Prometheus + Grafana (2) mysql、redis、Docker容器、服务端点以及预警

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接着上一节 《Prometheus + Grafana (1) 监控 》,我们继续探讨 Prometheus + Grafana 的复杂应用

实现目标

这节我们的目标是搭建一个多维度监控微服务的可视化平台,包括Docker容器监控、MySQL监控、Redis监控和微服务JVM监控等,并且在必要的情况下可以发送预警邮件。

主要用到的组件有Prometheus、Grafana、alertmanager、node_exporter、mysql_exporter、redis_exporter、cadvisor。各自作用如下所示:

  1. Prometheus:获取、存储监控数据,供第三方查询;
  2. Grafana:提供Web页面,从Prometheus获取监控数据可视化展示;
  3. alertmanager:定义预警规则,发送预警信息;
  4. node_exporter:收集微服务端点监控数据(与Prometheus一套);
  5. mysql_exporter:收集MySQL数据库监控数据;
  6. redis_exporter:收集Redis监控数据;
  7. cadvisor:收集Docker容器监控数据。

使用docker安装 Grafana、Prometheus及监控服务

上一节我们是直接使用的Windows下的安装软件安装Grafana和Prometheus,但是在我们的日常生产=环境中多是用的Linux,所以我们选择了方便的docker进行安装部署。

  • 在自己的挂载目录下创建 prometheus.yml
#创建Prometheus挂载目录
mkdir -p /dimples/volumes/prometheus

#在该目录下创建Prometheus配置文件
vim /dimples/volumes/prometheus/prometheus.yml
# 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:
      # - alertmanager:9093

# Load rules once and periodically evaluate them according to the global 'evaluation_interval'.
rule_files:
  # - "first_rules.yml"
  # - "second_rules.yml"

# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs:
  # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
  - job_name: 'prometheus'

    # metrics_path defaults to '/metrics'
    # scheme defaults to 'http'.

    static_configs:
    - targets: ['localhost:9090']
  • 在自己的挂载目录下创建 alertmanager.yml
global:
  smtp_smarthost: 'smtp.qq.com:465'
  smtp_from: '1126834403@qq.com'
  smtp_auth_username: '1126834403@qq.com'
  # qq邮箱获取的授权码
  smtp_auth_password: 'xxxxxxxxxxxxxxxxx'
  smtp_require_tls: false

#templates:
#  - '/alertmanager/template/*.tmpl'

route:
  group_by: ['alertname']
  group_wait: 10s
  group_interval: 5m
  repeat_interval: 5m
  receiver: 'default-receiver'

receivers:
  - name: 'default-receiver'
    email_configs:
      - to: '2119713895@qq.com'
        send_resolved: true
  • 创建创建 docker-compose.yml 文件
version: '3'

services:
  prometheus:
    image: prom/prometheus
    container_name: prometheus
    volumes:
      - /dimples/volumes/prometheus/:/etc/prometheus/
    ports:
      - 9090:9090
    restart: on-failure
    command: 
      - '--web.enable-lifecycle '
  grafana:
    image: grafana/grafana
    container_name: grafana
    ports:
      - 3000:3000
  node_exporter:
    image: prom/node-exporter
    container_name: node_exporter
    ports:
      - 9100:9100
  redis_exporter:
    image: oliver006/redis_exporter
    container_name: redis_exporter
    command:
      - "--redis.addr=redis://127.0.0.1:6379"
      - "--redis.password 'ZHONG9602.class'"    # 认证密码,如果没有密码,该参数不需要
    ports:
      - 9101:9121
    restart: on-failure
  mysql_exporter:
    image: prom/mysqld-exporter
    container_name: mysql_exporter
    environment:
      - DATA_SOURCE_NAME=root:123456@(127.0.0.1:3306)/
    ports:
      - 9102:9104
  cadvisor:
    image: google/cadvisor
    container_name: cadvisor
    volumes:
      - /:/rootfs:ro
      - /var/run:/var/run:rw
      - /sys:/sys:ro
      - /var/lib/docker/:/var/lib/docker:ro
    ports:
      - 9103:8080
  alertmanager:
    image: prom/alertmanager
    container_name: alertmanager
    volumes:
      - /dimples/volumes/alertmanager/alertmanager.yml:/etc/alertmanager/alertmanager.yml
    ports:
      - 9104:9093

