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Jul 19, 2015 · You can programmatically create a folder on your computer, create a subfolder, create a file in the subfolder, and write data to the file.

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Hadoop Core provides a robust framework for distributing tasks across large numbers of general-purpose computers. Application developers just need to write the map and reduce methods for their data, and use one of the existing input and output formats. The framework provides a rich set of input and output handlers, and you can create custom handlers, if necessary.



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Getting over the installation hurdle can be difficult, but it is getting simpler as more people and organizations understand the issues and refine the processes and procedures. Cloudera (http://www.cloudera.com) now provides a self-installing Hadoop distribution in RPM format. New features and functionality are being tried. Read the information on the http:// hadoop.apache.org/core web site, join the mailing lists referenced there (to join the Core user mailing list, send an e-mail to core-user-subscribe@hadoop.apache.org), and have fun writing your applications. The chapters to come will guide you through the trouble spots as you develop your own applications with Hadoop.

The MIDP user interface consists of screens such as List or Form that in turn contain GUI items such as StringItem or TextField. In the following sections, you will learn all the tricks for making the most out of your screens.





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Likewise, this assembly shouldn t be written to disk, because it won t be used outside of the runtime of the host application. Therefore, set the GenerateInMemory property to true: oCompilerParameters.GenerateInMemory = true; Then, pass the CompilerParameters object to the CodeDomProvider s CompileAssemblyFromSource() method to produce a CompilerResults object like this: oCompilerResults = oCodeDomProvider.CompileAssemblyFromSource(oCompilerParameters, szCode); Of course, you aren t restricted to compiling only a text-string representation of source code. You can use the CompileAssemblyFromFile() method to compile a series of files on disk, as shown in Listing 3-3.

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his chapter walks you through what is involved in a MapReduce job. You will be able to write and run simple stand-alone MapReduce programs by the end of the chapter. The examples in this chapter assume the setup as described in 1. They should be explicitly run in a special local mode configuration for executing on a single machine, with no requirements for a running the Hadoop Core framework. This single machine (local) configuration is also ideal for debugging and for unit tests. The code for the examples is available from this book s details page at the Apress web site (http://www.apress.com). The downloadable code also includes a JAR file you can use to run the examples. Let s start by examining the parts that make up a MapReduce job.

Designing the background of the screen is easy: just choose one of the available backgrounds and apply it to the screen style. A popular option is to use an image as the background. Since the background is always painted from the very top to the very bottom of the screen (regardless of whether there is a title or a commands menu present), you need to take this into account. Figure 12-10 shows the used background image, and Figure 12-11 shows the background in action in a List screen. You can even use animated backgrounds like the pulsating-circles background.

The user configures and submits a MapReduce job (or just job for short) to the framework, which will decompose the job into a set of map tasks, shuffles, a sort, and a set of reduce tasks. The framework will then manage the distribution and execution of the tasks, collect the output, and report the status to the user. The job consists of the parts shown in Figure 2-1 and listed in Table 2-1. Table 2-1. Parts of a MapReduce Job

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