2.13 毕业设计
1.neo4j创建新的数据库
由于使用Neo3.x创建新数据库而不删除现有数据库,所以只需在$NEO4J_HOME的conf的目录编辑neo4j.conf。
搜寻dbms.active_database=,其默认值应为graph.db。用其他名称替换它,然后再次启动neo4j。现在,将在该目录名下创建一个新数据库。若要切换回以前的db,请重复这些步骤,只需将新值替换为graph.db在配置文件中。
2.pycharm永久激活
https://shimo.im/docs/ckprWXRyDcTydv6t/read
3.csv保存文件时默认位ansi编码,需要使用记事本打开保存问utf-8编码
4.label!!!!!!因为一个字母拼写错误浪费一下午时间
5.今日大头
neo4j 导入csv文件
参考知乎教程
admin-import 或 neo4j-import
- 适用场景:千万以上 nodes
- 速度:非常快 (xw/s)
- 优点:官方出品,占用更少的资源
- 缺点:需要转成CSV;必须停止neo4j;只能生成新的数据库,而不能在已存在的数据库中插入数据。
-
admin-import 基本语法
neo4j-admin import[--mode=csv][--database=<name>][--additional-config=<config-file-path>][--report-file=<filename>][--nodes[:Label1:Label2]=<"file1,file2,...">][--relationships[:RELATIONSHIP_TYPE]=<"file1,file2,...">][--id-type=<STRING|INTEGER|ACTUAL>][--input-encoding=<character-set>][--ignore-extra-columns[=<true|false>]][--ignore-duplicate-nodes[=<true|false>]][--ignore-missing-nodes[=<true|false>]][--multiline-fields[=<true|false>]][--delimiter=<delimiter-character>][--array-delimiter=<array-delimiter-character>][--quote=<quotation-character>][--max-memory=<max-memory-that-importer-can-use>][--f=<File containing all arguments to thisimport>][--high-io=<true/false>]-
- --ignore-extra-columns=true 忽略多余列参数
- --ignore-missing-nodes=true 忽略失去节点参数
- --ignore-duplicate-nodes=true 忽略重复节点参数
导入数据示例:
示例一
三个csv
movies.csv
movie:ID,name,:LABELtt0133093,TheMatrix,moviett0234215,TheMatrixReloaded,moviett0242653,TheMatrixRevolutions,movie
actors.csv
person:ID,name,:LABELkeanu,KeanuReeves,personlaurence,LaurenceFishburne,personcarrieanne,Carrie-AnneMoss,person
roles.csv
:START_ID,role,:END_IDkeanu,Neo,tt0133093keanu,Neo,tt0234215keanu,Neo,tt0242653laurence,Morpheus,tt0133093laurence,Morpheus,tt0234215laurence,Morpheus,tt0242653carrieanne,Trinity,tt0133093
需要一次性全部导入
neo4j-import 导入
.\bin\neo4j-import--into data\databases\graph.db --nodes .\import\practice\actors.csv --nodes .\import\practice\movies.csv --relationships:ACTED_IN .\import\practice\roles.csv --skip-duplicate-nodes=true--skip-bad-relationships=true--stacktrace --bad-tolerance=500000
neo4j-admin 导入
.\bin\neo4j-admin import--database=graph.db --nodes .\import\practice\actors.csv --nodes .\import\practice\movies.csv --relationships:ACTED_IN .\import\practice\roles.csv
movies3-header.csv
movieId:ID,title,year:int,:LABEL
movies3.csv
tt0133093,"The Matrix",1999,Moviett0234215,"The Matrix Reloaded",2003,Movie;Sequeltt0242653,"The Matrix Revolutions",2003,Movie;Sequel
actors3-header.csv
personId:ID,name,:LABEL
actors3.csv
keanu,"Keanu Reeves",Actorlaurence,"Laurence Fishburne",Actorcarrieanne,"Carrie-Anne Moss",Actor
roles3-header.csv
:START_ID,role,:END_ID,:TYPE
roles3.csv
keanu,"Neo",tt0133093,ACTED_INkeanu,"Neo",tt0234215,ACTED_INkeanu,"Neo",tt0242653,ACTED_INlaurence,"Morpheus",tt0133093,ACTED_INlaurence,"Morpheus",tt0234215,ACTED_INlaurence,"Morpheus",tt0242653,ACTED_INcarrieanne,"Trinity",tt0133093,ACTED_INcarrieanne,"Trinity",tt0234215,ACTED_INcarrieanne,"Trinity",tt0242653,ACTED_IN
导入脚本
neo4j_home$ bin/neo4j-admin import--nodes="import/movies3-header.csv,import/movies3.csv"--nodes="import/actors3-header.csv,import/actors3.csv"--relationships="import/roles3-header.csv,import/roles3.csv"
示例三
neo4j-admin import--mode=csv --database=userMovie.db --nodes data_test\movies.csv --nodes data_test\actors.csv --relationships data_test\roles.csvbin/neo4j-admin import--nodes:Movieimport/movie_node.csv --relationships:ACTED_IN=import/roles5b.csv
中途导入时可以用上述方法 - .\bin\neo4j-import --into data\databases\test.db --nodes .\import\node\person.csv --nodes .\import\node\movie.csv --relationships:ACTED_IN .\import\relation\actor.csv --nodes .\import\node\movie.csv --nodes .\import\node\country.csv --relationships:DISTRICT_IN .\import\relation\district.csv --nodes .\import\node\movie.csv --nodes .\import\node\person.csv --relationships:DIRECTOR_IN .\import\relation\director.csv --nodes .\import\node\movie.csv --nodes .\import\node\person.csv --relationships:COMPOSER_IN .\import\relation\composer.csv --skip-duplicate-nodes=true --skip-bad-relationships=true --stacktrace --bad-tolerance=500000

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