book

Data Science with Java

by Michael R. Brzustowicz

June 2017

Beginner to intermediate

233 pages

5h 57m

English

O'Reilly Media, Inc.

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Who Should Read This BookWhy I Wrote This BookA Word on Data Science TodayNavigating This BookConventions Used in This BookUsing Code ExamplesO’Reilly SafariHow to Contact UsAcknowledgments
What Is Data, Anyway?Data ModelsUnivariate ArraysMultivariate ArraysData ObjectsMatrices and VectorsJSONDealing with Real DataNullsBlank SpacesParse ErrorsOutliersManaging Data FilesUnderstanding File Contents FirstReading from a Text FileReading from a JSON FileReading from an Image FileWriting to a Text FileMastering Database OperationsCommand-Line ClientsStructured Query LanguageJava Database ConnectivityVisualizing Data with PlotsCreating Simple PlotsPlotting Mixed Chart TypesSaving a Plot to a File
Building Vectors and MatricesArray StorageBlock StorageMap StorageAccessing ElementsWorking with SubmatricesRandomizationOperating on Vectors and MatricesScalingTransposingAddition and SubtractionLengthDistancesMultiplicationInner ProductOuter ProductEntrywise ProductCompound OperationsAffine TransformationMapping a FunctionDecomposing MatricesCholesky DecompositionLU DecompositionQR DecompositionSingular Value DecompositionEigen DecompositionDeterminantInverseSolving Linear Systems
The Probabilistic Origins of DataProbability DensityCumulative ProbabilityStatistical MomentsEntropyContinuous DistributionsDiscrete DistributionsCharacterizing DatasetsCalculating MomentsDescriptive StatisticsMultivariate StatisticsCovariance and CorrelationRegressionWorking with Large DatasetsAccumulating StatisticsMerging StatisticsRegressionUsing Built-in Database Functions
Transforming Text DataExtracting Tokens from a DocumentUtilizing DictionariesVectorizing a DocumentScaling and Regularizing Numeric DataScaling ColumnsScaling RowsMatrix Scaling OperatorReducing Data to Principal ComponentsCovariance MethodSVD MethodCreating Training, Validation, and Test SetsIndex-Based ResamplingList-Based ResamplingMini-BatchesEncoding LabelsA Generic EncoderOne-Hot Encoding
Learning AlgorithmsIterative Learning ProcedureGradient Descent OptimizerEvaluating Learning ProcessesMinimizing a Loss FunctionMinimizing the Sum of VariancesSilhouette CoefficientLog-LikelihoodClassifier AccuracyUnsupervised Learningk-Means ClusteringDBSCANGaussian MixturesSupervised LearningNaive BayesLinear ModelsDeep Networks
Hadoop Distributed File SystemMapReduce ArchitectureWriting MapReduce ApplicationsAnatomy of a MapReduce JobHadoop Data TypesMappersReducersThe Simplicity of a JSON String as TextDeployment WizardryMapReduce ExamplesWord CountCustom Word CountSparse Linear Algebra
Anscombe’s QuartetSentimentGaussian MixturesIrisMNIST

Content preview from Data Science with Java

Chapter 6. Hadoop MapReduce

You write MapReduce jobs in Java when you need low-level control and want to optimize or streamline your big data pipeline. Using MapReduce is not required, but it is rewarding, because it is a beautifully designed system and API. Learning the basics can get you very far, very quickly, but before you embark on writing a customized MapReduce job, don’t overlook the fact that tools such as Apache Drill enable you to write standard SQL queries on Hadoop.

This chapter assumes you have a running Hadoop Distributed File System (HDFS) on your local machine or have access to a Hadoop cluster. To simulate how a real MapReduce job would run, we can run Hadoop in pseudodistributed mode on one node, either your localhost or a remote machine. Considering how much CPU, RAM, and storage resources we can fit on one box (laptop) these days, you can, in essence, create a mini supercomputer capable of running fairly massive distributed jobs. You can get pretty far on your localhost (on a subset of data) and then scale up to a full cluster when your application is ready.

If the Hadoop client is properly installed, you can get a complete listing of all available Hadoop operations by simply typing the following:

bash$ hadoop

Hadoop Distributed File System

Apache Hadoop comes with a command-line tool useful for accessing the Hadoop filesystem and launching MapReduce jobs. The filesystem access command fs is invoked as follows: