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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
Author Profile Icon James Cross
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

Working with the Spark Key/Value API

In this chapter, we'll be working with the Spark key/value API. We will start by looking at the available transformations on key/value pairs. We will then learn how to use the aggregateByKey method instead of the groupBy() method. Later, we'll be looking at actions on key/value pairs and looking at the available partitioners on key/value data. At the end of this chapter, we'll be implementing an advanced partitioner that will be able to partition our data by range.

In this chapter, we will be covering the following topics:

  • Available actions on key/value pairs
  • Using aggregateByKey instead of groupBy()
  • Actions on key/value pairs
  • Available partitioners on key/value data
  • Implementing a custom partitioner
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