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Hands-On Big Data Analytics with PySpark

You're reading from  Hands-On Big Data Analytics with PySpark

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Pages 182 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Rudy Lai Rudy Lai
Profile icon Rudy Lai
Bartłomiej Potaczek Bartłomiej Potaczek
Profile icon Bartłomiej Potaczek
View More author details
Toc

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 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 the reduce and reduceByKey methods to calculate the results

In this section, we will use the reduce and reduceBykey functions to calculate our results and understand the behavior of reduce. We will then compare the reduce and reduceBykey functions to check which of the functions should be used in a particular use case.

We will first focus on the reduce API. First, we need to create an input of UserTransaction. We have the user transaction A with amount 10, B with amount 1, and A with amount 101. Let's say that we want to find out the global maximum. We are not interested in the data for the specific key, but in the global data. We want to scan it, take the maximum, and return it, as shown in the following example:

test("should use reduce API") {
//given
val input = spark.makeRDD(List(
UserTransaction("A", 10),
UserTransaction("B...
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