Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (18 chapters)
Learning Apache Spark 2
Credits
About the Author
About the Reviewers
www.packtpub.com
Customer Feedback
Preface

PairRDDs


So far we have seen basic RDD where elements have been words, numbers, or lines of text. We'll now discuss PairRDD, which are essentially datasets of key/value pairs. People who have been using MapReduce will be familiar with the concept of key/value pairs and their benefits during aggregation, joining, sorting, counting, and other ETL operations. The beauty of having key value pairs is that you can operate on data belonging to a particular key in parallel, which includes operations such as aggregation or joining. The simplest example could be retail store sales with StoreId as the key, and the sales amount as the value. This helps you perform advanced analytics on StoreId, which can be used to operate the data in parallel.

Creating PairRDDs

The first step in understanding PairRDDs is to understand how they are created. As we have seen previously, it is not necessary that we have the data available in key/value form upon ingestion and hence there is a need to transform the data using...