K8S集群部署kube-Prometheus监控tomcat jvm

K8S集群部署kube-Prometheus监控tomcat jvm 一、背景描述

kube-Prometheus部署在K8S集群中！
kube-prometheus部署参考：K8S集群二进制部署之Prometheus监控告警
现在需要实现kube-prometheus 监控 tomcat jvm
tomcat 部署在 172.16.1.15 服务器上
jdk版本：java version "1.8.0_121
tomcat 版本：Apache Tomcat/8.5.59

二、tomcat环境配置

注：tomcat 安装目录 /usr/local/tomcat
所以以下的操作都是在 /usr/local/tomcat/bin 目录进行

①、下载jmx_exporter

[root@jumpserver01 bin]# pwd
/usr/local/tomcat/bin
[root@jumpserver01 bin]# wget https://repo1.maven.org/maven2/io/prometheus/jmx/jmx_prometheus_javaagent/0.16.0/jmx_prometheus_javaagent-0.16.0.jar

②、配置jmx_exporter

其实配置的时候，可以很简单的写成下面的格式如下，

#vim /usr/local/prometheus/jmx-exporter.yaml
---
rules:
 - pattern: '.*'

不过，个人觉得上面这种配置，可以快速的上手，正式使用的时候，不推荐这样，因为这样会导致prometheus收集的指标太多了，对于存储和网络都会有些许的影响，特别是手机的主机特别多的时候。比较推荐官方给的配置，如下：

#官方推荐配置实例：https://github.com/prometheus/jmx_exporter/blob/master/example_configs/tomcat.yml
#将文件下载下来放到下面文件中/usr/local/tomcat/bin/jmx-exporter.yaml

#cat /usr/local/tomcat/bin/jmx-exporter.yaml
---   
lowercaseOutputLabelNames: true
lowercaseOutputName: true
rules:
- pattern: 'Catalina<>(w+):'
  name: tomcat_$3_total
  labels:
    port: "$2"
    protocol: "$1"
  help: Tomcat global $3
  type: COUNTER
- pattern: 'Catalina<>(requestCount|maxTime|processingTime|errorCount):'
  name: tomcat_servlet_$3_total
  labels:
    module: "$1"
    servlet: "$2"
  help: Tomcat servlet $3 total
  type: COUNTER
- pattern: 'Catalina<>(currentThreadCount|currentThreadsBusy|keepAliveCount|pollerThreadCount|connectionCount):'
  name: tomcat_threadpool_$3
  labels:
    port: "$2"
    protocol: "$1"
  help: Tomcat threadpool $3
  type: GAUGE
- pattern: 'Catalina<>(processingTime|sessionCounter|rejectedSessions|expiredSessions):'
  name: tomcat_session_$3_total
  labels:
    context: "$2"
    host: "$1"
  help: Tomcat session $3 total
  type: COUNTER

③、配置tomcat

添加这一行配置

[root@jumpserver01 bin]# cat setenv.sh 
JAVA_OPTS=" -javaagent:/usr/local/tomcat/bin/jmx_prometheus_javaagent-0.16.0.jar=9901:/usr/local/tomcat/bin/jmx-exporter.yaml"

如果是jar包的话添加

java -javaagent:/opt/apps/prometheus/jmx_prometheus_javaagent-0.16.0.jar=9901:/opt/apps/prometheus/jmx-exporter.yaml -jar yourJar.jar

9901为jvm暴露监控数据的接口
启动tomcat 访问接口查看

三、prometheus新增targets配置

注：因为监控的tomcat 是K8S集群外部署的，所以配置方式参考：K8S集群部署kube-Prometheus监控Ceph（版本octopus）集群、并实现告警。

①、添加tomcat的 prometheus-servicemonitor

[root@k8s01 prometheus-tomcat]# cat prometheus-serviceMonitortomcat.yaml 
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: tomcat-k8s
  namespace: monitoring
  labels:
    app: tomcat
spec:
  jobLabel: k8s-app
  endpoints:
  - port: port
    interval: 15s
  selector:
    matchLabels:
      app: tomcat
  namespaceSelector:
    matchNames:
    - kube-system

