neo4j︱neo4j批量导入neo4j-import (五)

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neo4j数据批量导入

目前主要有以下几种数据插入方式:(转自:如何将大规模数据导入Neo4j) Cypher CREATE 语句,为每一条数据写一个CREATE Cypher LOAD CSV 语句,将数据转成CSV格式,通过LOAD CSV读取数据。 官方提供的Java API —— Batch Inserter 大牛编写的 Batch Import 工具 官方提供的 neo4j-import 工具

这边重点来说一下官方最快的neo4j-import,使用的前提条件:

  • graph.db需要清空;
  • neo4j需要停掉;
  • 接受CSV导入,而且格式较为固定;
  • 试用场景:首次导入
  • 节点名字需要唯一

比较适用:

首次导入,无法迭代更新

来看一下官方案例:Use the Import tool

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1 neo4j基本参数

1.1 启动与关闭:

bin\neo4j start
bin\neo4j stop
bin\neo4j restart
bin\neo4j status

1.2 neo4j-admin的参数:控制内存

来源:10.5. Memory recommendations

1.2.1 memrec 是查看参考内存设置

neo4j-admin memrec [--memory=<memory dedicated to Neo4j>] [--database=<name>]

Option

Default

Description

–memory

The memory capacity of the machine

The amount of memory to allocate to Neo4j. Valid units are: k, K, m, M, g, G.

–database

graph.db

The name of the database. This option will generate numbers for Lucene indexes, and for data volume and native indexes in the database. These can be used as an input into more detailed memory analysis.

参考:

$neo4j-home> bin/neo4j-admin memrec --memory=16g

1.2.2 指定缓存–pagecache

还有--pagecache单条命令指定缓存:

bin/neo4j-admin backup --from=192.168.1.34 --backup-dir=/mnt/backup --name=graph.db-backup --pagecache=4G

指的是,再该条导入数据的指令下,缓存设置。

1.3 neo4j-admin的参数:Dump and load databases - 线下备份

执行该两步操作,需要关闭数据库。参考:10.7. Dump and load databases

dump过程:把graph.db转存到.dump

需要关闭数据库

$neo4j-home> bin/neo4j-admin dump --database=graph.db --to=/backups/graph.db/2016-10-02.dump
$neo4j-home> ls /backups/graph.db
$neo4j-home> 2016-10-02.dump

load过程:把.dumpload进来

好像可以不用关闭

$neo4j-home> bin/neo4j stop
Stopping Neo4j.. stopped
$neo4j-home> bin/neo4j-admin load --from=/backups/graph.db/2016-10-02.dump --database=graph.db --force

如果带--force,那么load之后,会更新所有的存在着的.db(any existing database gets overwritten.

1.4 neo4j-admin的参数:backup and restore - 在线备份

参考:6.2. Perform a backup

在线备份backup :

$neo4j-home> export HEAP_SIZE=2G
$neo4j-home> mkdir /mnt/backup
$neo4j-home> bin/neo4j-admin backup --from=192.168.1.34 --backup-dir=/mnt/backup --name=graph.db-backup --pagecache=4G

backup 进临时文件夹之中。

追加备份:

$neo4j-home> export HEAP_SIZE=2G
$neo4j-home> bin/neo4j-admin backup --from=192.168.1.34 --backup-dir=/mnt/backup --name=graph.db-backup --fallback-to-full=true --check-consistency=true --pagecache=4G

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2 简单demo

movies.csv.

movieId:ID,title,year:int,:LABEL
tt0133093,"The Matrix",1999,Movie
tt0234215,"The Matrix Reloaded",2003,Movie;Sequel
tt0242653,"The Matrix Revolutions",2003,Movie;Sequel

其中,title是属性,注意此时需要有双引号;year:int也是属性,只不过该属性是数值型的; :LABEL:ID一样生成了一个新节点,也就是一套数据可以通过:生成双节点 actors.csv.

personId:ID,name,:LABEL
keanu,"Keanu Reeves",Actor
laurence,"Laurence Fishburne",Actor
carrieanne,"Carrie-Anne Moss",Actor

roles.csv. 其中,:LABEL非常有意思,是节点的附属属性,其中personId:ID一定是唯一的:LABEL可以不唯一。 而且,载入之后,:LABEL单独会成为新的节点,而且是去重的。

