Prometheus+Grafana+Alertmanager搭建全方位的監控告警系統
prometheus安裝和配置
prometheus組件介紹
1.Prometheus Server: 用於收集和存儲時間序列數據。node
2.Client Library: 客戶端庫，檢測應用程序代碼，當Prometheus抓取實例的HTTP端點時，客戶端庫會將全部跟蹤的metrics指標的當前狀態發送到prometheus server端。linux
3.Exporters: prometheus支持多種exporter，經過exporter能夠採集metrics數據，而後發送到prometheus server端git
4.Alertmanager: 從 Prometheus server 端接收到 alerts 後，會進行去重，分組，並路由到相應的接收方，發出報警，常見的接收方式有：電子郵件，微信，釘釘, slack等。github
5.Grafana：監控儀表盤web
6.pushgateway: 各個目標主機可上報數據到pushgatewy，而後prometheus server統一從pushgateway拉取數據。docker
prometheus架構圖
從上圖可發現，Prometheus整個生態圈組成主要包括prometheus server，Exporter，pushgateway，alertmanager，grafana，Web ui界面，Prometheus server由三個部分組成，Retrieval，Storage，PromQL 。數據庫
- retrieval負責在活躍的target主機上抓取監控指標數據
- storage主要是把採集到的數據存儲到磁盤中
- promQL是prometheus提供的查詢語言模塊
prometheus工做流程
- Prometheus server可按期從活躍的（up）目標主機上（target）拉取監控指標數據，目標主機的監控數據可經過配置靜態job或者服務發現的方式被prometheus server採集到，這種方式默認的pull方式拉取指標；也可經過pushgateway把採集的數據上報到prometheus server中；還可經過一些組件自帶的exporter採集相應組件的數據；
- Prometheus server把採集到的監控指標數據保存到本地磁盤或者數據庫；
- Prometheus採集的監控指標數據按時間序列存儲，經過配置報警規則，把觸發的報警發送到alertmanager
- Alertmanager經過配置報警接收方，發送報警到郵件，微信或者釘釘等
- Prometheus 自帶的web ui界面提供PromQL查詢語言，可查詢監控數據
- Grafana可接入prometheus數據源，把監控數據以圖形化形式展現出
安裝node-exporter組件
node-exporter是採集機器（物理機、虛擬機、雲主機等）的監控指標數據，可以採集到的指標包括CPU, 內存，磁盤，網絡，文件數等信息。json
實驗環境
一個master節點，一個node節點。c#
在master節點操做
cat >node-export.yaml <<EOF
apiVersion: apps/v1
kind: DaemonSet
metadata:
name: node-exporter
namespace: monitor-sa
labels:
name: node-exporter
spec:
selector:
matchLabels:
name: node-exporter
template:
metadata:
labels:
name: node-exporter
spec:
hostPID: true
hostIPC: true
hostNetwork: true
containers:
- name: node-exporter
image: prom/node-exporter:v0.16.0
ports:
- containerPort: 9100
resources:
requests:
cpu: 0.15
securityContext:
privileged: true
args:
- --path.procfs
- /host/proc
- --path.sysfs
- /host/sys
- --collector.filesystem.ignored-mount-points
- '"^/(sys|proc|dev|host|etc)($|/)"'
volumeMounts:
- name: dev
mountPath: /host/dev
- name: proc
mountPath: /host/proc
- name: sys
mountPath: /host/sys
- name: rootfs
mountPath: /rootfs
tolerations:
- key: "node-role.kubernetes.io/master"
operator: "Exists"
effect: "NoSchedule"
volumes:
- name: proc
hostPath:
path: /proc
- name: dev
hostPath:
path: /dev
- name: sys
hostPath:
path: /sys
- name: rootfs
hostPath:
path: /
EOF
經過node-exporter採集數據
curl http://主機ip:9100/metrics
在k8s集羣中部署promethues
-
建立namespace、sa帳號，在k8s集羣的master節點操做api
kubectl create ns monitor-sa kubectl create serviceaccount monitor -n monitor-sa #把sa帳號monitor經過clusterrolebing綁定到clusterrole上 kubectl create clusterrolebinding moniror-clusterrolebinding -n monitor-sa --clusterrole=cluster-admin --serviceaccount=monitor-sa:monitor
-
建立數據目錄
# 在k8s集羣的任何一個node節點操做，本實驗在node1上操做 mkdir /data chmod 777 /data/
-
安裝prometheus，在master節點操做
#建立一個configmap存儲卷，用來存放prometheus配置信息 #prometheus-cfg.yaml kind: ConfigMap apiVersion: v1 metadata: labels: app: prometheus name: prometheus-config namespace: monitor-sa data: prometheus.yml: | global: scrape_interval: 15s scrape_timeout: 10s evaluation_interval: 1m scrape_configs: - job_name: 'kubernetes-node' kubernetes_sd_configs: - role: node relabel_configs: - source_labels: [__address__] regex: '(.*):10250' replacement: ':9100' target_label: __address__ action: replace - action: labelmap regex: __meta_kubernetes_node_label_(.