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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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Toc

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

Simple DataFrame queries


Now that you have created the swimmersJSON DataFrame, we will be able to run the DataFrame API, as well as SQL queries against it. Let's start with a simple query showing all the rows within the DataFrame.

DataFrame API query

To do this using the DataFrame API, you can use the show(<n>) method, which prints the first n rows to the console:

Tip

Running the.show() method will default to present the first 10 rows.

# DataFrame API
swimmersJSON.show()

This gives the following output:

SQL query

If you prefer writing SQL statements, you can write the following query:

spark.sql("select * from swimmersJSON").collect()

This will give the following output:

We are using the .collect() method, which returns all the records as a list of Row objects. Note that you can use either the collect() or show() method for both DataFrames and SQL queries. Just make sure that if you use .collect(), this is for a small DataFrame, since it will return all of the rows in the DataFrame and move them...

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