![CQL MATCH
MATCH (a)-->(b)
RETURN a, b;
MATCH (a)-->()
RETURN a.name;
MATCH (n)-[r]->(m)
RETURN n, r, m;
MATCH (a)-[r]->()
RETURN id(a), labels(a), keys(a), type(r);
MATCH (a)-[r:ACTED_IN]->(m)
RETURN a.name, r.roles, m.title;
12](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-12-2048.jpg)
![CQL MATCH
MATCH (a)-[:ACTED_IN]->(m)<-[:DIRECTED]-(d)
RETURN a.name, m.title, d.name;
MATCH (a)-[:ACTED_IN]->(m)<-[:DIRECTED]-(d)
RETURN a.name AS actor, m.title AS movie, d.name AS director;
MATCH (a)-[:ACTED_IN]->(m)
OPTIONAL MATCH (d)-[:DIRECTED]->(m)
RETURN a.name, m.title, d.name;
MATCH (a)-[:ACTED_IN]->(m), (d)-[:DIRECTED]->(m)
RETURN a.name, m.title, d.name;
MATCH p=(a)-[:ACTED_IN]->(m)<-[:DIRECTED]-(d)
RETURN nodes(p);
13](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-13-2048.jpg)
![CQL WHERE
MATCH (tom{name:"Tom Hanks"})-[:ACTED_IN]->(movie)
WHERE movie.released < 1992
RETURN DISTINCT movie.title;
MATCH (actor{name:"Keanu Reeves"})-[r:ACTED_IN]->(movie)
WHERE "Neo" IN r.roles
RETURN DISTINCT movie.title;
MATCH (tom{name:"Tom Hanks"})-[:ACTED_IN]->(movie)<-[:ACTED_IN]-(a)
WHERE a.born < tom.born
RETURN DISTINCT a.name, (tom.born - a.born) AS diff;
MATCH (kevin {name:"Kevin Bacon"})-[:ACTED_IN]->(movie)
RETURN DISTINCT movie.title;
MATCH (kevin)-[:ACTED_IN]->(movie)
WHERE kevin.name =~ '.*Kevin.*‘ // Regular expressions
RETURN DISTINCT movie.title;
15](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-15-2048.jpg)
![CQL WHERE
MATCH (gene {name:"Gene Hackman"})-[:ACTED_IN]->(movie)<-[:ACTED_IN]-(n)
WHERE (n)-[:DIRECTED]->()
RETURN DISTINCT n.name;
MATCH (a)-[:ACTED_IN]->()
RETURN a.name, count(*) AS count
ORDER BY count DESC LIMIT 5;
MATCH (keanu {name:"Keanu Reeves"})-[:ACTED_IN]->()<-[:ACTED_IN]-(c),
(c)-[:ACTED_IN]->()<-[:ACTED_IN]-(coc)
WHERE NOT((keanu)-[:ACTED_IN]->()<-[:ACTED_IN]-(coc)) AND coc <> keanu
RETURN coc.name, count(coc)
ORDER BY count(coc) DESC LIMIT 3;
// Recommend 5 actors that Keanu Reeves should work with (but hasn't)
16](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-16-2048.jpg)
![CQL CREATE
CREATE ({title:"Mystic River", released:1993});
MATCH (movie {title:"Mystic River"})
SET movie.tagline = "We bury our sins here, Dave. We wash them clean."
