Neo4j - The Benefits of Graph Databases (OSCON 2009)
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This document discusses the benefits of graph databases like Neo4j compared to relational databases. It notes that data is becoming more connected and semi-structured, and graph databases can better represent these types of connections. It provides examples of building a node space and traversing relationships in Neo4j using code snippets. It also discusses scaling Neo4j through replication and partitioning. In summary, graph databases like Neo4j are well-suited for complex, connected data and can provide better performance than relational databases for network and semantic queries.
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Neo4j - The Benefits of Graph Databases (OSCON 2009)
- 1. No SQL? Image credit: http://browsertoolkit.com/fault-tolerance.png
- 2. Neo4j the benefits of graph databases Emil Eifrem CEO, Neo Technology
- 5. Death? Community experimentation: CouchDB SimpleDB Hypertable Cassandra Scalaris ...
- 6. ?
- 7. Trend 1: data is getting more connected Giant Global Information connectivity Graph (GGG) Ontologies RDF Folksonomies Tagging User- Wikis generated content Blogs RSS Hypertext Text documents web 1.0 web 2.0 “web 3.0” 1990 2000 2010 2020
- 8. Trend 2: ... and more semi-structured Individualization of content! In the salary lists of the 1970s, all elements had exactly one job In the salary lists of the 2000s, we need 5 job columns! Or 8? Or 15? Trend accelerated by the decentralization of content generation that is the hallmark of the age of participation (“web 2.0”)
- 9. Relational database Salary List Performance Majority of Webapps Social network Semantic } Trading custom Information complexity
- 10. We = hackers! So that’s vCPU... what about vhackers?
- 11. Whiteboard friendly? ? owns Björn Big Car build transport , Kids & DayCare Veggies
- 13. Alternative? a graph database
- 14. The Graph DB model: representation Core abstractions: name = “Emil” age = 29 Nodes sex = “yes” Relationships between nodes 1 2 Properties on both type = KNOWS time = 4 years 3 type = car vendor = “SAAB” model = “95 Aero”
- 15. Example: The Matrix name = “The Architect” name = “Morpheus” rank = “Captain” name = “Thomas Anderson” occupation = “Total badass” 42 age = 29 disclosure = public KNOWS KNOWS CODED_BY 1 KN O 7 3 WS 13 S KN name = “Cypher” KNOW OW last name = “Reagan” S name = “Agent Smith” disclosure = secret version = 1.0b age = 3 days age = 6 months language = C++ 2 name = “Trinity”
- 16. Code (1): Building a node space NeoService neo = ... // Get factory // Create Thomas 'Neo' Anderson Node mrAnderson = neo.createNode(); mrAnderson.setProperty( "name", "Thomas Anderson" ); mrAnderson.setProperty( "age", 29 ); // Create Morpheus Node morpheus = neo.createNode(); morpheus.setProperty( "name", "Morpheus" ); morpheus.setProperty( "rank", "Captain" ); morpheus.setProperty( "occupation", "Total bad ass" ); // Create a relationship representing that they know each other mrAnderson.createRelationshipTo( morpheus, RelTypes.KNOWS ); // ...create Trinity, Cypher, Agent Smith, Architect similarly
- 17. Code (1): Building a node space NeoService neo = ... // Get factory Transaction tx = neo.beginTx(); // Create Thomas 'Neo' Anderson Node mrAnderson = neo.createNode(); mrAnderson.setProperty( "name", "Thomas Anderson" ); mrAnderson.setProperty( "age", 29 ); // Create Morpheus Node morpheus = neo.createNode(); morpheus.setProperty( "name", "Morpheus" ); morpheus.setProperty( "rank", "Captain" ); morpheus.setProperty( "occupation", "Total bad ass" ); // Create a relationship representing that they know each other mrAnderson.createRelationshipTo( morpheus, RelTypes.KNOWS ); // ...create Trinity, Cypher, Agent Smith, Architect similarly tx.commit();
