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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

Transformations and Actions

Transformations and actions are the main building blocks of an Apache Spark program. In this chapter, we will look at Spark transformations to defer computations and then look at which transformations should be avoided. We will then use the reduce and reduceByKey methods to carry out calculations from a dataset. We will then perform actions that trigger actual computations on graphs. By the end of this chapter, we will also have learned how to reuse the same rdd for different actions.

In this chapter, we will cover the following topics:

  • Using Spark transformations to defer computations to a later time
  • Avoiding transformations
  • Using the reduce and reduceByKey methods to calculate the result
  • Performing actions that trigger actual computations of our Directed Acyclic Graph (DAG)
  • Reusing the same rdd for different actions
...
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