CPU
Node性能 node-exporter 节点CPU 内存利用率
K8S资源对象 kube-state-metrics Pod/Deployment/Service

服务发现：
https://prometheus.io/docs/prometheus/latest/configuration/configuration/#kubernetes_sd_config

3 在K8S平台部署Prometheus

3.1集群环境
ip地址 角色 备注
192.168.73.136 nfs
192.168.73.138 k8s-master
192.168.73.139 k8s-node01
192.168.73.140 k8s-node02
192.168.73.135 k8s-node03

3.2 项目地址

可以直接选择git clone项目代码，也可以后面自己编写，后面的操作也会有实现代码

[root@k8s-master src]# git clone https://github.com/zhangdongdong7/k8s-prometheus.git
Cloning into 'k8s-prometheus'...
remote: Enumerating objects: 3, done.
remote: Counting objects: 100% (3/3), done.
remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 0
Unpacking objects: 100% (3/3), done.
[root@k8s-master src]# cd k8s-prometheus/
[root@k8s-master k8s-prometheus]# ls
alertmanager-configmap.yaml         kube-state-metrics-rbac.yaml             prometheus-rbac.yaml
alertmanager-deployment.yaml        kube-state-metrics-service.yaml          prometheus-rules.yaml
alertmanager-pvc.yaml               node_exporter-0.17.0.linux-amd64.tar.gz  prometheus-service.yaml
alertmanager-service.yaml           node_exporter.sh                         prometheus-statefulset-static-pv.yaml
grafana.yaml                        OWNERS                                   prometheus-statefulset.yaml
kube-state-metrics-deployment.yaml  prometheus-configmap.yaml                README.md

3.3 使用RBAC进行授权

RBAC（Role-Based Access Control，基于角色的访问控制）：负责完成授权（Authorization）工作。
编写授权yaml

[root@k8s-master prometheus-k8s]# vim prometheus-rbac.yaml
apiVersion: v1
kind: ServiceAccount
metadata:
  name: prometheus
  namespace: kube-system
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
  name: prometheus
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
rules:
  - apiGroups:
      - ""
    resources:
      - nodes
      - nodes/metrics
      - services
      - endpoints
      - pods
    verbs:
      - get
      - list
      - watch
  - apiGroups:
      - ""
    resources:
      - configmaps
    verbs:
      - get
  - nonResourceURLs:
      - "/metrics"
    verbs:
      - get
---
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
  name: prometheus
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: prometheus
subjects:
- kind: ServiceAccount
  name: prometheus
  namespace: kube-system

创建

[root@k8s-master prometheus-k8s]# kubectl apply -f prometheus-rbac.yaml

3.4 配置管理

使用Configmap保存不需要加密配置信息
其中需要把nodes中ip地址根据自己的地址进行修改，其他不需要改动

[root@k8s-master prometheus-k8s]# vim prometheus-configmap.yaml
# Prometheus configuration format https://prometheus.io/docs/prometheus/latest/configuration/configuration/
apiVersion: v1
kind: ConfigMap #
metadata:
  name: prometheus-config
  namespace: kube-system
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: EnsureExists
data:
  prometheus.yml: |
    rule_files:
    - /etc/config/rules/*.rules

    scrape_configs:
    - job_name: prometheus
      static_configs:
      - targets:
        - localhost:9090

    - job_name: kubernetes-nodes
      scrape_interval: 30s
      static_configs:
      - targets:
        - 192.168.73.135:9100   #ip地址根据自己的地址进行修改
        - 192.168.73.138:9100
        - 192.168.73.139:9100
        - 192.168.73.140:9100

    - job_name: kubernetes-apiservers
      kubernetes_sd_configs:
      - role: endpoints
      relabel_configs:
      - action: keep
        regex: default;kubernetes;https
        source_labels:
        - __meta_kubernetes_namespace
        - __meta_kubernetes_service_name
        - __meta_kubernetes_endpoint_port_name
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
        insecure_skip_verify: true
      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token

    - job_name: kubernetes-nodes-kubelet
      kubernetes_sd_configs:
      - role: node
      relabel_configs:
      - action: labelmap
        regex: __meta_kubernetes_node_label_(.+)
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
        insecure_skip_verify: true
      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token

    - job_name: kubernetes-nodes-cadvisor
      kubernetes_sd_configs:
      - role: node
      relabel_configs:
      - action: labelmap
        regex: __meta_kubernetes_node_label_(.+)
      - target_label: __metrics_path__
        replacement: /metrics/cadvisor
      scheme: https
      tls_config:
        ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
        insecure_skip_verify: true
      bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token

