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Fast Data Processing with Spark 2

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

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Product type Paperback
Published in Oct 2016
Publisher Packt
ISBN-13 9781785889271
Length 274 pages
Edition 3rd Edition
Languages
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Authors (2):
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Krishna Sankar Krishna Sankar
Author Profile Icon Krishna Sankar
Krishna Sankar
Holden Karau Holden Karau
Author Profile Icon Holden Karau
Holden Karau
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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 the context; it also unifies...

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