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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

Integration testing using SparkSession

Let's now learn about integration testing using SparkSession.

In this section, we will cover the following topics:

  • Leveraging SparkSession for integration testing
  • Using a unit tested component

Here, we are creating the Spark engine. The following line is crucial for the integration test:

 val spark: SparkContext = SparkSession.builder().master("local[2]").getOrCreate().sparkContext

It is not a simple line just to create a lightweight object. SparkSession is a really heavy object and constructing it from scratch is an expensive operation from the perspective of resources and time. Tests such as creating SparkSession will take more time compared to the unit testing from the previous section.

For the same reason, we should use unit tests often to convert all edge cases and use integration testing only for the smaller part of...

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