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You now have a working example for zooming in on a spot on the map. A suggestion for further refinement is to change the link in the InfoWindow to a Zoom out link once it s been clicked. I will, however, leave it up to you to work out how to implement it.

Nagios (http://www.nagios.org) provides a flexible customizable framework for collecting data about the state of a complex system and triggering various levels of alerts based on the collected data. A service of this type is essential for your cluster administration and operations team. The University of Nebraska has a web page (http://t2.unl.edu/documentation/hadoop/ monitoring-guide/) that details how to use the Nagios check_jmx plug-in to monitor Hadoop servers. The information is reproduced here. This example assumes that you understand how to construct the JMX password file and access control file.

Table 9-2. HTC-Specific Events*

Summary

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To enable JMX monitoring on Hadoop, add the following lines to hadoop-env.sh: export HADOOP_NAMENODE_OPTS=" -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.port=8004 -Dcom.sun.management.jmxremote.password.file= $HADOOP_HOME/conf/jmxremote.password -Dcom.sun.management.jmxremote.access.file=$HADOOP_HOME/conf/jmxremote.access" export HADOOP_DATANODE_OPTS=" -Dcom.sun.management.jmxremote.authenticate=false -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.port=8004 -Dcom.sun.management.jmxremote.password.file= $HADOOP_HOME/conf/jmxremote.password -Dcom.sun.management.jmxremote.access.file=$HADOOP_HOME/conf/jmxremote.access" The following lines add check_jmx to the Nagios deployment: ./check_jmx -U service:jmx:rmi:///jndi/rmi://node182:8004/jmxrmi -O hadoop.dfs:service=DataNode,name=DataNodeStatistics -A BlockReportsMaxTime -w 10 -c 150 ./check_jmx -U service:jmx:rmi:///jndi/rmi://node182:8004/jmxrmi -O java.lang:type=Memory -A HeapMemoryUsage -K used -C 10000000

Fires when the content of the element, to which the behavior is attached, has been completely parsed Fires just before the content of an element that is attached to an element behavior is saved or copied Fires before a behavior is detached from an element Fires when the behavior s containing document has been completely parsed

Hadoop has built-in support for Ganglia version 3.0 through Hadoop 0.19.0. Support for Ganglia 3.1 is expected for Hadoop 0.20. The Ganglia framework is available from http:// ganglia.sourceforge.net. Ganglia by itself is a highly scalable cluster monitoring tool, and provides visual information on the state of individual machines in a cluster or summary information for a cluster or sets of clusters. Ganglia provides the ability to view different time windows into the past, normally one hour, one day, one week, one month, and so on.

In this chapter, you examined a few things that can be accomplished with InfoWindows. First, you learned how to add full HTML as the content of an InfoWindow. Then you looked at how to add video to it using HTML5. Lastly you looked at how to create a detail map and a Zoom in link in the InfoWindow. Knowing these concepts, you re equipped with the knowledge to create a lot of functionality. Your own imagination sets the limit.

Caution Due to some limitations in the Ganglia support in Hadoop through at least Hadoop 0.19.1, the

* Source: MSDN (http://msdn.microsoft.com/library/default.asp url=/workshop/components/htc/ reference/htcref.asp)

Ganglia is composed of two servers: the gmetad server, which provides historical data and collects current data, and the gmond server, which collects and serves current statistics. The Ganglia web interface is generally installed on the host(s) running the gmetad servers, and in coordination with the host s httpd provides a graphical view of the cluster information. In general, each node will run gmond, but only one or a small number of nodes will also run gmetad. For Hadoop reporting to work with Ganglia, the configuration changes shown in Table 6-9 must be made in the conf/hadoop-metrics.properties file. Each Hadoop cluster must be

The Google Maps API has two classes for dealing with geometric shapes. These are polylines and polygons. These shapes provide you with the necessary tools for marking roads, borders, and other areas. One area where polylines are particularly useful is to track different paths, such as creating driving directions or tracking a jogging path. Polygons, on the other hand, are very useful when you want to highlight a certain geographic area, such as a state or a country. In this chapter, you ll learn how to harness the power of polylines and polygons and how to do some pretty amazing stuff with them.

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