Neo4j is a graphical Database management system, which is highly scalable, high-performance, and supports ACID transactions. It was launched by Neo4j Company in 2007, founded by Emil Eifrem and Johan Svensson. Neo4j was originally developed to address large-scale collaboration issues between artists and website developers. Applicable scenario: 1. Social network analysis: Neo4j can easily handle relationships in social networks and support real-time social network analysis. 2. Recommendation system: Neo4j's graphical data model is very suitable for building personalized recommendation systems, which can discover the association relationships between users through graphical algorithms. 3. Knowledge graph: Neo4j can be used to build a Knowledge graph and perform relational reasoning and knowledge discovery through graphical algorithms. Advantages: 1. High performance: Neo4j adopts a graphical storage structure and supports highly concurrent data read and write operations, demonstrating excellent performance. 2. Flexibility: Neo4j's data model is very flexible and can easily represent complex association relationships. 3. Easy to use: Neo4j has a friendly graphic Query language, Cypher, and provides a rich graphic algorithm library, so that users can easily query and analyze complex graphics. Disadvantages: 1. Storage consumption: Compared to traditional relational databases, Neo4j requires more storage space to store graphic data. This is due to the need to store the connection relationship between nodes and relationships. 2. Learning cost: For developers who have not used graphic databases, learning and understanding the concept of Neo4j and the Query language may require some time and effort. Technical principles: Neo4j uses a graphical structure to store data and uses a model called 'Label Property Graph'. It is centered around nodes and relationships, representing complex data structures through connections between nodes and relationships. Neo4j uses NIO (New I/O) for high-performance data read and write operations, and supports online Transaction processing processing. In addition, Neo4j also provides a series of graphic algorithms, such as shortest path algorithm, community discovery algorithm, etc., for the analysis and mining of graphic data. Performance analysis: Neo4j has excellent performance in reading and querying graphic data. It supports index to speed up data query. For complex graph query, you can use Cypher Query language to carry out efficient data retrieval. In addition, Neo4j also supports distributed deployment, which can improve system throughput and concurrency performance through horizontal scaling. Official website: https://neo4j.com/ Summary: Neo4j is a high-performance and flexible graphical Database management system, which is suitable for social network analysis, recommendation system, Knowledge graph and other scenarios. It has high scalability, flexible data models, and a rich library of graphic algorithms. However, Neo4j also has some disadvantages, such as high storage consumption and steep Learning curve. But overall, Neo4j is a very powerful and useful tool that can help users process and analyze complex graphic data.

ArangoDB is an open source Multi-model database, which supports graphic database, document database and key/value database. It provides a unified Query language (AQL) to perform complex query operations, and has distributed, highly available, and extensible features. ArangoDB was established in 2011 by the German Limited Liability Company and launched by its technology founder Claudius Weinberger. It aims to provide a flexible, high-performance, and easy-to-use database solution. ArangoDB is suitable for a range of different scenarios. For many applications, different data models may be advantageous, so ArangoDB's multi model support makes it a solution suitable for various scenarios. For example, for social Web application, the graphical database model can provide powerful query capabilities; For Content management system, document database model can effectively organize structured and unstructured data; For caching and simple key/value storage, the key/value database model is more suitable. The main advantages of ArangoDB include: 1. Multiple model support: ArangoDB allows developers to use multiple data models within the same database, providing greater flexibility and convenience. 2. High performance: ArangoDB has excellent performance and can handle a large number of concurrent read and write operations. 3. Distributed and High Availability: ArangoDB supports distributed architecture and can horizontally scale storage and computing resources. It also has high availability features, which can automatically handle node failures and data replication. 4. Easy to use: ArangoDB provides an intuitive API and Query language (AQL), enabling developers to easily use and operate the database. However, ArangoDB also has some drawbacks: 1. Relatively small community support: Compared to some mainstream database systems, ArangoDB has a relatively small community size, which may result in less support for third-party tools and plugins. 2. Relatively new databases: ArangoDB is relatively new compared to other databases, so there may be some shortcomings in reliability and stability. The technical principle of ArangoDB is based on a document storage model, which uses B+tree indexing to accelerate queries. It adopts a distributed architecture from Apache Cassandra, and uses Multiversion concurrency control (MVCC) to achieve transaction consistency. Regarding performance analysis, ArangoDB performs well in most scenarios. It has excellent read performance and can handle large-scale concurrent read operations. However, in terms of write performance, relatively high consistency requirements may lead to certain performance losses. You can find more information about ArangoDB on its official website: https://www.arangodb.com/ To sum up, ArangoDB is a Multi-model database with rich functions, flexibility and high performance, which is suitable for various application scenarios. It has advantages in multiple model support, high performance, distribution, and high availability, but there are some shortcomings in community support and database age.

