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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
Author Profile Icon James Cross
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

Actions on key/value pairs

In this section, we'll be looking at the actions on key/value pairs.

We will cover the following topics:

  • Examining actions on key/value pairs
  • Using collect()
  • Examining the output for the key/value RDD

In the first section of this chapter, we covered transformations that are available on key/value pairs. We saw that they are a bit different compared to RDDs. Also, for actions, it is slightly different in terms of result but not in the method name.

Therefore, we'll be using collect() and we'll be examining the output of our action on these key/value pairs.

First, we will create our transactions array and RDD according to userId, as shown in the following example:

 val keysWithValuesList =
Array(
UserTransaction("A", 100),
UserTransaction("B", 4),
UserTransaction("A", 100001),
UserTransaction("B&quot...
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