Data Algorithms: Recipes for Scaling Up with Hadoop and by Mahmoud Parsian

By Mahmoud Parsian

When you are able to dive into the MapReduce framework for processing huge datasets, this useful ebook takes you step-by-step in the course of the algorithms and instruments you must construct allotted MapReduce functions with Apache Hadoop or Apache Spark. every one bankruptcy offers a recipe for fixing a big computational challenge, similar to development a advice procedure. You'll how to enforce the best MapReduce resolution with code so you might use on your projects.

Dr. Mahmoud Parsian covers easy layout styles, optimization options, and information mining and desktop studying strategies for difficulties in bioinformatics, genomics, information, and social community research. This e-book additionally contains an summary of MapReduce, Hadoop, and Spark.

Topics include:
•Market basket research for a wide set of transactions
•Data mining algorithms (K-means, KNN, and Naive Bayes)
•Using large genomic facts to series DNA and RNA
•Naive Bayes theorem and Markov chains for information and industry prediction
•Recommendation algorithms and pairwise record similarity
•Linear regression, Cox regression, and Pearson correlation
•Allelic frequency and mining DNA
•Social community research (recommendation structures, counting triangles, sentiment analysis)

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Additional info for Data Algorithms: Recipes for Scaling Up with Hadoop and Spark

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Registers the application driver to the cluster manager. ) • Obtains a list of executors for executing your application driver. Example 1-10. Step 3: Connect to the Spark master 1 2 // Step 3: connect to the Spark master by creating a JavaSparkContext object final JavaSparkContext ctx = new JavaSparkContext(); Step 4: Use the JavaSparkContext to create a JavaRDD This step, illustrated in Example 1-11, reads an HDFS file and creates a Jav aRDD (which represents a set of records where each record is a String object).

26 } Custom partitioner In a nutshell, the partitioner decides which mapper’s output goes to which reducer based on the mapper’s output key. For this, we need two plug-in classes: a custom partitioner to control which reducer processes which keys, and a custom Comparator to sort reducer values. 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.

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. Typically, a design pattern is not presented in a specific programming language but instead can be implemented by many programming languages.

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