## 配置邮箱信息 1. 打开`/data/alertmanager/alertmanager.yml`文件，将原配置删除，加入如下内容并重启`alertmanger服务` ~~~yaml global: # 在没有报警的情况下声明为已解决的时间 resolve_timeout: 5m # 配置邮件发送信息 smtp_smarthost: 'smtp.163.com:25' # 邮箱地址 smtp_from: 'youremail@163.com' # 邮箱地址 smtp_auth_username: 'youremail@163.com' # 邮箱授权码，需要自行开启设置，非邮箱密码 smtp_auth_password: 'xxxxxxxx' # 邮箱地址 smtp_hello: 'youremail@163.com' smtp_require_tls: false route: # 接收到告警后到自定义分组 group_by: ["alertname"] # 分组创建后初始化等待时长 group_wait: 10s # 告警信息发送之前的等待时长 group_interval: 30s # 重复报警的间隔时长 repeat_interval: 5m # 默认消息接收 receiver: "email" receivers: # 邮件 - name: 'email' email_configs: - to: 'your email' send_resolved: true ~~~ 2. 在prometheus文件夹根目录增加`alert_rules.yaml`配置文件，内容如下 ~~~yaml groups: - name: alert_rules rules: - alert: CpuUsageAlertWarning expr: sum(avg(irate(node_cpu_seconds_total{mode!='idle'}[5m])) without (cpu)) by (instance) > 0.60 for: 2m labels: level: warning annotations: summary: "Instance {{ $labels.instance }} CPU usage high" description: "{{ $labels.instance }} CPU usage above 60% (current value: {{ $value }})" - alert: CpuUsageAlertSerious #expr: sum(avg(irate(node_cpu_seconds_total{mode!='idle'}[5m])) without (cpu)) by (instance) > 0.85 expr: (100 - (avg by (instance) (irate(node_cpu_seconds_total{job=~".*",mode="idle"}[5m])) * 100)) > 85 for: 3m labels: level: serious annotations: summary: "Instance {{ $labels.instance }} CPU usage high" description: "{{ $labels.instance }} CPU usage above 85% (current value: {{ $value }})" - alert: MemUsageAlertWarning expr: avg by(instance) ((1 - (node_memory_MemFree_bytes + node_memory_Buffers_bytes + node_memory_Cached_bytes) / node_memory_MemTotal_bytes) * 100) > 70 for: 2m labels: level: warning annotations: summary: "Instance {{ $labels.instance }} MEM usage high" description: "{{$labels.instance}}: MEM usage is above 70% (current value is: {{ $value }})" - alert: MemUsageAlertSerious expr: (node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes)/node_memory_MemTotal_bytes > 0.90 for: 3m labels: level: serious annotations: summary: "Instance {{ $labels.instance }} MEM usage high" description: "{{ $labels.instance }} MEM usage above 90% (current value: {{ $value }})" - alert: DiskUsageAlertWarning expr: (1 - node_filesystem_free_bytes{fstype!="rootfs",mountpoint!="",mountpoint!~"/(run|var|sys|dev).*"} / node_filesystem_size_bytes) * 100 > 80 for: 2m labels: level: warning annotations: summary: "Instance {{ $labels.instance }} Disk usage high" description: "{{$labels.instance}}: Disk usage is above 80% (current value is: {{ $value }})" - alert: DiskUsageAlertSerious expr: (1 - node_filesystem_free_bytes{fstype!="rootfs",mountpoint!="",mountpoint!~"/(run|var|sys|dev).*"} / node_filesystem_size_bytes) * 100 > 90 for: 3m labels: level: serious annotations: summary: "Instance {{ $labels.instance }} Disk usage high" description: "{{$labels.instance}}: Disk usage is above 90% (current value is: {{ $value }})" - alert: NodeFileDescriptorUsage expr: avg by (instance) (node_filefd_allocated{} / node_filefd_maximum{}) * 100 > 60 for: 2m labels: level: warning annotations: summary: "Instance {{ $labels.instance }} File Descriptor usage high" description: "{{$labels.instance}}: File Descriptor usage is above 60% (current value is: {{ $value }})" - alert: NodeLoad15 expr: avg by (instance) (node_load15{}) > 80 for: 2m labels: level: