kafka监控实战(jmxtrans+InfluxDb+Grafana)

一、需求分析

一直在调研找一款好用的kafka监控，我测试使用过的KafkaOffsetMonitor、Burrow、kafka-monitor、Kafka-Manager，他们各有优缺点，下面会介绍，并且它们基本上都是监控topic的写入和读取等等，没有提供对于整体集群的监控信息，比如集群的分片、延时、内存使用情况等等，无意中发现了jmxtrans，jmxtrans它是一个通过jmx采集java应用的数据采集器，他的输出可以是Graphite、StatsD、Ganglia、InfluxDb等等通过Grafana做展示，下面就给大家介绍一下jmxtrans+InfluxDb+Grafana监控kafka的整体解决方案，并且不需要任何额外的开发工作，完全使用原生的。

其余三种监控程序的优缺点

Kafka Web Console：监控功能较为全面，可以预览消息，监控Offset、Lag等信息，但存在bug，不建议在生产环境中使用

Kafka Manager：偏向Kafka集群管理，若操作不当，容易导致集群出现故障。对Kafka实时生产和消费消息是通过JMX实现的。没有记录Offset、Lag等信息。

KafkaOffsetMonitor：程序一个jar包的形式运行，部署较为方便。只有监控功能，使用起来也较为安全。若只需要监控功能，推荐使用KafkaOffsetMonito，若偏重Kafka集群管理，推荐使用Kafka Manager。因为都是开源程序，稳定性欠缺。故需先了解清楚目前已存在哪些Bug，多测试一下，避免出现类似于Kafka Web Console的问题。

二、环境介绍

1、角色

a、10.10.10.10   InfluxDb
b、10.10.10.100  Grafana
c、10.10.30.69   jmxtrans

d、kafka集群

10.10.20.14  node1
10.10.20.15 node2
10.10.20.16 node3
10.10.20.17 node4

2、软件版本

influxdb-1.2.4-1.x86_64
grafana-4.1.1-1484211277.x86_64
jmxtrans-266.rpm
kafka_2.10-0.9.0.0.jar.asc

3、架构图

三、配置规划

1、jmxtrans我们可以分别在每台kafka节点上部署，也可以部署到一台机器上，我这里是选择了后者，因为我的集群小，这样配置文件可以集中管理，如果集群比较大，可以考虑分散部署。

2、关于jmxtrans的配置文件，分全局指标（每个kafka节点）和topic指标，全局指标每个节点一个配置文件，命名规则：base_10.10.20.14.json，topic指标是每个topic一个配置文件，命名规则：falcon_monitor_us_17.json

四、监控指标

1、全局指标

每秒输入的流量

"obj" : "kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec""attr" : [ "Count" ]"resultAlias":"BytesInPerSec""tags" : {"application" : "BytesInPerSec"}

每秒输入的流量

"obj" : "kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec""attr" : [ "Count" ]"resultAlias":"BytesOutPerSec""tags" : {"application" : "BytesOutPerSec"}

每秒输入的流量

"obj" : "kafka.server:type=BrokerTopicMetrics,name=BytesRejectedPerSec""attr" : [ "Count" ]"resultAlias":"BytesRejectedPerSec""tags" : {"application" : "BytesRejectedPerSec"}

每秒的消息写入总量

"obj" : "kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec""attr" : [ "Count" ]"resultAlias":"MessagesInPerSec""tags" : {"application" : "MessagesInPerSec"}

每秒FetchFollower的请求次数

"obj" : "kafka.network:type=RequestMetrics,name=RequestsPerSec,request=FetchFollower""attr" : [ "Count" ]"resultAlias":"RequestsPerSec""tags" : {"request" : "FetchFollower"}

每秒FetchConsumer的请求次数

"obj" : "kafka.network:type=RequestMetrics,name=RequestsPerSec,request=FetchConsumer""attr" : [ "Count" ]"resultAlias":"RequestsPerSec""tags" : {"request" : "FetchConsumer"}

每秒Produce的请求次数

"obj" : "kafka.network:type=RequestMetrics,name=RequestsPerSec,request=Produce""attr" : [ "Count" ]"resultAlias":"RequestsPerSec""tags" : {"request" : "Produce"}

