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The core concept of MapReduce in Hadoop is that input may be split into logical chunks, and each chunk may be initially processed independently, by a map task. The results of these individual processing chunks can be physically partitioned into distinct sets, which are then

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This chapter introduces JavaScript as a potential general-purpose scripting language outside the browser. We learned how CommonJS and the CommonJS Modules system make this possible. We also got a glimpse of MooTools on the server side and Meso, a cross-engine toolkit for MooTools and CommonJS. In the next chapter, we ll expand on some of the concepts we learned here and take a look at a realworld example of how MooTools can be used on the server side. So put on that astronaut suit you stashed in your closet and get ready for launch, as we explore the space beyond the browser.

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InfoPath does not natively implement WSE it only natively connects to standard SOAP web services. However, you can always use managed code behind an InfoPath form to implement anything managed code can do (including WSE). WSE is currently at version 3.0. Moving forward, WSE is being deprecated in favor of Windows Communication Foundation (WCF, previously code-named Indigo ) in the .NET Framework 3.0.

sorted. Each sorted chunk is passed to a reduce task. Figure 1-1 illustrates how the MapReduce model works.

Key1 Key7 Record Record Record Record Record Input Dataset Record Record Record Record Record Record Record Record Record Split Map Task Key1 Key3 Key5 Key6 Key8 Split Map Task Key3 Key2 Key2 Key4 Split Map Task Key2 Key4 Key8

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Before CommonJS, there was ServerJS, a movement to advance the state of JavaScript on the server. Server-side JavaScript isn t something new: there have been several attempts to bring JavaScript to the backend of applications, but none of them was really successful. ServerJS (eventually renamed CommonJS) changed that. The idea of JavaScript on the server might seem weird to some people. After all, there are already dozens of established languages that can be used to write the backend of web applications. There s a big allure, however, to being able to write both parts of an application server and client in the same language, and that alone is enough to warrant exploration into server-side JavaScript. In this chapter, we ll take a quick look at the server-side aspect of applications and how CommonJS expands JavaScript to work on the backend. We ll also take a look at Deck, a cross-engine server-side JavaScript application layer written in MooTools.

Figure 1-1. The MapReduce model A map task may run on any compute node in the cluster, and multiple map tasks may be running in parallel across the cluster. The map task is responsible for transforming the input records into key/value pairs. The output of all of the maps will be partitioned, and each partition will be sorted. There will be one partition for each reduce task. Each partition s sorted keys and the values associated with the keys are then processed by the reduce task. There may be multiple reduce tasks running in parallel on the cluster. The application developer needs to provide only four items to the Hadoop framework: the class that will read the input records and transform them into one key/value pair per record, a map method, a reduce method, and a class that will transform the key/value pairs that the reduce method outputs into output records. My first MapReduce application was a specialized web crawler. This crawler received as input large sets of media URLs that were to have their content fetched and processed. The media items were large, and fetching them had a significant cost in time and resources. The job had several steps: 1. Ingest the URLs and their associated metadata. 2. Normalize the URLs. 3. Eliminate duplicate URLs.

Windows Communication Foundation (WCF)

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