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The Benefits of Graph Databases and Neo4j.rb
- 2. Neo4j.rb The Benefits of Graph Database Andreas Ronge
- 3. Who am I ?
- 6. Complex and evolving data models Choose the right tools - N ot O nly SQL
- 8. A vailability – tolerant of node failures
- 10. Eventual consistency instead of ACID (but not Neo4j)
- 14. Language binding - Python, Ruby, Scala, Groovy
- 16. A Ruby Crash Course
- 18. What is a Graph Database ?
- 19. Graph DB vs. SQL id name 1 andreas 2 peter id p1_id p2_id since 1234 1 2 2002 People Friends friend since: 2002 name: andreas name: peter
- 20. Graph DB vs SQL
- 21. Benefit 1 Domain Modeling
- 22. Benefit 2 No O/R mismatch
- 23. Benefit 3 Semi-structured information
- 25. Benefit 4 No Schema
- 26. DEMO TIME Does Thomas Andersson know someone [who knows]* called Agent Smith ? Benefit 5 Deep traversals
- 31. “Drop in” replacement for Rails Active Model
- 32. Improved/extended API (lucene, rules, migrations,...)
- 33. Embedded Easier to install, deploy & test Is running in same thread as your application No network connection to DB needed No Database Tier
- 34. Another Ruby Crash Course Ruby Java
- 36. only write locks, no read locks
- 37. Object Oriented Mapping Neo4j and a Dynamic Language – A Perfect Match ! Ruby Class A Neo4j Node _class: Person name: andreas
- 39. Graph as an Index
- 40. Use Lucene
- 41. Reference Node Find Thomas Andersson Neo4j.ref_node.outgoing(:root).first REF_NODE Thomas Andersson
- 42. Use the Graph as an Index A A B Neo4j.ref_node.outgoing('A').each {...} RefNode name andreas name anders name bertil
- 44. Ranked searching
- 45. Sorting
- 46. Date-range
- 47. Sorting by any field
- 49. NodeMixin Lucene Integration Declared properties DSL for relationship traversal Migrations Lists Cascade Delete Works with inheritance
- 50. Ruby Class Mapping: Relationships friend friend name andreas name peter name david
- 51. Incoming Relationship Same relationship different direction Same _class = Actor name = 'keanu' _class = Movie name = 'matrix'
- 52. Ruby on Rails/Active Record “drop in” replacement
- 59. Inheritance
- 60. Recommendation Engine Likes Likes Likes Recommend This 1 2 4 3 5 Bertil Caesar Adam
- 61. Example, Recommendation likes other_people 4 2 5 3 1
- 62. Included Graph Algorithms Shortest paths, Simple paths, Graph measures ... node1 node2
- 63. High Availability Online Backup - hot spare Read-slave replication Write master election
- 65. Lucene Optimized for Graph Algorithms
- 67. No O/R mismatch
- 68. Efficient storage of Semi Structured Information
Editor's Notes
- I'm going to talk about one of the most popular graph databases - neo4j, and of course the ruby binding - neo4j.rb which I have written over the last few years.
- Here is a photo of me playing the Brahms d-minor concerto with the swedish radio symphony. As you can see, it's an old photo. I don't do that much playing any longer but I still passionate about piano playing Another passion I have is Ruby. Why I'm I standing here talking about Neo4j.rb ? I fell in love with Ruby a few years ago and really wanted to learn it. The only way to learn is by reading/writing code. As it turns out I happened to know the people behind the Neo4j database and they suggested I would write a Ruby binding for it.
- The left picture is actually a picture generated by the NeoEclipse – a tool for visualize the graph
- Often you don't need to create your own search tree or index since the domain it self contains a search tree. For an recommendation algorithm it is enough to traverse only a limited number of node of a given distance from one node.
- As you can see Neo4j.rb is like an Object Database except that it does not have a query language The two main benefits of the Graph Database are that it's natural to express the requirements in code (just like an Object Database) since often the real world is best expressed as a graph. You don't need to ruin model that the customer described by putting it in tables. The other benefit is the traversal pattern. As you saw in the previous slide it is easy to express algorithms for things like an recommendation engine with traversals. It is still possible to do this in SQL (since you only need two joins) but I think it is more natural expressed as traversals. Also the traversal speed will remain the same regardless of the depth of traversal or the number of nodes.