内存使用的使用情况

"obj" : "java.lang:type=Memory""attr" : [ "HeapMemoryUsage", "NonHeapMemoryUsage" ]"resultAlias":"MemoryUsage""tags" : {"application" : "MemoryUsage"}

GC的耗时和次数

"obj" : "java.lang:type=GarbageCollector,name=*""attr" : [ "CollectionCount","CollectionTime" ]"resultAlias":"GC""tags" : {"application" : "GC"}

线程的使用情况

"obj" : "java.lang:type=Threading""attr" : [ "PeakThreadCount","ThreadCount" ]"resultAlias":"Thread""tags" : {"application" : "Thread"}

副本落后主分片的最大消息数量

"obj" : "kafka.server:type=ReplicaFetcherManager,name=MaxLag,clientId=Replica""attr" : [ "Value" ]"resultAlias":"ReplicaFetcherManager""tags" : {"application" : "MaxLag"}

该broker上的partition的数量

"obj" : "kafka.server:type=ReplicaManager,name=PartitionCount""attr" : [ "Value" ]"resultAlias":"ReplicaManager""tags" : {"application" : "PartitionCount"}

正在做复制的partition的数量

"obj" : "kafka.server:type=ReplicaManager,name=UnderReplicatedPartitions""attr" : [ "Value" ]"resultAlias":"ReplicaManager""tags" : {"application" : "UnderReplicatedPartitions"}

Leader的replica的数量

"obj" : "kafka.server:type=ReplicaManager,name=LeaderCount""attr" : [ "Value" ]"resultAlias":"ReplicaManager""tags" : {"application" : "LeaderCount"}

一个请求FetchConsumer耗费的所有时间

"obj" : "kafka.network:type=RequestMetrics,name=TotalTimeMs,request=FetchConsumer""attr" : [ "Count","Max" ]"resultAlias":"TotalTimeMs""tags" : {"application" : "FetchConsumer"}

一个请求FetchFollower耗费的所有时间

"obj" : "kafka.network:type=RequestMetrics,name=TotalTimeMs,request=FetchFollower""attr" : [ "Count","Max" ]"resultAlias":"TotalTimeMs""tags" : {"application" : "FetchFollower"}

一个请求Produce耗费的所有时间

"obj" : "kafka.network:type=RequestMetrics,name=TotalTimeMs,request=Produce""attr" : [ "Count","Max" ]"resultAlias":"TotalTimeMs""tags" : {"application" : "Produce"}

2、topic的监控指标

falcon_monitor_us每秒的写入流量

"kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec,topic=falcon_monitor_us""attr" : [ "Count" ]"resultAlias":"falcon_monitor_us""tags" : {"application" : "BytesInPerSec"}

falcon_monitor_us每秒的输出流量

"kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec,topic=falcon_monitor_us""attr" : [ "Count" ]"resultAlias":"falcon_monitor_us""tags" : {"application" : "BytesOutPerSec"}

falcon_monitor_us每秒写入消息的数量

"obj" : "kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec,topic=falcon_monitor_us""attr" : [ "Count" ]"resultAlias":"falcon_monitor_us""tags" : {"application" : "MessagesInPerSec"}

falcon_monitor_us在每个分区最后的Offset

"obj" : "kafka.log:type=Log,name=LogEndOffset,topic=falcon_monitor_us,partition=*""attr" : [ "Value" ]"resultAlias":"falcon_monitor_us""tags" : {"application" : "LogEndOffset"}

PS：

1、参数说明

"obj"对应jmx的ObjectName，就是我们要监控的指标

"attr"对应ObjectName的属性，可以理解为我们要监控的指标的值

"resultAlias"对应metric 的名称，在InfluxDb里面就是MEASUREMENTS名

"tags" 对应InfluxDb的tag功能，对与存储在同一个MEASUREMENTS里面的不同监控指标可以做区分，我们在用Grafana绘图的时候会用到，建议对每个监控指标都打上tags

2、对于全局监控，每一个监控指标对应一个MEASUREMENTS，所有的kafka节点同一个监控指标数据写同一个MEASUREMENTS ，对于topc监控的监控指标，同一个topic所有kafka节点写到同一个MEASUREMENTS，并且以topic名称命名

