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Learning PySpark

You're reading from   Learning PySpark Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Table of Contents (13) Chapters Close

Preface 1. Understanding Spark 2. Resilient Distributed Datasets FREE CHAPTER 3. DataFrames 4. Prepare Data for Modeling 5. Introducing MLlib 6. Introducing the ML Package 7. GraphFrames 8. TensorFrames 9. Polyglot Persistence with Blaze 10. Structured Streaming 11. Packaging Spark Applications Index

Creating RDDs


There are two ways to create an RDD in PySpark: you can either .parallelize(...) a collection (list or an array of some elements):

data = sc.parallelize(
    [('Amber', 22), ('Alfred', 23), ('Skye',4), ('Albert', 12), 
     ('Amber', 9)])

Or you can reference a file (or files) located either locally or somewhere externally:

data_from_file = sc.\    
    textFile(
        '/Users/drabast/Documents/PySpark_Data/VS14MORT.txt.gz',
        4)

Note

We downloaded the Mortality dataset VS14MORT.txt file from (accessed on July 31, 2016) ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/DVS/mortality/mort2014us.zip; the record schema is explained in this document http://www.cdc.gov/nchs/data/dvs/Record_Layout_2014.pdf. We selected this dataset on purpose: The encoding of the records will help us to explain how to use UDFs to transform your data later in this chapter. For your convenience, we also host the file here: http://tomdrabas.com/data/VS14MORT.txt.gz

The last parameter in sc.textFile...

You have been reading a chapter from
Learning PySpark
Published in: Feb 2017
Publisher: Packt
ISBN-13: 9781786463708
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