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Frederick P. The next recipe explains how to run it within a distributed Hadoop cluster. Cross correlation detects the number of times two things occur together. For example, in the Amazon dataset, if two buyers have bought the same item, we say that they are cross correlated. Through cross correlation, we count the number of times two customers have bought a same item. Text search is one of the first use cases for MapReduce, and according to Google, they built MapReduce as the programming model for text processing related to their search platform. Search is generally implemented with an inverted index.

An inverted index is a mapping of words to the data items that includes that word. Given a search query, we find all documents that have the words in the query.

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One of the complexities of web search is that there are too many results and we only need to show important queries. However, ranking the documents based on their importance is out of the scope of this discussion. This recipe explains how to build a simple inverted index based search using MapReduce. Graphs are another type of data that we often encounter.

One of the primary use cases for graphs is social networking; people want to search graphs for interesting patterns.

This recipe explains how to perform a simple graph operation, graph traversal, using MapReduce. This recipe uses the results from the Cross correlation with MapReduce Intermediate recipe. Each buyer is a node, and if two buyers have bought the same item, there is an edge between these nodes. A sample input is shown as follows:. Here the first token is node, and the comma-separated values are lists of nodes to which the first node has an edge. The last value is the color of the node.

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This is a construct we use for the graph traversal algorithm. Given a buyer a node , this recipe walks though the graph and calculates the distance from the given node to all other nodes. This recipe and the next recipe belong to a class called iterative MapReduce where we cannot solve the problem by processing data once. Iterative MapReduce processes the data many times using a MapReduce job until we have calculated the distance from the given node to all other nodes.

When we try to find or calculate interesting information from large datasets, often we need to calculate more complicated algorithms than the algorithms we discussed so far. There are many such algorithms available for example clustering, collaborative filtering, and data mining algorithms. This recipe will implement one such algorithm called Kmeans that belongs to clustering algorithms. Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. This is a Packt Instant How-to guide, which provides concise and clear recipes for getting started with Hadoop.

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ISBN 13: 9781782167709

This book is for big data enthusiasts and would-be Hadoop programmers. It is also meant for Java programmers who either have not worked with Hadoop at all, or who know Hadoop and MapReduce but are not sure how to deepen their understanding. Catalog Educational suggestions. Remember me Forgot password?


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