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Hands-On Big Data Analytics with PySpark

You're reading from   Hands-On Big Data Analytics with PySpark Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Length 182 pages
Edition 1st Edition
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Authors (3):
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James Cross James Cross
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James Cross
Bartłomiej Potaczek Bartłomiej Potaczek
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Bartłomiej Potaczek
Rudy Lai Rudy Lai
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Rudy Lai
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Toc

Table of Contents (15) Chapters Close

Preface 1. Installing Pyspark and Setting up Your Development Environment FREE CHAPTER 2. Getting Your Big Data into the Spark Environment Using RDDs 3. Big Data Cleaning and Wrangling with Spark Notebooks 4. Aggregating and Summarizing Data into Useful Reports 5. Powerful Exploratory Data Analysis with MLlib 6. Putting Structure on Your Big Data with SparkSQL 7. Transformations and Actions 8. Immutable Design 9. Avoiding Shuffle and Reducing Operational Expenses 10. Saving Data in the Correct Format 11. Working with the Spark Key/Value API 12. Testing Apache Spark Jobs 13. Leveraging the Spark GraphX API 14. Other Books You May Enjoy

Available actions on key/value pairs

In this section, we will be covering the following topics:

  • Available transformations on key/value pairs
  • Using countByKey()
  • Understanding the other methods

So, this is our well-known test in which we will be using transformations on key/value pairs.

First, we will create an array of user transactions for users A, B, A, B, and C for some amount, as per the following example:

 val keysWithValuesList =
Array(
UserTransaction("A", 100),
UserTransaction("B", 4),
UserTransaction("A", 100001),
UserTransaction("B", 10),
UserTransaction("C", 10)
)

We then need to key our data by a specific field, as per the following example:

val keyed = data.keyBy(_.userId)

We will key it by userId, by invoking the keyBy method with a userId parameter.

Now, our data is assigned to the keyed variable and its type...

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