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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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Toc

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

Implementing a custom partitioner

In this section, we'll implement a custom partitioner and create a partitioner that takes a list of parses with ranges. If our key falls into a specific range, we will assign the partition number index of the list.

We will cover the following topics:

  • Implementing a custom partitioner
  • Implementing a range partitioner
  • Testing our partitioner

We will implement the logic range partitioning based on our own range partitioning and then test our partitioner. Let's start with the black box test without looking at the implementation.

The first part of the code is similar to what we have used already, but this time we have keyBy amount of data, as shown in the following example:

 val keysWithValuesList =
Array(
UserTransaction("A", 100),
UserTransaction("B", 4),
UserTransaction("A", 100001),
UserTransaction(&quot...
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