使用Prometheus+Alertmanager告警JVM异常情况

原文地址

在前一篇文章中提到了如何使用Prometheus+Grafana来监控JVM。本文介绍如何使用Prometheus+Alertmanager来对JVM的某些情况作出告警。

本文所提到的脚本可以在这里下载。

摘要

用到的工具：

Docker，本文大量使用了Docker来启动各个应用。
Prometheus，负责抓取/存储指标信息，并提供查询功能，本文重点使用它的告警功能。
Grafana，负责数据可视化（本文重点不在于此，只是为了让读者能够直观地看到异常指标）。
Alertmanager，负责将告警通知给相关人员。
JMX exporter，提供JMX中和JVM相关的metrics。
Tomcat，用来模拟一个Java应用。

先讲一下大致步骤：

利用JMX exporter，在Java进程内启动一个小型的Http server
配置Prometheus抓取那个Http server提供的metrics。
配置Prometheus的告警触发规则
- heap使用超过最大上限的50%、80%、90%
- instance down机时间超过30秒、1分钟、5分钟
- old gc时间在最近5分钟里超过50%、80%
配置Grafana连接Prometheus，配置Dashboard。
配置Alertmanager的告警通知规则

告警的大致过程如下：

Prometheus根据告警触发规则查看是否触发告警，如果是，就将告警信息发送给Alertmanager。
Alertmanager收到告警信息后，决定是否发送通知，如果是，则决定发送给谁。

第一步：启动几个Java应用

1) 新建一个目录，名字叫做prom-jvm-demo。

2) 下载JMX exporter到这个目录。

3) 新建一个文件simple-config.yml内容如下：

---
lowercaseOutputLabelNames: true
lowercaseOutputName: true
whitelistObjectNames: ["java.lang:type=OperatingSystem"]
rules:
 - pattern: 'java.lang<>((?!process_cpu_time)\w+):'
   name: os_$1
   type: GAUGE
   attrNameSnakeCase: true

4) 运行以下命令启动3个Tomcat，记得把替换成正确的路径（这里故意把-Xmx和-Xms设置的很小，以触发告警条件）：

docker run -d \
  --name tomcat-1 \
  -v :/jmx-exporter \
  -e CATALINA_OPTS="-Xms32m -Xmx32m -javaagent:/jmx-exporter/jmx_prometheus_javaagent-0.3.1.jar=6060:/jmx-exporter/simple-config.yml" \
  -p 6060:6060 \
  -p 8080:8080 \
  tomcat:8.5-alpine

docker run -d \
  --name tomcat-2 \
  -v :/jmx-exporter \
  -e CATALINA_OPTS="-Xms32m -Xmx32m -javaagent:/jmx-exporter/jmx_prometheus_javaagent-0.3.1.jar=6060:/jmx-exporter/simple-config.yml" \
  -p 6061:6060 \
  -p 8081:8080 \
  tomcat:8.5-alpine

docker run -d \
  --name tomcat-3 \
  -v :/jmx-exporter \
  -e CATALINA_OPTS="-Xms32m -Xmx32m -javaagent:/jmx-exporter/jmx_prometheus_javaagent-0.3.1.jar=6060:/jmx-exporter/simple-config.yml" \
  -p 6062:6060 \
  -p 8082:8080 \
  tomcat:8.5-alpine

5) 访问http://localhost:8080|8081|8082看看Tomcat是否启动成功。

6) 访问对应的http://localhost:6060|6061|6062看看JMX exporter提供的metrics。

备注：这里提供的simple-config.yml仅仅提供了JVM的信息，更复杂的配置请参考JMX exporter文档。

第二步：启动Prometheus

1) 在之前新建目录prom-jvm-demo，新建一个文件prom-jmx.yml，内容如下：

scrape_configs:
  - job_name: 'java'
    static_configs:
    - targets:
      - ':6060'
      - ':6061'
      - ':6062'

# alertmanager的地址
alerting:
  alertmanagers:
  - static_configs:
    - targets:
      - ':9093'

# 读取告警触发条件规则
rule_files:
  - '/prometheus-config/prom-alert-rules.yml'

2) 新建文件prom-alert-rules.yml，该文件是告警触发规则：

# severity按严重程度由高到低：red、orange、yello、blue
groups:
  - name: jvm-alerting
    rules:

    # down了超过30秒
    - alert: instance-down
      expr: up == 0
      for: 30s
      labels:
        severity: yellow
      annotations:
        summary: "Instance {{ $labels.instance }} down"
        description: "{{ $labels.instance }} of job {{ $labels.job }} has been down for more than 30 seconds."