RETURN movie;
MATCH (kevin {name:"Kevin Bacon"}),(movie {title:"Mystic River"})
CREATE UNIQUE (kevin)-[:ACTED_IN {roles:["Sean"]}]->(movie);
MATCH (kevin {name:"Kevin Bacon"})-[r:ACTED_IN]->(movie {title:"Mystic River"})
SET r.roles = ["Sean Devine"]
RETURN r.roles;
MATCH (clint {name:"Clint Eastwood"})-[r:ACTED_IN]->(movie {title:"Mystic River"})
CREATE UNIQUE (clint)-[:DIRECTED]->(movie);
17](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-17-2048.jpg)
![CQL DELETE
MATCH (matrix {title:"The Matrix"})<-[r:ACTED_IN]-(a)
WHERE "Emil" IN r.roles
RETURN a;
// Emil Eifrem is CEO of Neo Technology and co-founder of the Neo4j project
MATCH (emil{name:"Emil Eifrem"})
DELETE emil;
MATCH (emil{name:"Emil Eifrem"}) -[r]-()
DELETE r;
MATCH (emil{name:"Emil Eifrem"}) -[r]-()
DELETE r, emil;
MATCH (node) where ID(node)=1
OPTIONAL MATCH (node)-[r]-()
DELETE r, node;
18](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-18-2048.jpg)
![SQL VS Cypher
MATCH (keanu:Person { name: 'Keanu Reeves' })-[:ACTED_IN]->(movie:Movie),
(director:Person)-[:DIRECTED]->(movie)
RETURN director.name, count(*)
ORDER BY count(*) DESC
19
SELECT director.name, count(*) FROM person Keanu
JOIN acted_in ON keanu.id = acted_in.person_id
JOIN directed ON acted_in.movie_id = directed.movie_id
JOIN person AS director ON directed.person_id = director.id
WHERE keanu.name = 'Keanu Reeves‘
GROUP BY director.name ORDER BY count(*) DESC
Now let’s find out a bit about the directors in movies that Keanu Reeves acted in. We want to know
how many of those movies each of them directed.](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-19-2048.jpg)
![CQL Do-It-Yourself
Add KNOWS relationships between all actors who were in the same movie
23
MATCH (a)-[:ACTED_IN]->()<-[:ACTED_IN]-(b)
CREATE UNIQUE (a)-[:KNOWS]->(b);
MATCH (a)-[:ACTED_IN|DIRECTED]->()<-[:ACTED_IN|DIRECTED]-(b)
CREATE UNIQUE (a)-[:KNOWS]->(b);](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-23-2048.jpg)
![CQL Useful Tricks
Find friends of friends
MATCH (keanu{name:"Keanu Reeves"})-[:KNOWS*2]->(fof)
WHERE NOT((keanu)-[:KNOWS]-(fof))
RETURN DISTINCT fof.name;
Find shortest path
MATCH p=shortestPath(
(charlize{name:"Charlize Theron"})-[:KNOWS*]->(bacon{name:"Kevin Bacon"}))
RETURN length(rels(p));
Return the names of the people joining Charlize to Kevin.
MATCH p=shortestPath(
(charlize{name:"Charlize Theron"})-[:KNOWS*]->(bacon{name:"Kevin Bacon"}))
RETURN extract(n IN nodes(p)| n.name) AS names
24](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-24-2048.jpg)
![CQL Useful Tricks
Find movies and actors up to 4 "hops" away from Kevin Bacon
MATCH (bacon:Person {name:"Kevin Bacon"})-[*1..4]-(hollywood)
RETURN DISTINCT Hollywood
Find someone to introduce Tom Hanks to Tom Cruise
MATCH (tom:Person {name:"Tom Hanks"})-[:ACTED_IN]->(m)<-[:ACTED_IN]-(coActors),
(coActors)-[:ACTED_IN]->(m2)<-[:ACTED_IN]-(cruise:Person {name:"Tom Cruise"})
RETURN tom, m, coActors, m2, cruise
25](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-25-2048.jpg)
![Social Network Database (2)
CREATE (ann:Person { name: 'Ann', email:'ann@neo.4j', dob:487119060000 })
RETURN ann;
CREATE (john:Person { name: 'John', email:'john@neo.4j', dob:435679060000 })
RETURN john;
MATCH (ann:Person { name: 'Ann' }), (john:Person { name: 'John' })
CREATE UNIQUE (ann)-[:IS_FRIEND{since:'2009'}]-(john);
MATCH (ann:Person { name: 'Ann' })