    - job_name: kubernetes-service-endpoints
      kubernetes_sd_configs:
      - role: endpoints
      relabel_configs:
      - action: keep
        regex: true
        source_labels:
        - __meta_kubernetes_service_annotation_prometheus_io_scrape
      - action: replace
        regex: (https?)
        source_labels:
        - __meta_kubernetes_service_annotation_prometheus_io_scheme
        target_label: __scheme__
      - action: replace
        regex: (.+)
        source_labels:
        - __meta_kubernetes_service_annotation_prometheus_io_path
        target_label: __metrics_path__
      - action: replace
        regex: ([^:]+)(?::\d+)?;(\d+)
        replacement: $1:$2
        source_labels:
        - __address__
        - __meta_kubernetes_service_annotation_prometheus_io_port
        target_label: __address__
      - action: labelmap
        regex: __meta_kubernetes_service_label_(.+)
      - action: replace
        source_labels:
        - __meta_kubernetes_namespace
        target_label: kubernetes_namespace
      - action: replace
        source_labels:
        - __meta_kubernetes_service_name
        target_label: kubernetes_name

    - job_name: kubernetes-services
      kubernetes_sd_configs:
      - role: service
      metrics_path: /probe
      params:
        module:
        - http_2xx
      relabel_configs:
      - action: keep
        regex: true
        source_labels:
        - __meta_kubernetes_service_annotation_prometheus_io_probe
      - source_labels:
        - __address__
        target_label: __param_target
      - replacement: blackbox
        target_label: __address__
      - 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

    - job_name: kubernetes-pods
      kubernetes_sd_configs:
      - role: pod
      relabel_configs:
      - action: keep
        regex: true
        source_labels:
        - __meta_kubernetes_pod_annotation_prometheus_io_scrape
      - action: replace
        regex: (.+)
        source_labels:
        - __meta_kubernetes_pod_annotation_prometheus_io_path
        target_label: __metrics_path__
      - action: replace
        regex: ([^:]+)(?::\d+)?;(\d+)
        replacement: $1:$2
        source_labels:
        - __address__
        - __meta_kubernetes_pod_annotation_prometheus_io_port
        target_label: __address__
      - action: labelmap
        regex: __meta_kubernetes_pod_label_(.+)
      - action: replace
        source_labels:
        - __meta_kubernetes_namespace
        target_label: kubernetes_namespace
      - action: replace
        source_labels:
        - __meta_kubernetes_pod_name
        target_label: kubernetes_pod_name
    alerting:
      alertmanagers:
      - static_configs:
          - targets: ["alertmanager:80"]

创建

[root@k8s-master prometheus-k8s]# kubectl apply -f prometheus-configmap.yaml

3.5 有状态部署prometheus

这里使用storageclass进行动态供给，给prometheus的数据进行持久化，具体实现办法，可以查看之前的文章《k8s中的NFS动态存储供给》，除此之外可以使用静态供给的prometheus-statefulset-static-pv.yaml进行持久化

[root@k8s-master prometheus-k8s]# vim prometheus-statefulset.yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: prometheus
  namespace: kube-system
  labels:
    k8s-app: prometheus
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
    version: v2.2.1
spec:
  serviceName: "prometheus"
  replicas: 1
  podManagementPolicy: "Parallel"
  updateStrategy:
   type: "RollingUpdate"
  selector:
    matchLabels:
      k8s-app: prometheus
  template:
    metadata:
      labels:
        k8s-app: prometheus
      annotations:
        scheduler.alpha.kubernetes.io/critical-pod: ''
    spec:
      priorityClassName: system-cluster-critical
      serviceAccountName: prometheus
      initContainers:
      - name: "init-chown-data"
        image: "busybox:latest"
        imagePullPolicy: "IfNotPresent"
        command: ["chown", "-R", "65534:65534", "/data"]
        volumeMounts:
        - name: prometheus-data
          mountPath: /data
          subPath: ""
      containers:
        - name: prometheus-server-configmap-reload
          image: "jimmidyson/configmap-reload:v0.1"
          imagePullPolicy: "IfNotPresent"
          args:
            - --volume-dir=/etc/config
            - --webhook-url=http://localhost:9090/-/reload
          volumeMounts:
            - name: config-volume
              mountPath: /etc/config
              readOnly: true
          resources:
            limits:
              cpu: 10m
              memory: 10Mi
            requests:
              cpu: 10m
              memory: 10Mi