使用 docker-compose up -d 启动服务

# 不使用docker-compose安装
docker run -d --name prometheus -p 9090:9090 -v /dimples/volumes/prometheus/:/etc/prometheus/ prom/prometheus --config.file=/etc/prometheus/prometheus.yml --web.enable-lifecycle

docker run -d --name redis_exporter -p 9101:9121 oliver006/redis_exporter --redis.addr redis://127.0.0.1:6379 --redis.password 'ZHONG9602.class'
  • 测试是否监控到数据

http://127.0.0.1:9090/alerts

如上图所示,我们刚刚定义的两个警告规则已经成功加载

接着访问 http://127.0.0.1:9090/targets 观察在Prometheus配置文件里定义的各个job的状态:

可以看的都是监控的UP状态。

还可以点击上面这个页面的各个 Endpoint 的链接,如果页面显示出了收集的数据,则说明各个Endpoint已经成功采集到了数据,以mysql_exporter为例子,访问 http://127.0.0.1:9102/metrics

访问http://127.0.0.1:9104/#/status看看我们在alertmanager.yml配置的规则是否已经生效:

配置Java程序监控

在上面的配置中我们简单的将Prometheus采集的对于自身的数据通过Grafana进行了展示,而我们的核心是通过Prometheus去采集Java应用的数据,这就需要针对前面提到的通过Prometheus的pull模式定时去拉取SpringBoot通过Actuator暴露的Micrometer采集的监控指标

  • 首先需要的做的是完成Java应用的Micrometer集成,访问actuator/prometheus或者/prometheus能够正常的返回Micrometer采集的数据指标(这一步操作在上节中已经很详细的介绍了,此处不再赘述)
  • 进入部署Prometheus的文件目录,打prometheus.yml进行拉取节点的配置,在配置文件的scrape_configs节点添加针对java的配置

修改 prometheus.yml 配置所有监控服务

在上面启动的 prometheus,我们没有配置任何的监控,所以我们要修改 prometheus.yml 文件,使其监控我们想监控的数据源,具体的修改内容如下图所示

global:
  scrape_interval:     15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['127.0.0.1:9090']
  - job_name: 'node_exporter'
    static_configs:
      - targets: ['127.0.0.1:9100']
        labels:
          instance: 'node_exporter'
  - job_name: 'redis_exporter'
    static_configs:
      - targets: ['127.0.0.1:9101']
        labels:
          instance: 'redis_exporter'
  - job_name: 'mysql_exporter'
    static_configs:
      - targets: ['127.0.0.1:9102']
        labels:
          instance: 'mysql_exporter'
  - job_name: 'cadvisor'
    static_configs:
      - targets: ['127.0.0.1:9103']
        labels:
          instance: 'cadvisor'

  - job_name: 'server-demo-actuator'
    metrics_path: '/actuator/prometheus'
    scrape_interval: 5s
    static_configs:
      - targets: ['127.0.0.1:8001']
        labels:
          instance: 'server-demo'
rule_files:
  - 'memory_over.yml'
  - 'server_down.yml'
alerting:
  alertmanagers:
    - static_configs:
        - targets: ["127.0.0.1:9104"]

PS: 每个服务的targets都是一个数组,可以收集多个服务器下的exporter提供的监控数据。

接着创建上面提到的两个监控规则 memory_over.yml 和 server_down.yml

# 创建 memory_over.yml
vim /dimples/volumes/prometheus/memory_over.yml

内容如下:

groups:
  - name: server_down
    rules:
      - alert: InstanceDown
        expr: up == 0
        for: 20s
        labels:
          user: Dimples
        annotations:
          summary: "Instance {{ $labels.instance }} down"
          description: "{{ $labels.instance }} of job {{ $labels.job }} has been down for more than 20 s."

当某个节点的内存使用率大于80%,并且持续时间大于20秒后,触发监控预警。

接着创建 server_down.yml:

# server_down.yml
vim /dimples/volumes/prometheus/server_down.yml

内容如下:

groups:
  - name: server_down
    rules:
      - alert: InstanceDown
        expr: up == 0
        for: 20s
        labels:
          user: Dimples
        annotations:
          summary: "Instance {{ $labels.instance }} down"
          description: "{{ $labels.instance }} of job {{ $labels.job }} has been down for more than 20 s."

