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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 keyBy() operations to reduce shuffle

In this section, we will use keyBy() operations to reduce shuffle. We will cover the following topics:

  • Loading randomly partitioned data
  • Trying to pre-partition data in a meaningful way
  • Leveraging the keyBy() function

We will load randomly partitioned data, but this time using the RDD API. We will repartition the data in a meaningful way and extract the information that is going on underneath, similar to DataFrame and the Dataset API. We will learn how to leverage the keyBy() function to give our data some structure and to cause the pre-partitioning in the RDD API.

Here is the test we will be using in this section. We are creating two random input records. The first record has a random user ID, user_1, the second one has a random user ID, user_1, and the third one has a random user ID, user_2:

test("Should use keyBy to distribute...
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