        - name: prometheus-server
          image: "prom/prometheus:v2.2.1"
          imagePullPolicy: "IfNotPresent"
          args:
            - --config.file=/etc/config/prometheus.yml
            - --storage.tsdb.path=/data
            - --web.console.libraries=/etc/prometheus/console_libraries
            - --web.console.templates=/etc/prometheus/consoles
            - --web.enable-lifecycle
          ports:
            - containerPort: 9090
          readinessProbe:
            httpGet:
              path: /-/ready
              port: 9090
            initialDelaySeconds: 30
            timeoutSeconds: 30
          livenessProbe:
            httpGet:
              path: /-/healthy
              port: 9090
            initialDelaySeconds: 30
            timeoutSeconds: 30
          # based on 10 running nodes with 30 pods each
          resources:
            limits:
              cpu: 200m
              memory: 1000Mi
            requests:
              cpu: 200m
              memory: 1000Mi

          volumeMounts:
            - name: config-volume
              mountPath: /etc/config
            - name: prometheus-data
              mountPath: /data
              subPath: ""
            - name: prometheus-rules
              mountPath: /etc/config/rules

      terminationGracePeriodSeconds: 300
      volumes:
        - name: config-volume
          configMap:
            name: prometheus-config
        - name: prometheus-rules
          configMap:
            name: prometheus-rules

  volumeClaimTemplates:
  - metadata:
      name: prometheus-data
    spec:
      storageClassName: managed-nfs-storage #存储类根据自己的存储类名字修改
      accessModes:
        - ReadWriteOnce
      resources:
        requests:
          storage: "16Gi"

创建

[root@k8s-master prometheus-k8s]# kubectl apply -f prometheus-statefulset.yaml

检查状态

[root@k8s-master prometheus-k8s]# kubectl get pod -n kube-system
NAME                                    READY   STATUS    RESTARTS   AGE
coredns-5bd5f9dbd9-wv45t                1/1     Running   1          8d
kubernetes-dashboard-7d77666777-d5ng4   1/1     Running   5          14d
prometheus-0                            2/2     Running   6          14d

可以看到一个prometheus-0的pod，这就刚才使用statefulset控制器进行的有状态部署，两个容器的状态为Runing则是正常，如果不为Runing可以使用kubectl describe pod  prometheus-0 -n kube-system查看报错详情

3.6 创建service暴露访问端口

此处使用nodePort固定一个访问端口，不适用随机端口，便于访问

[root@k8s-master prometheus-k8s]# vim prometheus-service.yaml
kind: Service
apiVersion: v1
metadata:
  name: prometheus
  namespace: kube-system
  labels:
    kubernetes.io/name: "Prometheus"
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
spec:
  type: NodePort
  ports:
    - name: http
      port: 9090
      protocol: TCP
      targetPort: 9090
      nodePort: 30090    #固定的对外访问的端口
  selector:
    k8s-app: prometheus

创建

[root@k8s-master prometheus-k8s]# kubectl apply -f prometheus-service.yaml

检查

[root@k8s-master prometheus-k8s]# kubectl get pod,svc -n kube-system
NAME                                        READY   STATUS    RESTARTS   AGE
pod/coredns-5bd5f9dbd9-wv45t                1/1     Running   1          8dpod/kubernetes-dashboard-7d77666777-d5ng4   1/1     Running   5          14dpod/prometheus-0                            2/2     Running   6          14dNAME                           TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)             AGE
service/kube-dns               ClusterIP   10.0.0.2     <none>        53/UDP,53/TCP       13dservice/kubernetes-dashboard   NodePort    10.0.0.127   <none>        443:30001/TCP       16dservice/prometheus             NodePort    10.0.0.33    <none>        9090:30090/TCP      13d

3.7 web访问

使用任意一个NodeIP加端口进行访问，访问地址：http://NodeIP:Port ,此例就是：http://192.168.73.139:30090
访问成功的界面如图所示：

图片

4 在K8S平台部署Grafana

通过上面的web访问，可以看出prometheus自带的UI界面是没有多少功能的，可视化展示的功能不完善，不能满足日常的监控所需，因此常常我们需要再结合Prometheus+Grafana的方式来进行可视化的数据展示
官网地址：
https://github.com/kubernetes/kubernetes/tree/master/cluster/addons/prometheus
https://grafana.com/grafana/download
刚才下载的项目中已经写好了Grafana的yaml，根据自己的环境进行修改

