摘要：，負責抓取存儲指標信息，并提供查詢功能，本文重點使用它的告警功能。，負責將告警通知給相關人員。配置的告警觸發規則使用超過最大上限的機時間超過秒分鐘分鐘時間在最近分鐘里超過配置連接，配置。

原文地址

在前一篇文章中提到了如何使用Prometheus+Grafana來監控JVM。本文介紹如何使用Prometheus+Alertmanager來對JVM的某些情況作出告警。

本文所提到的腳本可以在這里下載。

用到的工具：

Docker，本文大量使用了Docker來啟動各個應用。

Prometheus，負責抓取/存儲指標信息，并提供查詢功能，本文重點使用它的告警功能。

Grafana，負責數據可視化（本文重點不在于此，只是為了讓讀者能夠直觀地看到異常指標）。

Alertmanager，負責將告警通知給相關人員。

JMX exporter，提供JMX中和JVM相關的metrics。

Tomcat，用來模擬一個Java應用。

先講一下大致步驟：

利用JMX exporter，在Java進程內啟動一個小型的Http server

配置Prometheus抓取那個Http server提供的metrics。

配置Prometheus的告警觸發規則

heap使用超過最大上限的50%、80%、90%

instance down機時間超過30秒、1分鐘、5分鐘

old gc時間在最近5分鐘里超過50%、80%

配置Grafana連接Prometheus，配置Dashboard。

配置Alertmanager的告警通知規則

告警的大致過程如下：

Prometheus根據告警觸發規則查看是否觸發告警，如果是，就將告警信息發送給Alertmanager。

Alertmanager收到告警信息后，決定是否發送通知，如果是，則決定發送給誰。

1) 新建一個目錄，名字叫做prom-jvm-demo。

2) 下載JMX exporter到這個目錄。

3) 新建一個文件simple-config.yml內容如下：

---
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) 運行以下命令啟動3個Tomcat，記得把替換成正確的路徑（這里故意把-Xmx和-Xms設置的很小，以觸發告警條件）：

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) 訪問http://localhost:8080|8081|8082看看Tomcat是否啟動成功。

6) 訪問對應的http://localhost:6060|6061|6062看看JMX exporter提供的metrics。

備注：這里提供的simple-config.yml僅僅提供了JVM的信息，更復雜的配置請參考JMX exporter文檔。

1) 在之前新建目錄prom-jvm-demo，新建一個文件prom-jmx.yml，內容如下：

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

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

# 讀取告警觸發條件規則
rule_files:
  - "/prometheus-config/prom-alert-rules.yml"

2) 新建文件prom-alert-rules.yml，該文件是告警觸發規則：

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

    # down了超過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了超過1分鐘
    - 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了超過5分鐘
    - 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."

    # 堆空間使用超過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 }}%)"

    # 堆空間使用超過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 }}%)"
    
    # 堆空間使用超過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分鐘里，Old GC花費時間超過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分鐘里，Old GC花費時間超過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分鐘里，Old GC花費時間超過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) 啟動Prometheus：

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

4) 訪問http://localhost:9090/alerts應該能看到之前配置的告警規則：

如果沒有看到三個instance，那么等一會兒再試。

參考使用Prometheus+Grafana監控JVM

1) 新建一個文件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: ""

修改里面關于smtp_*的部分和最下面user-a的郵箱地址。

備注：因為國內郵箱幾乎都不支持TLS，而Alertmanager目前又不支持SSL，因此請使用Gmail或其他支持TLS的郵箱來發送告警郵件，見這個issue，這個問題已經修復，下面是阿里云企業郵箱的配置例子：

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，這個是郵件內容模板：

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

Description

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

3） 運行下列命令啟動：

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

4) 訪問http://localhost:9093，看看有沒有收到Prometheus發送過來的告警(如果沒有看到稍等一下)：

等待一會兒（最多5分鐘）看看是否收到郵件。如果沒有收到，檢查配置是否正確，或者docker logs alertmanager看看alertmanager的日志，一般來說都是郵箱配置錯誤導致。