Neo4j Desktop 2 本地部署与图数据库实战 | 极客日志

Neo4j Desktop 2 本地部署与图数据库实战 | 极客日志

// 创建人物节点
CREATE (p:Person {name: 'Alice', age: 30, occupation: 'Engineer'})

// 批量创建多个节点
CREATE 
  (p1:Person {name: 'Bob', age: 25}),
  (p2:Person {name: 'Charlie', age: 35}),
  (c:Company {name: 'Tech Corp', industry: 'Technology'})

// 先匹配节点，然后创建关系
MATCH 
  (a:Person {name: 'Alice'}),
  (b:Person {name: 'Bob'})
CREATE (a)-[:FRIENDS_WITH {since: '2023-01-01'}]->(b)

// 创建节点和关系同时进行
CREATE 
  (alice:Person {name: 'Alice'})-[:WORKS_AT {position: 'Developer'}]->(company:Company {name: 'Neo4j Inc'})

// 添加属性
MATCH (p:Person {name: 'Alice'})
SET p.email = '[email protected]'

// 更新多个属性
MATCH (p:Person {name: 'Bob'})
SET p.age = 26, p.department = 'Engineering'

// 添加标签
MATCH (p:Person {name: 'Charlie'})
SET p:Employee:Manager

// 查询所有人物
MATCH (p:Person) 
RETURN p

// 查询特定属性
MATCH (p:Person) 
WHERE p.age > 25 
RETURN p.name, p.age

// 查询关系和路径
MATCH (p1:Person)-[r:FRIENDS_WITH]->(p2:Person)
RETURN p1.name, p2.name, r.since

// 查找朋友的朋友（两步以内）
MATCH (p:Person {name: 'Alice'})-[:FRIENDS_WITH*2]->(friendOfFriend)
RETURN friendOfFriend.name

// 聚合查询
MATCH (p:Person)
RETURN p.occupation, COUNT(*) as count, AVG(p.age) as avgAge

// 路径查询
MATCH path = (start:Person)-[:FRIENDS_WITH|WORKS_AT*1..3]->(end)
WHERE start.name = 'Alice'
RETURN path

from neo4j import GraphDatabase

uri = "neo4j://127.0.0.1:7687"
driver = GraphDatabase.driver(uri, auth=("neo4j", "你的密码"))

with driver.session() as session:
    result = session.run("MATCH (n) RETURN n LIMIT 1")
    print(result.single())