4.1 使用StatefulSet部署grafana

[root@k8s-master prometheus-k8s]# vim grafana.yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: grafana
  namespace: kube-system
spec:
  serviceName: "grafana"
  replicas: 1
  selector:
    matchLabels:
      app: grafana
  template:
    metadata:
      labels:
        app: grafana
    spec:
      containers:
      - name: grafana
        image: grafana/grafana
        ports:
          - containerPort: 3000
            protocol: TCP
        resources:
          limits:
            cpu: 100m
            memory: 256Mi
          requests:
            cpu: 100m
            memory: 256Mi
        volumeMounts:
          - name: grafana-data
            mountPath: /var/lib/grafana
            subPath: grafana
      securityContext:
        fsGroup: 472
        runAsUser: 472
  volumeClaimTemplates:
  - metadata:
      name: grafana-data
    spec:
      storageClassName: managed-nfs-storage #和prometheus使用同一个存储类
      accessModes:
        - ReadWriteOnce
      resources:
        requests:
          storage: "1Gi"

---

apiVersion: v1
kind: Service
metadata:
  name: grafana
  namespace: kube-system
spec:
  type: NodePort
  ports:
  - port : 80
    targetPort: 3000
    nodePort: 30091
  selector:
    app: grafana

4.2 Grafana的web访问

使用任意一个NodeIP加端口进行访问，访问地址：http://NodeIP:Port ,此例就是：http://192.168.73.139:30091
成功访问界面如下，会需要进行账号密码登陆，默认账号密码都为admin，登陆之后会让修改密码

图片

登陆之后的界面如下

图片

第一步需要进行数据源添加，点击create your first data source数据库图标，根据下图所示进行添加即可

图片

第二步，添加完了之后点击底部的绿色的Save&Test，会成功提示Data sourse is working，则表示数据源添加成功

4.3 监控K8S集群中Pod、Node、资源对象数据的方法

1）Pod
kubelet的节点使用cAdvisor提供的metrics接口获取该节点所有Pod和容器相关的性能指标数据，安装kubelet默认就开启了
暴露接口地址：
https://NodeIP:10255/metrics/cadvisor
https://NodeIP:10250/metrics/cadvisor

2）Node
需要使用node_exporter收集器采集节点资源利用率。
https://github.com/prometheus/node_exporter
使用文档：https://prometheus.io/docs/guides/node-exporter/

  • 使用node_exporter.sh脚本分别在所有服务器上部署node_exporter收集器，不需要修改可直接运行脚本

[root@k8s-master prometheus-k8s]# cat node_exporter.sh #!/bin/bashwget https://github.com/prometheus/node_exporter/releases/download/v0.17.0/node_exporter-0.17.0.linux-amd64.tar.gz

tar zxf node_exporter-0.17.0.linux-amd64.tar.gz
mv node_exporter-0.17.0.linux-amd64 /usr/local/node_exporter

cat <<EOF >/usr/lib/systemd/system/node_exporter.service
[Unit]
Description=https://prometheus.io

[Service]
Restart=on-failure
ExecStart=/usr/local/node_exporter/node_exporter --collector.systemd --collector.systemd.unit-whitelist=(docker|kubelet|kube-proxy|flanneld).service

[Install]
WantedBy=multi-user.target
EOF

systemctl daemon-reload
systemctl enable node_exporter
systemctl restart node_exporter
[root@k8s-master prometheus-k8s]# ./node_exporter.sh

  • 检测node_exporter的进程，是否生效

[root@k8s-master prometheus-k8s]# ps -ef|grep node_exporter
root       6227      1  0 Oct08 ?        00:06:43 /usr/local/node_exporter/node_exporter --collector.systemd --collector.systemd.unit-whitelist=(docker|kubelet|kube-proxy|flanneld).service
root     118269 117584  0 23:27 pts/0    00:00:00 grep --color=auto node_exporter

3）资源对象
kube-state-metrics采集了k8s中各种资源对象的状态信息，只需要在master节点部署就行
https://github.com/kubernetes/kube-state-metrics

