Welcome to the connected world. In the information age, everything around us is based on entities, relations, and above all, connectivity. Data is becoming exponentially more complex, which is affecting the performance of existing data stores. The most natural form in which data is visualized is in the form of graphs. In recent years, there has been an explosion of technologies to manage, process, and analyze graphs. While companies such as Facebook and LinkedIn have been the most well-known users of graph technologies for social web properties, a quiet revolution has been spreading across other industries. More than 30 of the Forbes Global 2000 companies, and many times as many start-ups have quietly been working to apply graphs to a wide array of business-critical use cases.
Neo4j, a graph database by Neo Technologies, is the leading player in the market for handling related data. It is not only efficient and easier to use, but it also includes all security and reliability features of tabulated databases.
We are entering an era of connected data where companies that can master the connections between their data—the lines and patterns linking the dots and not just the dots—will outperform the organizations that fail to recognize connectedness. It will be a long time before relational databases ebb into oblivion. However, their role is no longer universal. Graph databases are here to stay, and for now, Neo4j is setting the standard for the rest of the market.
This book presents an insight into how Neo4j can be applied to practical industry scenarios and also includes tweaks and optimizations for developers and administrators to make their systems more efficient and high performing.
Chapter 1, Getting Started with Neo4j, introduces Neo4j, its functionality, and norms in general, briefly outlining the fundamentals. The chapter also gives an overview of graphs, NOSQL databases and their features and Neo4j in particular, ACID compliance, basic CRUD operations, and setup. So, if you are new to Neo4j and need a boost, this is your chapter.
Chapter 2, Querying and Indexing in Neo4j, deals with querying Neo4j using Cypher, optimizations to data model and queries for better Cypher performance. The basics of Gremlin are also touched upon. Indexing in Neo4j and its types are introduced along with how to migrate from existing SQL stores and data import/export techniques.
Chapter 3, Efficient Data Modeling with Graphs, explores the data modeling concepts and techniques associated with graph data in Neo4j, in particular, property graph model, design constraints for Neo4j, the designing of schemas, and modeling across multiple domains.
Chapter 4, Neo4j for High-volume Applications, teaches you how to develop applications with Neo4j to handle high volumes of data. We will define how to develop an efficient architecture and transactions in a scalable way. We will also take a look at built-in graph algorithms for better traversals and also introduce Spring Data Neo4j.
Chapter 5, Testing and Scaling Neo4j Applications, teaches how to test Neo4j applications using the built-in tools and the GraphAware framework for unit and performance tests. We will also discuss how a Neo4j application can scale.
Chapter 6, Neo4j Internals, takes a look under the hood of Neo4j, skimming the concepts from the core classes in the source into the internal storage structure, caching, transactions, and related operations. Finally, the chapter deals with HA functions and master election.
Chapter 7, Administering Neo4j, throws light upon some useful tools and adapters that have been built to interface Neo4j with the most popular languages and frameworks. The chapter also deals with tips and configurations for administrators to optimize the performance of the Neo4j system. The essential security aspects are also dealt with in this chapter.
Chapter 8, Use Case – Similarity-based Recommendation System, is an example-oriented chapter. It provides a demonstration on how to go about building a similarity-based recommendation system with Neo4j and highlights the utility of graph visualization.