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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

Testing Apache Spark Jobs

In this chapter, we will test Apache Spark jobs and learn how to separate logic from the Spark engine.

We will first cover unit testing of our code, which will then be used by the integration test in SparkSession. Later, we will be mocking data sources using partial functions, and then learn how to leverage ScalaCheck for property-based testing for a test as well as types in Scala. By the end of this chapter, we will have performed tests in different versions of Spark.

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

  • Separating logic from Spark engine-unit testing
  • Integration testing using SparkSession
  • Mocking data sources using partial functions
  • Using ScalaCheck for property-based testing
  • Testing in different versions of Spark
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