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)

What is GraphX?


Spark GraphX is not the first system to bring graph parallel computation to the mainstream, as even before this project, people used to perform graph computation on Hadoop, although they had to spend considerable time to build a graph on Hadoop. This resulted in creation of specialized systems such as Apache Giraph (an open source version of Google's Pregel), which ensured that the graph processing times come down to a fraction of what they were on Hadoop. However, graph processing is not isolated, and is very similar to MLLib where you have to spend time to load the data and pre-process it before running a machine learning pipeline. Similarly, the full data processing pipeline isn't just about running a graph algorithm, and graph creation is an important aspect of the problem, including performing post-processing, that is, what to do with the result. This was beautifully presented in a UC Berkley AmpLab talk in 2013 by Joseph Gonzalez and Reynold Xin.

The following figure...