k8s(七)、监控--Prometheus部署篇

淩亂°似流年 2022-05-21 00:37 248阅读 0赞

## 前言 ##

前面几篇文章介绍了k8s的部署、对外服务、集群网络、微服务支持，在生产环境中使用，离不开运行状态监控，本篇开始部署使用prometheus，被各大公司广泛使用的容器监控工具。

## 工作方式 ##

Prometheus工作示意图：  
![这里写图片描述][70]

在k8s中，关于集群的资源有metrics度量值的概念，有各种不同的exporter可以通过api接口对外提供各种度量值的及时数据，prometheus在与k8s融合工作的过程，就是通过与这些提供metric值得exporter进行交互，获取数据，整合数据，展示数据，触发告警的过程。  
**一、获取metrics：**  
1.对短暂生命周期的任务，采取拉的形式获取metrics (不常见)  
2.对于exporter提供的metrics，采取拉的方式获取metrics(通常方式),对接的exporter常见的有：kube-apiserver 、cadvisor、node-exporter，也可根据应用类型部署相应的exporter，获取该应用的状态信息，目前支持的应用有：nginx/haproxy/mysql/redis/memcache等。

**二、数据汇总及按需获取：**  
可以按照官方定义的expr表达式格式，以及PromQL语法对相应的指标进程过滤，数据展示及图形展示。不过自带的webui较为简陋，但prometheus同时提供获取数据的api，grafana可通过api获取prometheus数据源，来绘制更精细的图形效果用以展示。

expr书写格式及语法参考官方文档：  
https://prometheus.io/docs/prometheus/latest/querying/basics/

**三、告警推送**  
prometheus支持多种告警媒介，对满足条件的告警自动触发告警，并可对告警的发送规则进行定制，例如重复间隔、路由等，可以实现非常灵活的告警触发。

## 部署 ##

1.配置configmap，在部署前将Prometheus主程序配置文件准备好，以configmap的形式挂载进deployment中。  
prometheus-configmap.yaml:

apiVersion: v1
    kind: ConfigMap
    metadata:
      name: prometheus-config
      namespace: kube-system
    data:
      prometheus.yml: | global: scrape_interval: 15s evaluation_interval: 15s rule_files: - /etc/prometheus/rules.yml alerting: alertmanagers: - static_configs: - targets: ["alertmanager:9093"] scrape_configs:
    
        - job_name: 'kubernetes-apiservers'
          kubernetes_sd_configs:
          - role: endpoints
          scheme: https
          tls_config:
            ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
          bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
          relabel_configs:
          - source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]
            action: keep
            regex: default;kubernetes;https
    
        - job_name: 'kubernetes-cadvisor'
          kubernetes_sd_configs:
          - role: node
          scheme: https
          tls_config:
            ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
          bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
          relabel_configs:
          - action: labelmap
            regex: __meta_kubernetes_node_label_(.+)
          - target_label: __address__
            replacement: kubernetes.default.svc:443
          - source_labels: [__meta_kubernetes_node_name]
            regex: (.+)
            target_label: __metrics_path__
            replacement: /api/v1/nodes/${ 1}/proxy/metrics/cadvisor
    
        - job_name: 'kubernetes-service-endpoints'
          kubernetes_sd_configs:
          - role: endpoints
          relabel_configs:
          - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape]
            action: keep
            regex: true
          - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]
            action: replace
            target_label: __scheme__
            regex: (https?)
          - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
            action: replace
            target_label: __metrics_path__
            regex: (.+)
          - source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]
            action: replace
            target_label: __address__
            regex: ([^:]+)(?::\d+)?;(\d+)
            replacement: $1:$2
          - action: labelmap
            regex: __meta_kubernetes_service_label_(.+)
          - source_labels: [__meta_kubernetes_namespace]
            action: replace
            target_label: kubernetes_namespace
          - source_labels: [__meta_kubernetes_service_name]
            action: replace
            target_label: kubernetes_name
    