    # down了超过1分钟
    - alert: instance-down
      expr: up == 0
      for: 1m
      labels:
        severity: orange
      annotations:
        summary: "Instance {{ $labels.instance }} down"
        description: "{{ $labels.instance }} of job {{ $labels.job }} has been down for more than 1 minutes."

    # down了超过5分钟
    - alert: instance-down
      expr: up == 0
      for: 5m
      labels:
        severity: red
      annotations:
        summary: "Instance {{ $labels.instance }} down"
        description: "{{ $labels.instance }} of job {{ $labels.job }} has been down for more than 5 minutes."

    # 堆空间使用超过50%
    - alert: heap-usage-too-much
      expr: jvm_memory_bytes_used{job="java", area="heap"} / jvm_memory_bytes_max * 100 > 50
      for: 1m
      labels:
        severity: yellow
      annotations:
        summary: "JVM Instance {{ $labels.instance }} memory usage > 50%"
        description: "{{ $labels.instance }} of job {{ $labels.job }} has been in status [heap usage > 50%] for more than 1 minutes. current usage ({{ $value }}%)"

    # 堆空间使用超过80%
    - alert: heap-usage-too-much
      expr: jvm_memory_bytes_used{job="java", area="heap"} / jvm_memory_bytes_max * 100 > 80
      for: 1m
      labels:
        severity: orange
      annotations:
        summary: "JVM Instance {{ $labels.instance }} memory usage > 80%"
        description: "{{ $labels.instance }} of job {{ $labels.job }} has been in status [heap usage > 80%] for more than 1 minutes. current usage ({{ $value }}%)"
    
    # 堆空间使用超过90%
    - alert: heap-usage-too-much
      expr: jvm_memory_bytes_used{job="java", area="heap"} / jvm_memory_bytes_max * 100 > 90
      for: 1m
      labels:
        severity: red
      annotations:
        summary: "JVM Instance {{ $labels.instance }} memory usage > 90%"
        description: "{{ $labels.instance }} of job {{ $labels.job }} has been in status [heap usage > 90%] for more than 1 minutes. current usage ({{ $value }}%)"

    # 在5分钟里，Old GC花费时间超过30%
    - alert: old-gc-time-too-much
      expr: increase(jvm_gc_collection_seconds_sum{gc="PS MarkSweep"}[5m]) > 5 * 60 * 0.3
      for: 5m
      labels:
        severity: yellow
      annotations:
        summary: "JVM Instance {{ $labels.instance }} Old GC time > 30% running time"
        description: "{{ $labels.instance }} of job {{ $labels.job }} has been in status [Old GC time > 30% running time] for more than 5 minutes. current seconds ({{ $value }}%)"

    # 在5分钟里，Old GC花费时间超过50%        
    - alert: old-gc-time-too-much
      expr: increase(jvm_gc_collection_seconds_sum{gc="PS MarkSweep"}[5m]) > 5 * 60 * 0.5
      for: 5m
      labels:
        severity: orange
      annotations:
        summary: "JVM Instance {{ $labels.instance }} Old GC time > 50% running time"
        description: "{{ $labels.instance }} of job {{ $labels.job }} has been in status [Old GC time > 50% running time] for more than 5 minutes. current seconds ({{ $value }}%)"

    # 在5分钟里，Old GC花费时间超过80%
    - alert: old-gc-time-too-much
      expr: increase(jvm_gc_collection_seconds_sum{gc="PS MarkSweep"}[5m]) > 5 * 60 * 0.8
      for: 5m
      labels:
        severity: red
      annotations:
        summary: "JVM Instance {{ $labels.instance }} Old GC time > 80% running time"
        description: "{{ $labels.instance }} of job {{ $labels.job }} has been in status [Old GC time > 80% running time] for more than 5 minutes. current seconds ({{ $value }}%)"