+) - job_name: 'kubernetes-node-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//proxy/metrics/cadvisor - job_name: 'kubernetes-apiserver' 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-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: : - 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 --- #經過deployment部署prometheus #prometheus-deploy.yaml apiVersion: apps/v1 kind: Deployment metadata: name: prometheus-server namespace: monitor-sa labels: app: prometheus spec: replicas: 1 selector: matchLabels: app: prometheus component: server template: metadata: labels: app: prometheus component: server annotations: prometheus.io/scrape: 'false' spec: nodeName: node1 serviceAccountName: monitor containers: - name: prometheus image: prom/prometheus:v2.2.1 imagePullPolicy: IfNotPresent command: - prometheus - --config.file=/etc/prometheus/prometheus.yml - --storage.tsdb.path=/prometheus - --storage.tsdb.retention=720h ports: - containerPort: 9090 protocol: TCP volumeMounts: - mountPath: /etc/prometheus/prometheus.yml name: prometheus-config subPath: prometheus.yml - mountPath: /prometheus/ name: prometheus-storage-volume volumes: - name: prometheus-config configMap: name: prometheus-config items: - key: prometheus.yml path: prometheus.yml mode: 0644 - name: prometheus-storage-volume hostPath: path: /data type: Directory注意：經過上面命令生成的promtheus-cfg.yaml文件會有一些問題，$1和$2這種變量在文件裏沒有，須要在k8s的master1節點打開promtheus-cfg.yaml文件，手動把$1和$2這種變量寫進文件裏，promtheus-cfg.yaml文件須要手動修改部分以下：
22行的replacement: ':9100'變成replacement: '${1}:9100' 42行的replacement: /api/v1/nodes//proxy/metrics/cadvisor變成 replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor 73行的replacement: 變成replacement: $1:$2給prometheus pod 建立一個service
cat > prometheus-svc.yaml << EOF --- apiVersion: v1 kind: Service metadata: name: prometheus namespace: monitor-sa labels: app: prometheus spec: type: NodePort ports: - port: 9090 targetPort: 9090 protocol: TCP selector: app: prometheus component: server EOF#查看service在物理機映射的端口 kubectl get svc -n monitor-sa #訪問prometheus web ui 界面 http://172.16.9.3:30426/graph #點擊頁面的Status->Targets，可看到以下,說明咱們配置的服務發現能夠正常採集數據
prometheus熱更新
#爲了每次修改配置文件能夠熱加載prometheus，也就是不中止prometheus，就可使配置生效，如修改prometheus-cfg.yaml，想要使配置生效可用以下熱加載命令：
curl -X POST http://10.244.1.125:9090/-/reload#10.244.1.66是prometheus的pod的ip地址
#熱加載速度比較慢，能夠暴力重啓prometheus，如修改上面的prometheus-cfg.yaml文件以後，可執行以下強制刪除：
kubectl delete -f prometheus-cfg.yaml
kubectl delete -f prometheus-deploy.yaml
而後再經過apply更新：
kubectl apply -f prometheus-cfg.yaml
kubectl apply -f prometheus-deploy.yaml
注意：
線上最好熱加載，暴力刪除可能形成監控數據的丟失
Grafana安裝和配置
下載安裝Grafana須要的鏡像
上傳heapster-grafana-amd64_v5_0_4.tar.gz鏡像到k8s的各個master節點和k8s的各個node節點，而後在各個節點手動解壓：
docker load -i heapster-grafana-amd64_v5_0_4.tar.gz
鏡像所在的百度網盤地址以下：
連接：https://pan.baidu.com/s/1TmVGKxde_cEYrbjiETboEA 提取碼：052u
在k8s的master節點建立grafana.yaml
cat >grafana.yaml << EOF
apiVersion: apps/v1
kind: Deployment
metadata:
name: monitoring-grafana
namespace: kube-system
spec:
replicas: 1
selector:
matchLabels:
task: monitoring
k8s-app: grafana
template:
metadata:
labels:
task: monitoring
k8s-app: grafana
spec:
containers:
- name: grafana
image: k8s.gcr.io/heapster-grafana-amd64:v5.0.4
ports:
- containerPort: 3000
protocol: TCP
volumeMounts:
- mountPath: /etc/ssl/certs
name: ca-certificates
readOnly: true
- mountPath: /var
name: grafana-storage
env:
- name: INFLUXDB_HOST
value: monitoring-influxdb
- name: GF_SERVER_HTTP_PORT
value: "3000"