        - job_name: 'kubernetes-services'
          kubernetes_sd_configs:
          - role: service
          metrics_path: /probe
          params:
            module: [http_2xx]
          relabel_configs:
          - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_probe]
            action: keep
            regex: true
          - source_labels: [__address__]
            target_label: __param_target
          - target_label: __address__
            replacement: blackbox-exporter.example.com:9115
          - 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-ingresses'
          kubernetes_sd_configs:
          - role: ingress
          relabel_configs:
          - source_labels: [__meta_kubernetes_ingress_annotation_prometheus_io_probe]
            action: keep
            regex: true
            regex: (.+)
          - source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
            action: replace
            regex: ([^:]+)(?::\d+)?;(\d+)
            replacement: $1:$2
            target_label: __address__
          - action: labelmap
            regex: __meta_kubernetes_pod_label_(.+)
          - source_labels: [__meta_kubernetes_namespace]
            action: replace
            target_label: kubernetes_namespace
          - source_labels: [__meta_kubernetes_pod_name]
            action: replace
            target_label: kubernetes_pod_name
            
        - job_name: 'kubernetes_node'
          tls_config:
            ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
          bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
          kubernetes_sd_configs:
          # 基于endpoint的服务发现，不再经过service代理层面
          - role: endpoints
          relabel_configs:
          - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape, __meta_kubernetes_endpoint_port_name]
            regex: true;prometheus-node-exporter
            action: keep
          - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme]
            action: replace
            target_label: __scheme__
            regex: (https?)
          - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path]
            action: replace
            target_label: __metrics_path__
            regex: (.+)
          - source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port]
            action: replace
            target_label: __address__
            regex: (.+)(?::\d+);(\d+)
            replacement: $1:$2
          # 去掉label name中的前缀__meta_kubernetes_service_label_
          - action: labelmap
            regex: __meta_kubernetes_service_label_(.+)
          # 为了区分所属node,把instance 从node-exporter ep的实例，替换成ep所在node的ip
          - source_labels: [__meta_kubernetes_pod_host_ip]
            regex: '(.*)'
            replacement: '${1}'
            target_label: instance

2.部署prometheus工作主程序，注意挂载上面的configmap：  
prometheus.deploy.yml:

apiVersion: apps/v1beta2
    kind: Deployment
    metadata:
      labels:
        name: prometheus-deployment
      name: prometheus
      namespace: kube-system
    spec:
      replicas: 1
      selector:
        matchLabels:
          app: prometheus
      template:
        metadata:
          labels:
            app: prometheus
        spec:
          containers:
          - image: prom/prometheus:v2.0.0
            name: prometheus
            command:
            - "/bin/prometheus"
            args:
            - "--config.file=/etc/prometheus/prometheus.yml"
            - "--storage.tsdb.path=/prometheus"
            - "--storage.tsdb.retention=24h"
            ports:
            - containerPort: 9090
              protocol: TCP
            volumeMounts:
            - mountPath: "/prometheus"
              name: data
            - mountPath: "/etc/prometheus"
              name: config-volume
            resources:
              requests:
                cpu: 100m
                memory: 100Mi
              limits:
                cpu: 500m
                memory: 2500Mi
          serviceAccountName: prometheus    
          volumes:
          - name: data
            emptyDir: { }
          - name: config-volume
            configMap:
              name: prometheus-config

3.部署svc、ingress、rbac授权。  
注意：在本地是使用traefik做对外服务代理的，因此修改了默认的NodePort的svc.type为ClusterIP的方式，添加ingress后，可以以域名方式直接访问。若不做代理，可以无需部署ingress，svc.type使用默认的NodePort，然后通过node ip+port的形式访问。Ingress如何使用，请参考此前的文章：[使用traefik做ingress controller][traefik_ingress controller]  
prometheus.svc.yaml:

kind: Service
    apiVersion: v1
    metadata:
      labels:
        app: prometheus
      name: prometheus
      namespace: kube-system
    spec:
      type: ClusterIP
      ports:
      - port: 80
        protocol: TCP
        targetPort: 9090
      selector:
        app: prometheus

prometheus.ing.yaml：

apiVersion: extensions/v1beta1
    kind: Ingress
    metadata:
      name: prometheus
      namespace: kube-system
      selfLink: /apis/extensions/v1beta1/namespaces/default/ingresses/prometheus
    spec:
      rules:
      - host: prometheusv19.abc.com
        http:
          paths:
          - backend:
              serviceName: prometheus
              servicePort: 80
            path: /

