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Python 图算法系列29-大规模图关系建立-step1导入数据

news 2024/9/23 20:06:09

说明

这里指的是从文件类型开始，将数据规范为所需的格式，然后存储。

内容

1 文件操作

压缩文件

rar a some_file.rar some_file.csv

获取文件

rsync -rvltz -e 'ssh -p 22' --progress root@IP:/data/graph_data graph_data

解压文件

apt install unrar# e 忽略文件结构解压 , x 保持文件结构
unrar e filname.csv.rar

在获得了文件之后，需要对超大文件操作Python一些可能用的到的函数系列28 超大文本文件分割读取。

获取文件末尾n行：

import os 
# 采用偏置方法读取末尾三百行文本
def read_tail_n(fname, n_lines):off = - 100 # 偏置量min_size = - os.path.getsize(fname) # 最小量with open(fname,'rb+') as f:while True:if off < min_size:f.seek(min_size, 2)res_lines = f.readlines()breakf.seek(off, 2) # 2 代表文件末尾，往回追 off个字符lines= f.readlines()if len(lines) >  n_lines + 1:res_lines = lines[-n_lines:]breakoff *= 2return res_lines

读取中间n行

def read_somelines_mem(filename,  start_line = 1 , end_line = 100):f = open(filename, 'r')line_count = 0start_line -= 1 # 和数组索引对齐lines = []for i in range(end_line): # 从头开始循环line = f.readline()if line_count >= start_line:lines.append(line)line_count +=1  # line_count是行指针f.close()return lines

获取文件总行数

# 读取大文件行数
def nrow_big_txt(filename):cnt = 0with open(filename,'r') as f:for line in f:cnt+=1print('total lines: %s' % cnt)return cnt

处理时，假设我们每次只处理1千万条

# 读取5000万~6000万行
test1 = read_somelines_mem('filename.csv', 50000000, 60000000)
# 对可能存在的极少数问题清洗（理论上，4个字段应该一个也不缺）
test2 = [x.replace(',,',',').replace('\n','').split(',') for x in test1]
test3 = [x for x in test2 if len(x) ==4]

最终获得的文件大约为30G。

2.9G	company_node_batch1.csv
2.8G	company_node_batch2.csv
2.8G	company_node_batch3.csv
2.0G	company_node_batch4.csv
2.1G	contact_rel_part1.csv
2.1G	contact_rel_part2.csv
2.1G	contact_rel_part3.csv
2.1G	contact_rel_part4.csv
1.9G	xxx.csv
310M	invest_rel_v2.csv
2.3G	xxx1.csv

2 数据规范

该保留节点的就保留节点

之前犯过一个迷糊，就是去掉了一类联系节点，之间建立两点间的边。然后就发生了数据爆炸，因为联系节点会有多个节点与之关联，去掉后产生了n平方的边数。(100*100=1万）。这应该还是属于基本的思维方式错误。

按图的思想，每一类实体，该分类的就分类，而不必在意业务场景的特殊性。只要最后可以用流畅的自然语言表达就行，例如：Company(A) HasContact Phone(B)

将数据保存为csv形式，大致的格式如下。

对于节点来说， ID和LABEL是必须的。ID可以简单理解为数据的主键，LABEL则是数据的表(Table)或集合(Collection)。

对于边来说， STARD_ID、END_ID、TYPE构成了一条边，STARD_ID和END_ID是起点和终点，需要注意的是，边声明的节点必须已存在于图中，否则会失败。

额外的一点是，被声明为ID的列会被默认为字符型。所以即使id看起来是数值的，也会被当成字符处理。

company.csv 
id:ID,name,:LABEL
100,a01,Company
101,a02,Company
102,a03,Companycontact.csv
id:ID,name,:LABEL
200,1311111111,Phone
201,1322222222,Phonecompany_invest_rel.csv 
:START_ID,:END_ID,per:float,:TYPE
100,101,0.5,Investcontact_rel.csv
:START_ID,:END_ID,:TYPE
100,200,HasContact
101,200,HasContact
101,201,HasContact
102,201,HasContact

在使用pandas保存相应csv时可以参考以下语句

invest_rel_df.to_csv('invest_rel_v2.csv', index=False, encoding='utf-8', quoting=1)
数据模型【Nice To Have】