# The following env variables are required to make Grafana accessible via
# the kubernetes api-server proxy. On production clusters, we recommend
# removing these env variables, setup auth for grafana, and expose the grafana
# service using a LoadBalancer or a public IP.
- name: GF_AUTH_BASIC_ENABLED
value: "false"
- name: GF_AUTH_ANONYMOUS_ENABLED
value: "true"
- name: GF_AUTH_ANONYMOUS_ORG_ROLE
value: Admin
- name: GF_SERVER_ROOT_URL
# If you're only using the API Server proxy, set this value instead:
# value: /api/v1/namespaces/kube-system/services/monitoring-grafana/proxy
value: /
volumes:
- name: ca-certificates
hostPath:
path: /etc/ssl/certs
- name: grafana-storage
emptyDir: {}
---
apiVersion: v1
kind: Service
metadata:
labels:
# For use as a Cluster add-on (https://github.com/kubernetes/kubernetes/tree/master/cluster/addons)
# If you are NOT using this as an addon, you should comment out this line.
kubernetes.io/cluster-service: 'true'
kubernetes.io/name: monitoring-grafana
name: monitoring-grafana
namespace: kube-system
spec:
# In a production setup, we recommend accessing Grafana through an external Loadbalancer
# or through a public IP.
# type: LoadBalancer
# You could also use NodePort to expose the service at a randomly-generated port
# type: NodePort
ports:
- port: 80
targetPort: 3000
selector:
k8s-app: grafana
type: NodePort
EOF
經過kubectl get sac -n cube-system看到grafana暴漏的蘇主機端口是32351，咱們能夠訪問k8s集羣的master節點ip:32351便可訪問grafana的web界面
Grafana界面接入prometheus數據源
-
登陸Grafana,172.16.9.3:32351，帳號密碼都是admin
-
配置grafana界面，選擇create your first data source
Name:Prometheus Type:Prometheus HTTP出的URL：http://prometheus.monitor-sa.svc:9090
點擊左下角Save&Test，出現Data source is working，說明prometheus數據源成功的被grafana接入了。
導入監控模板，可在以下連接搜索
https://grafana.com/dashboards?dataSource=prometheus&search=kubernetes
也可直接導入node_exporter.json監控模板，這個能夠把node節點指標顯示出來，node_exporter.json在百度網盤地址以下：連接：https://pan.baidu.com/s/1vF1kAMRbxQkUGPlZt91MWg 提取碼：kyd6
還可直接導入docker_rev1.json，能夠把容器相關的數據展現出來
docker_rev1.json在百度網盤地址以下連接：https://pan.baidu.com/s/17o_nja5N2R-g9g5PkJ3aFA 提取碼：vinv
導入監控模版步驟：點擊左側+號下面的Import，選擇Upload json file,選擇一個本地的json文件便可。
安裝配置kube-state-metrics組件
kube-state-metrics經過監聽API Server生成有關資源對象的狀態指標，好比Deployment、Node、Pod，須要注意的是kube-state-metrics只是簡單的提供一個metrics數據，並不會存儲這些指標數據，因此咱們可使用Prometheus來抓取這些數據而後存儲，主要關注的是業務相關的一些元數據，好比Deployment、Pod、副本狀態等；調度了多少個replicas？如今可用的有幾個？多少個Pod是running/stopped/terminated狀態？Pod重啓了多少次？我有多少job在運行中。
安裝kube-state-metrics組件
-
建立sa，並對sa受權，在master節點操做
cat > kube-state-metrics-rbac.yaml <<EOF --- apiVersion: v1 kind: ServiceAccount metadata: name: kube-state-metrics namespace: kube-system --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: kube-state-metrics rules: - apiGroups: [""] resources: ["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: ClusterRoleBinding metadata: name: kube-state-metrics roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: kube-state-metrics subjects: - kind: ServiceAccount name: kube-state-metrics namespace: kube-system EOF
-
安裝cube-state-metrics組件，在master節點操做
cat > kube-state-metrics-deploy.yaml <<EOF apiVersion: apps/v1 kind: Deployment metadata: name: kube-state-metrics namespace: kube-system spec: replicas: 1 selector: matchLabels: app: kube-state-metrics template: metadata: labels: app: kube-state-metrics spec: serviceAccountName: kube-state-metrics containers: - name: kube-state-metrics # image: gcr.io/google_containers/kube-state-metrics-amd64:v1.3.1 image: quay.io/coreos/kube-state-metrics:v1.9.0 ports: - containerPort: 8080 EOF -
建立service，在master節點操做
