全手动部署prometheus-operator监控K8S集群以及一些坑

栏目: 数据库 · 发布时间: 5年前

内容简介：所有的监控的agent底层最终都是查询的/proc和/sys里的信息推送(如果错了轻喷),因为收集宿主机信息方面也想用pod跑,会面临到问题常见的zabbix_agent默认读取fs的/proc和/sys,容器跑agent会导致读取的不是宿主机的/proc和/sys而prometheus的

所有的监控的agent底层最终都是查询的/proc和/sys里的信息推送(如果错了轻喷),因为收集宿主机信息方面也想用pod跑,会面临到问题

常见的zabbix_agent默认读取fs的/proc和/sys,容器跑agent会导致读取的不是宿主机的/proc和/sys

而prometheus的 node-exporter 有选项 --path.procfs 和 --path.sysfs 来指定从这俩选项的值的proc和sys读取,容器跑 node-exporter 只需要挂载宿主机的/proc和/sys到容器fs的某个路径挂载属性设置为readonly后用这两个选项指定即可,zabbix4.0看了文档和容器都找不到类似选项应该不支持

虽说上prometheus但是k8s监控这方面,经常看到如下问题:

如何部署
用prometheus的话pod ip会变咋整之类的
我的target怎么是0/0
官方yaml怎么用
operator和传统的prometheus有啥差异
operator相对手动部署的prometheus有啥优秀之处
…..

上面问题里大多都是对prometheus-operator不了解的,也就是说大多不看官方文档的,这里我几个例子加介绍说说怎样部署 prometheus-operator ,和一些常见的坑

另外网上大多是helm部署的以及管理组件是二进制下有几个target是0/0发现不了的解决办法

需要看懂本文要具备一下知识点

svc实现原理和会应用以及svc和endpoint关系
了解prometheus(不是operator的)工作机制
知道什么是metrics(不过有了prometheus-operator似乎不是必须)

速补基础

什么是metrics

前面知识点第一条都考虑到k8s集群监控了想必都会了,第二条因为有operator的存在不太关心底层可能不太急需可以后面去稍微学学,第三条无论etcd还是k8s的管理组件基本都有metrics端口

这里来介绍啥什么是metrics

例如我们要查看etcd的metrics,先查看etcd的运行参数找到相关的值,这里我是所有参数写在一个yml文件里,非yml自行查看systemd文件或者运行参数找到相关参数和值即可

[root@k8s-m1 ~]# ps aux | grep -P '/etc[d] '
root      13531  2.8  0.8 10631072 140788 ?     Ssl   2018 472:58 /usr/local/bin/etcd --config-file=/etc/etcd/etcd.config.yml
[root@k8s-m1 ~]# cat /etc/etcd/etcd.config.yml
...
listen-client-urls: 'https://172.16.0.2:2379'
...
client-transport-security:
  ca-file: '/etc/etcd/ssl/etcd-ca.pem'
  cert-file: '/etc/etcd/ssl/etcd.pem'
  key-file: '/etc/etcd/ssl/etcd-key.pem'
...

我们需要两部分信息

https://172.16.0.2:2379

然后使用下面的curl，带上各自证书路径访问https的url执行

curl --cacert /etc/etcd/ssl/etcd-ca.pem --cert /etc/etcd/ssl/etcd.pem --key /etc/etcd/ssl/etcd-key.pem https://172.16.0.2:2379/metrics

