Neo4j中的图由节点(Node)和关系(Relation)组成,节点和关系也可以具有属性。节点代表的是实体,比如概念、事件、地点和事物。关系将节点连接起来。
节点和关系是图的基本单元,但是真正让图表示复杂知识的是连接起来的节点和关系构成的patterns。单一的节点和关系仅仅只能携带少量的信息但是pattern却可以携带或表示复杂的信息。
Cypher是Neo4j的查询语言就是基于pattern。通常pattern用来匹配期望找到的图结构,一旦找到或者创建,就可以进行进一步处理。
Cypher中简单的pattern比如:a Person LIVES_IN a City or a City is PART_OF a Country
或者更为复杂一点的:(:Person) -[:LIVES_IN]-> (:City) -[:PART_OF]-> (:Country)
Cypher的长处在于擅长识别各种给定的模式(pattern)。就跟人脑一样,比如人脑就很擅长识别图片中的人、物体等,因为人、物体都具备一定的模式。
Cypher跟SQL语言类似,有比如MATCH,WHERE,DELETE之类的关键字。
Node使用一对括号表示:
()
((matrix))
(:Movie)
(matrix:Movie)
(matrix:Movie {title: "The Matrix"})
(matrix:Movie {title: "The Matrix", released: 1997})
Movie是Node的标签,表明节点的类型。不同的标签可以组成不同的模式,比如Movie和Actor就可以组成一种模式。Neo4j的节点使用标签索引的,每一个索引由标签和属性组成。
节点的属性由大括号包含一系列key/value。比如
{title: "The Matrix", released: 1997}
Cypher中--
表示无方向性的关系,<--,-->
表示指向性的关系,[....]
表示对关系的说明,可以是变量、属性等。
-->
-[role]->
-[:ACTED_IN]->
-[role:ACTED_IN]->
-[role:ACTED_IN {roles: ["Neo"]}]->
ACTED_IN是关系的标签,指明关系的类型。role是关系变量,可以在别处使用。roles是属性值,和节点的属性一样。
(keanu:Person:Actor {name: "Keanu Reeves"} )-[role:ACTED_IN {roles: ["Neo"] } ]->(matrix:Movie {title: "The Matrix"} )
Pattern指明在图中符合给定pattern的节点以及连接的关系。 Pattern还可以被声明为一个变量:
acted_in = (:Person)-[:ACTED_IN]->(:Movie)
打开命令行执行:
sudo bin/neo4j console
打开浏览器打开:
http://localhost:7474/browser/
CREATE (:Movie { title:"The Matrix",released:1997 })
执行完毕可以看到:
如果创建之后需要返回值:
CREATE (p:Person { name:"Keanu Reeves", born:1964 })
RETURN p
执行完毕可以看到:
更为复杂的一个例子:
CREATE (a:Person { name:"Tom Hanks",
born:1956 })-[r:ACTED_IN { roles: ["Forrest"]}]->(m:Movie { title:"Forrest Gump",released:1994 })
CREATE (d:Person { name:"Robert Zemeckis", born:1951 })-[:DIRECTED]->(m)
RETURN a,d,r,m
比方说查找所有的Movie:
MATCH (m:Movie)
RETURN m
也可以找某个具体的人:
MATCH (p:Person { name:"Keanu Reeves" })
RETURN p
还可以查找某个关系:
MATCH (p:Person { name:"Tom Hanks" })-[r:ACTED_IN]->(m:Movie)
RETURN m, r
MATCH (p:Person { name:"Tom Hanks" })
CREATE (m:Movie { title:"Cloud Atlas",released:2012 })
CREATE (p)-[r:ACTED_IN { roles: ['Zachry']}]->(m)
RETURN p,r,m
Merge表示为节点或者关系添加新的pattern或属性。
添加新的属性:
MERGE (m:Movie { title:"Cloud Atlas" })
ON CREATE SET m.released = 2012
RETURN m
添加新的关系:
MATCH (m:Movie { title:"Cloud Atlas" })
MATCH (p:Person { name:"Tom Hanks" })
MERGE (p)-[r:ACTED_IN]->(m)
ON CREATE SET r.roles =['Zachry']
RETURN p,r,m
添加新的节点和关系:
CREATE (y:Year { year:2014 })
MERGE (y)<-[:IN_YEAR]-(m10:Month { month:10 })
MERGE (y)<-[:IN_YEAR]-(m11:Month { month:11 })
RETURN y,m10,m11
#1 WHERE
MATCH (m:Movie)
WHERE m.title = "The Matrix"
RETURN m
#2 WHERE
MATCH (p:Person)-[r:ACTED_IN]->(m:Movie)
WHERE p.name =~ "K.+" OR m.released > 2000 OR "Neo" IN r.roles
RETURN p,r,m
#3 NOT
MATCH (p:Person)-[:ACTED_IN]->(m)
WHERE NOT (p)-[:DIRECTED]->()
RETURN p,m
#4 ALIAS
MATCH (p:Person)
RETURN p, p.name AS name, toUpper(p.name), coalesce(p.nickname,"n/a") AS nickname, { name: p.name,
label:head(labels(p))} AS person
#5 COUNT
MATCH (:Person)
RETURN count(*) AS people
OR
RETURN count(DISTINCT role)
MATCH (actor:Person)-[:ACTED_IN]->(movie:Movie)<-[:DIRECTED]-(director:Person)
RETURN actor,director,count(*) AS collaborations
#6 ORDER
MATCH (a:Person)-[:ACTED_IN]->(m:Movie)
RETURN a,count(*) AS appearances
ORDER BY appearances DESC LIMIT 10;
#7 COLLECT
MATCH (m:Movie)<-[:ACTED_IN]-(a:Person)
RETURN m.title AS movie, collect(a.name) AS cast, count(*) AS actors
#8 UNION
MATCH (actor:Person)-[r:ACTED_IN]->(movie:Movie)
RETURN actor.name AS name, type(r) AS acted_in, movie.title AS title
UNION
MATCH (director:Person)-[r:DIRECTED]->(movie:Movie)
RETURN director.name AS name, type(r) AS acted_in, movie.title AS title
#9 WITH
MATCH (person:Person)-[:ACTED_IN]->(m:Movie)
WITH person, count(*) AS appearances, collect(m.title) AS movies
WHERE appearances > 1
RETURN person.name, appearances, movies
查询的结果可能是数字、字符串、或者列表、数组。
# UNIQUE
CREATE CONSTRAINT ON (movie:Movie) ASSERT movie.title IS UNIQUE
# INDEX
CREATE INDEX ON :Actor(name)
CREATE (actor:Actor { name:"Tom Hanks" }),(movie:Movie { title:'Sleepless IN Seattle' }),
(actor)-[:ACTED_IN]->(movie);