cat >kube-state-metrics-svc.yaml <<EOF apiVersion: v1 kind: Service metadata: annotations: prometheus.io/scrape: 'true' name: kube-state-metrics namespace: kube-system labels: app: kube-state-metrics spec: ports: - name: kube-state-metrics port: 8080 protocol: TCP selector: app: kube-state-metrics EOF在Grafana web界面導入kubernetes Cluster和kubernetes cluster monitoring
連接：https://pan.baidu.com/s/1QAMqT8scsXx-lzEPI6MPgA 提取碼：i4yd
安裝和配置Alertmanager-發送報警到qq郵箱
在k8s的master節點建立alertmanager-cm.yaml文件
cat >alertmanager-cm.yaml <<EOF
kind: ConfigMap
apiVersion: v1
metadata:
name: alertmanager
namespace: monitor-sa
data:
alertmanager.yml: |-
global:
resolve_timeout: 1m
smtp_smarthost: 'smtp.163.com:25'
smtp_from: '15011572657@163.com'
smtp_auth_username: '15011572657'
smtp_auth_password: 'BDBPRMLNZGKWRFJP'
smtp_require_tls: false
route:
group_by: [alertname]
group_wait: 10s
group_interval: 10s
repeat_interval: 10m
receiver: default-receiver
receivers:
- name: 'default-receiver'
email_configs:
- to: 'y1486170457@qq.com'
send_resolved: true
EOF
Alertmanager配置文件解釋說明:
smtp_smarthost: 'smtp.163.com:25' #用於發送郵件的郵箱的SMTP服務器地址+端口 smtp_from: '15011572657@163.com' #這是指定從哪一個郵箱發送報警 smtp_auth_username: '15011572657' #這是發送郵箱的認證用戶，不是郵箱名 smtp_auth_password: 'BDBPRMLNZGKWRFJP' #這是發送郵箱的受權碼而不是登陸密碼 email_configs: - to: 'y1486170457@qq.com' #to後面指定發送到哪一個郵箱，我發送到個人qq郵箱，你們須要寫本身的郵箱地址，不該該跟smtp_from的郵箱名字重複
在master節點從新生成prometheus-cfg.yaml文件
kind: ConfigMap
apiVersion: v1
metadata:
labels:
app: prometheus
name: prometheus-config
namespace: monitor-sa
data:
prometheus.yml: |
rule_files:
- /etc/prometheus/rules.yml
alerting:
alertmanagers:
- static_configs:
- targets: ["localhost:9093"]
global:
scrape_interval: 15s
scrape_timeout: 10s
evaluation_interval: 1m
scrape_configs:
- job_name: 'kubernetes-node'
kubernetes_sd_configs:
- role: node
relabel_configs:
- source_labels: [__address__]
regex: '(.*):10250'
replacement: '${1}:9100'
target_label: __address__
action: replace
- action: labelmap
regex: __meta_kubernetes_node_label_(.+)
- job_name: 'kubernetes-node-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-apiserver'
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-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-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:$2
source_labels:
- __address__
- __meta_kubernetes_pod_annotation_prometheus_io_port
target_label: __address__
- action: labelmap
regex: __meta_kubernetes_pod_label_(.+)
- action: replace
source_labels:
- __meta_kubernetes_namespace
target_label: kubernetes_namespace
- action: replace
source_labels:
- __meta_kubernetes_pod_name
target_label: kubernetes_pod_name
- job_name: 'kubernetes-schedule'
scrape_interval: 5s
static_configs:
- targets: ['172.16.9.3:10251']
- job_name: 'kubernetes-controller-manager'
scrape_interval: 5s
static_configs:
- targets: ['172.16.9.3:10252']
- job_name: 'kubernetes-kube-proxy'
scrape_interval: 5s
static_configs:
- targets: ['172.16.9.3:10249','172.16.9.4:10249']
- job_name: 'kubernetes-etcd'
scheme: https
tls_config:
ca_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/ca.crt
cert_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.crt
key_file: /var/run/secrets/kubernetes.io/k8s-certs/etcd/server.key
scrape_interval: 5s
static_configs:
- targets: ['172.16.9.3:2379']
rules.yml: |
groups:
- name: example
rules:
- alert: kube-proxy的cpu使用率大於80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過80%"
- alert: kube-proxy的cpu使用率大於90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-kube-proxy"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$lables.instance}}的{{$labels.job}}組件的cpu使用率超過90%"
- alert: scheduler的cpu使用率大於80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過80%"
