Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Fast Data Processing with Spark 2

You're reading from   Fast Data Processing with Spark 2 Accelerate your data for rapid insight

Arrow left icon
Product type Paperback
Published in Oct 2016
Publisher Packt
ISBN-13 9781785889271
Length 274 pages
Edition 3rd Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Krishna Sankar Krishna Sankar
Author Profile Icon Krishna Sankar
Krishna Sankar
Holden Karau Holden Karau
Author Profile Icon Holden Karau
Holden Karau
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Installing Spark and Setting Up Your Cluster 2. Using the Spark Shell FREE CHAPTER 3. Building and Running a Spark Application 4. Creating a SparkSession Object 5. Loading and Saving Data in Spark 6. Manipulating Your RDD 7. Spark 2.0 Concepts 8. Spark SQL 9. Foundations of Datasets/DataFrames – The Proverbial Workhorse for DataScientists 10. Spark with Big Data 11. Machine Learning with Spark ML Pipelines 12. GraphX

Building a SparkSession object

In the Scala and Python programs, you build a SparkSession object with the following build pattern:

val sparkSession = new SparkSession.builder.master(master_path).appName("application name").config("optional configuration parameters").getOrCreate() 

Tip

While you can hardcode all these values, it's better to read them from the environment with reasonable defaults. This approach provides maximum flexibility to run the code in a changing environment without having to recompile. Using local as the default value for the master makes it easy to launch your application in a test environment locally. By carefully selecting the defaults, you can avoid having to overspecify this.

The spark-shell/pyspark creates the SparkSession object automatically and assigns to the spark variable.

The SparkSession object has the SparkContext object, which you can access with spark.sparkContext.

As we will see later, the SparkSession object unifies more than...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image