zhangguanzhang's Blog

全手动部署prometheus-operator监控K8S集群以及一些坑

字数统计: 5.9k阅读时长: 28 min
2018/10/12 Share

写这篇文章原因

        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--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

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[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

  • listen-client-urls的httpsurl,我这里是https://172.16.0.2:2379
  • 允许客户端证书信息

    然后使用下面的curl,带上各自证书路径访问https的url执行

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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

    也可以etcd用选项和值--listen-metrics-urls http://interface_IP:port设置成非https的metrics端口可以不用证书即可访问,我们会看到etcd的metrics输出信息如下

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....
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

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$ 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

prom

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
prom-operator

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

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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

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$ 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

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$ 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

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$ 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

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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

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$ 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

相关CRD介绍

这四个CRD作用如下

  • Prometheus: 由 Operator 依据一个自定义资源kind: Prometheus类型中,所描述的内容而部署的 Prometheus Server 集群,可以将这个自定义资源看作是一种特别用来管理Prometheus Server的StatefulSets资源。
  • ServiceMonitor: 一个Kubernetes自定义资源(和kind: Prometheus一样是CRD),该资源描述了Prometheus Server的Target列表,Operator 会监听这个资源的变化来动态的更新Prometheus Server的Scrape targets并让prometheus server去reload配置(prometheus有对应reload的http接口/-/reload)。而该资源主要通过Selector来依据 Labels 选取对应的Service的endpoints,并让 Prometheus Server 通过 Service 进行拉取(拉)指标资料(也就是metrics信息),metrics信息要在http的url输出符合metrics格式的信息,ServiceMonitor也可以定义目标的metrics的url.
  • Alertmanager:Prometheus Operator 不只是提供 Prometheus Server 管理与部署,也包含了 AlertManager,并且一样通过一个 kind: Alertmanager 自定义资源来描述信息,再由 Operator 依据描述内容部署 Alertmanager 集群。
  • PrometheusRule:对于Prometheus而言,在原生的管理方式上,我们需要手动创建Prometheus的告警文件,并且通过在Prometheus配置中声明式的加载。而在Prometheus Operator模式中,告警规则也编程一个通过Kubernetes API 声明式创建的一个资源.告警规则创建成功后,通过在Prometheus中使用想servicemonitor那样用ruleSelector通过label匹配选择需要关联的PrometheusRule即可

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

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$ 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

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$ 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

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$ 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

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$ 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

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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:

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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passwordusername值来源于同ns下的一个名为basic-auth的Secret

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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

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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

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$ 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

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$ 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
target
config

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

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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

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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

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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

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$ 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

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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

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kubectl -n monitoring get all

常见坑的说明和解决方法

坑一

config
        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-controllerkube-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

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$ 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

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$ 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

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$ 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

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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

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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

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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

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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
config

        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)

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$ 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

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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

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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.localgrafana.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。
prometheus
grafana

最后

可以多看看官方写的yaml,里面有更多的字段没介绍可以看yaml来了解

参考文档

https://github.com/coreos/prometheus-operator/tree/master/Documentation
https://github.com/coreos/prometheus-operator/tree/master/contrib/kube-prometheus
https://coreos.com/operators/prometheus/docs/latest/user-guides/getting-started.html

CATALOG
  1. 1. 写这篇文章原因
  2. 2. 速补基础
    1. 2.1. 什么是metrics
    2. 2.2. 为什么需要prometheus-operator
  3. 3. demo部署学习
    1. 3.1. 获取相关文件
    2. 3.2. 创建prometheus-operator的pod
    3. 3.3. 相关CRD介绍
    4. 3.4. 部署kind: Prometheus
    5. 3.5. 部署一组pod及其svc
    6. 3.6. 部署kind: ServiceMonitor
  4. 4. 部署官方的prometheus-operator
    1. 4.1. 分类文件
    2. 4.2. 部署operator
    3. 4.3. 部署整套CRD
    4. 4.4. 常见坑的说明和解决方法
      1. 4.4.1. 坑一
      2. 4.4.2. 解决办法
      3. 4.4.3. 坑二
      4. 4.4.4. 解决办法
    5. 4.5. 访问相关页面
  5. 5. 最后
  6. 6. 参考文档