CREATE (ann)-[:UPLOADED]->(status:Status{text:'Happy Birthday', date:1451610010000});
MATCH (john:Person { name: 'John' }), (status:Status{text:'Happy Birthday'})
CREATE (john)-[:LIKES]->(status);
27](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-27-2048.jpg)
![Social Network Database (3)
MATCH (john:Person { name: 'John' })
CREATE (john)-[:UPLOADED]->(photo:Photo{text:'Birthday Party', date:1452410019386});
MATCH (ann:Person { name: 'Ann' }), (photo:Photo{text:'Birthday Party'})
CREATE (ann)-[:LIKES]->(photo);
MATCH (ann:Person { name: 'Ann' }), (photo:Photo{text:'Birthday Party'})
CREATE (ann)-[:WROTE_COMMENT]->(comment:Comment{text:'Happy Birthday. The party was
great!', date:1452410478569})<-[:HAS_COMMENT]-(photo);
MATCH (ann:Person { name: 'Ann' }), (photo:Photo{text:'Birthday Party'})
CREATE (ann)-[:TAGGED_IN]->(photo);
28](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-28-2048.jpg)
![Social Network Database (4)
MATCH (john:Person { name: 'John' })
RETURN (john)-[:UPLOADED]->(:Photo);
MATCH (ann:Person { name: 'Ann' }) -[:LIKES]->(photo:Photo)
RETURN ann, photo;
MATCH (ann:Person { name: 'Ann' })-[:IS_FRIEND]-(:Person)-[:UPLOADED]-(photo:Photo)
RETURN ann, photo
MATCH (ann:Person { name: 'Ann' })-[:IS_FRIEND]-(friend:Person),
(friend:Person)-[:UPLOADED]-(photo:Photo)
RETURN ann, photo, friend
29](https://image.slidesharecdn.com/490d3cfa-7342-4e8c-b4a7-1c4ff02229d0-160726075922/75/Neo4j-29-2048.jpg)
More Related Content
Neo4j
- 1. The world's leadinggraph DB Georgios Eleftheriadis Software/Database Engineer
- 2. What is NOSQL? It’s not “No to SQL” It’s not “Never SQL” It’s “Not Only SQL” as they may support SQL-like query languages NOSQL describes ongoing trend where developers increasingly opt for non-relational databases to help solve their problems, in an effort to use the right tool for the right job. NOSQL example databases Document Oriented (CouchDB, MongoDB) Key-Value (Memcached, Redis) Graph Database (Neo4J, InfiniteGraph) Multi-model (ArangoDB, OrientDB) 2
- 3. Graphs are everywhere Relationships in Politics, Economics, History, Science, Transportation Biology, Chemistry, Physics, Sociology Body, Ecosphere, Reaction, Interactions Internet Hardware, Software, Interaction Social Networks Family, Friends Work, Communities Neighbors, Cities, Society 3
- 4. A sample socialgraph # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms with ~1,000 persons average 50 friends per person pathExists(a,b) limited to depth 4 caches warmed up to eliminate disk I/O 4
- 5. A sample socialgraph # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms with ~1,000 persons average 50 friends per person pathExists(a,b) limited to depth 4 caches warmed up to eliminate disk I/O 5
- 6. A sample socialgraph # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms with ~1,000 persons average 50 friends per person pathExists(a,b) limited to depth 4 caches warmed up to eliminate disk I/O 6
- 7. Two Ways toWork with Neo4j Embeddable on JVM Java Jruby Scala Tomcat Rails Server with REST API every language on the planet flexible deployment scenarios DIY server or cloud managed Embedded capability == Server capability same scalability, transactionality, and availability 7
