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Figure introduction to database neo4j

2022-06-13 09:05:00 【Q z1997】

1. Graph database Neo4j Introduce

1.1 What is graph database （graph database）

With social interaction 、 Online retailers 、 Finance 、 retail 、 Rapid development of Internet of things and other industries , The real society has formed a huge and complex network of relations , Traditional database is difficult to deal with relational operation . The relationship between the data to be processed in big data industry grows geometrically with the amount of data , There is an urgent need for a database that supports massive and complex data relational operations , Figure database came into being . Many famous companies in the world are using graph database , such as ：

Social sphere ：Facebook, Twitter,Linkedin Use it to manage social relationships , Realize friend recommendation
Retail area ：eBay, Wal Mart uses it to realize real-time recommendation of goods , Better shopping experience for buyers
Financial field ： Morgan chase , Citigroup, UBS and other banks are using graph database for risk control
Automotive manufacturing ： Volvo , Top automakers such as Daimler and Toyota rely on graphic databases to drive innovative manufacturing solutions
Telecom field ：Verizon, Orange and AT&T And other telecom companies rely on graph database to manage the network , Control access and support customers 360
Hotel Area ： Top hotel companies, such as Marriott and Accor hotels, manage complex and rapidly changing inventory map databases by using map databases, which do not refer to databases that store pictures , Instead, data is stored and queried in a graph data structure .

Graph database is a kind of database based on graph theory NoSQL database , Its data storage structure and data query method are based on graph theory , Graph database is mainly used to store more connection data .

graph theory 〔Graph Theory〕 It's a branch of mathematics . It takes graph as its research object. Graph in graph theory is a graph composed of several given points and lines connecting two points , This kind of graph is usually used to describe a certain relationship between certain things , Use dots to represent things , A line connecting two points indicates that there is such a relationship between two things .
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Use Google+（GooglePlus） Applications to understand the real world Graph Database requirements . Look at the chart below . ad locum , We use circles to show Google+ Apply profile .

In the diagram above , outline “A” Have circles to connect to other outlines ： Family circle （B,C,D） With friends （B,C）. Again , If we open the configuration file “B”, We can observe the following connected data .
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like this , These applications contain a large number of structured , Semi structured and unstructured connected data . stay RDBMS It is not easy to represent such unstructured connected data in a database . If we were RDBMS This more connected data is stored in the database , So retrieval or traversal is very difficult and slow . So you want to represent or store this more connected data , We should choose a popular graph database .

The graph database can easily store this more connected data . It stores each profile data internally as a node , The nodes that it connects to adjacent nodes , They are connected by relationships . They store the same data for this connection as in the chart above , This is very easy and faster to retrieve or traverse .

Relationship query performance comparison In the data relationship Center , Graph database is very efficient in query speed , Even for deep and complex queries . In relational database and graph database (Neo4j) The experiment was carried out between ： The maximum depth found in a social network is 5 Friends of friends of , Their datasets include 100 ten thousand people , About everyone. 50 A friend .

The experimental results are as follows ：
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Compare relational databases

In a relational database ,Person and Department The relationship between tables is represented by foreign keys ：

Compare with others NoSQL database

NoSQL Databases can be roughly divided into four categories ：

Key value (key/value) database
Column store database
Document database
Graph database

1.2 What is? Neo4j

Neo4j It's an open source NoSQL Graphic database ,2003 Development started in , Use scala and java Language ,2007 Launched in .

It is one of the most advanced graph databases in the world , Provide native graph data storage , Retrieve and process ;
Adopt attribute graph model （Property graph model）, Greatly improve and enrich the graph data model ;
Proprietary query language Cypher, intuitive , Efficient ;
Official website ： https://neo4j.com/

Neo4j Characteristics of ：

SQL It's like a simple query language Neo4j CQL
It follows the attribute graph data model
It uses Apache Lucence Support the index
It supports UNIQUE constraint
It contains a for executing CQL Ordered UI：Neo4j Data browser
It supports complete ACID（ Atomicity , Uniformity , Isolation and persistence ） The rules
It uses native graphics library with native GPE（ Graphics engine ）
It supports the export of query data to JSON and XLS Format
It provides REST API, It can be used in any programming language （ Such as Java,Spring,Scala etc. ） visit
It provides access to any UI MVC frame （ Such as Node JS） Access to the Java Script
It supports two kinds of Java API：Cypher API and Native Java API To develop Java Applications

