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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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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

Using ScalaCheck for property-based testing

In this section, we will cover the following topics:

  • Property-based testing
  • Creating a property-based test

Let's look at a simple property-based test. We need to import a dependency before we define properties. We also need a dependency for the ScalaCheck library, which is a library for property-based tests.

In the previous section, every test extended FunSuite. We used functional tests, but we had to provide arguments explicitly. In this example, we're extending Properties from the ScalaCheck library and testing a StringType, as follows:

object PropertyBasedTesting extends Properties("StringType")

Our ScalaCheck will generate a random string for us. If we create a property-based test for a custom type, then that is not known to the ScalaCheck. We need to provide a generator that will generate instances of that...

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