3) 启动Prometheus：

docker run -d \
  --name=prometheus \
  -p 9090:9090 \
  -v :/prometheus-config \
  prom/prometheus --config.file=/prometheus-config/prom-jmx.yml

4) 访问http://localhost:9090/alerts应该能看到之前配置的告警规则：

如果没有看到三个instance，那么等一会儿再试。

第三步：配置Grafana

参考使用Prometheus+Grafana监控JVM

第四步：启动Alertmanager

1) 新建一个文件alertmanager-config.yml：

global:
  smtp_smarthost: ''
  smtp_from: ''
  smtp_auth_username: ''
  smtp_auth_password: ''

# The directory from which notification templates are read.
templates: 
- '/alertmanager-config/*.tmpl'

# The root route on which each incoming alert enters.
route:
  # The labels by which incoming alerts are grouped together. For example,
  # multiple alerts coming in for cluster=A and alertname=LatencyHigh would
  # be batched into a single group.
  group_by: ['alertname', 'instance']

  # When a new group of alerts is created by an incoming alert, wait at
  # least 'group_wait' to send the initial notification.
  # This way ensures that you get multiple alerts for the same group that start
  # firing shortly after another are batched together on the first 
  # notification.
  group_wait: 30s

  # When the first notification was sent, wait 'group_interval' to send a batch
  # of new alerts that started firing for that group.
  group_interval: 5m

  # If an alert has successfully been sent, wait 'repeat_interval' to
  # resend them.
  repeat_interval: 3h 

  # A default receiver
  receiver: "user-a"

# Inhibition rules allow to mute a set of alerts given that another alert is
# firing.
# We use this to mute any warning-level notifications if the same alert is 
# already critical.
inhibit_rules:
- source_match:
    severity: 'red'
  target_match_re:
    severity: ^(blue|yellow|orange)$
  # Apply inhibition if the alertname and instance is the same.
  equal: ['alertname', 'instance']
- source_match:
    severity: 'orange'
  target_match_re:
    severity: ^(blue|yellow)$
  # Apply inhibition if the alertname and instance is the same.
  equal: ['alertname', 'instance']
- source_match:
    severity: 'yellow'
  target_match_re:
    severity: ^(blue)$
  # Apply inhibition if the alertname and instance is the same.
  equal: ['alertname', 'instance']

receivers:
- name: 'user-a'
  email_configs:
  - to: ''

修改里面关于smtp_*的部分和最下面user-a的邮箱地址。

~~备注：因为国内邮箱几乎都不支持TLS，而Alertmanager目前又不支持SSL，因此请使用Gmail或其他支持TLS的邮箱来发送告警邮件，见这个issue~~，这个问题已经修复，下面是阿里云企业邮箱的配置例子：

smtp_smarthost: 'smtp.qiye.aliyun.com:465'
smtp_hello: 'company.com'
smtp_from: '[email protected]'
smtp_auth_username: '[email protected]'
smtp_auth_password: password
smtp_require_tls: false

2) 新建文件alert-template.tmpl，这个是邮件内容模板：

{{ define "email.default.html" }}
Summary
  
{{ .CommonAnnotations.summary }}

Description

{{ .CommonAnnotations.description }}
{{ end}}

3）运行下列命令启动：

docker run -d \
  --name=alertmanager \
  -v :/alertmanager-config \
  -p 9093:9093 \
  prom/alertmanager:master --config.file=/alertmanager-config/alertmanager-config.yml

4) 访问http://localhost:9093，看看有没有收到Prometheus发送过来的告警(如果没有看到稍等一下)：

第五步：等待邮件

等待一会儿（最多5分钟）看看是否收到邮件。如果没有收到，检查配置是否正确，或者docker logs alertmanager看看alertmanager的日志，一般来说都是邮箱配置错误导致。

使用Prometheus+Alertmanager告警JVM异常情况

摘要

第一步：启动几个Java应用

第二步：启动Prometheus

第三步：配置Grafana

第四步：启动Alertmanager

Summary

Description

第五步：等待邮件

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