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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

Using RDD in an immutable way

Now that we know how to create a chain of execution using RDD inheritance, let's learn how to use RDD in an immutable way.

In this section, we will go through the following topics:

  • Understating DAG immutability
  • Creating two leaves from the one root RDD
  • Examining results from both leaves

Let's first understand directed acyclic graph immutability and what it gives us. We will then be creating two leaves from one node RDD, and checking if both leaves are behaving totally independently if we create a transformation on one of the leaf RDD's. We will then examine results from both leaves of our current RDD and check if any transformation on any leaf does not change or impact the root RDD. It is imperative to work like this because we have found that we will not be able to create yet another leaf from the root RDD, because the root RDD will...

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