Graph Databases in Action

Name: Graph Databases in Action | Examples in Gremlin
Brand: Manning
Price: 37.67 EUR
Availability: OnlineOnly

Examples in Gremlin

Josh Perryman Dave Bechberger(Autor*in)

Manning (Verlag)

1. Auflage

Erschienen am 17. Oktober 2020

336 Seiten

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Beschreibung

Graph Databases in Action introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization.

Summary
Relationships in data often look far more like a web than an orderly set of rows and columns. Graph databases shine when it comes to revealing valuable insights within complex, interconnected data such as demographics, financial records, or computer networks. In Graph Databases in Action, experts Dave Bechberger and Josh Perryman illuminate the design and implementation of graph databases in real-world applications. You'll learn how to choose the right database solutions for your tasks, and how to use your new knowledge to build agile, flexible, and high-performing graph-powered applications!

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Isolated data is a thing of the past! Now, data is connected, and graph databases-like Amazon Neptune, Microsoft Cosmos DB, and Neo4j-are the essential tools of this new reality. Graph databases represent relationships naturally, speeding the discovery of insights and driving business value.

About the book
Graph Databases in Action introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization.

What's inside
Graph databases vs. relational databases
Systematic graph data modeling
Querying and navigating a graph
Graph patterns
Pitfalls and antipatterns

About the reader
For software developers. No experience with graph databases required.

About the author
Dave Bechberger and Josh Perryman have decades of experience building complex data-driven systems and have worked with graph databases since 2014.

