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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

Basic statistics


Let's read the car mileage data and then compute some basic statistics. In Spark 2.0.0, DataFrameReader has the capability to read CSV files and create Datasets. And the Dataset has the describe() function, which calculates the count, mean, standard deviation, min, and max values. For correlation and covariance, we use the stat.corr() and stat.cov() methods. Spark 2.0.0 Datasets have made our statistics work a lot easier.

Now let's run the program, parse the code, and compare the results.

The code files are in fdps-v3/code and the data files in fdps-v3/data. You can run the code either from a Scala IDE or just from the Spark shell startup.

Start the Spark shell from the bin directory where you have installed Spark:

/Volumes/sdxc-01/spark-2.0.0/bin/spark-shell

Inside the shell, you'll find this command:

load /Users/ksankar/fdps-v3/code/ML01v2.scala

This command loads the source as follows:

It creates the ML01v2 object. To run the object, use the following command:

ML01v2.main...
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