图数据库-Neo4j介绍与Cypher入门

1、Neo4j简介

2、单机安装和简单使用（社区版）

3、Cypher Query Language

3.1.基本语法

3.2.模式实践

3.3.Getting the Results You Want

3.4.Compose Large Statements：编写大型语句

3.5.Utilizing Data Structures

3.6.Labels, Constraints and Indexes

1、Neo4j简介

Neo4j是一个高性能的,NOSQL图形数据库，它将结构化数据存储在网络上形成图谱而不是表中。Neo4j也可以被看作是一个高性能的图引擎，该引擎具有成熟数据库的所有特性，如：事务，索引等。程序员工作在一个面向对象的、灵活的网络结构下而不是严格、静态的表中——但是他们可以享受到具备完全的事务特性、企业级的数据库的所有好处。

在一个图中包含两种基本的数据类型：Nodes（节点） 和 Relationships（关系）。Nodes 和 Relationships 包含key/value形式的属性。Nodes通过Relationships所定义的关系相连起来，形成关系型网络结构。

目前的客户端接口方式包括：neo4j-shell，REST API，Driver(Java\.NET\JS\Python\Ruby\PHP)等等。截止此时，最新版本为2.3.0社区版和企业版，其中企业版支持HA。Neo4j中的语言采用Cypher，一种类似于SQL的语法格式。Neo4j中的数据处理分析可采用Spark的Gragh图计算处理。

Neo4j因其嵌入式、高性能、轻量级等优势，越来越受到关注。

2、单机安装和简单使用（社区版）

下载地址：http://neo4j.com/download/

直接解压启动：tar -zxcf  neo4j-community-2.3.0-M02-unix.tar.gz

配置外网访问IP地址：conf/neo4f-server.properties

org.neo4j.server.webserver.address=0.0.0.0

执行命令启动数据库服务：neo4j/bin/neo4j start

浏览器http://localhost:7474/browser/启动，进行Gragh操作。默认的账号和密码为neo4j/neo4j。进入后需要修改密码。以下是创建图语句和结果视图：

3、Cypher 查询语言简单使用

3.1.基本语法

Node语法：

Cypher使用一对圆括号来表示一个节点：提供了多种格式如下：

() 匿名节点

(matrix)  为节点添加一个ID

(:Movie) Movie label标签，声明的是节点类型。noe4j的索引使用label，每个索引由标签和属性组成

(matrix:Movie)

(matrix:Movie {title: "The Matrix"}) 节点属性(如：title)代表一个key\value 的List

(matrix:Movie {title: "The Matrix", released: 1997})

RelationShip语法：

--  表示一个无指向的关系

--> 表示一个有指向的关系

[] 能够添加ID，属性，类型等信息

-[role]->

-[:ACTED_IN]->

-[role:ACTED_IN]->

-[role:ACTED_IN {roles: ["Neo"]}]->

Pattern 语法：

节点和关系语法的合并就表示模式。

(keanu:Person:Actor   {name: "Keanu Reeves"} )

-[role:ACTED_IN   {roles: ["Neo"] } ]->

(matrix:Movie    {title: "The Matrix"} )

Pattern Identifiers ：

为模式分配ID，为例增加模块化和重复使用

acted_in = (:Person)-[:ACTED_IN]->(:Movie)

3.2.模式实践

bin/neo4j-shell

创建一个节点数据：

CREATE (:Movie { title:"The Matrix",released:1997 }) ;

如果想返回创建的数据，需要指定ID：

create (p:Person {name:"weiw",born:2000}) return p;

创建多个节点数据，多个元素间用逗号或者用create分开：

create (a:Person {name:"jiaj",born:2003})-[r:ACTED_IN {roles:["student"]}]->(m:School {name:"CDLG",address:"chengdu"})

create (d:Person {name:"weiw",born:2001})-[:DIRECTED]->(m)

return a,d,r,m;

Matching Patterns :模式匹配

我们想连接新的数据到已经存在的结构，这个需求需要我们知道怎样找到在图中已经存在的模式。

match (m:School) return m;

match (p:Person {name:"weiw"}) return p;

match (p:Person {name:"jiaj"})-[r:ACTED_IN]->(m:School) return m.name,r.roles;

Attaching Structures:

将match和create进行合并使用。将匹配到的节点连接到一个新的节点上。

match (p:Person {name:"jiaj"})

create (m:School {name:"DEJY",address:"deyang"})

create (p)-[r:ACTED_IN {roles:["studeng"]}]->(m)

return p,r,m;

Completing Patterns :

merge在查找时，如果找到则返回，如果没找到则创建。可以避免创建重复的节点

merge (m:School {name:"SCDX"})

on create set m.address="chegndu"

return m;

MATCH (m:School { name:"CDLG" })

MATCH (p:Person { name:"jiaj" })

MERGE (p)-[r:ACTED_IN]->(m)

ON CREATE SET r.roles =['teacher']

RETURN p,r,m ;

之前的案列中，关联的方向是随意的，你可以改变箭头的指向。MERGE 会检查关联两边的方向，如果没有匹配到关系，则创建一个新的方向的关系。

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 ;

3.3.Getting the Results You Want

数据准备：以人在电影中扮演的角色为例

CREATE (matrix:Movie { title:"The Matrix",released:1997 })

CREATE (cloudAtlas:Movie { title:"Cloud Atlas",released:2012 })

CREATE (forrestGump:Movie { title:"Forrest Gump",released:1994 })

CREATE (keanu:Person { name:"Keanu Reeves", born:1964 })

