使用Prometheus+Alertmanager告警JVM异常情况

摘要：，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。，负责将告警通知给相关人员。配置的告警触发规则使用超过最大上限的机时间超过秒分钟分钟时间在最近分钟里超过配置连接，配置。

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。

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。

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。

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。

参考使用Prometheus+Grafana监控JVM

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: "username@company.com"
smtp_auth_username: "username@company.com"
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)：

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。