book

Graph Databases in Action

Name: Graph Databases in Action
ISBN: 9781617296376

by Dave Bechberger, Josh Perryman

November 2020

Intermediate to advanced

336 pages

10h 11m

English

Manning Publications

Audiobook available

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Graph Databases in Action
copyright
contents
foreword
preface
acknowledgments
about this book
about the authors
about the cover illustration
Part 1 Getting started with graph databases

1 Introduction to graphs
1.1 What is a graph?1.1.1 What is a graph database?1.1.2 Comparison with other types of databases1.1.3 Why can’t I use SQL?1.2 Is my problem a graph problem?1.2.1 Explore the questions1.2.2 I’m still confused… . Is this a graph problem?
2 Graph data modeling
2.1 The data modeling process2.1.1 Data modeling terms2.1.2 Four-step process for data modeling2.2 Understand the problem2.2.1 Domain and scope questions2.2.2 Business entity questions2.2.3 Functionality questions2.3 Developing the whiteboard model2.3.1 Identifying and grouping entities2.3.2 Identifying relationships between entities2.4 Constructing the logical data model2.4.1 Translating entities to vertices2.4.2 Translating relationships to edges2.4.3 Finding and assigning properties2.5 Checking our model
3 Running basic and recursive traversals
3.1 Setting up your environment3.1.1 Starting the Gremlin Server3.1.2 Starting the Gremlin Console, connecting to the Gremlin Server, and loading the data3.2 Traversing a graph3.2.1 Using a logical data model (schema) to plan traversals3.2.2 Planning the steps through the graph data3.2.3 Fundamental concepts of traversing a graph3.2.4 Writing traversals in Gremlin3.2.5 Retrieving properties with values steps3.3 Recursive traversals3.3.1 Using recursive logic3.3.2 Writing recursive traversals in Gremlin
4 Pathfinding traversals and mutating graphs
4.1 Mutating a graph4.1.1 Creating vertices and edges4.1.2 Removing data from our graph4.1.3 Updating a graph4.1.4 Extending our graph4.2 Paths4.2.1 Cycles in graphs4.2.2 Finding the simple path4.3 Traversing and filtering edges4.3.1 Introducing the E and V steps for traversing edges4.3.2 Filtering with edge properties4.3.3 Include edges in path results4.3.4 Performant edge counts and denormalization
5 Formatting results
5.1 Review of values steps5.2 Constructing our result payload5.2.1 Applying aliases in Gremlin5.2.2 Projecting results instead of aliasing5.3 Organizing our results5.3.1 Ordering results returned from a graph traversal5.3.2 Grouping results returned from a graph traversal5.3.3 Limiting results5.4 Combining steps into complex traversals
6 Developing an application
6.1 Starting the project6.1.1 Selecting our tools6.1.2 Setting up the project6.1.3 Obtaining a driver6.1.4 Preparing the database server Instance6.2 Connecting to our database6.2.1 Building the cluster configuration6.2.2 Setting up the GraphTraversalSource6.3 Retrieving data6.3.1 Retrieving a vertex6.3.2 Using Gremlin language variants (GLVs)6.3.3 Adding terminal steps6.3.4 Creating the Java method in our application6.4 Adding, modifying, and deleting data6.4.1 Adding vertices6.4.2 Adding edges6.4.3 Updating properties6.4.4 Deleting elements6.5 Translating our list and path traversals6.5.1 Getting a list of results6.5.2 Implementing recursive traversals6.5.3 Implementing paths
Part 2 Building on Graph Databases
7 Advanced data modeling techniques
7.1 Reviewing our current data models7.2 Extending our logical data model7.3 Translating entities to vertices7.3.1 Using generic labels7.3.2 Denormalizing graph data7.3.3 Translating relationships to edges7.3.4 Finding and assigning properties7.3.5 Moving properties to edges7.3.6 Checking our model7.4 Extending our data model for personalization7.5 Comparing the results
8 Building traversals using known walks
8.1 Preparing to develop our traversals8.1.1 Identifying the required elements8.1.2 Selecting a starting place8.1.3 Setting up test data8.2 Writing our first traversal8.2.1 Designing our traversal8.2.2 Developing the traversal code8.3 Pagination and graph databases8.4 Recommending the highest-rated restaurants8.4.1 Designing our traversal8.4.2 Developing the traversal code8.5 Writing the last recommendation engine traversal8.5.1 Designing our traversal8.5.2 Adding this traversal to our application
9 Working with subgraphs
9.1 Working with subgraphs9.1.1 Extracting a subgraph9.1.2 Traversing a subgraph9.2 Building a subgraph for personalization9.3 Building the traversal9.3.1 Reversing the traversing direction9.3.2 Evaluating the individualized results of the subgraph9.4 Implementing a subgraph with a remote connection9.4.1 Connecting with TinkerPop’s Client class9.4.2 Adding this traversal to our application
Part 3 Moving Beyond the Basics
10 Performance, pitfalls, and anti-patterns
10.1 Slow-performing traversals10.1.1 Explaining our traversal10.1.2 Profiling our traversal10.1.3 Indexes10.2 Dealing with supernodes10.2.1 It’s about instance data10.2.2 It’s about the database10.2.3 What makes a supernode?10.2.4 Monitoring for supernodes10.2.5 What to do if you have a supernode10.3 Application anti-patterns10.3.1 Using graphs for non-graph use cases10.3.2 Dirty data10.3.3 Lack of adequate testing10.4 Traversal anti-patterns10.4.1 Not using parameterized traversals10.4.2 Using unlabeled filtering steps
11 What’s next: Graph analytics, machine learning, and resources
11.1 Graph analytics11.1.1 Pathfinding11.1.2 Centrality11.1.3 Community detection11.1.4 Graphs and machine learning11.1.5 Additional resources11.2 Final thoughts
appendix A Apache TinkerPop installation and overview
A.1 OverviewA.1.1 Gremlin traversal languageA.1.2 TinkerGraphA.1.3 Gremlin ConsoleA.1.4 Gremlin Language Variants (GLVs)A.1.5 Gremlin ServerA.1.6 DocumentationA.2 InstallationA.2.1 Installing and verifying the Java RuntimeA.2.2 Installing Gremlin ConsoleA.2.3 Installing Gremlin ServerA.2.4 Configuring the Gremlin Console to connect to the Gremlin ServerA.2.5 Gremlin Console command modes: Local versus remoteA.2.6 Using the Gremlin Console

Content preview from Graph Databases in Action

2 Graph data modeling

This chapter covers

Defining project goals and terminology with business or end users
Building a conceptual data model for the entities and their relationships
Translating a conceptual data model into a graph data model
Comparing graph data modeling concepts to relational data modeling concepts
Constructing the graph data model for our social network use case

Let’s say you want to build a fire pit in your backyard. How would you approach this problem? Would you just start building something and hope that it comes out all right, or would you sit back and draw a picture of what you want to accomplish? When building anything, be it software or a backyard fire pit, it’s crucial that you start with a good mental picture ...

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Graph Databases in Action

by Dave Bechberger, Josh Perryman