- 8. Pros & Conswith Neo4j Database Pros Most of the data is connected High performance to access connected data Least or no impact if changes to the data model Can be mixed with JPA with Spring Data Neo4j Integrate with Spring Cons Could not reuse SQL queries Migration could be pain Not effective to use Neo4j if data is not connected 8
- 9. Enterprise VS CommunityEdition Features Enterprise Community Property Graph Model YES YES Native Graph Processing & Storage YES YES Cypher – Graph Query Language YES YES Language Drivers YES YES REST & High-Performance Native API YES YES Enterprise Lock Manager YES NO Cache Sharding YES NO Clustered Replication YES NO Hot Backups YES NO Advanced Monitoring YES NO 9
- 10. RDBMS vs GraphDB 10 ID Name Grade 1510 Jordan 9 1689 Gabriel 9 1381 Tiffany 9 1709 Cassandra 9 1101 Haley 10 1782 Andrew 10 1468 Kris 10 1641 Brittany 10 1247 Alexis 11 1316 Austin 11 1911 Gabriel 11 1501 Jessica 11 1304 Jordan 12 1025 John 12 1934 Kyle 12 1661 Logan 12 ID1 ID2 1510 1381 1510 1689 1689 1709 1381 1247 1709 1247 1689 1782 1782 1468 1782 1316 1782 1304 1468 1101 1468 1641 1101 1641 … … ID1 ID2 1689 1709 1709 1689 1782 1709 1911 1247 1247 1468 1641 1468 1316 1304 1501 1934 1934 1501 1025 1101
- 11. Cypher Query Language Declarative query language Describe what you want, not how Based on pattern matching 11
- 12. CQL MATCH MATCH(a)-->(b) RETURN a, b; MATCH (a)-->() RETURN a.name; MATCH (n)-[r]->(m) RETURN n, r, m; MATCH (a)-[r]->() RETURN id(a), labels(a), keys(a), type(r); MATCH (a)-[r:ACTED_IN]->(m) RETURN a.name, r.roles, m.title; 12
- 13. CQL MATCH MATCH(a)-[:ACTED_IN]->(m)<-[:DIRECTED]-(d) RETURN a.name, m.title, d.name; MATCH (a)-[:ACTED_IN]->(m)<-[:DIRECTED]-(d) RETURN a.name AS actor, m.title AS movie, d.name AS director; MATCH (a)-[:ACTED_IN]->(m) OPTIONAL MATCH (d)-[:DIRECTED]->(m) RETURN a.name, m.title, d.name; MATCH (a)-[:ACTED_IN]->(m), (d)-[:DIRECTED]->(m) RETURN a.name, m.title, d.name; MATCH p=(a)-[:ACTED_IN]->(m)<-[:DIRECTED]-(d) RETURN nodes(p); 13
- 14. CQL MATCH Functions count(x) - add up the number of occurrences min(x) - get the lowest value max(x) - get the highest value avg(x) - get the average of a numeric value collect(x) - collected all the occurrences into an array 14
- 15. CQL WHERE MATCH(tom{name:"Tom Hanks"})-[:ACTED_IN]->(movie) WHERE movie.released < 1992 RETURN DISTINCT movie.title; MATCH (actor{name:"Keanu Reeves"})-[r:ACTED_IN]->(movie) WHERE "Neo" IN r.roles RETURN DISTINCT movie.title; MATCH (tom{name:"Tom Hanks"})-[:ACTED_IN]->(movie)<-[:ACTED_IN]-(a) WHERE a.born < tom.born RETURN DISTINCT a.name, (tom.born - a.born) AS diff; MATCH (kevin {name:"Kevin Bacon"})-[:ACTED_IN]->(movie) RETURN DISTINCT movie.title; MATCH (kevin)-[:ACTED_IN]->(movie) WHERE kevin.name =~ '.*Kevin.*‘ // Regular expressions RETURN DISTINCT movie.title; 15
- 16. CQL WHERE MATCH(gene {name:"Gene Hackman"})-[:ACTED_IN]->(movie)<-[:ACTED_IN]-(n) WHERE (n)-[:DIRECTED]->() RETURN DISTINCT n.name; MATCH (a)-[:ACTED_IN]->() RETURN a.name, count(*) AS count ORDER BY count DESC LIMIT 5; MATCH (keanu {name:"Keanu Reeves"})-[:ACTED_IN]->()<-[:ACTED_IN]-(c), (c)-[:ACTED_IN]->()<-[:ACTED_IN]-(coc) WHERE NOT((keanu)-[:ACTED_IN]->()<-[:ACTED_IN]-(coc)) AND coc <> keanu RETURN coc.name, count(coc) ORDER BY count(coc) DESC LIMIT 3; // Recommend 5 actors that Keanu Reeves should work with (but hasn't) 16