我们会看到etcd的metrics输出信息如下

....
grpc_server_started_total{grpc_method="RoleList",grpc_service="etcdserverpb.Auth",grpc_type="unary"} 0
grpc_server_started_total{grpc_method="RoleRevokePermission",grpc_service="etcdserverpb.Auth",grpc_type="unary"} 0
grpc_server_started_total{grpc_method="Snapshot",grpc_service="etcdserverpb.Maintenance",grpc_type="server_stream"} 0
grpc_server_started_total{grpc_method="Status",grpc_service="etcdserverpb.Maintenance",grpc_type="unary"} 0
grpc_server_started_total{grpc_method="Txn",grpc_service="etcdserverpb.KV",grpc_type="unary"} 259160
grpc_server_started_total{grpc_method="UserAdd",grpc_service="etcdserverpb.Auth",grpc_type="unary"} 0
grpc_server_started_total{grpc_method="UserChangePassword",grpc_service="etcdserverpb.Auth",grpc_type="unary"} 0
grpc_server_started_total{grpc_method="UserDelete",grpc_service="etcdserverpb.Auth",grpc_type="unary"} 0
grpc_server_started_total{grpc_method="UserGet",grpc_service="etcdserverpb.Auth",grpc_type="unary"} 0
grpc_server_started_total{grpc_method="UserGrantRole",grpc_service="etcdserverpb.Auth",grpc_type="unary"} 0
grpc_server_started_total{grpc_method="UserList",grpc_service="etcdserverpb.Auth",grpc_type="unary"} 0
grpc_server_started_total{grpc_method="UserRevokeRole",grpc_service="etcdserverpb.Auth",grpc_type="unary"} 0
grpc_server_started_total{grpc_method="Watch",grpc_service="etcdserverpb.Watch",grpc_type="bidi_stream"} 86
# HELP process_cpu_seconds_total Total user and system CPU time spent in seconds.
# TYPE process_cpu_seconds_total counter
process_cpu_seconds_total 28145.45
# HELP process_max_fds Maximum number of open file descriptors.
# TYPE process_max_fds gauge
process_max_fds 65536
# HELP process_open_fds Number of open file descriptors.
# TYPE process_open_fds gauge
process_open_fds 121
# HELP process_resident_memory_bytes Resident memory size in bytes.
# TYPE process_resident_memory_bytes gauge
process_resident_memory_bytes 1.46509824e+08
# HELP process_start_time_seconds Start time of the process since unix epoch in seconds.
# TYPE process_start_time_seconds gauge
process_start_time_seconds 1.54557786888e+09
# HELP process_virtual_memory_bytes Virtual memory size in bytes.
# TYPE process_virtual_memory_bytes gauge
process_virtual_memory_bytes 1.0886217728e+10

同理kube-apiserver也有metrics信息

$ kubectl get --raw /metrics
...
rest_client_request_latency_seconds_bucket{url="https://[::1]:6443/apis?timeout=32s",verb="GET",le="0.512"} 39423
rest_client_request_latency_seconds_bucket{url="https://[::1]:6443/apis?timeout=32s",verb="GET",le="+Inf"} 39423
rest_client_request_latency_seconds_sum{url="https://[::1]:6443/apis?timeout=32s",verb="GET"} 24.781942557999795
rest_client_request_latency_seconds_count{url="https://[::1]:6443/apis?timeout=32s",verb="GET"} 39423
# HELP rest_client_requests_total Number of HTTP requests, partitioned by status code, method, and host.
# TYPE rest_client_requests_total counter
rest_client_requests_total{code="200",host="[::1]:6443",method="GET"} 2.032031e+06
rest_client_requests_total{code="200",host="[::1]:6443",method="PUT"} 1.106921e+06
rest_client_requests_total{code="201",host="[::1]:6443",method="POST"} 38
rest_client_requests_total{code="401",host="[::1]:6443",method="GET"} 17378
rest_client_requests_total{code="404",host="[::1]:6443",method="GET"} 3.546509e+06
rest_client_requests_total{code="409",host="[::1]:6443",method="POST"} 29
rest_client_requests_total{code="409",host="[::1]:6443",method="PUT"} 20
rest_client_requests_total{code="422",host="[::1]:6443",method="POST"} 1
rest_client_requests_total{code="503",host="[::1]:6443",method="GET"} 5
# HELP ssh_tunnel_open_count Counter of ssh tunnel total open attempts
# TYPE ssh_tunnel_open_count counter
ssh_tunnel_open_count 0
# HELP ssh_tunnel_open_fail_count Counter of ssh tunnel failed open attempts
# TYPE ssh_tunnel_open_fail_count counter
ssh_tunnel_open_fail_count 0

