Book Image

Learning Spark SQL

By : Aurobindo Sarkar
Book Image

Learning Spark SQL

By: Aurobindo Sarkar

Overview of this book

In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. However, designing web-scale production applications using Spark SQL APIs can be a complex task. Hence, understanding the design and implementation best practices before you start your project will help you avoid these problems. This book gives an insight into the engineering practices used to design and build real-world, Spark-based applications. The book's hands-on examples will give you the required confidence to work on any future projects you encounter in Spark SQL. It starts by familiarizing you with data exploration and data munging tasks using Spark SQL and Scala. Extensive code examples will help you understand the methods used to implement typical use-cases for various types of applications. You will get a walkthrough of the key concepts and terms that are common to streaming, machine learning, and graph applications. You will also learn key performance-tuning details including Cost Based Optimization (Spark 2.2) in Spark SQL applications. Finally, you will move on to learning how such systems are architected and deployed for a successful delivery of your project.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Preparing data for machine learning


In this section, we introduce the of preparing the data prior to applying Spark MLlib algorithms. Typically, we need to have two columns called label and features for using Spark MLlib classification algorithms. We will illustrate this with the following example described:

We import the required classes for this section:

scala> import org.apache.spark.ml.Pipeline
scala> import org.apache.spark.ml.classification.{RandomForestClassificationModel, RandomForestClassifier}
scala> import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
scala> import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer} 
scala> import org.apache.spark.ml.linalg.Vectors 

Pre-processing data for machine learning

We define a set of UDFs used in this section. These include, for example, checking whether a string contains a specific substring or not, and returning a 0.0 or 1.0 value to the label column. Another UDF is used to create...