  1. 创建rbac的yaml对metrics进行授权

[root@k8s-master prometheus-k8s]# vim kube-state-metrics-rbac.yaml
apiVersion: v1
kind: ServiceAccount
metadata:
  name: kube-state-metrics
  namespace: kube-system
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: kube-state-metrics
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
rules:
- apiGroups: [""]
  resources:
  - configmaps
  - secrets
  - nodes
  - pods
  - services
  - resourcequotas
  - replicationcontrollers
  - limitranges
  - persistentvolumeclaims
  - persistentvolumes
  - namespaces
  - endpoints
  verbs: ["list", "watch"]
- apiGroups: ["extensions"]
  resources:
  - daemonsets
  - deployments
  - replicasets
  verbs: ["list", "watch"]
- apiGroups: ["apps"]
  resources:
  - statefulsets
  verbs: ["list", "watch"]
- apiGroups: ["batch"]
  resources:
  - cronjobs
  - jobs
  verbs: ["list", "watch"]
- apiGroups: ["autoscaling"]
  resources:
  - horizontalpodautoscalers
  verbs: ["list", "watch"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: kube-state-metrics-resizer
  namespace: kube-system
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
rules:
- apiGroups: [""]
  resources:
  - pods
  verbs: ["get"]
- apiGroups: ["extensions"]
  resources:
  - deployments
  resourceNames: ["kube-state-metrics"]
  verbs: ["get", "update"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: kube-state-metrics
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: kube-state-metrics
subjects:
- kind: ServiceAccount
  name: kube-state-metrics
  namespace: kube-system
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: kube-state-metrics
  namespace: kube-system
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: kube-state-metrics-resizer
subjects:
- kind: ServiceAccount
  name: kube-state-metrics
  namespace: kube-system
[root@k8s-master prometheus-k8s]# kubectl apply -f kube-state-metrics-rbac.yaml

  1. 编写Deployment和ConfigMap的yaml进行metrics pod部署，不需要进行修改

[root@k8s-master prometheus-k8s]# cat kube-state-metrics-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: kube-state-metrics
  namespace: kube-system
  labels:
    k8s-app: kube-state-metrics
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
    version: v1.3.0
spec:
  selector:
    matchLabels:
      k8s-app: kube-state-metrics
      version: v1.3.0
  replicas: 1
  template:
    metadata:
      labels:
        k8s-app: kube-state-metrics
        version: v1.3.0
      annotations:
        scheduler.alpha.kubernetes.io/critical-pod: ''
    spec:
      priorityClassName: system-cluster-critical
      serviceAccountName: kube-state-metrics
      containers:
      - name: kube-state-metrics
        image: lizhenliang/kube-state-metrics:v1.3.0
        ports:
        - name: http-metrics
          containerPort: 8080
        - name: telemetry
          containerPort: 8081
        readinessProbe:
          httpGet:
            path: /healthz
            port: 8080
          initialDelaySeconds: 5
          timeoutSeconds: 5
      - name: addon-resizer
        image: lizhenliang/addon-resizer:1.8.3
        resources:
          limits:
            cpu: 100m
            memory: 30Mi
          requests:
            cpu: 100m
            memory: 30Mi
        env:
          - name: MY_POD_NAME
            valueFrom:
              fieldRef:
                fieldPath: metadata.name
          - name: MY_POD_NAMESPACE
            valueFrom:
              fieldRef:
                fieldPath: metadata.namespace
        volumeMounts:
          - name: config-volume
            mountPath: /etc/config
        command:
          - /pod_nanny
          - --config-dir=/etc/config
          - --container=kube-state-metrics
          - --cpu=100m
          - --extra-cpu=1m
          - --memory=100Mi
          - --extra-memory=2Mi
          - --threshold=5
          - --deployment=kube-state-metrics
      volumes:
        - name: config-volume
          configMap:
            name: kube-state-metrics-config
---
# Config map for resource configuration.
apiVersion: v1
kind: ConfigMap
metadata:
  name: kube-state-metrics-config
  namespace: kube-system
  labels:
    k8s-app: kube-state-metrics
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
data:
  NannyConfiguration: |-
    apiVersion: nannyconfig/v1alpha1
    kind: NannyConfiguration
[root@k8s-master prometheus-k8s]# kubectl apply -f kube-state-metrics-deployment.yaml

      2.编写Service的yaml对metrics进行端口暴露

[root@k8s-master prometheus-k8s]# cat kube-state-metrics-service.yaml
apiVersion: v1
kind: Service
metadata:
  name: kube-state-metrics
  namespace: kube-system
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
    kubernetes.io/name: "kube-state-metrics"
  annotations:
    prometheus.io/scrape: 'true'
spec:
  ports:
  - name: http-metrics
    port: 8080
    targetPort: http-metrics
    protocol: TCP
  - name: telemetry
    port: 8081
    targetPort: telemetry
    protocol: TCP
  selector:
    k8s-app: kube-state-metrics
[root@k8s-master prometheus-k8s]# kubectl apply -f kube-state-metrics-service.yaml

     3.检查pod和svc的状态，可以看到正常运行了pod/kube-state-metrics-7c76bdbf68-kqqgd 和对外暴露了8080和8081端口