当某个节点宕机(up==0表示宕机,1表示正常运行)超过20秒后,则触发监控。

在 Grafana 中使用

使用浏览器访问 http://127.0.0.1:9090,用户名密码为admin/admin,首次登录需要修改密码。

第一步:首先需要添加数据源,上一节中已经详细介绍过了,此处不再赘述,结果如图:

添加数据源成功后,我们就可以添加监控面板了,同样的,我们可以去Grafana官方市场选择别人配置好的模板:grafana.com/grafana/das…

此处我收集了几个好用的监控模板,已经上传到微云网盘,只需要下载然后导入即可( 链接:share.weiyun.com/XDzICKtf

下面以 MySql 监控为例,演示导入模板:

点击 Upload JSON file 后,选择对应的文件,成功后会自动弹出一下界面,然后点击Import

额外补充

alertmanager 丰富的预警配置

groups:
- name: example #定义规则组
  rules:
  - alert: InstanceDown  #定义报警名称
    expr: up == 0   #Promql语句,触发规则
    for: 1m            # 一分钟
    labels:       #标签定义报警的级别和主机
      name: instance
      severity: Critical
    annotations:  #注解
      summary: " {{ $labels.appname }}" #报警摘要,取报警信息的appname名称
      description: " 服务停止运行 "   #报警信息
      value: "{{ $value }}%"  # 当前报警状态值
- name: Host
  rules:
  - alert: HostMemory Usage
    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:
      name: Memory
      severity: Warning
    annotations:
      summary: " {{ $labels.appname }} "
      description: "宿主机内存使用率超过80%."
      value: "{{ $value }}"
  - alert: HostCPU Usage
    expr: sum(avg without (cpu)(irate(node_cpu_seconds_total{mode!='idle'}[5m]))) by (instance,appname) > 0.65
    for: 1m
    labels:
      name: CPU
      severity: Warning
    annotations:
      summary: " {{ $labels.appname }} "
      description: "宿主机CPU使用率超过65%."
      value: "{{ $value }}"
  - alert: HostLoad 
    expr: node_load5 > 4
    for: 1m
    labels:
      name: Load
      severity: Warning
    annotations:
      summary: "{{ $labels.appname }} "
      description: " 主机负载5分钟超过4."
      value: "{{ $value }}"
  - alert: HostFilesystem Usage
    expr: 1-(node_filesystem_free_bytes / node_filesystem_size_bytes) >  0.8
    for: 1m
    labels:
      name: Disk
      severity: Warning
    annotations:
      summary: " {{ $labels.appname }} "
      description: " 宿主机 [ {{ $labels.mountpoint }} ]分区使用超过80%."
      value: "{{ $value }}%"
  - alert: HostDiskio
    expr: irate(node_disk_writes_completed_total{job=~"Host"}[1m]) > 10
    for: 1m
    labels:
      name: Diskio
      severity: Warning
    annotations:
      summary: " {{ $labels.appname }} "
      description: " 宿主机 [{{ $labels.device }}]磁盘1分钟平均写入IO负载较高."
      value: "{{ $value }}iops"
  - alert: Network_receive
    expr: irate(node_network_receive_bytes_total{device!~"lo|bond[0-9]|cbr[0-9]|veth.*|virbr.*|ovs-system"}[5m]) / 1048576  > 3 
    for: 1m
    labels:
      name: Network_receive
      severity: Warning
    annotations:
      summary: " {{ $labels.appname }} "
      description: " 宿主机 [{{ $labels.device }}] 网卡5分钟平均接收流量超过3Mbps."
      value: "{{ $value }}3Mbps"
  - alert: Network_transmit
    expr: irate(node_network_transmit_bytes_total{device!~"lo|bond[0-9]|cbr[0-9]|veth.*|virbr.*|ovs-system"}[5m]) / 1048576  > 3
    for: 1m
    labels:
      name: Network_transmit
      severity: Warning
    annotations:
      summary: " {{ $labels.appname }} "
      description: " 宿主机 [{{ $labels.device }}] 网卡5分钟内平均发送流量超过3Mbps."
      value: "{{ $value }}3Mbps"
- name: Container
  rules:
  - alert: ContainerCPU Usage
    expr: (sum by(name,instance) (rate(container_cpu_usage_seconds_total{image!=""}[5m]))*100) > 60
    for: 1m
    labels:
      name: CPU
      severity: Warning
    annotations:
      summary: "{{ $labels.name }} "
      description: " 容器CPU使用超过60%."
      value: "{{ $value }}%"
  - alert: ContainerMem Usage
#    expr: (container_memory_usage_bytes - container_memory_cache)  / container_spec_memory_limit_bytes   * 100 > 10  
    expr:  container_memory_usage_bytes{name=~".+"}  / 1048576 > 1024
    for: 1m
    labels:
      name: Memory
      severity: Warning
    annotations:
      summary: "{{ $labels.name }} "
      description: " 容器内存使用超过1GB."
      value: "{{ $value }}G"

预警除了使用邮件外,也可以使用企业微信接收,可以参考:songjiayang.gitbooks.io/prometheus/…