- 17. CQL CREATE CREATE({title:"Mystic River", released:1993}); MATCH (movie {title:"Mystic River"}) SET movie.tagline = "We bury our sins here, Dave. We wash them clean." RETURN movie; MATCH (kevin {name:"Kevin Bacon"}),(movie {title:"Mystic River"}) CREATE UNIQUE (kevin)-[:ACTED_IN {roles:["Sean"]}]->(movie); MATCH (kevin {name:"Kevin Bacon"})-[r:ACTED_IN]->(movie {title:"Mystic River"}) SET r.roles = ["Sean Devine"] RETURN r.roles; MATCH (clint {name:"Clint Eastwood"})-[r:ACTED_IN]->(movie {title:"Mystic River"}) CREATE UNIQUE (clint)-[:DIRECTED]->(movie); 17
- 18. CQL DELETE MATCH(matrix {title:"The Matrix"})<-[r:ACTED_IN]-(a) WHERE "Emil" IN r.roles RETURN a; // Emil Eifrem is CEO of Neo Technology and co-founder of the Neo4j project MATCH (emil{name:"Emil Eifrem"}) DELETE emil; MATCH (emil{name:"Emil Eifrem"}) -[r]-() DELETE r; MATCH (emil{name:"Emil Eifrem"}) -[r]-() DELETE r, emil; MATCH (node) where ID(node)=1 OPTIONAL MATCH (node)-[r]-() DELETE r, node; 18
- 19. SQL VS Cypher MATCH(keanu:Person { name: 'Keanu Reeves' })-[:ACTED_IN]->(movie:Movie), (director:Person)-[:DIRECTED]->(movie) RETURN director.name, count(*) ORDER BY count(*) DESC 19 SELECT director.name, count(*) FROM person Keanu JOIN acted_in ON keanu.id = acted_in.person_id JOIN directed ON acted_in.movie_id = directed.movie_id JOIN person AS director ON directed.person_id = director.id WHERE keanu.name = 'Keanu Reeves‘ GROUP BY director.name ORDER BY count(*) DESC Now let’s find out a bit about the directors in movies that Keanu Reeves acted in. We want to know how many of those movies each of them directed.
- 20. CQL INDEXES There isusually no need to specify which indexes to use in a query, Cypher will figure that out by itself. Indexes are also automatically used for equality comparisons and inequality (range) comparisons of an indexed property in the WHERE clause. USING is used to influence the decisions of the planner when building an execution plan for a query. CREATE INDEX ON :Person(name) MATCH (person:Person { name: 'Keanu Reeves' }) RETURN person MATCH (person:Person) WHERE person.name = 'Keanu Reeves' RETURN person MATCH (person:Person) WHERE person.name > 'Keanu' RETURN person MATCH (person:Person) WHERE person.name STARTS WITH 'Kea' // CONTAINS, ENDS WITH RETURN person 20
- 21. Embedded example code GraphDatabaseServicegraphDb = new GraphDatabaseFactory().newEmbeddedDatabase("var/neo-4j"); try (Transaction tx = graphDb.beginTx()) { Node firstNode = graphDb.createNode(); firstNode.setProperty("message", "Hello, "); Node secondNode = graphDb.createNode(); secondNode.setProperty("message", "World!"); Relationship relationship = firstNode.createRelationshipTo(secondNode, RelTypes.KNOWS); relationship.setProperty("message", "brave Neo4j "); System.out.print(firstNode.getProperty("message")); System.out.print(relationship.getProperty("message")); System.out.print(secondNode.getProperty("message")); tx.success(); } 21
- 22. Shortest path examplecode GraphDatabaseService graphDb = new GraphDatabaseFactory().newEmbeddedDatabase("var/neo-4j"); try (Transaction tx = graphDb.beginTx()) { static Index<Node> indexService = graphDb.index().forNodes("nodes"); Node neo = indexService.get(“name", “Neo").getSingle(); Node agentSmith = indexService.get(“name", "Agent Smith").getSingle(); PathFinder<Path> finder = GraphAlgoFactory.shortestPath( PathExpanders.forTypeAndDirection(KNOWS, Direction.BOTH), 4); Path foundPath = finder.findSinglePath(neo, agentSmith); System.out.println(Paths.simplePathToString(foundPath, NAME_KEY)); } 22
