By Mahmoud Parsian
While you're able to dive into the MapReduce framework for processing huge datasets, this sensible publication takes you step-by-step during the algorithms and instruments you must construct allotted MapReduce functions with Apache Hadoop or Apache Spark. each one bankruptcy presents a recipe for fixing a big computational challenge, comparable to development a suggestion procedure. You'll how one can enforce the proper MapReduce resolution with code so that you can use on your projects.
Dr. Mahmoud Parsian covers easy layout styles, optimization options, and knowledge mining and computer studying recommendations for difficulties in bioinformatics, genomics, information, and social community research. This e-book additionally contains an summary of MapReduce, Hadoop, and Spark.
•Market basket research for a wide set of transactions
•Data mining algorithms (K-means, KNN, and Naive Bayes)
•Using large genomic info to series DNA and RNA
•Naive Bayes theorem and Markov chains for facts and marketplace prediction
•Recommendation algorithms and pairwise record similarity
•Linear regression, Cox regression, and Pearson correlation
•Allelic frequency and mining DNA
•Social community research (recommendation platforms, counting triangles, sentiment analysis)
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Extra info for Data Algorithms: Recipes for Scaling Up with Hadoop and Spark
The custom partitioner ensures that all data with the same key (the natural key, not including the composite key with the temperature value) is sent to the same reducer. The custom Comparator does sorting so that the natural key (year-month) groups the data once it arrives at the reducer. Example 1-2. hashCode() % numberOfPartitions); } } Solutions to the Secondary Sort Problem | 5 Hadoop provides a plug-in architecture for injecting the custom partitioner code into the framework. Job; ... ; ...
Also, I want to thank Mike Loukides (VP of Content Strategy for O’Reilly Media) for believing in and supporting my book project. Thank you so much to my editor, Marie Beaugureau, data and development editor at O’Reilly, who has worked with me patiently for a long time and supported me during every phase of this project. Marie’s comments and suggestions have been very useful and helpful. A big thank you to Rachel Monaghan, copyeditor, for her superb knowledge of book editing and her valuable comments and suggestions.
Sometimes, it is called value-to-key conversion. The secondary sorting techni‐ que will enable us to sort the values (in ascending or descending order) passed to each reducer. I will provide concrete examples of how to achieve secondary sorting in ascending or descending order. The goal of this chapter is to implement the Secondary Sort design pattern in MapRe‐ duce/Hadoop and Spark. In software design and programming, a design pattern is a reusable algorithm that is used to solve a commonly occurring problem.