CREATE (robert:Person { name:"Robert Zemeckis", born:1951 })

CREATE (tom:Person { name:"Tom Hanks", born:1956 })

CREATE (tom)-[:ACTED_IN { roles: ["Forrest"]}]->(forrestGump)

CREATE (tom)-[:ACTED_IN { roles: ['Zachry']}]->(cloudAtlas)

CREATE (robert)-[:DIRECTED]->(forrestGump)

Filtering Results ：数据过滤

常用谓词：AND, OR, XOR and NOT

match (m:Movie)  where m.title="The Matrix" return m;

MATCH (p:Person)-[r:ACTED_IN]->(m:Movie)

W HERE p.name =~ "K.+" OR m.released > 2000 OR "Neo" IN r.roles 

RETURN p,r,m ;

最后一个角色条件没有完全匹配上：

MATCH (p:Person)-[:ACTED_IN]->(m)

WHERE NOT (p)-[:DIRECTED]->()

RETURN p,m ；

Returning Results ：结果返回

返回 numbers, strings and arrays as [1,2,3], and maps like  {name:"Tom Hanks", born:1964, movies:["Forrest Gump", ...], count:13}.

常用表达式：

 names[0] ，movies[1..-1].   length(array), toInt("12"), substring("2014-07-01",0,4), or coalesce(p.nickname,"n/a") ，DISTINCT

MATCH (p:Person) 

RETURN p, p.name AS name, upper(p.name), coalesce(p.nickname,"n/a") AS nickname, { name: p.name,  label:head(labels(p))} AS person;

Aggregating Information：聚合操作

常用聚合： count, sum, avg, min, max，count(DISTINCT role)， NULL值自动跳过

MATCH (:Person)

RETURN count(*) AS people

To find out how often an actor and director worked together, you’d run this statement:

MATCH (actor:Person)-[:ACTED_IN]->(movie:Movie)<-[:DIRECTED]-(director:Person)

RETURN actor,director,count(*) AS collaborations

Ordering and Pagination ：排序和分页

排序用法：ORDER BY person.age

分页用法：SKIP {offset} LIMIT {count}

MATCH (a:Person)-[:ACTED_IN]->(m:Movie)

RETURN a,count(*) AS appearances

ORDER BY appearances DESC LIMIT 10;

Collecting Aggregation：聚集聚合

collects all aggregated   values into a real array or list。

MATCH (m:Movie)<-[:ACTED_IN]-(a:Person)

RETURN m.title AS movie, collect(a.name) AS cast, count(*) AS actors

3.4.Compose Large Statements：编写大型语句

UNION:

MATCH (p:Person)-[r:ACTED_IN]->(m:Movie)

RETURN p,type(r) AS rel,m

UNION

MATCH (p:Person)-[r:DIRECTED]->(m:Movie)

RETURN p,type(r) AS rel,m

WITH:

3.5.Utilizing Data Structures

MATCH (m:Movie)<-[:ACTED_IN]-(a:Person)

RETURN m.title AS movie, collect(a.name)[0..5] AS five_of_cast

List谓词：

When using lists and arrays in comparisons you can use predicates like value IN list or any(x IN list

WHERE x = value). There are list predicates to satisfy conditions for all, any, none and single elements.

MATCH path =(:Person)-->(:Movie)<--(:Person)

WHERE ALL (r IN rels(path) WHERE type(r)= 'ACTED_IN') AND ANY (n IN nodes(path) WHERE n.name = 'Clint   Eastwood') 

RETURN path

List处理：

 you want to process lists to filter, aggregate (reduce) or transform (extract) their values.

WITH range(1,10) AS numbers

WITH extract(n IN numbers | n*n) AS squares

WITH filter(n IN squares WHERE n > 25) AS large_squares

RETURN reduce(a = 0, n IN large_squares | a + n) AS sum_large_squares;

MATCH (m:Movie)<-[r:ACTED_IN]-(a:Person)

WITH m.title AS movie, collect({ name: a.name, roles: r.roles }) AS cast

RETURN movie, extract(c2 IN filter(c1 IN cast WHERE c1.name =~ "T.*")| c2.roles)

Unwind Lists:列表展开

you have collected information into a list, but want to use each element individually as a  row。For instance, you might want to further match patterns in the graph.

MATCH (a:Person)-[:ACTED_IN]->(m:Movie)<-[:ACTED_IN]-(colleague:Person)

WITH colleague, count(*) AS frequency, collect(DISTINCT m) AS movies

ORDER BY frequency DESC LIMIT 5 UNWIND movies AS m

MATCH (m)<-[:ACTED_IN]-(a)

RETURN m.title AS movie, collect(a.name) AS cast

3.6.Labels, Constraints and Indexes

使用约束： title被唯一化约束

adding the unique constraint will add an index on that property。

CREATE CONSTRAINT ON (movie:Movie) ASSERT movie.title IS UNIQUE

查看索引：

CREATE INDEX ON :Actor(name)

CREATE (actor:Actor { name:"Tom Hanks" }),(movie:Movie { title:'Sleepless IN Seattle' }),  (actor)-[:ACTED_IN]->(movie); 

标签：

MATCH (actor:Actor { name: "Tom Hanks" })   SET actor :American return actor; 

删除标签：

MATCH (actor:Actor { name: "Tom Hanks" })   REMOVE actor:American;

本期关于neo4j的介绍只是做了入门级别的使用，后续会更加详细的介绍Cypher的所有语法、Neo4j-JDBC的使用以及HA的安装过程。

官方文档手册下载地址：http://download.csdn.net/detail/wangweislk/8983743