:START_ID,role,:END_ID,:TYPE
keanu,"Neo",tt0133093,ACTED_IN
keanu,"Neo",tt0234215,ACTED_IN
keanu,"Neo",tt0242653,ACTED_IN
laurence,"Morpheus",tt0133093,ACTED_IN
laurence,"Morpheus",tt0234215,ACTED_IN
laurence,"Morpheus",tt0242653,ACTED_IN
carrieanne,"Trinity",tt0133093,ACTED_IN
carrieanne,"Trinity",tt0234215,ACTED_IN
carrieanne,"Trinity",tt0242653,ACTED_IN

其中,这个节点的属性,role没有标注:,role是属性,可以加双引号,也可以不加。最好是指定一下格式,譬如:int为数值型,还有字符型roles:string[]

linux执行:

neo4j_home$ bin/neo4j-admin import --nodes import/movies.csv --nodes import/actors.csv --relationships import/roles.csv

其中,之前老版本批量导入是:neo4j-import,现在批量导入是:neo4j-admin

window执行:

neo4j-import.bat --into ../data/databases/graph.db --id-type string --nodes:attribute ../import/node_attribute.csv --relationships ../import/product_SecondLeaf.csv --relationships ../import/scene_isDemond.csv
  • --into,是指定存入名字,在不同的尝试,可以修改名字。
  • --nodes:attribute,其中,nodes:后面是用来指定节点大类的名称的
  • --id-type string,,The –id-type string is indicating that all :ID columns contain alphanumeric values (there is an optimization for numeric-only id’s).之前节点ID只能由数字组成,现在允许字符+数字共同定义。

linux最后启动:

./bin/neo4j start

window 最后启动:

neo4j.bat console

执行时候错误信息解析:

1 报错信息留存在bad.log

\data\databases\graph.db\bad.log

global id space的报错为节点未定义,或者节点重复

2 如果节点不唯一,直接报错: global id space,同时后续的内容中端上传,需要删除data/database /graph.db,重新操作一遍

.


.

3 其他导入情况列举

主要来源于:B.2. Use the Import tool

3.1 不同分隔符导入

如果导入的节点信息为:

:START_ID;role;:END_ID;:TYPE
keanu;'Neo';tt0133093;ACTED_IN
keanu;'Neo';tt0234215;ACTED_IN

那么可以通过--delimiter来进行指定。

neo4j_home$ bin/neo4j-admin import --nodes import/movies2.csv --nodes import/actors2.csv --relationships import/roles2.csv --delimiter ";" --array-delimiter "|" --quote "'"

3.2 不同数据集定义相同节点

movies5a.csv.

movieId:ID,title,year:int
tt0133093,"The Matrix",1999

sequels5a.csv.

movieId:ID,title,year:int
tt0234215,"The Matrix Reloaded",2003
tt0242653,"The Matrix Revolutions",2003

actors5a.csv.

personId:ID,name
keanu,"Keanu Reeves"
laurence,"Laurence Fishburne"
carrieanne,"Carrie-Anne Moss"

执行语句:

neo4j_home$ bin/neo4j-admin import --nodes:Movie import/movies5a.csv --nodes:Movie:Sequel import/sequels5a.csv --nodes:Actor import/actors5a.csv

执行的时候,把movies5a.csv定义一个节点名字nodes:Movie; 在sequels5a.csv定义节点名字有两个::Movie:Sequel

3.3 定义关系名称以及关系属性

roles5b.csv.

