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 a custom partitioner to reduce shuffle

In this section, we will use a custom partitioner to reduce shuffle. We will cover the following topics:

  • Implementing a custom partitioner
  • Using the partitioner with the partitionBy method on Spark
  • Validating that our data was partitioned properly

We will implement a custom partitioner with our custom logic, which will partition the data. It will inform Spark where each record should land and on which executor. We will be using the partitionBy method on Spark. In the end, we will validate that our data was partitioned properly. For the purposes of this test, we are assuming that we have two executors:

import com.tomekl007.UserTransaction
import org.apache.spark.sql.SparkSession
import org.apache.spark.{Partitioner, SparkContext}
import org.scalatest.FunSuite
import org.scalatest.Matchers._

class CustomPartitioner extends FunSuite {
val...