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Apache Spark Graph Processing
Apache Spark Graph Processing

Apache Spark Graph Processing: Build, process and analyze large-scale graph data effectively with Spark

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Apache Spark Graph Processing

Chapter 2. Building and Exploring Graphs

This chapter aims to teach us how to represent various types of networks and complex systems as property graphs in Spark and GraphX. Before we can describe the behavior, and analyze the inner structure of these systems, we first need to map their components to vertices or nodes, and map the interactions between the individual components to edges or links. Building on what we learned in the previous chapter, we will delve into the details on how graphs are stored and represented in GraphX. In addition, this chapter introduces the language of graph theory, and the basic characteristics of graphs. Throughout this chapter, we will use real-world datasets that we will map to the different types of graphs. The examples include e-mail communication networks, food flavor network, and social ego networks. On completing this chapter, you will understand how to:

  • Load data and build Spark graphs in many ways
  • Use the join operator to mix external data...

Network datasets

In the previous chapter, we constructed a small social network as a toy example. From this chapter onwards, we are going to work with real-world datasets, drawn from various applications. In fact, graphs are used to represent any complex system as it describes the interactions between the components of the system. Despite the diversity in form, size, nature, and granularity of different systems, graph theory provides a common language, and a set of tools, for representing and analyzing complex systems.

Note

In brief, a graph consists of a set of vertices connected by a set of edges. Each edge represents the relationship between a pair of connected vertices. In this book, we will sometimes use the less technical terms network nodes to refer to vertices, and links to refer to edges. Note that Spark supports multigraphs, that is, it is permitted to have multiple edges between any pair of nodes.

Let's get a preview of the networks that we are going to build in this chapter...

Graph builders

In GraphX, there are four functions for building a property graph. Each of these functions requires that the data from which the graph is constructed should be structured in a specified manner.

The Graph factory method

The first one is the Graph factory method that we have already seen in the previous chapter. It is defined in the apply method of the companion object called Graph, which is as follows:

def apply[VD, ED](
      vertices: RDD[(VertexId, VD)],
      edges: RDD[Edge[ED]],
      defaultVertexAttr: VD = null)
    : Graph[VD, ED]

As we have seen before, this function takes two RDD collections: RDD[(VertexId, VD)] and RDD[Edge[ED]] as parameters for the vertices and edges respectively, to construct a Graph[VD, ED] parameter. The defaultVertexAttr attribute is used to assign the default attribute for the vertices that are present in the edge RDD but not in the vertex RDD. The Graph factory method is convenient when the RDD collections of edges and vertices are readily...

Building graphs

Let's now open our Spark shell and build three types of graphs: a directed email communication network, a bipartite graph of ingredient-compound connections, and a multigraph using the previous graph builders.

Note

Unless otherwise stated, we always assume that the Spark shell is launched from the $SPARKHOME directory. It then becomes the current directory for any relative file path used in this book.

Building directed graphs

The first graph that we will build is the Enron email communication network. If you have restarted your Spark shell, you need to again import the GraphX library. First, create a new folder called data inside $SPARKHOME and copy the dataset into it. This file contains the adjacency list of the email communications between the employees. Assuming that the current directory is $SPARKHOME, we can pass the file path to the GraphLoader.edgeListFile method:

scala> import org.apache.spark.graphx._
import org.apache.spark.graphx._

scala> import org.apache...

Computing the degrees of the network nodes

We are now going to explore the three graphs, and introduce an important property of a network node, which is the degree of the node.

The degree of a node represents the number of links it has to other nodes. In a directed graph, we can make a distinction between the incoming degree of a node or an in-degree, which is the number of its incoming links, and its outgoing degree or out-degree, which is the number of nodes that it points to. In the following sections, we will explore the degree distributions of the three example networks.

In-degree and out-degree of the Enron email network

For the Enron email network, we can confirm that there are roughly ten times more links than nodes:

scala> emailGraph.numEdges
res: Long = 367662

scala> emailGraph.numVertices
res: Long = 36692

Indeed, the in-degree and out-degree of the employees are exactly the same in this example as the email graph is bi-directed. This can be confirmed by looking at the average...