[root@k8s-master prometheus-k8s]# kubectl get pod,svc -n kube-system
NAME                                        READY   STATUS    RESTARTS   AGE
pod/alertmanager-5d75d5688f-fmlq6           2/2     Running   0          9dpod/coredns-5bd5f9dbd9-wv45t                1/1     Running   1          9dpod/grafana-0                               1/1     Running   2          15dpod/kube-state-metrics-7c76bdbf68-kqqgd     2/2     Running   6          14dpod/kubernetes-dashboard-7d77666777-d5ng4   1/1     Running   5          16dpod/prometheus-0                            2/2     Running   6          15dNAME                           TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)             AGE
service/alertmanager           ClusterIP   10.0.0.207   <none>        80/TCP              13dservice/grafana                NodePort    10.0.0.74    <none>        80:30091/TCP        15dservice/kube-dns               ClusterIP   10.0.0.2     <none>        53/UDP,53/TCP       14dservice/kube-state-metrics     ClusterIP   10.0.0.194   <none>        8080/TCP,8081/TCP   14dservice/kubernetes-dashboard   NodePort    10.0.0.127   <none>        443:30001/TCP       17dservice/prometheus             NodePort    10.0.0.33    <none>        9090:30090/TCP      14d[root@k8s-master prometheus-k8s]#

5 使用Grafana可视化展示Prometheus监控数据

通常在使用Prometheus采集数据的时候我们需要监控K8S集群中Pod、Node、资源对象，因此我们需要安装对应的插件和资源采集器来提供api进行数据获取，在4.3中我们已经配置好，我们也可以使用Prometheus的UI界面中的Staus菜单下的Target中的各个采集器的状态情况，如图所示：

图片

图片

只有当我们各个Target的状态都是UP状态时，我们可以使用自带的的界面去获取到某一监控项的相关的数据，如图所示：

图片

从上面的图中可以看出Prometheus的界面可视化展示的功能较单一，不能满足需求，因此我们需要结合Grafana来进行可视化展示Prometheus监控数据，在上一章节，已经成功部署了Granfana，因此需要在使用的时候添加dashboard和Panel来设计展示相关的监控项，但是实际上在Granfana社区里面有很多成熟的模板，我们可以直接使用，然后根据自己的环境修改Panel中的查询语句来获取数据
https://grafana.com/grafana/dashboards

图片

推荐模板：

  • 集群资源监控的模板号：3119，如图所示进行添加

    图片

  • 图片

    图片

    当模板添加之后如果某一个Panel不显示数据，可以点击Panel上的编辑，查询PromQL语句，然后去Prometheus自己的界面上进行调试PromQL语句是否可以获取到值，最后调整之后的监控界面如图所示

    图片

    图片

  • 资源状态监控：6417
    同理，添加资源状态的监控模板，然后经过调整之后的监控界面如图所示，可以获取到k8s中各种资源状态的监控展示

    图片

  • Node监控：9276
    同理，添加资源状态的监控模板，然后经过调整之后的监控界面如图所示，可以获取到各个node上的基本情况

  • 图片

6 在K8S中部署Alertmanager

6.1 部署Alertmanager的实现步骤

图片

6.2 部署告警

我们以Email来进行实现告警信息的发送

  1. 首先需要准备一个发件邮箱，开启stmp发送功能

  2. 使用configmap存储告警规则，编写报警规则的yaml文件，可根据自己的实际情况进行修改和添加报警的规则，prometheus比zabbix就麻烦在这里，所有的告警规则需要自己去定义

[root@k8s-master prometheus-k8s]# vim prometheus-rules.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-rules
  namespace: kube-system
data:
  general.rules: |
    groups:
    - name: general.rules
      rules:
      - alert: InstanceDown
        expr: up == 0
        for: 1m
        labels:
          severity: error
        annotations:
          summary: "Instance {{ $labels.instance }} 停止工作"
          description: "{{ $labels.instance }} job {{ $labels.job }} 已经停止5分钟以上."
  node.rules: |
    groups:
    - name: node.rules
      rules:
      - alert: NodeFilesystemUsage
        expr: 100 - (node_filesystem_free_bytes{fstype=~"ext4|xfs"} / node_filesystem_size_bytes{fstype=~"ext4|xfs"} * 100) > 80
        for: 1m
        labels:
          severity: warning
        annotations:
          summary: "Instance {{ $labels.instance }} : {{ $labels.mountpoint }} 分区使用率过高"
          description: "{{ $labels.instance }}: {{ $labels.mountpoint }} 分区使用大于80% (当前值: {{ $value }})"

      - alert: NodeMemoryUsage
        expr: 100 - (node_memory_MemFree_bytes+node_memory_Cached_bytes+node_memory_Buffers_bytes) / node_memory_MemTotal_bytes * 10
0 > 80
        for: 1m
        labels:
          severity: warning
        annotations:
          summary: "Instance {{ $labels.instance }} 内存使用率过高"
          description: "{{ $labels.instance }}内存使用大于80% (当前值: {{ $value }})"