- alert: scheduler的cpu使用率大於90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-schedule"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過90%"
- alert: controller-manager的cpu使用率大於80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過80%"
- alert: controller-manager的cpu使用率大於90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-controller-manager"}[1m]) * 100 > 0
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過90%"
- alert: apiserver的cpu使用率大於80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過80%"
- alert: apiserver的cpu使用率大於90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-apiserver"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過90%"
- alert: etcd的cpu使用率大於80%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過80%"
- alert: etcd的cpu使用率大於90%
expr: rate(process_cpu_seconds_total{job=~"kubernetes-etcd"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}組件的cpu使用率超過90%"
- alert: kube-state-metrics的cpu使用率大於80%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}組件的cpu使用率超過80%"
value: "{{ $value }}%"
threshold: "80%"
- alert: kube-state-metrics的cpu使用率大於90%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-state-metrics"}[1m]) * 100 > 0
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}組件的cpu使用率超過90%"
value: "{{ $value }}%"
threshold: "90%"
- alert: coredns的cpu使用率大於80%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 80
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}組件的cpu使用率超過80%"
value: "{{ $value }}%"
threshold: "80%"
- alert: coredns的cpu使用率大於90%
expr: rate(process_cpu_seconds_total{k8s_app=~"kube-dns"}[1m]) * 100 > 90
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.k8s_app}}組件的cpu使用率超過90%"
value: "{{ $value }}%"
threshold: "90%"
- alert: kube-proxy打開句柄數>600
expr: process_open_fds{job=~"kubernetes-kube-proxy"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>600"
value: "{{ $value }}"
- alert: kube-proxy打開句柄數>1000
expr: process_open_fds{job=~"kubernetes-kube-proxy"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>1000"
value: "{{ $value }}"
- alert: kubernetes-schedule打開句柄數>600
expr: process_open_fds{job=~"kubernetes-schedule"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>600"
value: "{{ $value }}"
- alert: kubernetes-schedule打開句柄數>1000
expr: process_open_fds{job=~"kubernetes-schedule"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>1000"
value: "{{ $value }}"
- alert: kubernetes-controller-manager打開句柄數>600
expr: process_open_fds{job=~"kubernetes-controller-manager"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>600"
value: "{{ $value }}"
- alert: kubernetes-controller-manager打開句柄數>1000
expr: process_open_fds{job=~"kubernetes-controller-manager"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>1000"
value: "{{ $value }}"
- alert: kubernetes-apiserver打開句柄數>600
expr: process_open_fds{job=~"kubernetes-apiserver"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>600"
value: "{{ $value }}"
- alert: kubernetes-apiserver打開句柄數>1000
expr: process_open_fds{job=~"kubernetes-apiserver"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>1000"
value: "{{ $value }}"
- alert: kubernetes-etcd打開句柄數>600
expr: process_open_fds{job=~"kubernetes-etcd"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>600"
value: "{{ $value }}"
- alert: kubernetes-etcd打開句柄數>1000
expr: process_open_fds{job=~"kubernetes-etcd"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "{{$labels.instance}}的{{$labels.job}}打開句柄數>1000"
value: "{{ $value }}"
- alert: coredns
expr: process_open_fds{k8s_app=~"kube-dns"} > 600
for: 2s
labels:
severity: warnning
annotations:
