Grafana Prometheus 通过JMX监控kafka 【2023最新方式】

第三方kafka exporter方案

目前网上关于使用Prometheus 监控kafka的大部分资料都是使用一个第三方的

kafka exporter，他的原理大概就是启动一个kafka客户端，获取kafka服务器的信息，然后提供一些metric接口供Prometheus使用，随意它能展示的监控信息比较有限，只有每个主题的分区数，每秒/分钟消息数，消费组的lag数。但是kafka本身的JMX有提供500+的监控信息可以进行监控，当然不是说这这么监控指标都很重要，相比kafka exporter直接使用JMX可监控的选项会更多。

Prometheus官方方案

Prometheus官方提供的jmx_exporter可以将JMX转换为Prometheus Metrics格式。

Prometheus JMX exporter使用方式选择

jmx_exporter提供两种用法：

• 一种是启动独立的进程。JVM 启动时指定参数，暴露 JMX 的 RMI 接口，JMX_Exporter 调用 RMI 获取 JVM 运行时状态数据，转换为 Prometheus metrics 格式，并暴露端口让 Prometheus 采集。
• 一种是JVM进程内启动，通过java agent的形式运行，进程内读取 JVM 运行时状态数据，转换为 Prometheus metrics 格式，并暴露端口让 Prometheus 采集。官方比较推荐使用这种方式。

  使用JMX exporter监控kafka

  在kafka-server-start.sh最上面添加下面的代码：

  export KAFKA_OPTS="-javaagent:/opt/kafka_2.11-1.1.0/bin/jmx_prometheus_javaagent-0.19.0.jar=9990:/opt/kafka_2.11-1.1.0/bin/kafka-jmx.yml"
  

  jmx_exporter官网下载最新的jmx_prometheus_javaagent-0.19.0.jar包。

  kafka-jmx.yml

  lowercaseOutputName: true
  rules:
  # Special cases and very specific rules
  - pattern : kafka.server<>Value
    name: kafka_server_$1_$2
    type: GAUGE
    labels:
      clientId: "$3"
      topic: "$4"
      partition: "$5"
  - pattern : kafka.server<>Value
    name: kafka_server_$1_$2
    type: GAUGE
    labels:
      clientId: "$3"
      broker: "$4:$5"
  - pattern : kafka.coordinator.(\w+)<>Value
    name: kafka_coordinator_$1_$2_$3
    type: GAUGE
  # Generic per-second counters with 0-2 key/value pairs
  - pattern: kafka.(\w+)<>Count
    name: kafka_$1_$2_$3_total
    type: COUNTER
    labels:
      "$4": "$5"
      "$6": "$7"
  - pattern: kafka.(\w+)<>Count
    name: kafka_$1_$2_$3_total
    type: COUNTER
    labels:
      "$4": "$5"
  - pattern: kafka.(\w+)<>Count
    name: kafka_$1_$2_$3_total
    type: COUNTER
  - pattern: kafka.server<>([a-z-]+)
    name: kafka_server_quota_$3
    type: GAUGE
    labels:
      resource: "$1"
      clientId: "$2"
  - pattern: kafka.server<>([a-z-]+)
    name: kafka_server_quota_$4
    type: GAUGE
    labels:
      resource: "$1"
      user: "$2"
      clientId: "$3"
  # Generic gauges with 0-2 key/value pairs
  - pattern: kafka.(\w+)<>Value
    name: kafka_$1_$2_$3
    type: GAUGE
    labels:
      "$4": "$5"
      "$6": "$7"
  - pattern: kafka.(\w+)<>Value
    name: kafka_$1_$2_$3
    type: GAUGE
    labels:
      "$4": "$5"
  - pattern: kafka.(\w+)<>Value
    name: kafka_$1_$2_$3
    type: GAUGE
  # Emulate Prometheus 'Summary' metrics for the exported 'Histogram's.
  #
  # Note that these are missing the '_sum' metric!
  - pattern: kafka.(\w+)<>Count
    name: kafka_$1_$2_$3_count
    type: COUNTER
    labels:
      "$4": "$5"
      "$6": "$7"
  - pattern: kafka.(\w+)<>(\d+)thPercentile
    name: kafka_$1_$2_$3
    type: GAUGE
    labels:
      "$4": "$5"
      "$6": "$7"
      quantile: "0.$8"
  - pattern: kafka.(\w+)<>Count
    name: kafka_$1_$2_$3_count
    type: COUNTER
    labels:
      "$4": "$5"
  - pattern: kafka.(\w+)<>(\d+)thPercentile
    name: kafka_$1_$2_$3
    type: GAUGE
    labels:
      "$4": "$5"
      quantile: "0.$6"
  - pattern: kafka.(\w+)<>Count
    name: kafka_$1_$2_$3_count
    type: COUNTER
  - pattern: kafka.(\w+)<>(\d+)thPercentile
    name: kafka_$1_$2_$3
    type: GAUGE
    labels:
      quantile: "0.$4"	
  

  配置好kafka-server-start.sh后还需要重启kafka。

  Prometheus配置

  在Prometheus的prometheus.yml添加如下内容。注意端口号为KAFKA_OPTS配置的端口。

  - job_name: "kafka_jmx"
      metrics_path: /metrics
      static_configs:
        - targets: ['192.168.249.1:9990','192.168.249.2:9990','192.168.249.3:9990']
  

  配置完成后重新加载Prometheus配置文件就可以了。

  grafana 配置

  通过上面配置后，可以在grafan中找到对应的面板直接来用。

  https://grafana.com/grafana/dashboards/18276-kafka-dashboard/
  

  效果