OrientDB is an object-oriented Multi-model database management system. It provides the functions of graphic database, document database and key value pair database, and can support SQL and graphic Query language similar to Gremlin at the same time. OrientDB was created by Luca Garulli in 2010 and is currently maintained and supported by OrientDB LTD. Applicable scenario: 1. Social media and recommendation systems: OrientDB's graphical database function is particularly suitable for social network analysis, friend relationship management, and recommendation systems. 2. Log and event management: OrientDB's document database function is suitable for storing and analyzing a large amount of event data, such as logs and monitoring events. 3. Geographic Information System: Due to OrientDB's support for geospatial indexing and querying, it can provide fast and efficient data management for geographic information systems. 4. Real time applications: OrientDB's high-performance features and scalability make it an ideal choice for applications that require real-time data processing. Advantages: 1. Multiple model support: OrientDB supports multiple data models such as graphics, documents, and key values, and can choose the most suitable model according to the needs of the application. 2. Support SQL and graphical query: OrientDB supports SQL query and graphical Query language similar to Gremlin at the same time. Users can select query methods according to their needs and personal preferences. 3. High performance and scalability: OrientDB adopts an in memory storage engine and has a distributed architecture, which can handle large-scale datasets and high concurrency requests. 4. Transaction support: OrientDB provides ACID transaction support to ensure data consistency and integrity. Disadvantages: 1. The Learning curve is steep: Compared with traditional relational databases, OrientDB's data model and Query language have certain learning costs. 2. Relatively less community support: Compared to some mainstream database systems, OrientDB has relatively less community support and fewer third-party tools and solutions available. Technical principles: OrientDB uses B+tree index and WAL (write to log) internally, providing high performance and reliability. It also supports memory based data processing, which improves query and transaction performance by loading data into memory. OrientDB's multi model support is achieved by mapping different data models to a consistent internal structure at the data level. Performance analysis: OrientDB has high-performance characteristics and can be optimized for performance through the following methods: 1. Reasonably design and optimize indexes: Create appropriate indexes based on query requirements to avoid full table scans. 2. Use appropriate storage engines: OrientDB supports multiple different storage engines and selects the appropriate engine based on the requirements of the application. 3. Vertical and horizontal sharding: Depending on the amount of data and concurrent load, vertical or horizontal sharding is used to improve query and write performance. Summary: OrientDB is a powerful Multi-model database, which provides flexibility and diversity by supporting multiple data models such as graphics, documents and key values. It has the advantages of high performance, scalability, and transaction support, and is suitable for various application scenarios. Although the Learning curve is steep and the community support is limited, OrientDB is still a potential Database management system that can meet the needs of different types of applications. Official website: [OrientDB official website]（ https://orientdb.org/ ） Note: The above summary is based on the general characteristics and widely accepted viewpoints of OrientDB, and the specific situation may vary depending on the version and personal usage.

