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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 2. Getting Your Big Data into the Spark Environment Using RDDs FREE CHAPTER 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

Summary

In this chapter, we first learned how to separate logic from the Spark engine. We then looked at a component that was well-tested in separation without the Spark engine, and we carried out integration testing using SparkSession. For this, we created a SparkSession test by reusing the component that was already well-tested. By doing that, we did not have to cover all edge cases in the integration test and our test was much faster. We then learned how to leverage partial functions to supply mocked data that's provided at the testing phase. We also covered ScalaCheck for property-based testing. By the end of this chapter, we had tested our code in different versions of Spark and learned how to change our DataFrame mock test to RDD.

In the next chapter, we will learn how to leverage the Spark GraphX API.

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