这种就是prometheus的定义的metrics格式规范,缺省是在http(s)的url的/metrics输出

而metrics要么程序定义输出(模块或者自定义开发),要么用官方的各种exporter(node-exporter,mysqld-exporter,memcached_exporter…)采集要监控的信息占用一个web端口然后输出成metrics格式的信息,prometheus server去收集各个target的metrics存储起来(tsdb)

用户可以在prometheus的http页面上用promQL(prometheus的查询语言)或者(grafana数据来源就是用)api去查询一些信息,也可以利用pushgateway去统一采集然后prometheus从pushgateway采集(所以pushgateway类似于zabbix的proxy),prometheus的工作架构如下图

全手动部署prometheus-operator监控K8S集群以及一些坑

为什么需要prometheus-operator

因为是prometheus主动去拉取的,所以在k8s里pod因为调度的原因导致pod的ip会发生变化,人工不可能去维持,自动发现有基于DNS的,但是新增还是有点麻烦

Prometheus-operator的本职就是一组用户自定义的CRD资源以及Controller的实现，Prometheus Operator这个controller有BRAC权限下去负责监听这些自定义资源的变化，并且根据这些资源的定义自动化的完成如Prometheus Server自身以及配置的自动化管理工作

在Kubernetes中我们使用Deployment、DamenSet，StatefulSet来管理应用Workload，使用Service，Ingress来管理应用的访问方式，使用ConfigMap和Secret来管理应用配置。我们在集群中对这些资源的创建，更新，删除的动作都会被转换为事件(Event)，Kubernetes的Controller Manager负责监听这些事件并触发相应的任务来满足用户的期望。这种方式我们成为声明式，用户只需要关心应用程序的最终状态，其它的都通过Kubernetes来帮助我们完成，通过这种方式可以大大简化应用的配置管理复杂度。

而除了这些原生的Resource资源以外，Kubernetes还允许用户添加自己的自定义资源(Custom Resource)。并且通过实现自定义Controller来实现对Kubernetes的扩展,不需要用户去二开k8s也能达到给k8s添加功能和对象

因为svc的负载均衡,所以在K8S里监控metrics基本最小单位都是一个svc背后的pod为target,所以prometheus-operator创建了对应的CRD: kind: ServiceMonitor ,创建的 ServiceMonitor 里声明需要监控选中的svc的label以及metrics的url路径的和namespaces即可

工作架构如下图所示

demo部署学习

获取相关文件

先获取相关文件后面跟着文件来讲,直接用git客户端拉取即可,不过文件大概30多M,没梯子基本拉不下来

git clone https://github.com/coreos/prometheus-operator.git

拉取不下来可以在 katacoda的网页上随便一个课程的机器都有 docker 客户端,可以git clone下来后把文件构建进一个alpine镜像然后推到dockerhub上,再在自己的机器docker run这个镜像的时候docker cp到宿主机上

Prometheus Operator引入的自定义资源包括：

Prometheus
ServiceMonitor
Alertmanager

用户创建了prometheus-operator(也就是上面监听三个CRD的各种事件的controller)后,用户可以利用 kind: Prometheus 这种声明式创建对应的资源

下面我们部署简单的例子学习prometheus-operator

创建prometheus-operator的pod

拉取到文件后我们先创建prometheus-operator

$ cd prometheus-operator
$ kubectl apply -f bundle.yaml
clusterrolebinding.rbac.authorization.k8s.io/prometheus-operator created
clusterrole.rbac.authorization.k8s.io/prometheus-operator created
deployment.apps/prometheus-operator created
serviceaccount/prometheus-operator created

确认pod运行,以及我们可以发现operator的pod在有RBAC下创建了一个APIService

$ kubectl get pod
NAME                                   READY     STATUS    RESTARTS   AGE
prometheus-operator-6db8dbb7dd-djj6s   1/1       Running   0          1m
$ kubectl get APIService | grep monitor
v1.monitoring.coreos.com               2018-10-09T10:49:47Z