rbac-setup.yaml：

apiVersion: rbac.authorization.k8s.io/v1
    kind: ClusterRole
    metadata:
      name: prometheus
    rules:
    - apiGroups: [""]
      resources:
      - nodes
      - nodes/proxy
      - services
      - endpoints
      - pods
      verbs: ["get", "list", "watch"]
    - apiGroups:
      - extensions
      resources:
      - ingresses
      verbs: ["get", "list", "watch"]
    - nonResourceURLs: ["/metrics"]
      verbs: ["get"]
    ---
    apiVersion: v1
    kind: ServiceAccount
    metadata:
      name: prometheus
      namespace: kube-system
    ---
    apiVersion: rbac.authorization.k8s.io/v1
    kind: ClusterRoleBinding
    metadata:
      name: prometheus
    roleRef:
      apiGroup: rbac.authorization.k8s.io
      kind: ClusterRole
      name: prometheus
    subjects:
    - kind: ServiceAccount
      name: prometheus
      namespace: kube-system

依次部署上方几个yaml文件，待初始化完成后，配置好dns记录，即可打开浏览器访问：  
![这里写图片描述][70 1]  
随便选取一个metric，点击execute，查看是否能正常获取结果输出。点击status—target，可以看到metrics的数据来源，即各exporter，点击相应exporter上的链接可查看这个exporter提供的metrics明细。  
![这里写图片描述][70 2]

为了更好的展示图形效果，需要部署grafana，因此前已经部署有grafana，这里不再部署，贴一个all-in-one.yaml部署文件。  
grafana-all-in-one.yaml:

apiVersion: extensions/v1beta1
    kind: Deployment
    metadata:
      name: grafana-core
      namespace: kube-system
      labels:
        app: grafana
        component: core
    spec:
      replicas: 1
      template:
        metadata:
          labels:
            app: grafana
            component: core
        spec:
          containers:
          - image: grafana/grafana:4.2.0
            name: grafana-core
            imagePullPolicy: IfNotPresent
            # env:
            resources:
              # keep request = limit to keep this container in guaranteed class
              limits:
                cpu: 100m
                memory: 100Mi
              requests:
                cpu: 100m
                memory: 100Mi
            env:
              # The following env variables set up basic auth twith the default admin user and admin password.
              - name: GF_AUTH_BASIC_ENABLED
                value: "true"
              - name: GF_AUTH_ANONYMOUS_ENABLED
                value: "false"
              # - name: GF_AUTH_ANONYMOUS_ORG_ROLE
              # value: Admin
              # does not really work, because of template variables in exported dashboards:
              # - name: GF_DASHBOARDS_JSON_ENABLED
              # value: "true"
            readinessProbe:
              httpGet:
                path: /login
                port: 3000
              # initialDelaySeconds: 30
              # timeoutSeconds: 1
            volumeMounts:
            - name: grafana-persistent-storage
              mountPath: /var
          volumes:
          - name: grafana-persistent-storage
            emptyDir: { }
    
    ---
    apiVersion: v1
    kind: Service
    metadata:
      name: grafana
      namespace: kube-system
      labels:
        app: grafana
        component: core
    spec:
      type: NodePort
      ports:
        - port: 3000
      selector:
        app: grafana
        component: core

**访问grafana，添加prometheus数据源：**  
默认管理账号密码为admin admin  
![这里写图片描述][70 3]  
选择资源类型，填入prometheus的服务地址及端口号，点击保存  
![这里写图片描述][70 4]

**导入展示模板：**  
点击dashboard，点击import dashboard，在弹出框内填写数字315，会自动加载官方提供的315号模板，然后选择数据源为刚添加的数据源，模板就创建好了，非常easy。  
![这里写图片描述][70 5]  
![这里写图片描述][70 6]  
![这里写图片描述][70 7]  
![这里写图片描述][70 8]

基本部署到这里就结束了，下篇介绍一下prometheus的告警相关规则。

===========================================================================================

## 7.19更新： ##

最近发现，采用daemon-set方式部署的node-exporterc采集到的度量值不准确，最后发现需要将host的/proc和/sys目录挂载进node-exporter的容器内。**(已解决，更新后的node-exporter.yaml文件）**：

