docker 安装 prometheus、grafana 和 alertmanager

最新推荐文章于 2025-05-20 14:21:20 发布

asdfsaas

最新推荐文章于 2025-05-20 14:21:20 发布

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文章标签：运维

本文链接：https://blog.youkuaiyun.com/asdfsaas/article/details/129624016

本文介绍了如何使用Docker来安装和配置Prometheus、Grafana和Alertmanager，以实现全面的系统监控。首先，详细讲述了Prometheus的安装步骤和配置文件设置；接着，介绍了Grafana的安装过程，用于可视化监控数据；最后，讨论了Alertmanager的安装，并提及了配置文件的重要性。

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安装 prometheus

docker run \
        --name=prometheus \
        --volume=/data/app/prometheus/config/:/etc/prometheus/ \
        --volume=/etc/localtime:/etc/localtime \
        --volume=/data/app/prometheus/data:/prometheus \
        --network=host \
        --expose=9090/tcp \
        --restart=always \
        prom/prometheus \
        --config.file=/etc/prometheus/prometheus.yml --web.enable-lifecycle

配置文件(prometheus)

#my global config
global:
  scrape_interval:     15s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
  evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute.
  # scrape_timeout is set to the global default (10s).
# Alertmanager configuration
alerting:
  alertmanagers:
  - static_configs:
    - targets:
       - XXX:9093
rule_files:
  - /etc/prometheus/rules.yml
scrape_configs:
  - job_name: 'XXX'
    scrape_interval: 10s
    file_sd_configs:
      - refresh_interval: 1m
        files: ["/etc/prometheus/json/*.json"]

rules配置文件(sample)

groups:
- name: test
  rules:
  - alert: InstanceDown
    expr: up == 0
    for: 5s
    labels:
      severity: fire
      alertName: Instance
    annotations:
      summary: "Target：{{ $labels.instance }} Down"
      description: "{{ $labels.instance }} has been down for more than 5s"

  - alert: CPUUsage 90%
    expr: 100 - (avg by (instance,hostName,hostIp)(irate(node_cpu_seconds_total{mode="idle"}[1m]) )) * 100 > 90
    for: 1m
    labels:
      severity: fire
      alertName: CPU
    annotations:
      summary: "Target：{{ $labels.instance }} CPU使用率超过90%"
      description: "{{ $labels.instance }} CPU使用率超过90% 已经持续1m"

  - alert: CPUUsage 85%
    expr: 100 - (avg by (instance,hostName,hostIp)(irate(node_cpu_seconds_total{mode="idle"}[1m]) )) * 100 > 85
    for: 1m
    labels:
      severity: critical
      alertName: CPU
    annotations:
      summary: "Target：{{ $labels.instance }} CPU使用率超过85%"
      description: "{{ $labels.instance }} CPU使用率超过85% 已经持续1m"

  - alert: CPUUsage 70%
    expr: 100 - (avg by (instance,hostName,hostIp)(irate(node_cpu_seconds_total{mode="idle"}[1m]) )) * 100 > 70
    for: 5m
    labels:
      severity: warning
      alertName: CPU
    annotations:
      summary: "Target：{{ $labels.instance }} CPU使用率超过70%"
      description: "{{ $labels.instance }} CPU使用率超过70% 已经持续5m"

  - alert: MEMUsage 90%
    expr: (node_memory_MemTotal_bytes - (node_memory_MemFree_bytes+node_memory_Buffers_bytes+node_memory_Cached_bytes )) / node_memory_MemTotal_bytes * 100 > 90
    for: 1m
    labels:
      severity: fire
      alertName: MEM
    annotations:
      summary: "Target：{{ $labels.instance }} 内存使用率超过90%"
      description: "{{ $labels.instance }} 内存使用率超过90% 已经持续1m"

  - alert: MEMUsage 80%
    expr: (node_memory_MemTotal_bytes - (node_memory_MemFree_bytes+node_memory_Buffers_bytes+node_memory_Cached_bytes )) / node_memory_MemTotal_bytes * 100 > 80
    for: 1m
    labels:
      severity: critical
      alertName: MEM
    annotations:
      summary: "Target：{{ $labels.instance }} 内存使用率超过80%"
      description: "{{ $labels.instance }} 内存使用率超过80% 已经持续1m"

  - alert: MEMUsage 70%
    expr: (node_memory_MemTotal_bytes - (node_memory_MemFree_bytes+node_memory_Buffers_bytes+node_memory_Cached_bytes )) / node_memory_MemTotal_bytes * 100 > 70
    for: 5m
    labels:
      severity: warning
      alertName: MEM
    annotations:
      summary: "Target：{{ $labels.instance }} 内存使用率超过70%"
      description: "{{ $labels.instance }} 内存使用率超过70% 已经持续5m"

  - alert: DISKUsage 90%
    expr: (node_filesystem_size_bytes - node_filesystem_avail_bytes) / node_filesystem_size_bytes * 100 > 90
    for: 1m
    labels:
      severity: fire
      alertName: DISK
    annotations:
      summary: "Target：{{ $labels.instance }} 磁盘使用率超过90%"
      description: "{{ $labels.instance }} 磁盘使用率超过90% 已经持续1m"

  - alert: DISKUsage 85%
    expr: (node_filesystem_size_bytes - node_filesystem_avail_bytes) / node_filesystem_size_bytes * 100 > 80
    for: 1m
    labels:
      severity: critical
      alertName: DISK
    annotations:
      summary: "Target：{{ $labels.instance }} 磁盘使用率超过80%"
      description: "{{ $labels.instance }} 磁盘使用率超过80% 已经持续1m"

  - alert: DISKUsage 70%
    expr: (node_filesystem_size_bytes - node_filesystem_avail_bytes) / node_filesystem_size_bytes * 100 > 70
    for: 5m
    labels:
      severity: warning
      alertName: DISK
    annotations:
      summary: "Target：{{ $labels.instance }} 磁盘使用率超过70%"
      description: "{{ $labels.instance }} 磁盘使用率超过70% 已经持续5m"

安装 grafana

dockerrun \
        --name=grafana \
        -p 3000:3000 \
        --restart=always \
        grafana/grafana

安装 alertmanager

docker run \
        --name=alertmanager \
        --volume=/data/app/alertmanager/conf/alertmanager.yml:/etc/alertmanager/alertmanager.yml \
        --network=host \
        --restart=no \
        prom/alertmanager

配置文件

global:
  resolve_timeout: 30m

route:
  group_by: ['instance']
  group_wait: 1sgroup_interval: 1srepeat_interval: 5m
  receiver: "webhook"receivers:
- name: "webhook"webhook_configs:
  - url: 'http://xxx/xx/xx/'send_resolved: true
inhibit_rules:
  - source_match:
      severity: 'fire'target_match:
      severity: 'critical'equal: ['alertName', 'instance']
  - source_match:
      severity: 'critical'target_match:
      severity: 'warning'equal: ['alertName','alertName']