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

By Mahmoud Parsian

ISBN-10: 1491906154

ISBN-13: 9781491906156

While you are able to dive into the MapReduce framework for processing huge datasets, this functional ebook takes you step-by-step throughout the algorithms and instruments you want to construct disbursed MapReduce purposes with Apache Hadoop or Apache Spark. each one bankruptcy offers a recipe for fixing a huge computational challenge, comparable to development a advice process. You'll how to enforce the right MapReduce answer with code so you might use on your projects.

Dr. Mahmoud Parsian covers uncomplicated layout styles, optimization innovations, and information mining and computer studying options for difficulties in bioinformatics, genomics, records, and social community research. This booklet additionally comprises 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 info 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 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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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). , they cannot be altered or modified). Note that Spark’s RDDs are the basic abstraction for parallel execution. Note also that you may use textFile() to read HDFS or non-HDFS files.

So, you should implement sorting explicitly using an RDD operator. Parti tioner), but it does not preserve the order of the original RDD elements. 0). 24 | Chapter 1: Secondary Sort: Introduction Further Reading on Secondary Sorting To support secondary sorting in Spark, you may extend the JavaPairRDD class and add additional methods such as groupByKeyAndSortValues(). com/tresata/spark-sorted Chapter 2 provides a detailed implementation of the Secondary Sort design pattern using the MapReduce and Spark frameworks.

The answer is the temperature data field (because we want the reduc‐ ers’ values to be sorted by temperature). So, we have to indicate how DateTempera turePair objects should be sorted using the compareTo() method. We need to define a proper data structure for holding our key and value, while also providing the sort order of intermediate keys. In Hadoop, for custom data types (such as DateTempera turePair) to be persisted, they have to implement the Writable interface; and if we are going to compare custom data types, then they have to implement an additional interface called WritableComparable (see Example 1-1).

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