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Before you deploy your workflow, you need to edit the feature.xml, workflow.xml, and install.bat files to provide for your solution. Before you start into these, you need to ensure that your project is strongly named and build it. 1. In the Solution Explorer, right-click the project and select Properties. 2. Select the Signing tab. 3. Make sure Sign the assembly is checked. 4. If you don t have a key file already, select <New . . .> from Choose a strong name key file. 5. Put the key file in a place you ll remember, like wfsnk.snk. Uncheck Protect my key file with a password for now. 6. Now build the solution you shouldn t receive any errors.

over to the onStateChange method, which waits for the wrapper XHR object to reach the ready state of 4. When this happens, onStateChange prepares the response object property of the request, setting the raw responseText value for response.text and raw responseXML value for response.xml. The onStateChange method then calls the isSuccess function to check whether the request was successful. If the request was successful, the method calls the success method, which parses and prepares the response.text value to strip out script tags. This success method then passes this new formatted value to the onSuccess method, which dispatches the complete and success events. Unsuccessful requests, in contrast, will make the onStateChange method invoke the failure method, whose main job is to invoke the onFailure method that dispatches the complete and failure events. These four methods success, onSuccess, failure, and onFailure are the usual points for subclassing. Most Request subclasses will override these four methods in order to create a specialized version of the class.

5, MapReduce Details for Multimachine Clusters: This chapter gives you a detailed understanding of what a MapReduce job is and what the Hadoop Core framework actually does to execute your MapReduce job You will learn how to set your job classpath and use shared libraries It also covers the input and output formats used by MapReduce jobs 6, Tuning Your MapReduce Jobs: In this chapter, you will learn what you can tune, how to tell what needs tuning, and how to tune it With this knowledge, you will be able to achieve optimal performance for your clusters 7, Unit Testing and Debugging: When your job is run across many machines, debugging becomes quite a challenge 7 walks you through how to debug your jobs The examples and unit testing framework provided in this chapter also help you know when your job is working as designed.

8, Advanced and Alternate MapReduce Techniques: This chapter demonstrates how to use several advanced features of Hadoop Core: map-side joins, chain mapping, streaming, pipes, and aggregators You will also learn how to configure your jobs to continue running when some input is bad Streaming is a particularly powerful tool, as it allows scripts and other external programs to be used to provide the MapReduce functionality 9, Solving Problems with Hadoop: This chapter describes step-by-step development of a nontrivial MapReduce job, including the whys of the design decisions The sample MapReduce job performs range joins, and uses custom comparator and partitioner classes 10, Projects Based on Hadoop and Future Directions: This chapter provides a summary of several projects that are being built on top of Hadoop Core: distributed column-oriented databases, distributed search, matrix manipulation, and machine learning There are also references for training and support and future directions for Hadoop Core.