description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打開句柄數超過600"
value: "{{ $value }}"
- alert: coredns
expr: process_open_fds{k8s_app=~"kube-dns"} > 1000
for: 2s
labels:
severity: critical
annotations:
description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 打開句柄數超過1000"
value: "{{ $value }}"
- alert: kube-proxy
expr: process_virtual_memory_bytes{job=~"kubernetes-kube-proxy"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "組件{{$labels.job}}({{$labels.instance}}): 使用虛擬內存超過2G"
value: "{{ $value }}"
- alert: scheduler
expr: process_virtual_memory_bytes{job=~"kubernetes-schedule"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "組件{{$labels.job}}({{$labels.instance}}): 使用虛擬內存超過2G"
value: "{{ $value }}"
- alert: kubernetes-controller-manager
expr: process_virtual_memory_bytes{job=~"kubernetes-controller-manager"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "組件{{$labels.job}}({{$labels.instance}}): 使用虛擬內存超過2G"
value: "{{ $value }}"
- alert: kubernetes-apiserver
expr: process_virtual_memory_bytes{job=~"kubernetes-apiserver"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "組件{{$labels.job}}({{$labels.instance}}): 使用虛擬內存超過2G"
value: "{{ $value }}"
- alert: kubernetes-etcd
expr: process_virtual_memory_bytes{job=~"kubernetes-etcd"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "組件{{$labels.job}}({{$labels.instance}}): 使用虛擬內存超過2G"
value: "{{ $value }}"
- alert: kube-dns
expr: process_virtual_memory_bytes{k8s_app=~"kube-dns"} > 2000000000
for: 2s
labels:
severity: warnning
annotations:
description: "插件{{$labels.k8s_app}}({{$labels.instance}}): 使用虛擬內存超過2G"
value: "{{ $value }}"
- alert: HttpRequestsAvg
expr: sum(rate(rest_client_requests_total{job=~"kubernetes-kube-proxy|kubernetes-kubelet|kubernetes-schedule|kubernetes-control-manager|kubernetes-apiservers"}[1m])) > 1000
for: 2s
labels:
team: admin
annotations:
description: "組件{{$labels.job}}({{$labels.instance}}): TPS超過1000"
value: "{{ $value }}"
threshold: "1000"
- alert: Pod_restarts
expr: kube_pod_container_status_restarts_total{namespace=~"kube-system|default|monitor-sa"} > 0
for: 2s
labels:
severity: warnning
annotations:
description: "在{{$labels.namespace}}名稱空間下發現{{$labels.pod}}這個pod下的容器{{$labels.container}}被重啓,這個監控指標是由{{$labels.instance}}採集的"
value: "{{ $value }}"
threshold: "0"
- alert: Pod_waiting
expr: kube_pod_container_status_waiting_reason{namespace=~"kube-system|default"} == 1
for: 2s
labels:
team: admin
annotations:
description: "空間{{$labels.namespace}}({{$labels.instance}}): 發現{{$labels.pod}}下的{{$labels.container}}啓動異常等待中"
value: "{{ $value }}"
threshold: "1"
- alert: Pod_terminated
expr: kube_pod_container_status_terminated_reason{namespace=~"kube-system|default|monitor-sa"} == 1
for: 2s
labels:
team: admin
annotations:
description: "空間{{$labels.namespace}}({{$labels.instance}}): 發現{{$labels.pod}}下的{{$labels.container}}被刪除"
value: "{{ $value }}"
threshold: "1"
- alert: Etcd_leader
expr: etcd_server_has_leader{job="kubernetes-etcd"} == 0
for: 2s
labels:
team: admin
annotations:
description: "組件{{$labels.job}}({{$labels.instance}}): 當前沒有leader"
value: "{{ $value }}"
threshold: "0"
- alert: Etcd_leader_changes
expr: rate(etcd_server_leader_changes_seen_total{job="kubernetes-etcd"}[1m]) > 0
for: 2s
labels:
team: admin
annotations:
description: "組件{{$labels.job}}({{$labels.instance}}): 當前leader已發生改變"
value: "{{ $value }}"
threshold: "0"
- alert: Etcd_failed
expr: rate(etcd_server_proposals_failed_total{job="kubernetes-etcd"}[1m]) > 0
for: 2s
labels:
team: admin
annotations:
description: "組件{{$labels.job}}({{$labels.instance}}): 服務失敗"
value: "{{ $value }}"
threshold: "0"