查看这个APISerivce

$ kubectl get --raw /apis/monitoring.coreos.com/v1
{
  "kind": "APIResourceList",
  "apiVersion": "v1",
  "groupVersion": "monitoring.coreos.com/v1",
  "resources": [
    {
      "name": "alertmanagers",
      "singularName": "alertmanager",
      "namespaced": true,
      "kind": "Alertmanager",
      "verbs": [
        "delete",
        "deletecollection",
        "get",
        "list",
        "patch",
        "create",
        "update",
        "watch"
      ]
    },
    {
      "name": "prometheuses",
      "singularName": "prometheus",
      "namespaced": true,
      "kind": "Prometheus",
      "verbs": [
        "delete",
        "deletecollection",
        "get",
        "list",
        "patch",
        "create",
        "update",
        "watch"
      ]
    },
    {
      "name": "servicemonitors",
      "singularName": "servicemonitor",
      "namespaced": true,
      "kind": "ServiceMonitor",
      "verbs": [
        "delete",
        "deletecollection",
        "get",
        "list",
        "patch",
        "create",
        "update",
        "watch"
      ]
    },
    {
      "name": "prometheusrules",
      "singularName": "prometheusrule",
      "namespaced": true,
      "kind": "PrometheusRule",
      "verbs": [
        "delete",
        "deletecollection",
        "get",
        "list",
        "patch",
        "create",
        "update",
        "watch"
      ]
    }
  ]
}

这个是因为bundle.yml里有如下的 CLusterRole 和对应的 ClusterRoleBinding 来让prometheus-operator有权限对 monitoring.coreos.com 这个apiGroup里的这些CRD进行所有操作

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: prometheus-operator
rules:
- apiGroups:
  - apiextensions.k8s.io
  resources:
  - customresourcedefinitions
  verbs:
  - '*'
- apiGroups:
  - monitoring.coreos.com
  resources:
  - alertmanagers
  - prometheuses
  - prometheuses/finalizers
  - alertmanagers/finalizers
  - servicemonitors
  - prometheusrules
  verbs:
  - '*'

同时我们查看到pod里的log发现operator也在集群里创建了对应的CRD

$ kubectl logs prometheus-operator-6db8dbb7dd-dkhxc
ts=2018-10-09T11:21:09.389340424Z caller=main.go:165 msg="Starting Prometheus Operator version '0.26.0'."
level=info ts=2018-10-09T11:21:09.491464524Z caller=operator.go:377 component=prometheusoperator msg="connection established" cluster-version=v1.11.3
level=info ts=2018-10-09T11:21:09.492679498Z caller=operator.go:209 component=alertmanageroperator msg="connection established" cluster-version=v1.11.3
level=info ts=2018-10-09T11:21:12.085147219Z caller=operator.go:624 component=alertmanageroperator msg="CRD created" crd=Alertmanager
level=info ts=2018-10-09T11:21:12.085265548Z caller=operator.go:1420 component=prometheusoperator msg="CRD created" crd=Prometheus
level=info ts=2018-10-09T11:21:12.099210714Z caller=operator.go:1420 component=prometheusoperator msg="CRD created" crd=ServiceMonitor
level=info ts=2018-10-09T11:21:12.118721976Z caller=operator.go:1420 component=prometheusoperator msg="CRD created" crd=PrometheusRule
level=info ts=2018-10-09T11:21:15.182780757Z caller=operator.go:225 component=alertmanageroperator msg="CRD API endpoints ready"
level=info ts=2018-10-09T11:21:15.383456425Z caller=operator.go:180 component=alertmanageroperator msg="successfully synced all caches"
$ kubectl get crd
NAME                                    CREATED AT
alertmanagers.monitoring.coreos.com     2018-10-09T11:21:11Z
prometheuses.monitoring.coreos.com      2018-10-09T11:21:11Z
prometheusrules.monitoring.coreos.com   2018-10-09T11:21:12Z
servicemonitors.monitoring.coreos.com   2018-10-09T11:21:12Z