      - alert: NodeCPUUsage
        expr: 100 - (avg(irate(node_cpu_seconds_total{mode="idle"}[5m])) by (instance) * 100) > 60
        for: 1m
        labels:
          severity: warning
        annotations:
          summary: "Instance {{ $labels.instance }} CPU使用率过高"
          description: "{{ $labels.instance }}CPU使用大于60% (当前值: {{ $value }})"
[root@k8s-master prometheus-k8s]# kubectl apply -f prometheus-rules.yaml

     3.编写告警configmap的yaml文件部署，增加alertmanager告警配置，进行配置邮箱发送地址

[root@k8s-master prometheus-k8s]# vim alertmanager-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: alertmanager-config
  namespace: kube-system
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: EnsureExists
data:
  alertmanager.yml: |
    global:
      resolve_timeout: 5m
      smtp_smarthost: 'xxx.com.cn:25'  #登陆邮件进行查看
      smtp_from: 'xxx@126.com.cn' #根据自己申请的发件邮箱进行配置
      smtp_auth_username: 'xxx@qq.com.cn'
      smtp_auth_password: 'xxxxx'

    receivers:
    - name: default-receiver
      email_configs:
      - to: "zhangdongdong27459@goldwind.com.cn"

    route:
      group_interval: 1m
      group_wait: 10s
      receiver: default-receiver
      repeat_interval: 1m
[root@k8s-master prometheus-k8s]# kubectl apply -f alertmanager-configmap.yaml

     4.创建PVC进行数据持久化，我这个yaml文件使用的跟Prometheus安装时用的存储类来进行自动供给，需要根据自己的实际情况修改

[root@k8s-master prometheus-k8s]# vim alertmanager-pvc.yaml
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: alertmanager
  namespace: kube-system
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: EnsureExists
spec:
  storageClassName: managed-nfs-storage
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: "2Gi"
[root@k8s-master prometheus-k8s]# kubectl apply -f alertmanager-pvc.yaml

      5.编写deployment的yaml来部署alertmanager的pod

[root@k8s-master prometheus-k8s]# vim alertmanager-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: alertmanager
  namespace: kube-system
  labels:
    k8s-app: alertmanager
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
    version: v0.14.0
spec:
  replicas: 1
  selector:
    matchLabels:
      k8s-app: alertmanager
      version: v0.14.0
  template:
    metadata:
      labels:
        k8s-app: alertmanager
        version: v0.14.0
      annotations:
        scheduler.alpha.kubernetes.io/critical-pod: ''
    spec:
      priorityClassName: system-cluster-critical
      containers:
        - name: prometheus-alertmanager
          image: "prom/alertmanager:v0.14.0"
          imagePullPolicy: "IfNotPresent"
          args:
            - --config.file=/etc/config/alertmanager.yml
            - --storage.path=/data
            - --web.external-url=/
          ports:
            - containerPort: 9093
          readinessProbe:
            httpGet:
              path: /#/status
              port: 9093
            initialDelaySeconds: 30
            timeoutSeconds: 30
          volumeMounts:
            - name: config-volume
              mountPath: /etc/config
            - name: storage-volume
              mountPath: "/data"
              subPath: ""
          resources:
            limits:
              cpu: 10m
              memory: 50Mi
            requests:
              cpu: 10m
              memory: 50Mi
        - name: prometheus-alertmanager-configmap-reload
          image: "jimmidyson/configmap-reload:v0.1"
          imagePullPolicy: "IfNotPresent"
          args:
            - --volume-dir=/etc/config
            - --webhook-url=http://localhost:9093/-/reload
          volumeMounts:
            - name: config-volume
              mountPath: /etc/config
              readOnly: true
          resources:
            limits:
              cpu: 10m
              memory: 10Mi
            requests:
              cpu: 10m
              memory: 10Mi
      volumes:
        - name: config-volume
          configMap:
            name: alertmanager-config
        - name: storage-volume
          persistentVolumeClaim:
            claimName: alertmanager
[root@k8s-master prometheus-k8s]# kubectl apply -f alertmanager-deployment.yaml

        6.创建 alertmanager的service对外暴露的端口

[root@k8s-master prometheus-k8s]# vim alertmanager-service.yaml
apiVersion: v1
kind: Service
metadata:
  name: alertmanager
  namespace: kube-system
  labels:
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
    kubernetes.io/name: "Alertmanager"
spec:
  ports:
    - name: http
      port: 80
      protocol: TCP
      targetPort: 9093
  selector:
    k8s-app: alertmanager
  type: "ClusterIP"
  [root@k8s-master prometheus-k8s]# kubectl apply -f alertmanager-service.yaml

