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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

Performing actions that trigger computations

Spark has a lot more actions that issue DAG, and we should be aware of all of them because they are very important. In this section, we'll understand what can be an action in Spark, do a walk-through of actions, and test those actions if they behave as expected.

The first action we covered is collect. We also covered two actions besides that—we covered both reduce and reduceByKey in the previous section. Both methods are actions because they return a single result.

First, we will create the input of our transactions and then apply some transformations just for testing purposes. We will take only the user that contains A, using keyBy_.userId, and then take only the amount of the required transaction, as shown in the following example:

test("should trigger computations using actions") {
//given
val input...
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