prometheus redis集群 prometheus监控集群部署

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：

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 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：

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。

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

选择资源类型，填入prometheus的服务地址及端口号，点击保存

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。

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。