- 18. Code (1b): Defining RelationshipTypes // In package org.neo4j.api.core public interface RelationshipType { String name(); } // In package org.yourdomain.yourapp // Example on how to roll dynamic RelationshipTypes class MyDynamicRelType implements RelationshipType { private final String name; MyDynamicRelType( String name ){ this.name = name; } public String name() { return this.name; } } // Example on how to kick it, static-RelationshipType-like enum MyStaticRelTypes implements RelationshipType { KNOWS, WORKS_FOR, }
- 19. The Graph DB model: traversal Traverser framework for name = “Emil” high-performance traversing age = 29 sex = “yes” across the node space 1 2 type = KNOWS time = 4 years 3 type = car vendor = “SAAB” model = “95 Aero”
- 20. Example: Mr Anderson’s friends name = “The Architect” name = “Morpheus” rank = “Captain” name = “Thomas Anderson” occupation = “Total badass” 42 age = 29 disclosure = public KNOWS KNOWS CODED_BY 1 KN O 7 3 WS 13 S KN name = “Cypher” KNOW OW last name = “Reagan” S name = “Agent Smith” disclosure = secret version = 1.0b age = 3 days age = 6 months language = C++ 2 name = “Trinity”
- 21. Code (2): Traversing a node space // Instantiate a traverser that returns Mr Anderson's friends Traverser friendsTraverser = mrAnderson.traverse( Traverser.Order.BREADTH_FIRST, StopEvaluator.END_OF_GRAPH, ReturnableEvaluator.ALL_BUT_START_NODE, RelTypes.KNOWS, Direction.OUTGOING ); // Traverse the node space and print out the result System.out.println( "Mr Anderson's friends:" ); for ( Node friend : friendsTraverser ) { System.out.printf( "At depth %d => %s%n", friendsTraverser.currentPosition().getDepth(), friend.getProperty( "name" ) ); }
- 22. name = “The Architect” name = “Morpheus” rank = “Captain” name = “Thomas Anderson” occupation = “Total badass” 42 age = 29 disclosure = public KNOWS KNOWS CODED_BY 1 KN O 7 3 WS 13 S KN name = “Cypher” KNOW OW last name = “Reagan” S name = “Agent Smith” disclosure = secret version = 1.0b age = 3 days age = 6 months language = C++ 2 name = “Trinity” $ bin/start-neo-example Mr Anderson's friends: At depth 1 => Morpheus friendsTraverser = mrAnderson.traverse( Traverser.Order.BREADTH_FIRST, At depth 1 => Trinity StopEvaluator.END_OF_GRAPH, At depth 2 => Cypher ReturnableEvaluator.ALL_BUT_START_NODE, RelTypes.KNOWS, At depth 3 => Agent Smith Direction.OUTGOING ); $
- 23. Example: Friends in love? name = “The Architect” name = “Morpheus” rank = “Captain” name = “Thomas Anderson” occupation = “Total badass” 42 age = 29 disclosure = public KNOWS KNOWS CODED_BY 1 7 3 KN O WS 13 S KN KNOW name = “Cypher” OW last name = “Reagan” S name = “Agent Smith” LO disclosure = secret version = 1.0b VE age = 6 months language = C++ S 2 name = “Trinity”
- 24. Code (3a): Custom traverser // Create a traverser that returns all “friends in love” Traverser loveTraverser = mrAnderson.traverse( Traverser.Order.BREADTH_FIRST, StopEvaluator.END_OF_GRAPH, new ReturnableEvaluator() { public boolean isReturnableNode( TraversalPosition pos ) { return pos.currentNode().hasRelationship( RelTypes.LOVES, Direction.OUTGOING ); } }, RelTypes.KNOWS, Direction.OUTGOING );
- 25. Code (3a): Custom traverser // Traverse the node space and print out the result System.out.println( "Who’s a lover?" ); for ( Node person : loveTraverser ) { System.out.printf( "At depth %d => %s%n", loveTraverser.currentPosition().getDepth(), person.getProperty( "name" ) ); }
- 26. name = “The Architect” name = “Morpheus” rank = “Captain” name = “Thomas Anderson” occupation = “Total badass” 42 age = 29 disclosure = public KNOWS KNOWS KN O CODED_BY 1 7 3 WS 13 S KN KNOW name = “Cypher” OW last name = “Reagan” S name = “Agent Smith” LO disclosure = secret version = 1.0b VE age = 6 months language = C++ S 2 name = “Trinity” $ bin/start-neo-example new ReturnableEvaluator() Who’s a lover? { public boolean isReturnableNode( TraversalPosition pos) At depth 1 => Trinity { $ return pos.currentNode(). hasRelationship( RelTypes.LOVES, Direction.OUTGOING ); } },
- 27. Bonus code: domain model How do you implement your domain model? Use the delegator pattern, i.e. every domain entity wraps a Neo4j primitive: // In package org.yourdomain.yourapp class PersonImpl implements Person { private final Node underlyingNode; PersonImpl( Node node ){ this.underlyingNode = node; } public String getName() { return this.underlyingNode.getProperty( "name" ); } public void setName( String name ) { this.underlyingNode.setProperty( "name", name ); } }