五、安装

1、kafka

这里不详细介绍kafka集群的安装，主要说一下kafka的启动方式，因为我们需要通过jmx采集kafka的监控数据，所以在kafka的启动时候需要启动jmx端口，启动方式如下：

cd /data/kafka/bin/

JMX_PORT=9999 nohup ./kafka-server-start.sh ../config/server.properties >/dev/null 2>&1 &

2、InfluxDb

yum -y install influxdb ##安装

/etc/init.d/influxdb start ##启动服务

[root@ip-10-10-10-10 jmxtrans]# influx

Connected to http://localhost:8086 version 1.3.2

InfluxDB shell version: 1.3.2> CREATE USER "root" WITH PASSWORD '123456' WITH ALL PRIVILEGES ##添加一个账号>

3、Grafana

yum -y install grafana ##安装

/etc/init.d/grafana-server start ##启动服务

4、jmxtrans

wget http://central.maven.org/maven2/org/jmxtrans/jmxtrans/266/jmxtrans-266.rpm

rpm -ivh jmxtrans-266.rpm ##安装

/etc/init.d/jmxtrans start ##启动

六、配置

这里主要介绍jmxtrans采集数据的配置文件撰写和Grafana绘图的配置注意事项，kafka和InfluxDb的配置这里不做描述。

1、jmxtrans

a、jmxtrans默认读取/var/lib/jmxtrans下的配置文件去采集数据的，所以我们把采集kafka监控数据的配置文件都在这个目录下，下面是我的配置文件命名规范：

[root@ip-10-10-30-69 jmxtrans]# ll

total 96

-rw-r--r-- 1 root root 1657 Aug 18 17:03 article-feedback-10min-json_14.json

-rw-r--r-- 1 root root 1657 Aug 18 17:03 article-feedback-10min-json_15.json

-rw-r--r-- 1 root root 1657 Aug 18 17:04 article-feedback-10min-json_16.json

-rw-r--r-- 1 root root 1657 Aug 18 17:04 article-feedback-10min-json_17.json

-rw-r--r-- 1 root root 8430 Aug 22 08:24 base_10.10.20.14.json

-rw-r--r-- 1 root root 8431 Aug 22 08:24 base_10.10.20.15.json

-rw-r--r-- 1 root root 8431 Aug 22 08:25 base_10.10.20.16.json

-rw-r--r-- 1 root root 8431 Aug 22 08:25 base_10.10.20.17.json

-rw-r--r-- 1 root root 2027 Aug 21 16:19 falcon_monitor_us_14.json

-rw-r--r-- 1 root root 2027 Aug 21 16:20 falcon_monitor_us_15.json

-rw-r--r-- 1 root root 2484 Aug 21 20:58 falcon_monitor_us_16.json

-rw-r--r-- 1 root root 2027 Aug 21 16:20 falcon_monitor_us_17.json

-rw-r--r-- 1 root root 2147 Aug 21 17:43 highgmp-articles-through-primary_14.json

-rw-r--r-- 1 root root 2147 Aug 21 17:46 highgmp-articles-through-primary_15.json

-rw-r--r-- 1 root root 2147 Aug 21 17:46 highgmp-articles-through-primary_16.json

-rw-r--r-- 1 root root 2147 Aug 21 17:47 highgmp-articles-through-primary_17.json[root@ip-10-10-30-69 jmxtrans]# pwd

/var/lib/jmxtrans

b、全局监控的配置文件，以10.10.20.14为例：

[root@ip-10-10-30-69 jmxtrans]# cat base_10.10.20.14.json{

"servers" : [ {

"port" : "9999",

"host" : "10.10.20.14",

"queries" : [ {

"obj" : "kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec",

"attr" : [ "Count","OneMinuteRate" ],

"resultAlias":"BytesInPerSec",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "BytesInPerSec"}

} ]

},{

"obj" : "kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec",

"attr" : [ "Count","OneMinuteRate" ],

"resultAlias":"BytesOutPerSec",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "BytesOutPerSec"}

} ]

},{

"obj" : "kafka.server:type=BrokerTopicMetrics,name=BytesRejectedPerSec",

"attr" : [ "Count","OneMinuteRate" ],

"resultAlias":"BytesRejectedPerSec",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "BytesRejectedPerSec"}

} ]

