What is graph database Neo4j

The so-called graph database is generally composed of nodes and relationships, neo4j is one of them

It is superior to the traditional database mysql in seeking data association

And neo4j supports hundreds of millions of nodes and relationships

Traditional graph operations are generally performed in memory and cannot process the entire knowledge graph. Neo4j can complete graph operations on disk

Therefore, neo4j is often used as a data carrier in the question answering system to quickly search for keyword information

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Create a node:

create (n:person's name{name:Xiao Mingle)-[p:age{name:'age]->(m:age{name:183)
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show first degree relationship

match p-(n)-[ ] ->(m) where n.name ='Xiao Ming' return n,m

Show the relationship between the first level and the second level

match p=(n)-[*1…2]->(m) where n.name ='习近’return n,m
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delete node

match p=(n) where nname =' ' detach delete n
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In a graph with a large number of nodes and relationships, there is faster database operation speed

Supports distributed access, and can use clusters to expand memory and disk capacity

Support distributed high availability, can support large-scale

Data growth Data is safe and reliable, supporting real-time backup of data

Through the Cypher statement, the operation and display of graph data are more intuitive

Construct data in Neo4j supported formats

There are two ways to import general data into Neo4j:

1. Use fixed-format csv for data loading

2. Use code to import data

The two methods have their own advantages and disadvantages. Using CS to import data is faster, but relatively fixed; using code to import is more flexible, and there is a certain bottleneck in terms of processing speed.

If you use Csv to import, you generally need two pieces of data, entity.csv is used to define the entity; relationship.csv is used to describe the relationship, so the existing data needs to be reprocessed.

This is a sample of entity.csv
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This is a sample of relationship.csv

Preparation before data processing

import csv
from tqdm import tqdm
entity = open('entity.csv',w'encoding=utf-8newline=")
relationship =open(relationship.csv,wencoding='utf-8newline=")
#Open 2 logger
entities writer = csv.writer(entity)
relationship writer = csv.writer(relationship)
#The following are the two csvs accepted by neo4i, the required column names
entity writer.writerow([:IDname:LABELT)relationship writer.writerow([ :START IDname:ENDIDTYPET)

Formally write data

data = pdread csv(file name,chunksize=100000)
for ind,i in enumerate(data):
    for d in tqdm(range(len(0)):
        d = i.loc[d]
        entity writer.writerow([hash(d.实体)d.实体ENTITY)
        entity writer.writerow([hash(d.值)d值ENTITYT)
        relationship writer.writerow([hash(d.实体)d.属性hash(d.值)RELATIONSHIPT)
        break