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Graph Databases, by Ian Robinson, Jim Webber, Emil Eifrem
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Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.
Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.
- Model data with the Cypher query language and property graph model
- Learn best practices and common pitfalls when modeling with graphs
- Plan and implement a graph database solution in test-driven fashion
- Explore real-world examples to learn how and why organizations use a graph database
- Understand common patterns and components of graph database architecture
- Use analytical techniques and algorithms to mine graph database information
- Sales Rank: #277706 in Books
- Brand: Brand: O'Reilly Media
- Published on: 2013-06-17
- Released on: 2013-06-20
- Original language: English
- Number of items: 1
- Dimensions: 9.46" h x .47" w x 6.71" l, .78 pounds
- Binding: Paperback
- 224 pages
- Used Book in Good Condition
About the Author
Ian Robinson is the co-author of REST in Practice (O'Reilly Media, 2010). Ian is an engineer at Neo Technology, working on a distributed version of the Neo4j database. Prior to joining the engineering team, Ian served as Neo's Director of Customer Success, managing the training, professional services, and support arms of Neo, and working with customers to design and develop mission-critical graph database solutions. Ian came to Neo Technology from ThoughtWorks, where he was SOA Practice Lead and a member of the CTO's global Technical Advisory Board. Ian presents frequently at conferences worldwide on topics including the application of graph database technologies, and RESTful enterprise integration.
Dr. Jim Webber is Chief Scientist with Neo Technology, where he researches novel graph databases and writes open source software. Previously, Jim spent time working with big graphs like the Web for building distributed systems, which led him to being a co-author on the book REST in Practice (O'Reilly Media, 2010). Jim is active in the development community, presenting regularly around the world. His blog is located at http://jimwebber.org and he tweets often @jimwebber.
Emil Eifrem is CEO of Neo Technology and co-founder of the Neo4j project. Before founding Neo, he was the CTO of Windh AB, where he headed the development of highly complex information architectures for Enterprise Content Management Systems. Committed to sustainable open source, he guides Neo along a balanced path between free availability and commercial reliability. Emil is a frequent conference speaker and author on NOSQL databases.
Most helpful customer reviews
25 of 25 people found the following review helpful.
decent 3.7
By Vivace
The good:
- gives you concrete, real motivation for using graphDB
- decent discussion of modeling real world problems as graphs
- nice coverage of cypher
- nice coverage of neo4j
the bad:
- reads a bit like an advertisement for neo4j
- doesnt discuss much about pregel or other distributed graph systems.
- the last chapter is entitled 'predictive analytics' was a bit of a let down. The first half is just BFS, DFS and A* and the second half discusses `triadic closure' ...
- its a short book and a bit fluffy.
on the whole:
i was very excited to get this book, had it on preorder....its not the bombshell i was hoping for but it is a great step in the right direction.
16 of 17 people found the following review helpful.
Your grandfather's database?
By wiredweird
On the whole, I found this an adequate introduction. It goes over the basic notion of a graph database, built as a web of nuggets of information with explicit connections between them. The authors contrast this to the popular, table-oriented relational model, and describe cases where the graph approach offer better conceptual match to the real world, better performance, and many other advantages. Although this ties to specific products, it presents some examples in terms of a very non-SQL-like query language with some intriguing possibilities. It comes across more as a conceptual introduction than a detailed manual for the practitioner, but that's OK.
Not surprisingly, it skips over cases where relational's implicit, regular parallelism offers huge possibility for acceleration compared to the random access patterns of graph databases, but one could concoct cases for advantage in either direction. Also, despite the effort in computing a relational "join", there's great freedom in being able to create arbitrary new relationships between data elements - graph DBs show no straightforward way to step off the trails defined by the graph's existing edges. To boldly go through the data where no one has gone before seems not to be part of the paradigm.
My problem lies in presenting graph databases as shiny and new, the next great thing after relational. They're also shiny and old, the last great thing before relational. Network-based CODASYL databases held sway through the 1970s and early 1980s, before the relational model swept the industry in a seismic shift. For some reason, the authors omit all mention of this fact. The index doesn't even list the word CODASYL. Whether intentionally or not, the authors come across as Santayana's "those who cannot remember the past." In particular, they choose not to discuss database optimizations based on the physical, on-disk relationships between data items - something explicit in at least one CODASYL database I used back in the day, and that offered very worthwhile speedups.
This presents good conceptual foundation for graph databases. It's not the how-to for a serious programmer, though, and completely disregards decades of practice and hard-won experience.
-- wiredweird
11 of 13 people found the following review helpful.
New approach to data modelling explained in detail
By Krzysztof Ropiak
This book significantly help in understanding what graph databases are and how to use them properly. The authors introduce basic ideas behind graph databases. They write about why the need for such databases emerged, why there's a need for having database engine in which relationships are first class citizens.
I believe that most important chapter of this book is the one that explains data modeling with graphs. The way you need to think when using graph db is totally different that in other types of db. The authors based their teachings on a set of examples, with each being discussed in detail. Various use-cases are shown, and you'll be surprised how efficient data model can be, when used properly.
You will be also able to learn basics of Cypher, which is a language that is used for querying a graph database. It's not really comprehensive introduction, so therefore it cannot be used as a reference. The book shows examples for querying Neo4j, which is probably the most popular graph database implementation. I don't think that you will be very comfortable at using Neo4j immediately after reading this book. It rather intends to make you familiar with fundamental concepts of graph databases and showing how it differs from still more popular solutions like RDBMS.
Also, some additional topics were covered, like: overview of using graph database in agile (also tdd-based) manner, introduction to Neo4j internals (different available APIs or ways of running it) or overview of other NoSQL storage.
I really liked reading it and the book made me more interested in graph-dbs as it provided solid arguments for using it in various applications. On the other hand, after reading it, I still think there's a lot for me to learn (from other resources) before I become comfortable with Neo4j. I would recommend this book to all developers, who are new to concepts of graph databases and who wants to become familiar with its strong points, before they try start using concrete graph database solutions like Neo4j.
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