Neo4j Presentation
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The document discusses how property graph databases like Neo4j can model and query relationship data more effectively than relational or other NoSQL databases. It provides examples of modeling user, movie, and product data as graphs and executing queries in Cypher. It also discusses using the Java Core API and Traversal API to navigate graph data and developing recommendation systems and applications for fraud detection by analyzing patterns in user behaviors and connections.
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Neo4j Presentation
- 2. github.com/maxdemarzi About 200 public repositories Max De Marzi Neo4j Field Engineer About Me ! 01 02 03 04 maxdemarzi.com @maxdemarzi
- 3. Experience Technical Doesn’t Matter 75 % 50% 95%
- 4. You go home, thinking about graphs All that matters You
- 5. Property Graph It’s super simple. All you get is: Property Graph Model Properties Nodes Relationships
- 6. What you (probably) already know:
- 8. Joins are executed every time you query the relationship Executing a Join means to search for a key B-Tree Index: O(log(n)) Your data grows by 10x, your speed slows down by half More Data = More Searches Slower Performance The Problem 1 2 3 4
- 9. Same Data, Different Layout No more Tables, no more Foreign Keys, no more Joins
- 10. Relational Databases can’t handle Relationships Degraded Performance Speed plummets as data grows and as the number of joins grows Wrong Language SQL was built with Set Theory in mind, not Graph Theory Not Flexible New types of data and relationships require schema redesign Wrong Model They cannot model or store relationships without complexity1 2 3 4
- 11. NoSQL Databases can’t handle Relationships Degraded Performance Speed plummets as you try to join data together in the application Wrong Languages Lots of wacky “almost sql” languages terrible at “joins” Not ACID Eventually Consistent means Eventually Corrupt Wrong Model They cannot model or store relationships without complexity1 2 3 4
- 15. Fixed Sized Records “Joins” on Creation Spin Spin Spin through this data structure Pointers instead of Lookups 1 2 3 4 Neo4j Secret Sauce
- 16. Remains steady as database grows Real Time Query Performance Connectedness and Size of Data Set Response Time 0 to 2 hops 0 to 3 degrees Thousands of connections Tens to hundreds of hops Thousands of degrees Billions of connections Relational and Other NoSQL Databases Neo4j Neo4j is 1000x faster Reduces minutes to milliseconds
- 17. I don’t know the average height of all hollywood actors, but I do know the Six Degrees of Kevin Bacon But not for every query
- 18. Reimagine your Data as a Graph Better Performance Query relationships in real time Right Language Cypher was purpose built for Graphs Flexible and Consistent Evolve your schema seamlessly while keeping transactions Right Model Graphs simplify how you think 1 2 3 4 Agile, High Performance and Scalable without Sacrifice
- 19. Just draw stuff and “walla” there is your data model Graphs are Whiteboard Friendly
- 21. Movie Property Graph Some Models are Easy
- 23. Should Roles be their own Node? Some Models are Easy but not for all Questions
- 24. How do you model Flight Data?
- 25. Airports Nodes with Flying To Relationships How do you model Flight Data?
- 26. Maybe Flight should be its own Node? How do you model Flight Data?
- 27. Don’t we care about Flights only on particular Days? How do you model Flight Data?
- 28. What is this trick with the date in the relationship type? How do you model Flight Data?
- 29. We don’t need Airports if we model this way! How do you model Flight Data?
- 31. Group Destinations together! How do you model Flight Data?
- 32. OMG WAT! How do you model Flight Data?
- 33. Do not try and bend the data. That’s im possible.
- 34. If they can do it, you can do it! How do you model Comic Books?