},{

"obj" : "kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec",

"attr" : [ "Count","OneMinuteRate" ],

"resultAlias":"MessagesInPerSec",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "MessagesInPerSec"}

} ]

},{

"obj" : "kafka.network:type=RequestMetrics,name=RequestsPerSec,request=FetchConsumer",

"attr" : [ "Count" ],

"resultAlias":"RequestsPerSec",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"request" : "FetchConsumer"}

} ]

},{

"obj" : "kafka.network:type=RequestMetrics,name=RequestsPerSec,request=FetchFollower",

"attr" : [ "Count" ],

"resultAlias":"RequestsPerSec",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"request" : "FetchFollower"}

} ]

},{

"obj" : "kafka.network:type=RequestMetrics,name=RequestsPerSec,request=Produce",

"attr" : [ "Count" ],

"resultAlias":"RequestsPerSec",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"request" : "Produce"}

} ]

},{

"obj" : "java.lang:type=Memory",

"attr" : [ "HeapMemoryUsage", "NonHeapMemoryUsage" ],

"resultAlias":"MemoryUsage",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "MemoryUsage"}

} ]

},{

"obj" : "java.lang:type=GarbageCollector,name=*",

"attr" : [ "CollectionCount","CollectionTime" ],

"resultAlias":"GC",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "GC"}

} ]

},{

"obj" : "java.lang:type=Threading",

"attr" : [ "PeakThreadCount","ThreadCount" ],

"resultAlias":"Thread",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "Thread"}

} ]

},{

"obj" : "kafka.server:type=ReplicaFetcherManager,name=MaxLag,clientId=Replica",

"attr" : [ "Value" ],

"resultAlias":"ReplicaFetcherManager",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "MaxLag"}

} ]

},{

"obj" : "kafka.server:type=ReplicaManager,name=PartitionCount",

"attr" : [ "Value" ],

"resultAlias":"ReplicaManager",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "PartitionCount"}

} ]

},{

"obj" : "kafka.server:type=ReplicaManager,name=UnderReplicatedPartitions",

"attr" : [ "Value" ],

"resultAlias":"ReplicaManager",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "UnderReplicatedPartitions"}

} ]

},{

"obj" : "kafka.server:type=ReplicaManager,name=LeaderCount",

"attr" : [ "Value" ],

"resultAlias":"ReplicaManager",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "LeaderCount"}

} ]

},{

"obj" : "kafka.network:type=RequestMetrics,name=TotalTimeMs,request=FetchConsumer",

"attr" : [ "Count","Max" ],

"resultAlias":"TotalTimeMs",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "FetchConsumer"}

} ]

},{

"obj" : "kafka.network:type=RequestMetrics,name=TotalTimeMs,request=FetchFollower",

"attr" : [ "Count","Max" ],

"resultAlias":"TotalTimeMs",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "FetchConsumer"}

} ]

},{

"obj" : "kafka.network:type=RequestMetrics,name=TotalTimeMs,request=Produce",

"attr" : [ "Count","Max" ],

"resultAlias":"TotalTimeMs",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "Produce"}

} ]

},{

"obj" : "kafka.server:type=ReplicaManager,name=IsrShrinksPerSec",

"attr" : [ "Count" ],

"resultAlias":"ReplicaManager",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "IsrShrinksPerSec"}

} ]

}]

} ]}

c、topic监控的配置文件，以falcon_monitor_us的10.10.20.14节点为例：

[root@ip-10-10-30-69 jmxtrans]# cat falcon_monitor_us_14.json{

"servers" : [ {

"port" : "9999",

"host" : "10.10.20.14",

"queries" : [ {

"obj" : "kafka.server:type=BrokerTopicMetrics,name=BytesInPerSec,topic=falcon_monitor_us",

"attr" : [ "Count" ],

"resultAlias":"falcon_monitor_us",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "BytesInPerSec"}

} ]

},{

"obj" : "kafka.server:type=BrokerTopicMetrics,name=BytesOutPerSec,topic=falcon_monitor_us",

"attr" : [ "Count" ],

"resultAlias":"falcon_monitor_us",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "BytesOutPerSec"}

} ]

},{

"obj" : "kafka.server:type=BrokerTopicMetrics,name=MessagesInPerSec,topic=falcon_monitor_us",