Table of Contents

PART 1 - GETTING STARTED WITH GRAPH DATABASES

1 Introduction to graphs

2 Graph data modeling

3 Running basic and recursive traversals

4 Pathfinding traversals and mutating graphs

5 Formatting results

6 Developing an application

PART 2 - BUILDING ON GRAPH DATABASES

7 Advanced data modeling techniques

8 Building traversals using known walks

9 Working with subgraphs

PART 3 - MOVING BEYOND THE BASICS

10 Performance, pitfalls, and anti-patterns

11 What's next: Graph analytics, machine learning, and resources

Weitere Details

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Inhalt

Intro
Graph Databases in Action
Copyright
contents
front matter
foreword
preface
acknowledgments
about this book
Who should read this book
How this book is organized: A roadmap
About the code
About the technologies
liveBook discussion forum
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 databases
1.1.3 Why can't I use SQL?
1.2 Is my problem a graph problem?
1.2.1 Explore the questions
1.2.2 I'm still confused. . . . Is this a graph problem?
Summary
2 Graph data modeling
2.1 The data modeling process
2.1.1 Data modeling terms
2.1.2 Four-step process for data modeling
2.2 Understand the problem
2.2.1 Domain and scope questions
2.2.2 Business entity questions
2.2.3 Functionality questions
2.3 Developing the whiteboard model
2.3.1 Identifying and grouping entities
2.3.2 Identifying relationships between entities
2.4 Constructing the logical data model
2.4.1 Translating entities to vertices
2.4.2 Translating relationships to edges
2.4.3 Finding and assigning properties
2.5 Checking our model
Summary
3 Running basic and recursive traversals
3.1 Setting up your environment
3.1.1 Starting the Gremlin Server
3.1.2 Starting the Gremlin Console, connecting to the Gremlin Server, and loading the data
3.2 Traversing a graph
3.2.1 Using a logical data model (schema) to plan traversals
3.2.2 Planning the steps through the graph data
3.2.3 Fundamental concepts of traversing a graph
3.2.4 Writing traversals in Gremlin
3.2.5 Retrieving properties with values steps
3.3 Recursive traversals
3.3.1 Using recursive logic
3.3.2 Writing recursive traversals in Gremlin
Summary
4 Pathfinding traversals and mutating graphs
4.1 Mutating a graph
4.1.1 Creating vertices and edges
4.1.2 Removing data from our graph
4.1.3 Updating a graph
4.1.4 Extending our graph
4.2 Paths
4.2.1 Cycles in graphs
4.2.2 Finding the simple path
4.3 Traversing and filtering edges
4.3.1 Introducing the E and V steps for traversing edges
4.3.2 Filtering with edge properties
4.3.3 Include edges in path results
4.3.4 Performant edge counts and denormalization
Summary
5 Formatting results
5.1 Review of values steps
5.2 Constructing our result payload
5.2.1 Applying aliases in Gremlin
5.2.2 Projecting results instead of aliasing
5.3 Organizing our results
5.3.1 Ordering results returned from a graph traversal
5.3.2 Grouping results returned from a graph traversal
5.3.3 Limiting results
5.4 Combining steps into complex traversals
Summary
6 Developing an application
6.1 Starting the project
6.1.1 Selecting our tools
6.1.2 Setting up the project
6.1.3 Obtaining a driver
6.1.4 Preparing the database server Instance
6.2 Connecting to our database
6.2.1 Building the cluster configuration
6.2.2 Setting up the GraphTraversalSource
6.3 Retrieving data
6.3.1 Retrieving a vertex
6.3.2 Using Gremlin language variants (GLVs)
6.3.3 Adding terminal steps
6.3.4 Creating the Java method in our application
6.4 Adding, modifying, and deleting data
6.4.1 Adding vertices
6.4.2 Adding edges
6.4.3 Updating properties
6.4.4 Deleting elements
6.5 Translating our list and path traversals
6.5.1 Getting a list of results
6.5.2 Implementing recursive traversals
6.5.3 Implementing paths
Summary
Part 2. Building on Graph Databases
7 Advanced data modeling techniques
7.1 Reviewing our current data models
7.2 Extending our logical data model
7.3 Translating entities to vertices
7.3.1 Using generic labels
7.3.2 Denormalizing graph data
7.3.3 Translating relationships to edges
7.3.4 Finding and assigning properties
7.3.5 Moving properties to edges
7.3.6 Checking our model
7.4 Extending our data model for personalization
7.5 Comparing the results
Summary
8 Building traversals using known walks
8.1 Preparing to develop our traversals
8.1.1 Identifying the required elements
8.1.2 Selecting a starting place
8.1.3 Setting up test data
8.2 Writing our first traversal
8.2.1 Designing our traversal
8.2.2 Developing the traversal code
8.3 Pagination and graph databases
8.4 Recommending the highest-rated restaurants
8.4.1 Designing our traversal
8.4.2 Developing the traversal code
8.5 Writing the last recommendation engine traversal
8.5.1 Designing our traversal
8.5.2 Adding this traversal to our application
Summary
9 Working with subgraphs
9.1 Working with subgraphs
9.1.1 Extracting a subgraph
9.1.2 Traversing a subgraph
9.2 Building a subgraph for personalization
9.3 Building the traversal
9.3.1 Reversing the traversing direction
9.3.2 Evaluating the individualized results of the subgraph
9.4 Implementing a subgraph with a remote connection
9.4.1 Connecting with TinkerPop's Client class
9.4.2 Adding this traversal to our application
Summary
Part 3. Moving Beyond the Basics
10 Performance, pitfalls, and anti-patterns
10.1 Slow-performing traversals
10.1.1 Explaining our traversal
10.1.2 Profiling our traversal
10.1.3 Indexes
10.2 Dealing with supernodes
10.2.1 It's about instance data
10.2.2 It's about the database
10.2.3 What makes a supernode?
10.2.4 Monitoring for supernodes
10.2.5 What to do if you have a supernode
10.3 Application anti-patterns
10.3.1 Using graphs for non-graph use cases
10.3.2 Dirty data
10.3.3 Lack of adequate testing
10.4 Traversal anti-patterns
10.4.1 Not using parameterized traversals
10.4.2 Using unlabeled filtering steps
Summary
11 What's next: Graph analytics, machine learning, and resources
11.1 Graph analytics
11.1.1 Pathfinding
11.1.2 Centrality
11.1.3 Community detection
11.1.4 Graphs and machine learning
11.1.5 Additional resources
11.2 Final thoughts
Summary
appendix. Apache TinkerPop installation and overview
A.1 Overview
A.1.1 Gremlin traversal language
A.1.2 TinkerGraph
A.1.3 Gremlin Console
A.1.4 Gremlin Language Variants (GLVs)
A.1.5 Gremlin Server
A.1.6 Documentation
A.2 Installation
A.2.1 Installing and verifying the Java Runtime
A.2.2 Installing Gremlin Console
A.2.3 Installing Gremlin Server
A.2.4 Configuring the Gremlin Console to connect to the Gremlin Server
A.2.5 Gremlin Console command modes: Local versus remote
A.2.6 Using the Gremlin Console
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