warning annotations: summary: "Instance {{ $labels.instance }} Load15 usage high" description: "{{$labels.instance}}: Load15 is above 80 (current value is: {{ $value }})" - alert: NodeAgentStatus expr: avg by (instance) (up{}) == 0 for: 2m labels: level: warning annotations: summary: "{{$labels.instance}}: has been down" description: "{{$labels.instance}}: Node_Exporter Agent is down (current value is: {{ $value }})" - alert: NodeProcsBlocked expr: avg by (instance) (node_procs_blocked{}) > 10 for: 2m labels: level: warning annotations: summary: "Instance {{ $labels.instance }} Process Blocked usage high" description: "{{$labels.instance}}: Node Blocked Procs detected! above 10 (current value is: {{ $value }})" - alert: NetworkTransmitRate #expr: avg by (instance) (floor(irate(node_network_transmit_bytes_total{device="ens192"}[2m]) / 1024 / 1024)) > 50 expr: avg by (instance) (floor(irate(node_network_transmit_bytes_total{}[2m]) / 1024 / 1024 * 8 )) > 40 for: 1m labels: level: warning annotations: summary: "Instance {{ $labels.instance }} Network Transmit Rate usage high" description: "{{$labels.instance}}: Node Transmit Rate (Upload) is above 40Mbps/s (current value is: {{ $value }}Mbps/s)" - alert: NetworkReceiveRate #expr: avg by (instance) (floor(irate(node_network_receive_bytes_total{device="ens192"}[2m]) / 1024 / 1024)) > 50 expr: avg by (instance) (floor(irate(node_network_receive_bytes_total{}[2m]) / 1024 / 1024 * 8 )) > 40 for: 1m labels: level: warning annotations: summary: "Instance {{ $labels.instance }} Network Receive Rate usage high" description: "{{$labels.instance}}: Node Receive Rate (Download) is above 40Mbps/s (current value is: {{ $value }}Mbps/s)" - alert: DiskReadRate expr: avg by (instance) (floor(irate(node_disk_read_bytes_total{}[2m]) / 1024 )) > 200 for: 2m labels: level: warning annotations: summary: "Instance {{ $labels.instance }} Disk Read Rate usage high" description: "{{$labels.instance}}: Node Disk Read Rate is above 200KB/s (current value is: {{ $value }}KB/s)" - alert: DiskWriteRate expr: avg by (instance) (floor(irate(node_disk_written_bytes_total{}[2m]) / 1024 / 1024 )) > 20 for: 2m labels: level: warning annotations: summary: "Instance {{ $labels.instance }} Disk Write Rate usage high" description: "{{$labels.instance}}: Node Disk Write Rate is above 20MB/s (current value is: {{ $value }}MB/s)" ~~~ 3. 修改`prometheys.yaml`,最上方三个节点改为如下配置，执行`curl -XPOST localhost:9090/-/reload`刷新配置 **注意⚠️：若prometheus为docker部署的服务，则需要关闭后重新启动，同时使用 -v 挂载目录才会读取到rules文件** ~~~yaml global: scrape_interval: 15s evaluation_interval: 15s alerting: alertmanagers: - static_configs: # alertmanager服务地址 - targets: ['11x.xx.x.7:9093'] rule_files: - "alert_rules.yml" ~~~ ## 验证配置 1. 打开prometheus服务，可以看到alerts栏出现了很多规则 ![image-20210209145250954](../../../images/image-20210209144437502.png) 2. 此时我们手动关闭一个节点 ~~~shell [root@JD ~]# docker stop mysqld-exporter ~~~ 3. 刷新prometheus，可以看到有一个节点颜色改变，进入了pending状态 ![image-20210209144712173](../../../images/image-20210209144712173.png) 4. 稍等片刻，alertmanager.yaml 配置为等待5m，颜色变为红色，进入了firing状态 ![image-20210209144855162](../../../images/image-20210209144855162.png) 5. 这时候我们可以看到，收到一封邮件，说明告警监控发送成功 ![image-20210209145617055](../../../images/image-20210209145617055.png) ## 后记 * 邮件告警配置相对简单，若大家无法成功，可以到谷歌搜索相关alertmanger的集成帖子，按照帖子更加详细的步骤进行配置 * 邮件告警仅为参考，企业微信告警才是我们推荐的方案，后续我们会讲解一下企业微信的具体配置