- 23. CQL Do-It-Yourself AddKNOWS relationships between all actors who were in the same movie 23 MATCH (a)-[:ACTED_IN]->()<-[:ACTED_IN]-(b) CREATE UNIQUE (a)-[:KNOWS]->(b); MATCH (a)-[:ACTED_IN|DIRECTED]->()<-[:ACTED_IN|DIRECTED]-(b) CREATE UNIQUE (a)-[:KNOWS]->(b);
- 24. CQL Useful Tricks Find friends of friends MATCH (keanu{name:"Keanu Reeves"})-[:KNOWS*2]->(fof) WHERE NOT((keanu)-[:KNOWS]-(fof)) RETURN DISTINCT fof.name; Find shortest path MATCH p=shortestPath( (charlize{name:"Charlize Theron"})-[:KNOWS*]->(bacon{name:"Kevin Bacon"})) RETURN length(rels(p)); Return the names of the people joining Charlize to Kevin. MATCH p=shortestPath( (charlize{name:"Charlize Theron"})-[:KNOWS*]->(bacon{name:"Kevin Bacon"})) RETURN extract(n IN nodes(p)| n.name) AS names 24
- 25. CQL Useful Tricks Find movies and actors up to 4 "hops" away from Kevin Bacon MATCH (bacon:Person {name:"Kevin Bacon"})-[*1..4]-(hollywood) RETURN DISTINCT Hollywood Find someone to introduce Tom Hanks to Tom Cruise MATCH (tom:Person {name:"Tom Hanks"})-[:ACTED_IN]->(m)<-[:ACTED_IN]-(coActors), (coActors)-[:ACTED_IN]->(m2)<-[:ACTED_IN]-(cruise:Person {name:"Tom Cruise"}) RETURN tom, m, coActors, m2, cruise 25
- 26. Social Network Database RelationshipType Properties IS_FRIEND since LIKES WROTE_COMMENT HAS_COMMENT UPLOADED SENT_MESSAGE RECEIVED_MESSAGE TAGGED_IN Node Labels Properties Person name, email, dob Photo caption, date Status text, date Comment text, date Message Text, date 26
- 27. Social Network Database(2) CREATE (ann:Person { name: 'Ann', email:'ann@neo.4j', dob:487119060000 }) RETURN ann; CREATE (john:Person { name: 'John', email:'john@neo.4j', dob:435679060000 }) RETURN john; MATCH (ann:Person { name: 'Ann' }), (john:Person { name: 'John' }) CREATE UNIQUE (ann)-[:IS_FRIEND{since:'2009'}]-(john); MATCH (ann:Person { name: 'Ann' }) CREATE (ann)-[:UPLOADED]->(status:Status{text:'Happy Birthday', date:1451610010000}); MATCH (john:Person { name: 'John' }), (status:Status{text:'Happy Birthday'}) CREATE (john)-[:LIKES]->(status); 27
- 28. Social Network Database(3) MATCH (john:Person { name: 'John' }) CREATE (john)-[:UPLOADED]->(photo:Photo{text:'Birthday Party', date:1452410019386}); MATCH (ann:Person { name: 'Ann' }), (photo:Photo{text:'Birthday Party'}) CREATE (ann)-[:LIKES]->(photo); MATCH (ann:Person { name: 'Ann' }), (photo:Photo{text:'Birthday Party'}) CREATE (ann)-[:WROTE_COMMENT]->(comment:Comment{text:'Happy Birthday. The party was great!', date:1452410478569})<-[:HAS_COMMENT]-(photo); MATCH (ann:Person { name: 'Ann' }), (photo:Photo{text:'Birthday Party'}) CREATE (ann)-[:TAGGED_IN]->(photo); 28
- 29. Social Network Database(4) MATCH (john:Person { name: 'John' }) RETURN (john)-[:UPLOADED]->(:Photo); MATCH (ann:Person { name: 'Ann' }) -[:LIKES]->(photo:Photo) RETURN ann, photo; MATCH (ann:Person { name: 'Ann' })-[:IS_FRIEND]-(:Person)-[:UPLOADED]-(photo:Photo) RETURN ann, photo MATCH (ann:Person { name: 'Ann' })-[:IS_FRIEND]-(friend:Person), (friend:Person)-[:UPLOADED]-(photo:Photo) RETURN ann, photo, friend 29
- 30. How to getstarted? Documentation http://neo4j.com/docs/ - tutorials & reference Neo4j in Action Graph Databases by O'Reilly Get Neo4j http://neo4j.org/download http://elements.heroku.com/addons/graphenedb Participate http://groups.google.com/group/neo4j http://neo4j.meetup.com http://stackoverflow.com/questions/tagged/neo4j 30