Neo4j The advantages of ：

It's easy to represent connected data
retrieval / Traverse / Navigating more connected data is very easy and fast
It's very easy to represent semi-structured data
Neo4j CQL Query language command is a human readable format , It's very easy to learn
Use a simple and powerful data model
It doesn't need complex connections to retrieve connected / Relevant data , Because it's easy to retrieve its neighbor node or relationship details without connection or index

1.3 Neo4j Data model

The basis of graph theory
A graph is a set of nodes and the relationships that connect them , Graphs store data in nodes and relationships in the form of attributes , Attributes are key value pairs used to represent data .

In graph theory , We can represent a node with a circle , The relationship between nodes is marked by an arrow .

The simplest possible graph is a single node ：
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We can use nodes to represent social networks （ Such as Google+（GooglePlus） The personal data ）, It does not contain any attributes . towards Google+ Add some attributes to your profile ：

Create a relationship between two nodes ：

Here you create the relationship name between the two profiles “ Follow ”. It means Profile-I follow Profile-II.

Attribute graph model

Neo4j The graph database follows the attribute graph model to store and manage its data .

Attribute graph model rules

Representation node , Data in relationships and properties
Both nodes and relationships contain attributes
Relationship connection nodes
Property is a key value pair
Nodes are represented by circles , Relations are represented by direction keys .
Relationships have directions ： One way and two way .
Each relationship contains “ Start node ” or “ From the node ” and “ To the node ” or “ The end node ”

In the attribute graph data model , The relationship should be directed . If we try to create relationships that have no direction , Then it will throw an error message . stay Neo4j in , Relationships should also be directional . If we try to create relationships that have no direction , that Neo4j An error message will be thrown ,“ Relationships should be directional ”.

Neo4j A graph database stores all its data in nodes and relationships , We don't need any extra RDBMS A database or NoSQL Database to store Neo4j Database data , It stores data in the form of graphs .Neo4j Use this machine GPE（ Graphics engine ） To use its native graph storage format .

Figure the main building blocks of the database data model are ：

node
Relationship
attribute

An example of a simple attribute graph ：
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Here we use circles to represent nodes . Use arrows to indicate relationships , Relationships are directional . We can use Properties（ Key value pair ） To express Node The data of . In this case , We are Node Of Circle Each... Is represented in Node Of Id attribute .

1.4 Neo4j Build elements for

Neo4j The diagram database mainly has the following building elements ：

node
attribute
Relationship
label
Data browser

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node

node （Node) Is a basic element in the graph database , Used to represent an entity record , Like a record in a relational database . stay Neo4j The middle node can contain multiple attributes (Property) And multiple tags (Label).

Node is the main data element
Nodes are connected to other nodes through relationships
A node can have one or more attributes （ namely , Store as key / Property of value pair ）
A node has one or more labels , Used to describe its role in the chart

attribute

attribute （Property） Is a key value pair used to describe graph nodes and relationships . among Key Is a string , Values can be by using any Neo4j Data type

Property is a named value , Which name （ Or key ） Is string
Properties can be indexed and constrained
Composite indexes can be created from multiple properties

Relationship

Relationship （Relationship） It is also the basic element of graph database . When a node already exists in the database , The nodes need to be connected to make up the diagram . A relationship is used to connect two nodes , Relation is also called the edge of graph theory (Edge) , Its start and end must be nodes , Relationships cannot point to or originate from emptiness . A relationship can contain multiple attributes like a node , But there can only be one type of relationship (Type) .

Relationship connects two nodes
Relationships are directional
Nodes can have multiple or even recursive relationships
A relationship can have one or more properties （ Stored as key / Property of value pair ）
Based on directionality ,Neo4j Relationships are divided into two main types ：
One way relationship
Two way relationship

label

label （Label） Associate a common name with a set of nodes or relationships , A node or relationship can contain one or more tags . We can create new labels for existing nodes or relationships , We can delete tags from existing nodes or relationships .