原始数据可能存在少量数据问题，为了确保后续可以正确的大批量处理，设定数据模型进行限定和转换。

from typing import List,Dict,Optional
from pydantic import BaseModelclass Relation(BaseModel):rid : int from_id: int to_id: int link_attr: str link_typr:int class Relation_s(BaseModel):data_list : List[Relation]test1 = read_somelines_mem('ds_lianxi_relation_e0.csv', 0,10000)
test2 = [x.replace(',,',',').replace('\n','').split(',') for x in test1]
test3 = [x for x in test2 if len(x) ==4]tem_df = pd.DataFrame(test3, columns = ['from_id','to_id','link_attr','link_typr'])
tem_df['rid']= list(range(len(tem_df)))sample_lod = tem_df.to_dict(orient='records')rs = Relation_s(data_list = sample_lod[:3])
rs1 = [x.dict() for x in rs.data_list][{'rid': 0,'from_id': 76247745,'to_id': 25278409,'link_attr': '111111111','link_typr': 1},{'rid': 1,'from_id': 24115962,'to_id': 22426271,'link_attr': '22222222','link_typr': 1},{'rid': 2,'from_id': 68525645,'to_id': 66453181,'link_attr': '3333333@qq.com','link_typr': 3}]

3 数据导入

当数据较多的时候，使用 neo4j-admin import是比较合适的方法。

假设是通过docker启动的neo4j。

通过bash方式启动容器（默认的entry point 会直接启动neo4j)

proc_path=/opt/aprojects/Neo4j_24535_36
data_path=/data/aprojects/Neo4j_24535_36
image_name="registry.cn-hangzhou.aliyuncs.com/andy08008/neo4j_5:v100"# 操作数据
docker run -it \--name='Neo4j_24535_36' \--restart=always \-v /etc/localtime:/etc/localtime  \-v /etc/timezone:/etc/timezone \-v /etc/hostname:/workspace/hostname \-e "LANG=C.UTF-8" \-v ${data_path}/data:/data \-v ${data_path}/logs:/logs \-v ${proc_path}/conf4:/var/lib/neo4j/conf/ \-v /data/neo4j_import:/var/lib/neo4j/import \-v ${proc_path}/plugins4:/var/lib/neo4j/plugins \--env NEO4J_AUTH=neo4j/xxxxxx \-p 24535:7474 \-p 24536:7687 \${image_name} bash

然后确保neo4j停止时导入(neo4j stop),可以导入多个节点和边的文件。

neo4j-admin database import full --nodes=import/company_node_batch1.csv --nodes=import/company_node_batch2.csv --nodes=import/company_node_batch3.csv --nodes=import/company_node_batch4.csv --nodes=import/xxx.csv --nodes=import/xxxx.csv --relationships=import/invest_rel_v2.csv  --relationships=import/contact_rel_part1.csv --relationships=import/contact_rel_part2.csv --relationships=import/contact_rel_part3.csv --relationships=import/contact_rel_part4.csv --overwrite-destination --verbose

