Book Image

Hands-On Big Data Analytics with PySpark

By : Rudy Lai, Bartłomiej Potaczek
Book Image

Hands-On Big Data Analytics with PySpark

By: Rudy Lai, Bartłomiej Potaczek

Overview of this book

Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.
Table of Contents (15 chapters)

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...