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Apache Spark 2.x for Java Developers

You're reading from  Apache Spark 2.x for Java Developers

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781787126497
Pages 350 pages
Edition 1st Edition
Languages
Authors (2):
Sourav Gulati Sourav Gulati
Profile icon Sourav Gulati
Sumit Kumar Sumit Kumar
Profile icon Sumit Kumar
View More author details

Table of Contents (19) Chapters

Title Page
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Introduction to Spark 2. Revisiting Java 3. Let Us Spark 4. Understanding the Spark Programming Model 5. Working with Data and Storage 6. Spark on Cluster 7. Spark Programming Model - Advanced 8. Working with Spark SQL 9. Near Real-Time Processing with Spark Streaming 10. Machine Learning Analytics with Spark MLlib 11. Learning Spark GraphX

Dataframe and dataset


Dataframes were introduced in Spark 1.3. Dataframe built on the concept of providing schemas over the data. An RDD basically consists of raw data. Although it provides various functions to process the data, it is a collection of Java objects and is involved in the overhead of garbage collection and serialization. Also, Spark SQL concepts can only be leveraged if it contains some schema. So, earlier version of a Spark provide another version of RDD called SchemaRDD.

SchemaRDD

As its name suggests, it is an RDD with schema. As it contains schema, run relation queries can be run on the data along with basic RDD functions. The SchemaRDD can be registered as a table so that SQL queries can be executed on it using Spark SQL. It was available in earlier version of a Spark. However, with Spark Version 1.3, the SchemaRDD was deprecated and dataframe was introduced.

Dataframe

In spite of being an evolved version of SchemaRDD, dataframe comes with big differences to RDDs. It was introduced...

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