②、kube-prometheus 添加 tomcat service和 endpoint ,把tomcat外部服务映射到k8s集群

[root@k8s01 prometheus-tomcat]# cat prometheus-tomcatService.yaml 
apiVersion: v1
kind: Endpoints
metadata:
  name: tomcat-k8s
  namespace: kube-system
  labels:
    app: tomcat
subsets:
- addresses:
  - ip: 172.16.1.15
  ports:
  - name: port
    port: 9901
    protocol: TCP

---
apiVersion: v1
kind: Service
metadata:
  name: tomcat-k8s
  namespace: kube-system
  labels:
    app: tomcat
spec:
  type: ClusterIP
  clusterIP: None
  ports:
  - name: port
    port: 9901
    protocol: TCP

应用yaml 文件

[root@k8s01 prometheus-tomcat]# kubectl apply -f prometheus-serviceMonitortomcat.yaml 
servicemonitor.monitoring.coreos.com/tomcat-k8s configured
[root@k8s01 prometheus-tomcat]# kubectl apply -f prometheus-tomcatService.yaml 
endpoints/tomcat-k8s created
service/tomcat-k8s created

③、查看prometheus

四、granfa展示

grafana的dashboards编号是8563，添加到grafana中即可。之后，就完成了tomcat的监控，如下图。

图

参考：https://bbs.huaweicloud.com/blogs/166271

五、jvm告警

vim /kube-prometheus/manifests/prometheus-rules.yaml

    - name: tomcat-jvm监控
      rules:
    # GC次数太多
    - alert: GcCountTooMuch
      expr: increase(jvm_gc_collection_seconds_count[1m]) > 30
      for: 1m
      labels:
        severity: red
      annotations:
        summary: "{{ $labels.app }} 1分钟GC次数>30次"
        message: "ns:{{ $labels.namespace }} pod:{{ $labels.pod }} 1分钟GC次数>30次，当前值({{ $value }})"
    - alert: GcTimeTooMuch
      expr: increase(jvm_gc_collection_seconds_sum[5m]) > 30
      for: 5m
      labels:
        severity: red
      annotations:
        summary: "{{ $labels.app }} GC时间占比超过10%"
        message: "ns:{{ $labels.namespace }} pod:{{ $labels.pod }} GC时间占比超过10%，当前值({{ $value }}%)"
    # FGC次数太多
    - alert: FgcCountTooMuch
      expr: increase(jvm_gc_collection_seconds_count{gc="ConcurrentMarkSweep"}[1h]) > 3
      for: 1m
      labels:
        severity: red
      annotations:
        summary: "{{ $labels.app }} 1小时的FGC次数>3次"
        message: "ns:{{ $labels.namespace }} pod:{{ $labels.pod }} 1小时的FGC次数>3次，当前值({{ $value }})"
    # 非堆内存使用超过80%
    - alert: NonheapUsageTooMuch
      expr: jvm_memory_bytes_used{job="jmx-metrics", area="nonheap"} / jvm_memory_bytes_max * 100 > 80
      for: 1m
      labels:
        severity: red
      annotations:
        summary: "{{ $labels.app }} 非堆内存使用>80%"
        message: "ns:{{ $labels.namespace }} pod:{{ $labels.pod }} 非堆内存使用率>80%，当前值({{ $value }}%)"
    # 内存使用预警
    - alert: HeighMemUsage
      expr: process_resident_memory_bytes{job="jmx-metrics"} / os_total_physical_memory_bytes * 100 > 85
      for: 1m
      labels:
        severity: red
      annotations:
        summary: "{{ $labels.app }} rss内存使用率大于85%"
        message: "ns:{{ $labels.namespace }} pod:{{ $labels.pod }} rss内存使用率大于85%，当前值({{ $value }}%)"