- 28. Domain layer frameworks Qi4j (www.qi4j.org) Framework for doing DDD in pure Java5 Defines Entities / Associations / Properties Sound familiar? Nodes / Rel’s / Properties! Neo4j is an “EntityStore” backend NeoWeaver (http://components.neo4j.org/neo-weaver) Weaves Neo4j-backed persistence into domain objects in runtime (dynamic proxy / cglib based) Veeeery alpha
- 29. Neo4j system characteristics Disk-based Native graph storage engine with custom (“SSD- ready”) binary on-disk format Transactional JTA/JTS, XA, 2PC, Tx recovery, deadlock detection, etc Scalable Several billions of nodes/rels/props on single JVM Robust 6+ years in 24/7 production
- 30. Social network pathExists() 12 ~1k persons 3 7 1 Avg 50 friends per person pathExists(a, b) limit 36 41 77 depth 4 5 Two backends Eliminate disk IO so warm up caches
- 31. Social network pathExists() 2 Emil 1 5 7 Mike Kevin 3 John Marcus 9 4 Bruce Leigh # persons query time Relational database 1 000 2 000 ms Graph database (Neo4j) 1 000 2 ms Graph database (Neo4j) 1 000 000 2 ms
- 34. Pros & Cons compared to RDBMS + No O/R impedance mismatch (whiteboard friendly) + Can easily evolve schemas + Can represent semi-structured info + Can represent graphs/networks (with performance) - Lacks in tool and framework support - No other implementations => potential lock in - No support for ad-hoc queries +
- 35. More consequences Ability to capture semi-structured information => allowing individualization of content No predefined schema => easier to evolve model => can capture ad-hoc relationships Can capture non-normative relations => easy to model specific links to specific sets All state is kept in transactional memory => improves application concurrency
- 36. The Neo4j ecosystem Neo4j is an embedded database Tiny teeny lil jar file Component ecosystem index-util neo-meta neo-utils owl2neo sparql-engine ... See http://components.neo4j.org
- 37. Example: NeoRDF NeoRDF triple/quad store OWL SPARQL RDF Metamodel Graph match Neo4j
- 38. Language bindings Neo4j.py – bindings for Jython and CPython http://components.neo4j.org/neo4j.py Neo4jrb – bindings for JRuby (incl RESTful API) http://wiki.neo4j.org/content/Ruby Clojure http://wiki.neo4j.org/content/Clojure Scala (incl RESTful API) http://wiki.neo4j.org/content/Scala … .NET? Erlang?
- 39. Scale out – replication Rolling out Neo4j HA before end-of-year Side note: ppl roll it today w/ REST frontends & onlinebackup Master-slave replication, 1st configuration MySQL style... ish Except all instances can write, synchronously between writing slave & master (strict consistency) Updates are asynchronously propagated to the other slaves (eventual consistency) This can handle billions of entities... … but not 100B
- 40. Scale out – partitioning Sharding possible today … but you have to do a lot of manual work … just as with MySQL Great option: shard on top of resilient, scalable OSS app server , see: www.codecauldron.org Transparent partitioning? Neo4j 2.0 100B? Easy to say. Sliiiiightly harder to do. Fundamentals: BASE & eventual consistency Generic clustering algorithm as base case, but give lots of knobs for developers
- 41. How ego are you? (aka other impls?) Franz’ AllegroGraph (http://agraph.franz.com) Proprietary, Lisp, RDF-oriented but real graphdb FreeBase graphd (http://bit.ly/13VITB) In-house at Metaweb Kloudshare (http://kloudshare.com) Graph database in the cloud, still stealth mode Google Pregel (http://bit.ly/dP9IP) We are oh-so-secret Some academic papers from ~10 years ago G = {V, E} #FAIL
- 42. Conclusion Graphs && Neo4j => teh awesome! Available NOW under AGPLv3 / commercial license AGPLv3: “if you’re open source, we’re open source” If you have proprietary software? Must buy a commercial license But up to 1M primitives it’s free for all uses! Download http://neo4j.org Feedback http://lists.neo4j.org
- 44. Questions? Image credit: lost again! Sorry :(