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)

Comparison between GraphFrames and GraphX


It is important to look at a quick comparison between GraphX and GraphFrames as it gives you an idea as to where GraphFrames are going. Joseph Bradley, who is a software Engineer at Databricks, gave a brilliant talk on GraphFrames and the difference between the two APIs. The talk is available at http://bit.ly/2hBrDwH. Here is a summary of the comparison:

GraphFrames

GraphX

Core APIs

Scala, Java, Python

Scala only

Programming Abstraction

DataFrames

RDDs

Use Cases

Algorithms, Queries, Motif Finding

Algorithms

VertexIds

Any type (in Catalyst)

Long

Vertex/edge attributes

Any number of DataFrame columns

Any type (VD,ED)

Return Types

GraphFrames/DataFrames

Graph [VD,ED] or RDD [Long,VD]

GraphX <=> GraphFrames

If you have invested heavily into GraphX already and are wondering how you will migrate your existing code to GraphFrames, you are about to receive some good news. Apache Spark provides seamless conversions...