部署kind: Prometheus

现在我们有了prometheus这个CRD,我们部署一个prometheus server只需要如下声明即可

$ cat<<EOF | kubectl apply -f -
apiVersion: v1
kind: ServiceAccount
metadata:
  name: prometheus
---
apiVersion: monitoring.coreos.com/v1
kind: Prometheus
metadata:
  name: prometheus
spec:
  serviceMonitorSelector:
    matchLabels:
      team: frontend
  serviceAccountName: prometheus
  resources:
    requests:
      memory: 400Mi
EOF

因为负载均衡,一个svc下的一组pod是监控的最小单位,要监控一个svc的metrics就声明创建一个 servicemonitors 即可

部署一组pod及其svc

首先,我们部署一个带metrics输出的简单程序的deploy,该镜像里的主进程会在8080端口上输出metrics信息

$ cat<<EOF | kubectl apply -f -
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: example-app
spec:
  replicas: 3
  template:
    metadata:
      labels:
        app: example-app
    spec:
      containers:
      - name: example-app
        image: zhangguanzhang/instrumented_app
        ports:
        - name: web
          containerPort: 8080
EOF

创建对应的svc

$ cat<<EOF | kubectl apply -f -
kind: Service
apiVersion: v1
metadata:
  name: example-app
  labels:
    app: example-app
spec:
  selector:
    app: example-app
  ports:
  - name: web
    port: 8080
EOF

部署kind: ServiceMonitor

现在创建一个 ServiceMonitor 来告诉prometheus server需要监控带有label app: example-app 的svc背后的一组pod的metrics

$ cat<<EOF | kubectl apply -f -
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: example-app
  labels:
    team: frontend
spec:
  selector:
    matchLabels:
      app: example-app
  endpoints:
  - port: web
EOF

默认情况下 ServiceMonitor 和监控对象必须是在相同Namespace下的,如果要关联非同ns下需要下面这样设置值

spec:
  namespaceSelector:
    matchNames:
    - target_ns_name

如果希望ServiceMonitor可以关联任意命名空间下的标签，则通过以下方式定义：

spec:
  namespaceSelector:
    any: true

如果需要监控的Target对象启用了BasicAuth认证，那在定义ServiceMonitor对象时，可以使用endpoints配置中定义basicAuth如下所示basicAuth中的 password 和 username 值来源于同ns下的一个名为 basic-auth 的Secret

spec
  endpoints:
  - basicAuth:
      password:
        name: basic-auth
        key: password
      username:
        name: basic-auth
        key: user
    port: web
---
apiVersion: v1
kind: Secret
metadata:
  name: basic-auth
type: Opaque
data:
  user: dXNlcgo= # base64编码后的用户名
  password: cGFzc3dkCg== # base64编码后的密码

上面要注意的是我创建prometheus server的时候有如下值

serviceMonitorSelector:
  matchLabels:
    team: frontend

该值字面意思可以知道就是指定prometheus server去选择哪些 ServiceMonitor ,这个概念和svc去选择pod一样,可能一个集群跑很多prometheus server来监控各自选中的 ServiceMonitor ,如果想一个prometheus server监控所有的则 spec.serviceMonitorSelector: {} 为空即可,而namespaces的范围同样的设置 spec.serviceMonitorSelector: {} ,后面官方的prometheus实例里我们可以看到设置了这两个值

给prometheus server设置相关的RBAC权限

$ cat<<EOF | kubectl apply -f -
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
  name: prometheus
rules:
- apiGroups: [""]
  resources:
  - nodes
  - 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
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: prometheus
subjects:
- kind: ServiceAccount
  name: prometheus
  namespace: default
EOF

创建svc使用 NodePort 方便我们访问prometheus的web页面,生产环境不建议使用 NodePort

$ cat<<EOF | kubectl apply -f -
apiVersion: v1
kind: Service
metadata:
  name: prometheus
spec:
  type: NodePort
  ports:
  - name: web
    nodePort: 30900
    port: 9090
    protocol: TCP
    targetPort: web
  selector:
    prometheus: prometheus
EOF

打开浏览器访问 ip:30900 进入target发现已经监听起来了,对应的config里也有配置生成和导入

先清理掉上面的,然后我们使用官方提供的全套yaml正式部署 prometheus-operator

kubectl delete svc prometheus example-app
kubectl delete ClusterRoleBinding prometheus 
kubectl delete ClusterRole prometheus
kubectl delete ServiceMonitor example-app
kubectl delete deploy example-app
kubectl delete  sa prometheus
kubectl delete prometheus prometheus
kubectl delete -f bundle.yaml

