Ganglia监控Hadoop及Hbase集群性能(安装配置)

[v5tech]

Ganglia监控Hadoop及Hbase集群性能(安装配置)

1. 在主节点上安装ganglia-webfrontend和ganglia-monitor

sudo apt-get install ganglia-webfrontend ganglia-monitor

在主节点上安装ganglia-webfrontend和ganglia-monitor。在其他监视节点上，只需要安装ganglia-monitor即可

将ganglia的文件链接到apache的默认目录下

sudo ln -s /usr/share/ganglia-webfrontend /var/www/ganglia

2. 安装ganglia-monitor

在其他监视节点上，只需要安装ganglia-monitor

sudo apt-get install ganglia-monitor

3. Ganglia配置

• gmond.conf

在每个节点上都需要配置/etc/ganglia/gmond.conf,配置相同如下所示

sudo vim /etc/ganglia/gmond.conf

修改后的/etc/ganglia/gmond.conf

globals {                    
  daemonize = yes  ##以后台的方式运行            
  setuid = yes             
  user = ganglia     #运行Ganglia的用户              
  debug_level = 0               
  max_udp_msg_len = 1472        
  mute = no             
  deaf = no             
  host_dmax = 0 /*secs */ 
  cleanup_threshold = 300 /*secs */ 
  gexec = no             
  send_metadata_interval = 10     #发送数据的时间间隔
} 

/* If a cluster attribute is specified, then all gmond hosts are wrapped inside 
 * of a <CLUSTER> tag.  If you do not specify a cluster tag, then all <HOSTS> will 
 * NOT be wrapped inside of a <CLUSTER> tag. */ 
cluster { 
  name = "hadoop-cluster"         #集群名称
  owner = "ganglia"               #运行Ganglia的用户
  latlong = "unspecified" 
  url = "unspecified" 
} 

/* The host section describes attributes of the host, like the location */ 
host { 
  location = "unspecified" 
} 

/* Feel free to specify as many udp_send_channels as you like.  Gmond 
   used to only support having a single channel */ 
udp_send_channel { 
  #mcast_join = 239.2.11.71     #注释掉组播
  host = master                 #发送给安装gmetad的机器
  port = 8649                   #监听端口
  ttl = 1 
} 

/* You can specify as many udp_recv_channels as you like as well. */ 
udp_recv_channel { 
  #mcast_join = 239.2.11.71     #注释掉组播
  port = 8649 
  #bind = 239.2.11.71 
} 

/* You can specify as many tcp_accept_channels as you like to share 
   an xml description of the state of the cluster */ 
tcp_accept_channel { 
  port = 8649 
} 

• gmetad.conf

在主节点上还需要配置/etc/ganglia/gmetad.conf,这里面的名字hadoop-cluster和上面gmond.conf中name应该一致。　

/etc/ganglia/gmetad.conf

sudo vim /etc/ganglia/gmetad.conf

修改为以下内容

data_source "hadoop-cluster" 10 master:8649 slave:8649
setuid_username "nobody"
rrd_rootdir "/var/lib/ganglia/rrds"
gridname "hadoop-cluster"

注:master:8649 slave:8649为要监听的主机和端口,data_source中hadoop-cluster与gmond.conf中name一致

4. Hadoop配置

在所有hadoop所在的节点，均需要配置hadoop-metrics2.properties，配置如下：

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# syntax: [prefix].[source|sink].[instance].[options]
# See javadoc of package-info.java for org.apache.hadoop.metrics2 for details

#注释掉以前原有配置

#*.sink.file.class=org.apache.hadoop.metrics2.sink.FileSink
# default sampling period, in seconds
#*.period=10

# The namenode-metrics.out will contain metrics from all context
#namenode.sink.file.filename=namenode-metrics.out
# Specifying a special sampling period for namenode:
#namenode.sink.*.period=8

