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 (12 chapters)

GraphFrames


Having seen GraphX over the course of this chapter, have you not wondered what happened to DataFrame? If you are reading/following this book cover to cover, you might be asking yourself why is there a switch between RDD and the DataFrame API? We saw that DataFrame has become the primary API for Spark, and all new optimizations can only be benefitted from if you are using a DataFrame API, so why is there no DataFrame API for GraphX?

Well the reality is that there is a lot of focus on GraphFrames, which is the DataFrame based API for graphs in Spark. There are certain motivations to have a DataFrame based API for Spark and some of these stem from some shortcomings of GraphX.

Why GraphFrames?

GraphX poses certain challenges, for example:

  • Supports Scala only: The promise of Spark lies in the fact that you can have the same set of algorithms available to a wide variety of users, who can program in Java, Scala, Python, or R. GraphX only supports Scala API. This is a serious limitation...