部署官方的prometheus-operator

分类文件

官方把所有文件都放在一起,这里我分类下

cd contrib/kube-prometheus/manifests/
mkdir -p operator node-exporter alertmanager grafana kube-state-metrics prometheus serviceMonitor adapter
mv *-serviceMonitor* serviceMonitor/
mv 0prometheus-operator* operator/
mv grafana-* grafana/
mv kube-state-metrics-* kube-state-metrics/
mv alertmanager-* alertmanager/
mv node-exporter-* node-exporter/
mv prometheus-adapter* adapter/
mv prometheus-* prometheus/
$ ll
total 40
drwxr-xr-x 9 root root 4096 Jan  6 14:19 ./
drwxr-xr-x 9 root root 4096 Jan  6 14:15 ../
-rw-r--r-- 1 root root   60 Jan  6 14:15 00namespace-namespace.yaml
drwxr-xr-x 3 root root 4096 Jan  6 14:19 adapter/
drwxr-xr-x 3 root root 4096 Jan  6 14:19 alertmanager/
drwxr-xr-x 2 root root 4096 Jan  6 14:17 grafana/
drwxr-xr-x 2 root root 4096 Jan  6 14:17 kube-state-metrics/
drwxr-xr-x 2 root root 4096 Jan  6 14:18 node-exporter/
drwxr-xr-x 2 root root 4096 Jan  6 14:17 operator/
drwxr-xr-x 2 root root 4096 Jan  6 14:19 prometheus/
drwxr-xr-x 2 root root 4096 Jan  6 14:17 serviceMonitor/

部署operator

先创建ns和operator,quay.io仓库拉取慢,可以使用我脚本拉取,其他镜像也可以这样去拉,不过在apply之前才能拉,一旦被docker接手拉取就只能漫长等

kubectl apply -f .
curl -s https://zhangguanzhang.github.io/bash/pull.sh | bash -s -- quay.io/coreos/prometheus-operator:v0.26.0
kubectl apply -f operator/

确认状态运行正常再往后执行,这里镜像是quay.io仓库的可能会很慢耐心等待或者自行修改成能拉取到的

$ kubectl -n monitoring get pod
NAME                                   READY     STATUS    RESTARTS   AGE
prometheus-operator-56954c76b5-qm9ww   1/1       Running   0          24s

部署整套CRD

创建相关的CRD,这里镜像可能也要很久

kubectl apply -f adapter/
kubectl apply -f alertmanager/
kubectl apply -f node-exporter/
kubectl apply -f kube-state-metrics/
kubectl apply -f grafana/
kubectl apply -f prometheus/
kubectl apply -f serviceMonitor/

可以通过get查看整体状态,这里镜像原因会等待很久,我们可以先往后看几个坑的地方

kubectl -n monitoring get all

常见坑的说明和解决方法

坑一

全手动部署prometheus-operator监控K8S集群以及一些坑这里要注意有一个坑,二进制部署k8s管理组件和新版本kubeadm部署的都会发现在prometheus server的页面上发现 kube-controller 和 kube-schedule 的target为0/0也就是上图所示

