需求:查询出满足3人及3案有关系的集合
# -*- coding: utf-8 -*- from py2neo import Graph import psycopg2 # 二维数组查找 def find(target, array): for i, val in enumerate(array): for j, temp in enumerate(val): if temp == target: return True; return False graph = Graph(host="192.168.3.186://7474", auth=("neo4j", "wabjtam123")) data = graph.run('match data=(p:anjian)-[:参与 *2..5]-(u:anjian) with p,collect( distinct u.AJBH) as ajbhs match (p:anjian)-[:参与 *1..5]-(c:xyr) with p,ajbhs, collect(distinct c.XYRBH) as xyrbh_list,(ajbhs+p.AJBH) as ajbh_list where size(xyrbh_list)>2 and size(ajbh_list)>2 return p.AJBH as ajbh,ajbh_list,xyrbh_list') aj = [] xyr_result = [] aj_result = [] for node in data: ajbh = node.get('ajbh') ajbh_list = node.get('ajbh_list') xyrbh_list = node.get('xyrbh_list') ajbh_list.append(ajbh) ajbh_list = list(set(node.get('ajbh_list'))) xyrbh_list = list(set(node.get('xyrbh_list'))) flag = True if len(aj_result) > 0: for i, val in enumerate(ajbh_list): f = find(val, aj_result) if f: flag = False break; if flag: if len(ajbh_list) > 2 and len(xyrbh_list) > 2: aj.append(ajbh) aj_result.append(ajbh_list) xyr_result.append(xyrbh_list) print('MATCH p=(:anjian{AJBH:"'+ajbh+'"})-[*]-() RETURN p LIMIT 25') name = str(len(xyrbh_list)) + "人" + str(len(ajbh_list)) + "案" print(name) print(len(aj_result)) print(aj) print('----------------------------') print(aj_result) conn = psycopg2.connect(database="zfgfh", user="postgres", password="postgres", host="192.168.3.202", port="5432") cursor = conn.cursor() for ajbh in aj_result: for temp in ajbh: insert_sql = "insert into test.gm (\"ajbh\") values ('%s')" insert_sql = insert_sql % temp print(insert_sql) # 插入 cursor.execute(insert_sql) # 提交 conn.commit() cursor.close() # 关闭Cursor conn.close() # 关闭数据库
6人3案 4人5案 3人6案 11人3案 3人13案 6人46案 8人4案 5人4案 7人4案 28人17案 4人4案 3人8案 4人4案 8人3案 6人6案 5人4案 4人6案 19人77案 4人22案 4人14案 4人5案 9人5案 12人8案 4人3案 17人4案 5人7案 18人30案 7人11案 3人22案 20人12案 30 ['A2301260500002019020002', 'A2301096000002018040010', 'A2301246400002018060001', 'A2301270500002018040003', 'A2301085100002018100001', 'A2301062020012018100099', 'A2301830517002018090003', 'A2301290501002019040001', 'A2301240500002018080001', 'A2301260200002018090006', 'A2301821414022018011647', 'A2301000514012018090001', 'A2301245100002018050014', 'A2301004908002018110004', 'A2301295500002018110005', 'A2301270300002018090005', 'A2301848900002018070017', 'A2301210504002018080011', 'A2301004902002018110002', 'A2301810500002018110038', 'A2301025103002018120002', 'A2301290500002019020003', 'A2301096000002018060043', 'A2301260200002018060002', 'A2301810500002018090002', 'A2301815100002018060014', 'A2301820506002018070006', 'A2301037200002018100154', 'A2301849000002018090001', 'A2301280200002018110002'] 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'A2301060504002018120004', 'A2301060504002018110018', 'A2301060504002018110020', 'A2301060504002018110034'] ['A2301830517002018090003', 'A2301830517002018090005', 'A2301830517002018090006', 'A2301830517002018090004'] ['A2301290501002019040001', 'A2301290501002019040002', 'A2301290501002019030002', 'A2301290501002019040004'] ['A2301240500002018080001', 'A2301240500002018080004', 'A2301240500002018080003', 'A2301240500002018080002'] ['A2301260200002018100006', 'A2301260200002018090002', 'A2301260200002018100013', 'A2301260200002018100008', 'A2301260200002018090006', 'A2301260200002018100003', 'A2301260200002018100001', 'A2301260200002018100010', 'A2301260200002018100005', 'A2301260200002018100012', 