- alert: Etcd_db_total_size
expr: etcd_debugging_mvcc_db_total_size_in_bytes{job="kubernetes-etcd"} > 10000000000
for: 2s
labels:
team: admin
annotations:
description: "組件{{$labels.job}}({{$labels.instance}})：db空間超過10G"
value: "{{ $value }}"
threshold: "10G"
- alert: Endpoint_ready
expr: kube_endpoint_address_not_ready{namespace=~"kube-system|default"} == 1
for: 2s
labels:
team: admin
annotations:
description: "空間{{$labels.namespace}}({{$labels.instance}}): 發現{{$labels.endpoint}}不可用"
value: "{{ $value }}"
threshold: "1"
- name: 物理節點狀態-監控告警
rules:
- alert: 物理節點cpu使用率
expr: 100-avg(irate(node_cpu_seconds_total{mode="idle"}[5m])) by(instance)*100 > 90
for: 2s
labels:
severity: ccritical
annotations:
summary: "{{ $labels.instance }}cpu使用率太高"
description: "{{ $labels.instance }}的cpu使用率超過90%,當前使用率[{{ $value }}],須要排查處理"
- alert: 物理節點內存使用率
expr: (node_memory_MemTotal_bytes - (node_memory_MemFree_bytes + node_memory_Buffers_bytes + node_memory_Cached_bytes)) / node_memory_MemTotal_bytes * 100 > 90
for: 2s
labels:
severity: critical
annotations:
summary: "{{ $labels.instance }}內存使用率太高"
description: "{{ $labels.instance }}的內存使用率超過90%,當前使用率[{{ $value }}],須要排查處理"
- alert: InstanceDown
expr: up == 0
for: 2s
labels:
severity: critical
annotations:
summary: "{{ $labels.instance }}: 服務器宕機"
description: "{{ $labels.instance }}: 服務器延時超過2分鐘"
- alert: 物理節點磁盤的IO性能
expr: 100-(avg(irate(node_disk_io_time_seconds_total[1m])) by(instance)* 100) < 60
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 流入磁盤IO使用率太高！"
description: "{{$labels.mountpoint }} 流入磁盤IO大於60%(目前使用:{{$value}})"
- alert: 入網流量帶寬
expr: ((sum(rate (node_network_receive_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 流入網絡帶寬太高！"
description: "{{$labels.mountpoint }}流入網絡帶寬持續5分鐘高於100M. RX帶寬使用率{{$value}}"
- alert: 出網流量帶寬
expr: ((sum(rate (node_network_transmit_bytes_total{device!~'tap.*|veth.*|br.*|docker.*|virbr*|lo*'}[5m])) by (instance)) / 100) > 102400
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 流出網絡帶寬太高！"
description: "{{$labels.mountpoint }}流出網絡帶寬持續5分鐘高於100M. RX帶寬使用率{{$value}}"
- alert: TCP會話
expr: node_netstat_Tcp_CurrEstab > 1000
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} TCP_ESTABLISHED太高！"
description: "{{$labels.mountpoint }} TCP_ESTABLISHED大於1000%(目前使用:{{$value}}%)"
- alert: 磁盤容量
expr: 100-(node_filesystem_free_bytes{fstype=~"ext4|xfs"}/node_filesystem_size_bytes {fstype=~"ext4|xfs"}*100) > 80
for: 2s
labels:
severity: critical
annotations:
summary: "{{$labels.mountpoint}} 磁盤分區使用率太高！"
description: "{{$labels.mountpoint }} 磁盤分區使用大於80%(目前使用:{{$value}}%)"
一樣須要手動添加$的變量。
在k8smaster節點從新生成一個prometheus-deploy.yaml文件
cat >prometheus-deploy.yaml <<EOF
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: prometheus-server
namespace: monitor-sa
labels:
app: prometheus
spec:
replicas: 1
selector:
matchLabels:
app: prometheus
component: server
#matchExpressions:
#- {key: app, operator: In, values: [prometheus]}
#- {key: component, operator: In, values: [server]}
template:
metadata:
labels:
app: prometheus
component: server
annotations:
prometheus.io/scrape: 'false'
spec:
nodeName: node1
serviceAccountName: monitor
containers:
- name: prometheus
image: prom/prometheus:v2.2.1
imagePullPolicy: IfNotPresent
command:
- "/bin/prometheus"
args:
- "--config.file=/etc/prometheus/prometheus.yml"
- "--storage.tsdb.path=/prometheus"
- "--storage.tsdb.retention=24h"
- "--web.enable-lifecycle"
ports:
- containerPort: 9090
protocol: TCP
volumeMounts:
- mountPath: /etc/prometheus
name: prometheus-config
- mountPath: /prometheus/
name: prometheus-storage-volume
- name: k8s-certs
mountPath: /var/run/secrets/kubernetes.io/k8s-certs/etcd/
