Book Image

Apache Spark 2.x Cookbook

By : Rishi Yadav
Book Image

Apache Spark 2.x Cookbook

By: Rishi Yadav

Overview of this book

While Apache Spark 1.x gained a lot of traction and adoption in the early years, Spark 2.x delivers notable improvements in the areas of API, schema awareness, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and more accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Further on, you will be introduced to working with RDDs, DataFrames and Datasets to operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning & recommendation engines in Spark. Last but not least, the final few chapters delve deeper into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Programmatically specifying the schema


There are a few cases where case classes might not work; one of these cases is where case classes cannot take more than 22 fields. Another case can be that you do not know about the schema beforehand. In this approach, data is loaded as an RDD of the Row objects. The schema is created separately using the StructType and StructField objects, which represent a table and a field, respectively. The schema is applied to the Row RDD to create a DataFrame.

How to do it...

  1. Start the Spark shell or Databricks Cloud Scala notebook:
$ spark-shell  
  1. Import the Spark SQL datatypes and Row objects:
        scala> import org.apache.spark.sql._
        scala> import org.apache.spark.sql.types._
  1. Create the schema using the StructType and StructField objects. The StructField object takes parameters in the form of param name, param type, and nullability:
scala> val schema = StructType(
    Array(StructField("first_name",StringType,true),
StructField("last_name",StringType...