这是因为serviceMonitor是根据label去选取svc的,我们可以看到对应的 serviceMonitor 是选取的ns范围是 kube-system

$ grep -2 selector serviceMonitor/prometheus-serviceMonitorKube*
serviceMonitor/prometheus-serviceMonitorKubeControllerManager.yaml-    matchNames:
serviceMonitor/prometheus-serviceMonitorKubeControllerManager.yaml-    - kube-system
serviceMonitor/prometheus-serviceMonitorKubeControllerManager.yaml:  selector:
serviceMonitor/prometheus-serviceMonitorKubeControllerManager.yaml-    matchLabels:
serviceMonitor/prometheus-serviceMonitorKubeControllerManager.yaml-      k8s-app: kube-controller-manager
--
serviceMonitor/prometheus-serviceMonitorKubelet.yaml-    matchNames:
serviceMonitor/prometheus-serviceMonitorKubelet.yaml-    - kube-system
serviceMonitor/prometheus-serviceMonitorKubelet.yaml:  selector:
serviceMonitor/prometheus-serviceMonitorKubelet.yaml-    matchLabels:
serviceMonitor/prometheus-serviceMonitorKubelet.yaml-      k8s-app: kubelet
--
serviceMonitor/prometheus-serviceMonitorKubeScheduler.yaml-    matchNames:
serviceMonitor/prometheus-serviceMonitorKubeScheduler.yaml-    - kube-system
serviceMonitor/prometheus-serviceMonitorKubeScheduler.yaml:  selector:
serviceMonitor/prometheus-serviceMonitorKubeScheduler.yaml-    matchLabels:
serviceMonitor/prometheus-serviceMonitorKubeScheduler.yaml-      k8s-app: kube-scheduler

而kube-system里默认只有这俩svc,且没有符合上面的label

$ kubectl -n kube-system get svc
NAME                      TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)         AGE
kube-dns                  ClusterIP   10.96.0.10   <none>        53/UDP,53/TCP   139m
kubelet                   ClusterIP   None         <none>        10250/TCP       103m

但是却有对应的ep(没有带任何label)被创建,这点想不通官方什么鬼操作,另外这里没有kubelet的ep,我博客部署的二进制的话会有

$ kubectl get ep -n kube-system
NAME                      ENDPOINTS                                                AGE
kube-controller-manager   <none>                                                   139m
kube-dns                  10.244.1.2:53,10.244.8.10:53,10.244.1.2:53 + 1 more...   139m
kube-scheduler            <none>                                                   139m

解决办法

所以这里我们创建两个管理组建的svc,名字无所谓,关键是svc的label要能被servicemonitor选中,svc的选择器的label是因为kubeadm的staticPod的label是这样

如果是二进制部署的这俩svc的selector部分不能要

apiVersion: v1
kind: Service
metadata:
  namespace: kube-system
  name: kube-controller-manager
  labels:
    k8s-app: kube-controller-manager
spec:
  selector:
    component: kube-controller-manager
  type: ClusterIP
  clusterIP: None
  ports:
  - name: http-metrics
    port: 10252
    targetPort: 10252
    protocol: TCP
---
apiVersion: v1
kind: Service
metadata:
  namespace: kube-system
  name: kube-scheduler
  labels:
    k8s-app: kube-scheduler
spec:
  selector:
    component: kube-scheduler
  type: ClusterIP
  clusterIP: None
  ports:
  - name: http-metrics
    port: 10251
    targetPort: 10251
    protocol: TCP

二进制的话需要我们手动填入svc对应的ep的属性,我集群是HA的,所有有三个,仅供参考,别傻傻得照抄,另外这个ep的名字得和上面的svc的名字和属性对应上

apiVersion: v1
kind: Endpoints
metadata:
  labels:
    k8s-app: kube-controller-manager
  name: kube-controller-manager
  namespace: kube-system
subsets:
- addresses:
  - ip: 172.16.0.2
  - ip: 172.16.0.7
  - ip: 172.16.0.8
  ports:
  - name: http-metrics
    port: 10252
    protocol: TCP
---
apiVersion: v1
kind: Endpoints
metadata:
  labels:
    k8s-app: kube-scheduler
  name: kube-scheduler
  namespace: kube-system
subsets:
- addresses:
  - ip: 172.16.0.2
  - ip: 172.16.0.7
  - ip: 172.16.0.8
  ports:
  - name: http-metrics
    port: 10251
    protocol: TCP