     7.检测部署状态，可以发现pod/alertmanager-5d75d5688f-fmlq6和service/alertmanager正常运行

[root@k8s-master prometheus-k8s]# kubectl get pod,svc -n kube-system -o wide
NAME                                        READY   STATUS    RESTARTS   AGE   IP            NODE             NOMINATED NODE   READINESS GATES
pod/alertmanager-5d75d5688f-fmlq6           2/2     Running   4          10d   172.17.15.2   192.168.73.140   <none>           <none>
pod/coredns-5bd5f9dbd9-qxvmz                1/1     Running   0          42m   172.17.33.2   192.168.73.138   <none>           <none>
pod/grafana-0                               1/1     Running   3          16d   172.17.31.2   192.168.73.139   <none>           <none>
pod/kube-state-metrics-7c76bdbf68-hv56m     2/2     Running   0          23h   172.17.15.3   192.168.73.140   <none>           <none>
pod/kubernetes-dashboard-7d77666777-d5ng4   1/1     Running   6          17d   172.17.31.4   192.168.73.139   <none>           <none>
pod/prometheus-0                            2/2     Running   8          16d   172.17.83.2   192.168.73.135   <none>           <none>

NAME                           TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)             AGE   SELECTOR
service/alertmanager           ClusterIP   10.0.0.207   <none>        80/TCP              14d   k8s-app=alertmanager
service/grafana                NodePort    10.0.0.74    <none>        80:30091/TCP        16d   app=grafana
service/kube-dns               ClusterIP   10.0.0.2     <none>        53/UDP,53/TCP       42m   k8s-app=kube-dns
service/kube-state-metrics     ClusterIP   10.0.0.194   <none>        8080/TCP,8081/TCP   15d   k8s-app=kube-state-metrics
service/kubernetes-dashboard   NodePort    10.0.0.127   <none>        443:30001/TCP       18d   k8s-app=kubernetes-dashboard
service/prometheus             NodePort    10.0.0.33    <none>        9090:30090/TCP      15d   k8s-app=prometheus

6.3 测试告警发送

登录prometheus默认的web界面，选择Alert菜单，则可以看到刚才我们使用prometheus-rules.yaml定义的的四个告警规则

图片

因为告警规则中定义了一个InstanceDown 的实例，所以我们可以停掉138服务器上的kubelet，来测试是否可以收到报警邮件发送

[root@k8s-master prometheus-k8s]# kubectl get nodeNAME             STATUS   ROLES    AGE   VERSION192.168.73.135   Ready    <none>   18d   v1.15.2192.168.73.138   Ready    <none>   17d   v1.15.2192.168.73.139   Ready    <none>   19d   v1.15.2192.168.73.140   Ready    <none>   19d   v1.15.2[root@k8s-master prometheus-k8s]# systemctl stop kubelet[root@k8s-master prometheus-k8s]# kubectl get node      NAME             STATUS     ROLES    AGE   VERSION192.168.73.135   NotReady   <none>   18d   v1.15.2192.168.73.138   Ready      <none>   17d   v1.15.2192.168.73.139   Ready      <none>   19d   v1.15.2192.168.73.140   Ready      <none>   19d   v1.15.2

稍微等一会，我们再刷新刚才的web上的告警规则界面，可以发现InstanceDown 的实例颜色变成粉红色了，并且显示2 active

图片

根据规则等待五分钟之后，我们去刷新刚才配置的告警收件箱，收到了一个封InstanceDown 的邮件提醒，邮件的发送间隔时间可以在alertmanager-configmap.yaml配置文件中进行设置，恢复刚才停止的kubelet，将不会收到告警邮件提醒

图片

如果各位大佬看到这里，相信已经对于如何在k8s集群中部署一套Prometheus监控平台来可视化进行数据的展示和告警有了一定了解，读万卷书,不如行万里路，还是希望大家能够自己多去实操

在此特别鸣谢李振良老师的指导，感兴趣的同学可以去腾旭课堂搜他相关的课程

写的不好，有问题欢迎大家一起讨论学习

写在最后

- 如果各位大佬看到这里，相信已经对于如何在k8s集群中部署一套Prometheus监控平台来可视化进行数据的展示和告警有了一定了解，读万卷书,不如行万里路，还是希望大家能够自己多去实操

- 在此特别鸣谢李振良老师的指导，感兴趣的同学可以去腾讯课堂搜他相关的课程

-写的不好，有问题欢迎大家一起讨论学习

图片

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