'A2301260200002018100002', 'A2301260200002018090004', 'A2301260200002018100011', 'A2301260200002018090003', 'A2301260200002018100014', 'A2301260200002018090005', 'A2301260200002018100007'] ['A2323037100002018120006', 'A2301827600002018090005', 'A2301821414022018011647', 'A2301820209332018024067'] ['A2301000514012018090001', 'A2301230500002018060001', 'A2301000514012018110004', 'A2301230300002018110005', 'A2301230500002018050005', 'A2301000514012019010001', 'A2301000514012018090002', 'A2301000514012018100001'] ['A2301245100002018050014', 'A2301245100002018070018', 'A2301286100002018050005', 'A2301286100002018060002'] ['A2303016200002018100001', 'A2301004908002018110004', 'A2301004908002018110006'] ['A2301295500002018110008', 'A2301295500002018110006', 'A2301295500002018110003', 'A2301295500002018110007', 'A2301295500002018110005', 'A2301002104002018100007'] ['A2301270300002018100004', 'A2301270300002018100002', 'A2301270300002018100001', 'A2301270300002018090005'] ['A2301848900002018070017', 'A2301845700002018070027', 'A2301840502002018070002', 'A2301848800002018070024', 'A2301840502002018070003', 'A2301840502002018070001'] ['A2301210504002018120003', 'A2301210504002018120019', 'A2301210504002018120013', 'A2301210504002018080007', 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'A2301210504002018120046', 'A2301210504002018120052', 'A2301210504002018110007', 'A2301210504002018120024', 'A2301210504002018080022', 'A2301210504002018080005', 'A2301210504002018120050', 'A2301210504002018080002', 'A2301210504002018120009', 'A2301210504002018120031', 'A2301210504002018120033', 'A2301210504002018120042', 'A2301210504002018120035', 'A2301210504002018120040', 'A2301210504002018120026', 'A2301210504002018120005', 'A2301210504002018120030', 'A2301210504002018120027', 'A2301210504002018120018', 'A2301210504002018120010', 'A2301210504002018120016', 'A2301210504002018120002', 'A2301210504002018120001', 'A2301210504002018120034', 'A2301210504002018120007', 'A2301210504002018120037', 'A2301210504002018120004', 'A2301210504002018080008', 'A2301210504002018080011', 'A2301210504002018080018', 'A2301210504002018120021', 'A2301210504002018080020', 'A2301210504002018120047', 'A2301210504002018080006', 'A2301210504002018120020', 'A2301210504002018080016'] ['A2301004906002019010005', 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以上这篇python 实现查询Neo4j多节点的多层关系就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持小牛知识库。
我对neo4j还是个新手。我已经编写了一个小型的RESTFUL服务,它将节点作为PoC通过REST插入neo4j db(服务器)。结构如下所示:根-[节点]- n节点是“居中”的节点。我计划将其他中心节点添加为不同的节点类型(例如产品/类别/文档/属性/用户…) 在我的第一个例子中,我添加了大约。1.000个节点。现在我试图增加节点的数量,并查询这些节点。现在我面临着一种性能“问题”。这是我的查询
本文向大家介绍python实现多层感知器,包括了python实现多层感知器的使用技巧和注意事项,需要的朋友参考一下 写了个多层感知器,用bp梯度下降更新,拟合正弦曲线,效果凑合。 效果图: 以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持呐喊教程。
本文向大家介绍Mybatis自关联查询一对多查询的实现示例,包括了Mybatis自关联查询一对多查询的实现示例的使用技巧和注意事项,需要的朋友参考一下 注:代码已托管在GitHub上,地址是:https://github.com/Damaer/Mybatis-Learning ,项目是mybatis-13-oneself-one2many,需要自取,需要配置maven环境以及mysql环境(sql
根据我的Spring Data Neo4j 4(SDN4)类层次结构,我有很多Neo4j节点,每个节点有大约7个标签。 我应该担心我的应用程序的性能,因为每个节点的标签数量或Neo4j标签(以及它们在SDN 4中的使用)不会影响性能吗?
主要内容:示例实际应用中,由于多对多的关系比较复杂,会增加理解和关联的复杂度,所以应用较少。MyBatis 没有实现多对多级联,推荐通过两个一对多级联替换多对多级联,以降低关系的复杂度,简化程序。 例如,一个订单可以有多种商品,一种商品可以对应多个订单,订单与商品就是多对多的级联关系。可以使用一个中间表(订单记录表)将多对多级联转换成两个一对多的关系。 示例 下面以订单和商品(实现“查询所有订单以及每个订单对应
我是新4J的新手。 (Node1)-[Rel1]->(Node3) (每个节点和发布在'Rights'属性中都有'admin'字符串。) 如果用户具有正确的'user',则路径应为: (Node1)-[Rel2]-(Node2)-[Rel3]-(Node3) (因为Rel1关系在“Rights”属性中没有“user”字符串。) 谢了。