apiVersion: extensions/v1beta1
    kind: DaemonSet
    metadata:
      labels:
        k8s-app: prometheus-node-exporter
      name: prometheus-node-exporter
      namespace: kube-system
    spec:
      selector:
        matchLabels:
          k8s-app: prometheus-node-exporter
      template:
        metadata:
          creationTimestamp: null
          labels:
            k8s-app: prometheus-node-exporter
        spec:
          containers:
          - args:
            - -collector.procfs
            - /host/proc
            - -collector.sysfs
            - /host/sys
            - -collector.filesystem.ignored-mount-points
            - ^/(proc|sys|host|etc|dev)($|/)
            - -collector.filesystem.ignored-fs-types
            - ^(tmpfs|cgroup|configfs|debugfs|devpts|efivarfs|nsfs|overlay|sysfs|proc)$
            image: prom/node-exporter:v0.14.0
            imagePullPolicy: IfNotPresent
            name: node-exporter
            ports:
            - containerPort: 9100
              hostPort: 9101
              name: http
              protocol: TCP
            resources: { }
            terminationMessagePath: /dev/termination-log
            terminationMessagePolicy: File
            volumeMounts:
            - mountPath: /host/proc
              name: proc
            - mountPath: /host/sys
              name: sys
            - mountPath: /rootfs
              name: root
          dnsPolicy: ClusterFirst
          restartPolicy: Always
          schedulerName: default-scheduler
          securityContext: { }
          terminationGracePeriodSeconds: 30
          volumes:
          - hostPath:
              path: /proc
              type: ""
            name: proc
          - hostPath:
              path: /sys
              type: ""
            name: sys
          - hostPath:
              path: /
              type: ""
            name: root
      templateGeneration: 17
      updateStrategy:
        type: OnDelete
    
    ---
    apiVersion: v1
    kind: Service
    metadata:
      annotations:
        prometheus.io/scrape: 'true'
        prometheus.io/app-metrics: 'true'
        prometheus.io/app-metrics-path: '/metrics'
      name: prometheus-node-exporter
      namespace: kube-system
      labels:
        app: prometheus-node-exporter
    spec:
      clusterIP: None
      ports:
        - name: prometheus-node-exporter
          port: 9100
          protocol: TCP
      selector:
        k8s-app: prometheus-node-exporter
      type: ClusterIP

但是发现，部署完成之后，采集到的node指标依然不准确，非常奇怪，尝试脱离k8s使用docker方式直接部署，结果采集到的node数值就很准确了，有点不明白原因，后续继续排查一下。  
**(11-12更新，数据采集不准问题已解决，是因为通过service代理后，采集到的数据是后端随机的ep，而非是你想要的指定主机上的ep，因此，prometheus端的服务发现，改发现的资源类型为endpoint，而不经过endpoint)**

========================================================  
**采集问题已解决，如下docker运行方式仅作参考，不要再使用，直接按上面的yaml文件部署即可。**

docker运行命令：

docker run -d \
      -p 9100:9100 \
      --name node-exporter \
      -v "/proc:/host/proc" \
      -v "/sys:/host/sys" \
      -v "/:/rootfs" \
      --net="host" \
      prom/node-exporter:v0.14.0 \
        -collector.procfs /host/proc \
        -collector.sysfs /host/sys \
        -collector.filesystem.ignored-mount-points "^/(sys|proc|dev|host|etc)($|/)"

最后，记得修改configmap内的job相关targets配置。

为什么依附于k8s集群内采集的node指标就不准确，这个问题后续得好好研究，这次先到这里。

--------------------

### 11.12 补充 ###

上面的node-exporter采集数据不准确的问题找到了，感谢下面评论区中的朋友 @架势糖007，指出node-exporter以service形式访问，会导致访问service时，按LB算法随机请求到某一个后端的ep pod上去，而非到达真正想要去的指定pod。这突然让我才想起，此前数据采集计算出来不准，就是因为采集到的大概率可能是来自其他node上的数据。因此，对上面的prometheus configMap文件，以及下方的exporter部署yaml文件作了一些修改，采集对象从service改为endpoint，绕过代理层，直接访问endpoint层，经过改正后，检查node数据不准的问题得到了解决。

[70]: /images/20220521/202ae61a4d024e7b991d1848ea0cee36.png
[traefik_ingress controller]: https://blog.csdn.net/ywq935/article/details/79886793
[70 1]: /images/20220521/a7b043003757440a8bf6e5123ebc1df6.png
[70 2]: /images/20220521/626029b0bae146ebb5d2e0f3519a11d7.png
[70 3]: /images/20220521/66e4b91f7f3b47599df4e952597efba1.png
[70 4]: /images/20220521/9a3f8bcc65c24f29bbd8ca78795ae1f4.png
[70 5]: /images/20220521/4c9b704b48c7481f8d8de3508d047e6b.png
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