图数据库：理论与实践

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"《Graph Databases》是由Ian Robinson, Jim Webber, 和 Emil Eifrem合著的一本关于图数据库的专业书籍，版权归属于2013年的NeoTechnology, Inc.。该书由O'Reilly Media, Inc.出版，并强调了在教育、商业和销售推广中的应用。此外，该书的在线版本也可通过Safari Books Online获取。" 《Graph Databases》这本书深入探讨了图数据库这一主题，特别提到了Neo4j，这是一个非常知名的图数据库管理系统。图数据库是NoSQL数据库的一种类型，它以图形结构来存储和处理数据，其中节点代表实体，边表示实体之间的关系，这使得处理复杂和相互关联的数据变得更加高效和直观。书中可能涵盖了以下关键知识点： 1. **图数据库基础**：介绍图数据库的基本概念，包括节点、边、属性以及如何构建和查询图模型。 2. **图数据库的优势**：讨论图数据库在处理高度互联数据、社交网络分析、推荐系统和复杂的查询场景中的优势。 3. **Neo4j介绍**：详述Neo4j的特性，如ACID事务、Cypher查询语言，以及如何安装和操作Neo4j数据库。 4. **Cypher查询语言**：详细解析Cypher的语法和用法，包括创建、查询、更新和删除节点、边及其属性。 5. **图数据库设计**：指导读者如何有效地设计和建模适合图数据库的数据结构。 6. **性能优化**：探讨图数据库的性能调优技巧，包括索引、批量导入数据和查询优化。 7. **案例研究**：提供实际应用案例，如欺诈检测、网络分析和知识图谱，展示图数据库在不同领域的应用。 8. **图数据库与其他数据库的比较**：对比图数据库与关系型数据库（RDBMS）、键值存储、文档数据库等其他类型的NoSQL数据库的优缺点。 9. **图算法**：介绍图论中的经典算法，如最短路径算法、社区检测算法在图数据库中的实现和应用。 10. **扩展和集成**：讨论如何将图数据库与其他系统集成，如与微服务架构、大数据平台的配合使用。 11. **安全性与管理**：涵盖Neo4j的安全措施、备份恢复策略以及数据库的日常管理。 12. **未来趋势**：对图数据库技术的发展趋势进行展望，包括新功能、工具和最佳实践的演变。这本书是图数据库领域的权威指南，对于希望理解和利用图数据库技术的开发者、数据科学家和IT专业人员来说，具有很高的参考价值。通过深入阅读和实践，读者可以掌握图数据库的核心原理并能够灵活应用到实际项目中。

business—social, intent, consumption, interest, and mobile—and says that the ability

to leverage these graphs provides a “sustainable competitive advantage.”

For example, Twitter’s data is easily represented as a graph. In Figure 1-1 we see a small

network of followers. The relationships are key here in establishing the semantic context:

namely, that Billy follows Harry, and that Harry, in turn, follows Billy. Ruth and Harry

likewise follow each other, but sadly, although Ruth follows Billy, Billy hasn’t (yet)

reciprocated.

Figure 1-1. A small social graph

Of course, T

witter’s real graph is hundreds of millions of times larger than the example

in Figure 1-1, but it works on precisely the same principles. In Figure 1-2 we’ve expanded

the graph to include the messages published by Ruth.

2 | Chapter 1: Introduction

2. See Rodriguez, M.A., Neubauer, P., “The Graph Traversal Pattern,” 2010.

Another way to slice the graph space is to look at the graph models

ployed by the various technologies. There are three dominant graph

data models: the property graph, Resource Description Framework

(RDF) triples, and hypergraphs. We describe these in detail in Appen‐

dix A. Most of the popular graph databases on the market use the prop‐

erty graph model, and in consequence, it’s the model we’ll use through‐

out the remainder of this book.

Graph Databases

A graph database

management system (henceforth, a graph database) is an online da‐

tabase management system with Create, Read, Update, and Delete (CRUD) methods

that expose a graph data model. Graph databases are generally built for use with trans‐

actional (OLTP) systems. Accordingly, they are normally optimized for transactional

performance, and engineered with transactional integrity and operational availability

in mind.

There are two properties of graph databases you should consider when investigating

graph database technologies:

The underlying storage

Some graph databases use native graph storage that is optimized and designed for

storing and managing graphs. Not all graph database technologies use native graph

storage, however. Some serialize the graph data into a relational database, an object-

oriented database, or some other general-purpose data store.

The processing engine

Some definitions require that a graph database use index-free adjacency, meaning

that connected nodes physically “point” to each other in the database.

Here we take

a slightly broader view: any database that from the user’s perspective behaves like a

graph database (i.e., exposes a graph data model through CRUD operations) quali‐

fies as a graph database. We do acknowledge, however, the significant performance

advantages of index-free adjacency, and therefore use the term native graph pro‐

cessing to describe graph databases that leverage index-free adjacency.

A High-Level View of the Graph Space | 5

It’s important to note that native graph storage and native graph pro‐

cessing are neither good nor bad—they’

re simply classic engineering

trade-offs. The benefit of native graph storage is that its purpose-built

stack is engineered for performance and scalability. The benefit of non‐

native graph storage, in contrast, is that it typically depends on a mature

nongraph backend (such as MySQL) whose production characteristics

are well understood by operations teams. Native graph processing

(index-free adjacency) benefits traversal performance, but at the ex‐

pense of making some nontraversal queries difficult or memory

intensive.

Relationships are first-class citizens of the graph data model, unlike other database

managemen

t systems, which require us to infer connections between entities using

contrived properties such as foreign keys, or out-of-band processing like map-reduce.

By assembling the simple abstractions of nodes and relationships into connected struc‐

tures, graph databases enable us to build arbitrarily sophisticated models that map

closely to our problem domain. The resulting models are simpler and at the same time

more expressive than those produced using traditional relational databases and the

other NOSQL stores.

Figure 1-3 shows a pictorial overview of some of the graph databases on the market

today based on their storage and processing models.

Graph Compute Engines

A graph compute engine is a technology that enables global graph computational algo‐

rithms to be run against large datasets. Graph compute engines are designed to do things

like identify clusters in your data, or answer questions such as, “how many relationships,

on average, does everyone in a social network have?”

Because of their emphasis on global queries, graph compute engines are normally op‐

timized for scanning and processing large amounts of information in batch, and in that

respect they are similar to other batch analysis technologies, such as data mining and

OLAP, that are familiar in the relational world. Whereas some graph compute engines

include a graph storage layer, others (and arguably most) concern themselves strictly

with processing data that is fed in from an external source, and returning the results.

6 | Chapter 1: Introduction

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