这里不知道为啥kubeadm部署的没有kubelet这个ep,我博客二进制部署后是会有kubelet这个ep的,下面仅供参考,IP根据实际写

另外kubeadm部署下kubelet的readonly的metrics端口(默认是10255)不会开放可以删掉ep的那部分port

apiVersion: v1
kind: Endpoints
metadata:
  labels:
    k8s-app: kubelet
  name: kubelet
  namespace: kube-system
subsets:
- addresses:
  - ip: 172.16.0.14
    targetRef:
      kind: Node
      name: k8s-n2
  - ip: 172.16.0.18
    targetRef:
      kind: Node
      name: k8s-n3
  - ip: 172.16.0.2
    targetRef:
      kind: Node
      name: k8s-m1
  - ip: 172.16.0.20
    targetRef:
      kind: Node
      name: k8s-n4
  - ip: 172.16.0.21
    targetRef:
      kind: Node
      name: k8s-n5
  ports:
  - name: http-metrics
    port: 10255
    protocol: TCP
  - name: cadvisor
    port: 4194
    protocol: TCP
  - name: https-metrics
    port: 10250
    protocol: TCP

至于prometheus server的服务访问,别再用效率不行的 NodePort 了,上ingress controller吧,怎么部署参照我博客IngressController

apiVersion: extensions/v1beta1
kind: Ingress
metadata:
  name: prometheus-ing
  namespace: monitoring
spec:
  rules:
  - host: prometheus.monitoring.k8s.local
    http:
      paths:
      - backend:
          serviceName: prometheus-k8s
          servicePort: 9090
---
apiVersion: extensions/v1beta1
kind: Ingress
metadata:
  name: grafana-ing
  namespace: monitoring
spec:
  rules:
  - host: grafana.monitoring.k8s.local
    http:
      paths:
      - backend:
          serviceName: grafana
          servicePort: 3000
---
apiVersion: extensions/v1beta1
kind: Ingress
metadata:
  name: alertmanager-ing
  namespace: monitoring
spec:
  rules:
  - host: alertmanager.monitoring.k8s.local
    http:
      paths:
      - backend:
          serviceName: alertmanager-main
          servicePort: 9093

坑二

访问prometheus server的web页面我们发现即使创建了svc和注入对应ep的信息在target页面发现prometheus server请求被拒绝

在宿主机上我们发现127.0.0.1才能访问,网卡ip不能访问(这里是另一个环境找的,所以ip是192不是前面的172)

$ hostname -i
192.168.15.223
$ curl -I http://192.168.15.223:10251/metrics
curl: (7) Failed connect to 192.168.15.223:10251; Connection refused
$ curl -I http://127.0.0.1:10251/metrics
HTTP/1.1 200 OK
Content-Length: 30349
Content-Type: text/plain; version=0.0.4
Date: Mon, 07 Jan 2019 13:33:50 GMT

解决办法

修改管理组件bind的ip

如果使用kubeadm启动的集群，初始化时的config.yml里可以加入如下参数

controllerManagerExtraArgs:
  address: 0.0.0.0
schedulerExtraArgs:
  address: 0.0.0.0

已经启动后的使用下面命令更改就会滚动更新

sed -ri '/--address/s#=.+#=0.0.0.0#' /etc/kubernetes/manifests/kube-*

二进制的话查看是不是bind的0.0.0.0如果不是就修改成0.0.0.0

多块网卡如果只想bind一个网卡就写对应的主机上的网卡ip,写0.0.0.0就会监听所有网卡的对应端口

访问相关页面

通过浏览器查看 prometheus.monitoring.k8s.local 与 grafana.monitoring.k8s.local 是否正常,若沒问题就可以看到下图结果,grafana初始用股名和密码是admin。

以上所述就是小编给大家介绍的《全手动部署prometheus-operator监控K8S集群以及一些坑》，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对码农网的支持！

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码农工具

全手动部署prometheus-operator监控K8S集群以及一些坑

速补基础

什么是metrics

为什么需要prometheus-operator

demo部署学习

获取相关文件

创建prometheus-operator的pod

相关CRD介绍

部署kind: Prometheus

部署一组pod及其svc

部署kind: ServiceMonitor

部署官方的prometheus-operator

分类文件

部署operator

部署整套CRD

常见坑的说明和解决方法

坑一

解决办法

坑二

解决办法

访问相关页面

无处不在的算法

图片转BASE64编码

URL 编码/解码

MD5 加密