"attr" : [ "Count" ],

"resultAlias":"falcon_monitor_us",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "MessagesInPerSec"}

} ]

},{

"obj" : "kafka.log:type=Log,name=LogEndOffset,topic=falcon_monitor_us,partition=*",

"attr" : [ "Value" ],

"resultAlias":"falcon_monitor_us",

"outputWriters" : [ {

"@class" : "com.googlecode.jmxtrans.model.output.InfluxDbWriterFactory",

"url" : "http://10.10.10.10:8086/",

"username" : "root",

"password" : "root",

"database" : "jmxDB",

"tags" : {"application" : "LogEndOffset"}

} ]

}]

} ]}

2、Grafana配置

a、添加数据源

Url、Database、User、Password需要和jmxtrans采集数据配置文件里面的写一致，然后点击Save&Test，提示成功就正常了

b、创建一个dashboard，然后在这里配置每一个监控指标的图

c、要点说明

1、对于监控指标为Count的监控项，需要通过Grafana做计算得到我们想要的监控，比如BytesInPerSec这个指标，它的监控值是一个累计值，我们想要取到每秒的流量，肯定需要计算，(本次采集的值-上次采集的值)/60 ,jmxtrans是一分钟采集一次数据，具体配置参考下面截图：

2、X轴的单位选择，比如流量的单位、时间的单位、每秒消息的个数无单位等等，下面分布举一个例子介绍说明因为我们是一分钟采集一次数据，所以group by 和derivative选1分钟；因为我们要每秒的流量，所以math这里除以60

设置流量的单位，点击需要设置的图，选择"Edit"进入编辑页面，切到Axes这个tab页，Unit--》data（Metric）--》bytes

设置时间的单位，点击需要设置的图，选择"Edit"进入编辑页面，切到Axes这个tab页，Unit--》time--》milliseconds(ms)

设置按原始值展示，无单位，点击需要设置的图，选择"Edit"进入编辑页面，切到Axes这个tab页，Unit--》none--》none

七、收获总结

1、关于jmx收集了kafka的那些指标，对应的值都是那些类型，对应这个问题走了很多弯路，各种谷歌百度拿到了有人整理过的，一个一个试，发现很多不能用，要不就是写的是错误的，要不就是版本不同，写法不一样，最后看到了jconsole这个工具，他可以连接到本地或者远程的jmx端口，能看到在收集的所有指标，在windows下装好jdk，在bin目录你可以找到这个工具。

2、关于consumer的延时，关官方介绍有一个type是 type=consumer-fetch-manager-metrics的指标，但是我这通过jconsole连进来死活没有找到，昨天我已经用 pyhon写了一个小脚本，通过crontab 一分钟采集一次，入库到InfluxDB，也可以通过Grafana做展示了。

a、数据采集的脚本

#!/usr/bin/env python
#-*-coding:utf8-*-
from influxdb import InfluxDBClient
from kafka.consumer import SimpleConsumer
from kazoo.client import KazooClient
from kafka.client import SimpleClientimport timeimport datetime#Zookeepers
zookeepers="10.10.20.14"#Kafka
kafka="10.10.20.14:9092"#consumer group
group_list = ["monitor","topic1"]#InfluxDB client
client = InfluxDBClient('10.10.10.10', 8086, 'root', 'root', 'jmxDB', timeout=2, retries=3)#time##now = time.strftime("%Y-%m-%dT%H:%M:%SZ")
now = ((datetime.datetime.now()-datetime.timedelta(hours=8)).strftime("%Y-%m-%d %H:%M"))if __name__ == '__main__':try:broker = SimpleClient(hosts=kafka) zk = KazooClient(hosts=zookeepers, read_only=True)zk.start()for group in group_list:print grouptopics = zk.get_children("/consumers/%s/owners" %(group))for topic in topics:try:consumer = SimpleConsumer(broker, group, str(topic))lag = int(consumer.pending())json_body = [{"measurement": "lag","tags": {"Consumer_id": group,"topic": topic,"addr": kafka},"time": now,"fields": {"value": int(lag)}}]try:client.write_points(json_body)except Exception as e:print eexcept Exception as e:print eexcept Exception as e:print efinally:   zk.stop()

b、Grafana的配置，下面这张图是topic1的两个Consumer的延时情况：