- 35. Cloning Twitter Building a News Feed 9:00 am @hipster This is what I had for breakfast! <Insert Image of squirrel food> 8:30 am @neo4j Automated tweet telling me about Graph Connect 2017 in NYC on Oct 23-24 8:12 am @ex-coworker Stuff I no longer care about. 8:03 am @someguy Inspirational Quote of the Day
- 37. How do others do it? Cloning Twitter
- 38. How do others do it? Cloning Twitter
- 39. The Wrong Way Modeling a Twitter Feed
- 40. A Better Way Modeling a Twitter Feed
- 41. Bigger Model Modeling a Twitter Feed
- 42. getDegree is your Friend
- 43. This is Java. What happened to Cypher?
- 44. Java Core API
- 45. Easy to Learn (no really) Java Core API • Step by Step from GraphDatabaseService • Start a transaction (reads and writes) • findNode(Label, Property, Value) • findNodes(Label, Property, Value) • findNodes(Label) • getNodeById(Long) • getRelationships(Direction, Type) • getProperty(Property, (optional) Default Value)
- 46. Get friends of a User Java Core API
- 47. Traversal API
- 48. Interesting to Learn Traversal API • Start with the Simple Defaults (order, relationships, depth, uniqueness, etc) • Custom Expanders • Where should I go next • Custom Evaluators • I’ve gone there… should I accept this path?
- 50. Cypher
- 51. Cypher: Powerful and Expressive Query Language MATCH (:Person { name:“Dan”} ) -[:LOVES]-> (:Person { name:“Ann”} ) LOVES Dan Ann Label Property Label Property Node Node
- 54. Learn from the Experts • Alex Beutel, CMU • Leman Akoglu, Stony Brook • Christos Faloutsos, CMU • Graph-Based User Behavior Modeling: From Prediction to Fraud Detection • http://www.cs.cmu.edu/~abeutel/kdd2015_tutorial/
- 57. User Behavior Challenges • How can we understand normal user behavior? • How can we find suspicious behavior? • How can we distinguish the two?
- 70. Modeling “Normal” Behavior • Predict Edges • Predict Node Attributes • Predict Edge Attributes • Clustering and Community Detection
- 72. Modeling “Normal” Behavior • Predict Edges • Predict Node Attributes • Predict Edge Attributes • Clustering and Community Detection • Fraud Detection
- 78. Movie Data Model
- 79. Cypher Query: Movie Recommendation MATCH (watched:Movie {title:"Toy Story”}) <-[r1:RATED]- () -[r2:RATED]-> (unseen:Movie) WHERE r1.rating > 7 AND r2.rating > 7 AND watched.genres = unseen.genres AND NOT( (:Person {username:”maxdemarzi"}) -[:RATED|WATCHED]-> (unseen) ) RETURN unseen.title, COUNT(*) ORDER BY COUNT(*) DESC LIMIT 25 What are the Top 25 Movies • that I haven't seen • with the same genres as Toy Story • given high ratings • by people who liked Toy Story
- 80. Movie Data Model
- 83. Combine any APIs Cypher Stored Procedures
- 84. Don’t use SOLR Facets for this! Multiple Dimensions AgeSize FeaturesProperty Cost
- 85. Multiple Dimensions Java Audio Book! What about Publisher? What about Author? What about Publication Year? What about Java Version? What About…. Left parentheses, n, right parentheses, semi-colon!
- 86. Bucket or Group Values if you have to Discrete Values for Each Dimension
- 87. Nodes for Discrete Dimensional Values Dimensional Model *Use Named Relationship Types instead of HAS
- 88. Stupid Glasses Loud Pants Skate Boards Neon Colors 1 2 3 4 Who remembers this?
- 89. Look at how thick they were, even back in 1902! It’s a Sears Catalogue!
- 90. Ares Predator Street Samurai Catalog
- 91. With free two day shipping! Cypher Version of the Catalog
- 92. A tree is a simple graph A Tree of Data
- 93. So fast, it’s not even funny. Promotions About 2-4M Traversals per second per core Traversing a 50 level Tree UP costs practically nothing.
- 97. Data Cleansing Look for Shared Features Calculate Similarity Connect Extract Features 1 2 3 4
- 98. Using an Anchor
- 100. Thank You!