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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

SparkSession versus SparkContext


You would have noticed that we are using SparkSession and SparkContext, and this is not an error. Let's revisit the annals of Spark history for a perspective. It is important to understand where we came from, as you will hear about these connection objects for some time to come.

Prior to Spark 2.0.0, the three main connection objects were SparkContext, SqlContext, and HiveContext. The SparkContext object was the connection to a Spark execution environment and created RDDs and others, SQLContext worked with SparkSQL in the background of SparkContext, and HiveContext interacted with the Hive stores.

Spark 2.0.0 introduced Datasets/DataFrames as the main distributed data abstraction interface and the SparkSession object as the entry point to a Spark execution environment. Appropriately, the SparkSession object is found in the namespace, org.apache.spark.sql.SparkSession (Scala), or pyspark.sql.sparkSession. A few points to note are as follows:

  • In Scala and Java...

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