导入过程中会不断给到反馈

Neo4j version: 5.23.0
Importing the contents of these files into /data/databases/neo4j:
Nodes:/var/lib/neo4j/import/company_node_batch1.csv/var/lib/neo4j/import/company_node_batch2.csv/var/lib/neo4j/import/company_node_batch3.csv/var/lib/neo4j/import/company_node_batch4.csv/var/lib/neo4j/import/xxx.csv/var/lib/neo4j/import/xxxx.csvRelationships:/var/lib/neo4j/import/invest_rel_v2.csv/var/lib/neo4j/import/contact_rel_part1.csv/var/lib/neo4j/import/contact_rel_part2.csv/var/lib/neo4j/import/contact_rel_part3.csv/var/lib/neo4j/import/contact_rel_part4.csvAvailable resources:Total machine memory: 47.04GiBFree machine memory: 20.71GiBMax heap memory : 11.77GiBMax worker threads: 8Configured max memory: 483.9MiBHigh parallel IO: trueCypher type normalization is enabled (disable with --normalize-types=false):Property type of 'regcap' normalized from 'float' --> 'double' in /var/lib/neo4j/import/company_node_batch1.csvProperty type of 'socnum' normalized from 'int' --> 'long' in /var/lib/neo4j/import/company_node_batch1.csvProperty type of 'regcap' normalized from 'float' --> 'double' in /var/lib/neo4j/import/company_node_batch2.csvProperty type of 'socnum' normalized from 'int' --> 'long' in /var/lib/neo4j/import/company_node_batch2.csvProperty type of 'regcap' normalized from 'float' --> 'double' in /var/lib/neo4j/import/company_node_batch3.csvProperty type of 'socnum' normalized from 'int' --> 'long' in /var/lib/neo4j/import/company_node_batch3.csvProperty type of 'regcap' normalized from 'float' --> 'double' in /var/lib/neo4j/import/company_node_batch4.csvProperty type of 'socnum' normalized from 'int' --> 'long' in /var/lib/neo4j/import/company_node_batch4.csv
Nodes, started 2024-09-23 06:28:28.070+0000
[*Nodes:0B/s 2.192GiB-------------------------------------------------------------------------] 224M ∆3.92M
Done in 3m 1s 796ms
Prepare node index, started 2024-09-23 06:31:29.907+0000
[*:3.030GiB-----------------------------------------------------------------------------------] 674M ∆    0
Done in 1m 25s 278ms
Relationships, started 2024-09-23 06:32:55.193+0000
[*Relationships:0B/s 3.030GiB-----------------------------------------------------------------] 245M ∆ 560K
Done in 6m 59s 93ms
Node Degrees, started 2024-09-23 06:39:57.991+0000
[*>(2)================================================|CALCULATE:2.593GiB(5)==================] 245M ∆ 8.4M
Done in 22s 771ms
Relationship --> Relationship 1/2, started 2024-09-23 06:40:21.526+0000
[>(2)=================|*LINK(4)==================================|v:130.4MiB/s----------------] 245M ∆7.32M
Done in 1m 1s 948ms
RelationshipGroup 1/2, started 2024-09-23 06:41:23.480+0000
[>:2.097GiB/s--|>|*v:240.6KiB/s---------------------------------------------------------------] 106M ∆ 106M
Done in 1s 469ms
Node --> Relationship, started 2024-09-23 06:41:24.958+0000
[>:122.6MiB/s---------|*>(3)========================|LINK--------------|v:196.1MiB/s(3)=======] 191M ∆35.7M
Done in 14s 914ms
Relationship <-- Relationship 1/2, started 2024-09-23 06:41:39.882+0000
[>--------------------------------|*LINK(5)===========================|v:142.0MiB/s-----------] 245M ∆ 7.2M
Done in 56s 596ms
Relationship --> Relationship 2/2, started 2024-09-23 06:42:39.009+0000
[>(2)================================|*LINK(4)============================|v:33.04MiB/s-------] 245M ∆8.64M
Done in 45s 314ms
RelationshipGroup 2/2, started 2024-09-23 06:43:24.325+0000
[*>(6)=============================================================================|v:8.104MiB] 220M ∆ 220M
Done in 1s 616ms
Relationship <-- Relationship 2/2, started 2024-09-23 06:43:25.965+0000
[*>(2)=============================================================|LINK(4)==========|v:40.18M] 245M ∆6.84M
Done in 37s 627ms
Count groups, started 2024-09-23 06:44:04.283+0000
[>|*>--------------------------------------------------------------------------------|COUNT:93] 349K ∆ 349K
Done in 143ms
Gather, started 2024-09-23 06:44:11.745+0000
[>----------------|*CACHE:2.619GiB------------------------------------------------------------] 349K ∆ 349K
Done in 179ms
Write, started 2024-09-23 06:44:11.935+0000
[*>:??-----------------------------------------------------------------------|EN|v:??---------] 349K ∆ 349K
Done in 676ms
Node --> Group, started 2024-09-23 06:44:12.717+0000
[>---|*FIRST-----------------------------------------------------------------------------|v:1.] 340K ∆ 119K
Done in 3s 319ms
Node counts and label index build, started 2024-09-23 06:44:18.512+0000
[*>(3)===================================|LABEL INDEX-------------------------|COUNT:2.174GiB(] 224M ∆24.1M
Done in 16s 843ms
Relationship counts and relationship type index build, started 2024-09-23 06:44:35.898+0000
[>-------------------------|RELATIONSHIP TYPE|*COUNT------------------------------------------] 245M ∆ 380K
Done in 1m 51s 188msIMPORT DONE in 18m 1s 685ms.
Imported:224894968 nodes245297705 relationships878241672 properties
Peak memory usage: 3.030GiB

成功后启动neo4j(neo4j start)

root@457931a3e173:/var/lib/neo4j# neo4j start
Directories in use:
home:         /var/lib/neo4j
config:       /var/lib/neo4j/conf
logs:         /logs
plugins:      /var/lib/neo4j/plugins
import:       /var/lib/neo4j
data:         /var/lib/neo4j/data
certificates: /var/lib/neo4j/certificates
licenses:     /var/lib/neo4j/licenses
run:          /var/lib/neo4j/run
Starting Neo4j.
Started neo4j (pid:2429). It is available at http://0.0.0.0:7474
There may be a short delay until the server is ready.

然后就可以在前端访问了，第一步导入数据结束。

在使用时需要先设置索引，整个过程也是比较快的。

从数据导入到索引完成，总共花费约30分钟。


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