Book Image

Apache Spark 2.x for Java Developers

By : Sourav Gulati, Sumit Kumar
Book Image

Apache Spark 2.x for Java Developers

By: Sourav Gulati, Sumit Kumar

Overview of this book

Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone. The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications.
Table of Contents (19 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Operations on feature vectors

Though the package uses the dataframe for ML workflows, depending on the use case one might need to extract data from raw dataframe or transform the dataframe in a format as required by the ML algorithms or at times one might just need a few selected parameters as feature vectors. All these different types of operations require usage of specially developed APIs that can be clubbed into the following categories.

Feature extractors

When the data present in a raw dataframe are not explicitly present in the form an ML algorithm expects we use feature extractors to extract those features. Common feature extractors are:

  • CountVectorizer: A CountVectorizer converts a collection of text documents into a vector representing the word count of text documents. CountVectorizer works in two different ways depending how the value of the dictionary gets populated. Let's first assume that the user has no prior information of the type of data that will populate the dataset...