#datanode.sink.file.filename=datanode-metrics.out

# the following example split metrics of different
# context to different sinks (in this case files)
#jobtracker.sink.file_jvm.context=jvm
#jobtracker.sink.file_jvm.filename=jobtracker-jvm-metrics.out
#jobtracker.sink.file_mapred.context=mapred
#jobtracker.sink.file_mapred.filename=jobtracker-mapred-metrics.out

#tasktracker.sink.file.filename=tasktracker-metrics.out

#maptask.sink.file.filename=maptask-metrics.out

#reducetask.sink.file.filename=reducetask-metrics.out

*.sink.ganglia.class=org.apache.hadoop.metrics2.sink.ganglia.GangliaSink31  
*.sink.ganglia.period=10

*.sink.ganglia.slope=jvm.metrics.gcCount=zero,jvm.metrics.memHeapUsedM=both  
*.sink.ganglia.dmax=jvm.metrics.threadsBlocked=70,jvm.metrics.memHeapUsedM=40  

namenode.sink.ganglia.servers=master:8649  
resourcemanager.sink.ganglia.servers=master:8649  

datanode.sink.ganglia.servers=master:8649    
nodemanager.sink.ganglia.servers=master:8649    


maptask.sink.ganglia.servers=master:8649    
reducetask.sink.ganglia.servers=master:8649

5. Hbase配置

在所有的hbase节点中均配置hadoop-metrics2-hbase.properties,配置如下:

# syntax: [prefix].[source|sink].[instance].[options]
# See javadoc of package-info.java for org.apache.hadoop.metrics2 for details

#*.sink.file*.class=org.apache.hadoop.metrics2.sink.FileSink
# default sampling period
#*.period=10

# Below are some examples of sinks that could be used
# to monitor different hbase daemons.

# hbase.sink.file-all.class=org.apache.hadoop.metrics2.sink.FileSink
# hbase.sink.file-all.filename=all.metrics

# hbase.sink.file0.class=org.apache.hadoop.metrics2.sink.FileSink
# hbase.sink.file0.context=hmaster
# hbase.sink.file0.filename=master.metrics

# hbase.sink.file1.class=org.apache.hadoop.metrics2.sink.FileSink
# hbase.sink.file1.context=thrift-one
# hbase.sink.file1.filename=thrift-one.metrics

# hbase.sink.file2.class=org.apache.hadoop.metrics2.sink.FileSink
# hbase.sink.file2.context=thrift-two
# hbase.sink.file2.filename=thrift-one.metrics

# hbase.sink.file3.class=org.apache.hadoop.metrics2.sink.FileSink
# hbase.sink.file3.context=rest
# hbase.sink.file3.filename=rest.metrics


*.sink.ganglia.class=org.apache.hadoop.metrics2.sink.ganglia.GangliaSink31  
*.sink.ganglia.period=10  

hbase.sink.ganglia.period=10  
hbase.sink.ganglia.servers=master:8649

6. 启动hadoop、hbase集群

start-dfs.sh
start-yarn.sh
start-hbase.sh

7. 启动Ganglia

先需要重启hadoop和hbase 。在各个节点上启动gmond服务，主节点还需要启动gmetad服务。

使用apt-get方式安装的Ganglia，可以直接用service方式启动。

• sudo service ganglia-monitor start（每台机器都需要启动）
• sudo service gmetad start（在安装了ganglia-webfrontend的机器上启动）

8. 检验

登录浏览器查看：http://master/ganglia，如果Hosts up为9即表示安装成功。

若安装不成功，有几个很有用的调试命令：

• 以调试模式启动gmetad：gmetad -d 9
• 查看gmetad收集到的XML文件：telnet master 8649

9. 截图

参考文章

• http://www.cnblogs.com/wukenaihe/archive/2013/03/21/2972837.html
• http://www.dataguru.cn/article-3816-1.html
• http://blog.csdn.net/iam333/article/details/16358509
• http://sofar.blog.51cto.com/353572/1367205