Summary

In this chapter, we have learned about the different ways to build graphs in Spark by working with concrete examples borrowed from online social networks, food science, and e-mail communications. We have seen that constructing a graph requires some data preparation and wrangling efforts. Nonetheless, GraphX offers various graph builder functions from which we can choose, depending on the graph representation that we need to create, and on the shape of the available datasets. Such usable functionalities are the advantages of GraphX against other similar graph-processing frameworks. Moreover, we looked at some basic statistics and properties of graphs, which are rather useful in characterizing their structure and in understanding their representation.

In the next chapter, we will go deeper into the analysis of graphs, using data visualization tools and new graph-theoretical concepts and algorithms, such as connectedness, triangle counting, and PageRank.

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Description

Apache Spark is the next standard of open-source cluster-computing engine for processing big data. Many practical computing problems concern large graphs, like the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. Apache Spark GraphX API combines the advantages of both data-parallel and graph-parallel systems by efficiently expressing graph computation within the Spark data-parallel framework. This book will teach the user to do graphical programming in Apache Spark, apart from an explanation of the entire process of graphical data analysis. You will journey through the creation of graphs, its uses, its exploration and analysis and finally will also cover the conversion of graph elements into graph structures. This book begins with an introduction of the Spark system, its libraries and the Scala Build Tool. Using a hands-on approach, this book will quickly teach you how to install and leverage Spark interactively on the command line and in a standalone Scala program. Then, it presents all the methods for building Spark graphs using illustrative network datasets. Next, it will walk you through the process of exploring, visualizing and analyzing different network characteristics. This book will also teach you how to transform raw datasets into a usable form. In addition, you will learn powerful operations that can be used to transform graph elements and graph structures. Furthermore, this book also teaches how to create custom graph operations that are tailored for specific needs with efficiency in mind. The later chapters of this book cover more advanced topics such as clustering graphs, implementing graph-parallel iterative algorithms and learning methods from graph data.

Who is this book for?

This book is for data scientists and big data developers who want to learn the processing and analyzing graph datasets at scale. Basic programming experience with Scala is assumed. Basic knowledge of Spark is assumed.

What you will learn

  • Write, build and deploy Spark applications with the Scala Build Tool.
  • Build and analyze largescale network datasets
  • Analyze and transform graphs using RDD and graphspecific operations
  • Implement new custom graph operations tailored to specific needs.
  • Develop iterative and efficient graph algorithms using message aggregation and Pregel abstraction
  • Extract subgraphs and use it to discover common clusters
  • Analyze graph data and solve various data science problems using realworld datasets.
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Length: 148 pages
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Language : English
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Language : English
ISBN-13 : 9781784391805
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Table of Contents

9 Chapters
1. Getting Started with Spark and GraphX Chevron down icon Chevron up icon
2. Building and Exploring Graphs Chevron down icon Chevron up icon
3. Graph Analysis and Visualization Chevron down icon Chevron up icon
4. Transforming and Shaping Up Graphs to Your Needs Chevron down icon Chevron up icon
5. Creating Custom Graph Aggregation Operators Chevron down icon Chevron up icon
6. Iterative Graph-Parallel Processing with Pregel Chevron down icon Chevron up icon
7. Learning Graph Structures Chevron down icon Chevron up icon
A. References Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
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nag Jun 18, 2016
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Reading it. will give full feedback later.
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MSastry Nov 01, 2015
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
This book is easy to read with a promise to provide step by step procedures to learn Apache Spark Graph Processing. To the most extent, Mr. Rindra Ramamonjison fulfills the promise. There are several errors in the book as such reader gets confused and can not be followed through the text. For example, spark-1.4.1-bin-hadoop2.6.tgz does not have the directories: core, graphx, mllib, sql, and streaming in SPARKHOME. Also, in the directory data, there are no files such as people.csv and links.csv.Apart from the above superficial errors, the book met my expectation to learn Graph Processing.
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