By Jimmy Lin, Chris Dyer, Graeme Hirst
Our global is being revolutionized through data-driven equipment: entry to massive quantities of information has generated new insights and opened interesting new possibilities in trade, technology, and computing purposes. Processing the large amounts of knowledge worthy for those advances calls for huge clusters, making dispensed computing paradigms extra the most important than ever. MapReduce is a programming version for expressing allotted computations on tremendous datasets and an execution framework for large-scale facts processing on clusters of commodity servers. The programming version presents an easy-to-understand abstraction for designing scalable algorithms, whereas the execution framework transparently handles many system-level information, starting from scheduling to synchronization to fault tolerance. This e-book specializes in MapReduce set of rules layout, with an emphasis on textual content processing algorithms universal in usual language processing, details retrieval, and computing device studying. We introduce the thought of MapReduce layout styles, which signify normal reusable suggestions to often taking place difficulties throughout quite a few challenge domain names. This publication not just intends to assist the reader ''think in MapReduce'', but additionally discusses boundaries of the programming version in addition. desk of Contents: advent / MapReduce fundamentals / MapReduce set of rules layout / Inverted Indexing for textual content Retrieval / Graph Algorithms / EM Algorithms for textual content Processing / remaining comments
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Extra resources for Data-Intensive Text Processing with MapReduce
Large-data problems have a penchant for uncovering obscure corner cases in code that is otherwise thought to be bug-free. Furthermore, any sufficiently large dataset will contain corrupted data or records that are mangled beyond a programmer’s imagination—resulting in errors that one would never think to check for or trap. The MapReduce execution framework must thrive in this hostile environment. 4 PARTITIONERS AND COMBINERS We have thus far presented a simplified view of MapReduce. There are two additional elements that complete the programming model: partitioners and combiners.
In Hadoop, there is no such restriction, and the reducer can emit an arbitrary number of output key-value pairs (with different keys). To provide a bit more implementation detail: pseudo-code provided in this book roughly mirrors how MapReduce programs are written in Hadoop. Mappers and reducers are objects that implement the Map and Reduce methods, respectively. In Hadoop, a mapper object is initialized for each map task (associated with a particular sequence of key-value pairs called an input split) and the Map method is called on each key-value pair by the execution framework.
There is no discussion of security in the original GFS paper, but HDFS explicitly assumes a datacenter environment where only authorized users have access. 20 • The system is built from unreliable but inexpensive commodity components. As a result, failures are the norm rather than the exception. HDFS is designed around a number of self-monitoring and self-healing mechanisms to robustly cope with common failure modes. Finally, some discussion is necessary to understand the single-master design of HDFS and GFS.
Data-Intensive Text Processing with MapReduce by Jimmy Lin, Chris Dyer, Graeme Hirst