Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Length 274 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Understanding Spark FREE CHAPTER 2. Resilient Distributed Datasets 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

Querying with SQL


Let's run the same queries, except this time, we will do so using SQL queries against the same DataFrame. Recall that this DataFrame is accessible because we executed the .createOrReplaceTempView method for swimmers.

Number of rows

The following is the code snippet to get the number of rows within your DataFrame using SQL:

spark.sql("select count(1) from swimmers").show()

The output is as follows:

Running filter statements using the where Clauses

To run a filter statement using SQL, you can use the where clause, as noted in the following code snippet:

# Get the id, age where age = 22 in SQL
spark.sql("select id, age from swimmers where age = 22").show()

The output of this query is to choose only the id and age columns where age = 22:

As with the DataFrame API querying, if we want to get back the name of the swimmers who have an eye color that begins with the letter b only, we can use the like syntax as well:

spark.sql(
"select name, eyeColor from swimmers where eyeColor like 'b%...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image