:START_ID,role,:END_ID
keanu,"Neo",tt0133093
keanu,"Neo",tt0234215
keanu,"Neo",tt0242653
laurence,"Morpheus",tt0133093
laurence,"Morpheus",tt0234215
laurence,"Morpheus",tt0242653
carrieanne,"Trinity",tt0133093

执行内容:

neo4j_home$ bin/neo4j-admin import --relationships:ACTED_IN import/roles5b.csv

其中,:ACTED_IN将关系名称定义为ACTED_IN;同时定义关系的属性也有role

3.4 拆分数据集上传提高效率

节点数据集,标题:movies4-header.csv.

movieId:ID,title,year:int,:LABEL

节点数据集,内容模块1:movies4-part1.csv.

tt0133093,"The Matrix",1999,Movie
tt0234215,"The Matrix Reloaded",2003,Movie;Sequel

节点数据集,内容模块2:movies4-part2.csv.

tt0242653,"The Matrix Revolutions",2003,Movie;Sequel

关系数据集,标题:roles4-header.csv.

:START_ID,role,:END_ID,:TYPE

关系数据集,内容1:roles4-part1.csv.

keanu,"Neo",tt0133093,ACTED_IN
keanu,"Neo",tt0234215,ACTED_IN

关系数据集,内容2:roles4-part2.csv.

laurence,"Morpheus",tt0242653,ACTED_IN
carrieanne,"Trinity",tt0133093,ACTED_IN

执行:

neo4j_home$ bin/neo4j-admin import --nodes "import/movies4-header.csv,import/movies4-part1.csv,import/movies4-part2.csv" --relationships "import/roles4-header.csv,import/roles4-part1.csv,import/roles4-part2.csv"

标题与内容单独分开,然后由:标题,内容模块1,内容模块2,分块导入。

3.5 两个节点集拥有相同的字段

这个会比较经常出现,两个节点集合中,拥有相同字段,如果不设置,就会出现报错。 movies7.csv.

movieId:ID(Movie-ID),title,year:int,:LABEL
1,"The Matrix",1999,Movie
2,"The Matrix Reloaded",2003,Movie;Sequel
3,"The Matrix Revolutions",2003,Movie;Sequel

其中,(Movie-ID),是将ID进行标记 actors7.csv.

personId:ID(Actor-ID),name,:LABEL
1,"Keanu Reeves",Actor
2,"Laurence Fishburne",Actor
3,"Carrie-Anne Moss",Actor

roles7.csv.

:START_ID(Actor-ID),role,:END_ID(Movie-ID)
1,"Neo",1
1,"Neo",2
1,"Neo",3
2,"Morpheus",1
2,"Morpheus",2
2,"Morpheus",3
3,"Trinity",1
3,"Trinity",2
3,"Trinity",3

执行:

neo4j_home$ bin/neo4j-admin import --nodes import/movies7.csv --nodes import/actors7.csv --relationships:ACTED_IN import/roles7.csv

在关联表中定义::START_ID(Actor-ID):END_ID(Movie-ID),来指定相应的ID。

3.6 错误信息跳过:错误的节点

错误的关系出现: roles8a.csv.

:START_ID,role,:END_ID,:TYPE
carrieanne,"Trinity",tt0242653,ACTED_IN
emil,"Emil",tt0133093,ACTED_IN

譬如多出了节点,emil 此时执行:

neo4j_home$ bin/neo4j-admin import --nodes import/movies8a.csv --nodes import/actors8a.csv --relationships import/roles8a.csv --ignore-missing-nodes

其中的--ignore-missing-nodes就是跳过报错的节点,其中,错误信息会记录在bad.log之中:

InputRelationship:
   source: roles8a.csv:11
   properties: [role, Emil]
   startNode: emil (global id space)
   endNode: tt0133093 (global id space)
   type: ACTED_IN
 referring to missing node emil

3.7 错误信息跳过:重复节点

actors8b.csv.

personId:ID,name,:LABEL
keanu,"Keanu Reeves",Actor
laurence,"Laurence Fishburne",Actor
carrieanne,"Carrie-Anne Moss",Actor
laurence,"Laurence Harvey",Actor

在节点数据集actors8b.csv. 中,由重复的节点:laurence 需要执行:

neo4j_home$ bin/neo4j-admin import --nodes import/actors8b.csv --ignore-duplicate-nodes

其中,–ignore-duplicate-nodes就是重复节点忽略 会在bad.log之中显示报错:

Id 'laurence' is defined more than once in global id space, at least at actors8b.csv:3 and actors8b.csv:5

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