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For your MapReduce jobs to be successful, the mapper must be able to ingest the input and process the input record, sending forward the records that can be passed to the reduce task or to the final output directly, if no reduce step is required. The reducer must be able to accept the key and value groups that passed through the mapper, and generate the final output of this MapReduce step. The job must be configured with the location and type of the input data, the mapper class to use, the number of reduce tasks required, and the reducer class and I/O types. The TaskTracker service will actually run your map and reduce tasks, and the JobTracker service will distribute the tasks and their input split to the various trackers. The cluster must be configured with the nodes that will run the TaskTrackers, and with the number of TaskTrackers to run per node. The TaskTrackers need to be configured with the JVM parameters, including the classpath for both the TaskTracker and the JVMs that will execute the individual tasks. There are three levels of configuration to address to configure MapReduce on your cluster. From the bottom up, you need to configure the machines, the Hadoop MapReduce framework, and the jobs themselves. We ll get started with these requirements by exploring how to launch your MapReduce jobs.

and library of form data. If you want a reusable library, you ll want to publish the form template as a content type. (See the following section, Publishing to a SharePoint Site As a Content Type, for more info.)

A Hadoop job is usually part of a production application, which may have many steps, some of Tip which are MapReduce jobs. Hadoop Core, as of version 0.19.0, provides a way of optimizing the data flows between a set of sequential MapReduce jobs. This framework for descriptively and efficiently running sequential MapReduce jobs together is called chaining, and uses the ChainMapper and the ChainReducer, as discussed in 8. An alternative is the cascading package, available from http://www.cascading.org/.

MooTools is a class-based framework not only because it provides the best JavaScript class system currently available, but because it uses this class system itself While other JavaScript frameworks provide some form of plug-in or extension system to add extra functionality to the framework, MooTools expects developers to use the built-in class system to do this Since the class system is central to MooTools, every developer who uses the framework is expected to know at least the basics of creating and extending classes After reading this and the previous chapter, I expect that you already understand the MooTools class system deeply and I won t be surprised if you re well on your way to mastering the system However, knowing how to create classes and knowing how to properly design classes are wholly separate things The first involves syntax and technique, which is easy enough to master.

Jobs within a MapReduce cluster can be launched by constructing a JobConf object (details on the JobConf object are provided in this book s appendix) and passing it to a JobClient object: JobConf conf = new JobConf(MyClass.class); /** Configuration setup deleted for clarity*/ /** Launch the Job by submitting it to the Framework. */ RunningJob job = JobClient.runJob(conf); You can launch the preceding example from the command line as follows: > bin/hadoop [-libjars jar1.jar,jar2.jar,jar3.jar] jar myjar.jar MyClass The optional -libjars jar1.jar... specifications add JARs for your job. The assumption is that MyClass is in the myjar.jar. For this to be successful requires a considerable amount of runtime environment setup. Hadoop Core provides a shell script, bin/hadoop, which manages the setup for a job. Using this script is the standard and recommended way to start a MapReduce job. This script sets up the process environment correctly for the installation, including inserting the Hadoop JARs and Hadoop configuration directory into the classpath, and launches your application. This behavior is triggered by providing the initial command-line argument jar to the bin/hadoop script. Hadoop Core provides several mechanisms for setting the classpath for your application: You can set up a fixed base classpath by altering hadoop-env.sh, via the HADOOP_CLASSPATH environment variable (on all of your machines) or by setting that environment variable in the runtime environment for the user that starts the Hadoop servers. You may run your jobs via the bin/hadoop jar command and supply a -libjars argument with a list of JARs. The DistributedCache object provides a way to add files or archives to your runtime classpath.

The second, on the other hand, involves a firm understanding of the principles and concepts of object-oriented programming, and it s a bit harder to master these These principles and concepts are, of course, outside the scope of this book, though we have touched on some of them, like code reuse, inheritance, encapsulation, and visibility The only way to learn them is by reading books on object-oriented programming and design, and by using what you ve learned in experimentation and in building complex JavaScript programs I will, however, provide you with some pointers that should come in handy: Follow the MooTools style and code conventions While coding style is mostly a personal preference, the MooTools project keeps its own set of conventions that is followed for all code that s part of the framework.