- name: alertmanager
image: prom/alertmanager:v0.14.0
imagePullPolicy: IfNotPresent
args:
- "--config.file=/etc/alertmanager/alertmanager.yml"
- "--log.level=debug"
ports:
- containerPort: 9093
protocol: TCP
name: alertmanager
volumeMounts:
- name: alertmanager-config
mountPath: /etc/alertmanager
- name: alertmanager-storage
mountPath: /alertmanager
- name: localtime
mountPath: /etc/localtime
volumes:
- name: prometheus-config
configMap:
name: prometheus-config
- name: prometheus-storage-volume
hostPath:
path: /data
type: Directory
- name: k8s-certs
secret:
secretName: etcd-certs
- name: alertmanager-config
configMap:
name: alertmanager
- name: alertmanager-storage
hostPath:
path: /data/alertmanager
type: DirectoryOrCreate
- name: localtime
hostPath:
path: /usr/share/zoneinfo/Asia/Shanghai
EOF
生成一個etch-certs，這個在部署prometheus須要
kubectl -n monitor-sa create secret generic etcd-certs --from-file=/etc/kubernetes/pki/etcd/server.key --from-file=/etc/kubernetes/pki/etcd/server.crt --from-file=/etc/kubernetes/pki/etcd/ca.crt
更新yaml文件，查看部署是否成功。
在k8smaster節點上從新生成一個alertmanager-svc.yaml文件
cat >alertmanager-svc.yaml <<EOF
---
apiVersion: v1
kind: Service
metadata:
labels:
name: prometheus
kubernetes.io/cluster-service: 'true'
name: alertmanager
namespace: monitor-sa
spec:
ports:
- name: alertmanager
nodePort: 30066
port: 9093
protocol: TCP
targetPort: 9093
selector:
app: prometheus
sessionAffinity: None
type: NodePort
EOF
#查看service在物理機映射的端口
kubectl get svc -n monitor-sa
訪問prometheus界面，點擊alerts，把controller-manager的cpu使用率大於90%展開，可看到status爲FIRING，表示prometheus已經將告警發給alertmanager，在Alertmanager 中能夠看到有一個 alert。
登陸alertmanager web界面查看
配置alertmanager報警-發送報警到釘釘
-
建立釘釘機器人
打開電腦版釘釘，建立一個羣，建立自定義機器人，按以下步驟建立 https://ding-doc.dingtalk.com/doc#/serverapi2/qf2nxq 我建立的機器人以下： 羣設置-->智能羣助手-->添加機器人-->自定義-->添加 機器人名稱：kube-event 接收羣組：釘釘報警測試 安全設置： 自定義關鍵詞：cluster1 上面配置好以後點擊完成便可，這樣就會建立一個kube-event的報警機器人，建立機器人成功以後怎麼查看webhook，按以下： 點擊智能羣助手，能夠看到剛纔建立的kube-event這個機器人，點擊kube-event，就會進入到kube-event機器人的設置界面 出現以下內容： 機器人名稱：kube-event 接受羣組：釘釘報警測試 消息推送：開啓 webhook：https://oapi.dingtalk.com/robot/send?access_token=9c03ff1f47b1d15a10d852398cafb84f8e81ceeb1ba557eddd8a79e5a5e5548e 安全設置： 自定義關鍵詞：cluster1
-
安裝釘釘的webhook插件，在master節點操做
tar zxvf prometheus-webhook-dingtalk-0.3.0.linux-amd64.tar.gz #壓縮包地址 #連接：https://pan.baidu.com/s/1_HtVZsItq2KsYvOlkIP9DQ #提取碼：d59o cd prometheus-webhook-dingtalk-0.3.0.linux-amd64 #啓動釘釘報警插件 nohup ./prometheus-webhook-dingtalk --web.listen-address="0.0.0.0:8060" --ding.profile="cluster1=https://oapi.dingtalk.com/robot/send?access_token=4372b6419ff1f198a9732dfb9f469f8c7eb7310dec00ede726a7ecd9d235c9b9" & #對原來的文件作備份 cp alertmanager-cm.yaml alertmanager-cm.yaml.bak #從新生成一個新的alertmanager-cm.yaml文件 cat >alertmanager-cm.yaml <<EOF kind: ConfigMap apiVersion: v1 metadata: name: alertmanager namespace: monitor-sa data: alertmanager.yml: |- global: resolve_timeout: 1m smtp_smarthost: 'smtp.163.com:25' smtp_from: '15011572657@163.com' smtp_auth_username: '15011572657' smtp_auth_password: 'BDBPRMLNZGKWRFJP' smtp_require_tls: false route: group_by: [alertname] group_wait: 10s group_interval: 10s repeat_interval: 10m receiver: cluster1 receivers: - name: cluster1 webhook_configs: - url: 'http://192.168.124.16:8060/dingtalk/cluster1/send' send_resolved: true EOF #經過kubectl apply使配置生效 kubectl delete -f alertmanager-cm.yaml kubectl apply -f alertmanager-cm.yaml kubectl delete -f prometheus-cfg.yaml kubectl apply -f prometheus-cfg.yaml kubectl delete -f prometheus-deploy.yaml kubectl apply -f prometheus-deploy.yaml #經過上面步驟，就能夠實現釘釘報警了
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