JanusGraph is a high-performance, distributed Graph database designed to store, query and manage large-scale graph data. It is built based on the Apache TinkerPop graph computing framework and has scalability and flexibility. Founding time and founder: JanusGraph was originally developed by TinkerPop community and became a top project of the Linux Foundation in 2017. Applicable scenario: JanusGraph is suitable for scenarios that require processing complex relationships and large-scale graph data. It can be used for social network analysis, recommendation system, network security, Knowledge graph and domain specific map applications. Advantages: 1. Scalability: JanusGraph supports horizontal scaling and can easily handle large-scale datasets and high concurrency access. 2. Flexibility: It allows users to use different data models (graphs, documents, key value pairs) and storage backend (HBase, Cassandra, etc.) to adapt to different needs. 3. Support for graph calculation: JanusGraph has a built-in graph calculation framework TinkerPop, which can perform complex graph queries and calculations. 4. Customizability: JanusGraph provides a rich API and Query language, which can flexibly customize queries and operations according to application needs. Disadvantages: 1. Learning curve: For beginners, using the Graph database and TinkerPop framework may require some time for learning and adaptation. 2. Complex deployment: deploying and managing the distributed Graph database requires some professional knowledge and experience. Technical principle: JanusGraph adopts a distributed, multi copy Master/Slave architecture for data storage. It distributes graph data across multiple nodes and uses distributed consistency protocols to maintain data consistency and reliability. Performance analysis: The performance of JanusGraph depends on the selection and configuration of the underlying storage backend. Normally, JanusGraph can support large-scale graph data with billions of nodes and billions of edges, and has good query response time and throughput. Official website: JanusGraph's official website is: https://janusgraph.org/ Summary: JanusGraph is a high-performance, distributed Graph database, which is suitable for the storage and query of large-scale graph data and complex relationships. It has the advantages of scalability, flexibility, and graph computing support, and can be applied to various scenarios. However, the Learning curve and deployment complexity can be challenges with JanusGraph.

Virtuoso is a powerful scalable, Distributed database management system (DBMS). The following is a detailed introduction to the Virtuoso database: Database Introduction: Virtuoso is a multi model and multi-purpose database system, which is compatible with RDBMS, Object database (OODBMS) and Graph database. It was developed by OpenLink Software and released under an open source license. The goal of the Virtuoso database is to provide a flexible solution that enables users to efficiently manage, query, and analyze massive amounts of data in various scenarios. Founding date and founder/company: The Virtuoso database was first released by Kingsley Idehen, founder of OpenLink Software, in 1998. Applicable scenario: Virtuoso database is applicable to various application scenarios, including but not limited to: Graph database, relational database, document database, semantic database, data integration and federated query, Web application, data mart and data analysis. Advantages: 1. Multi model support: Virtuoso database supports the functions of relational, object-oriented and Graph database, allowing users to process different types of data models on one platform. 2. Distributed architecture: The Virtuoso database has a highly scalable distributed architecture that can manage and process massive amounts of data in large-scale clusters. 3. Data integration and federated queries: Virtuoso supports data integration and federated queries across multiple data sources, enabling users to easily query and analyze dispersed data. 4. Semantic Database: The Virtuoso database has built-in RDF triplet storage and query functions, supports semantic web technology, and can perform complex semantic reasoning and queries. 5. Web application support: Virtuoso can serve as an integrated web application server, supporting various web development technologies such as SPARQL, SQL, web services, Dav services, etc. Disadvantages: 1. Complexity: Due to Virtuoso being a powerful and diverse system, using it requires a certain learning cost. 2. Performance: When processing large-scale data, Virtuoso's performance may be affected to some extent. Technical principles: The core technical principles of Virtuoso database include: data storage and index structure, query optimization and execution engine, data integration and federated queries, parallel processing and distributed architecture, semantic web technology, etc. Performance analysis: Virtuoso databases perform well in processing large-scale data and can effectively utilize distributed computing and storage resources. Its optimized query execution engine and index structure can provide fast query response time and high throughput. Official website: The official website of the Virtuoso database is: https://virtuoso.openlinksw.com/ Summary: Virtuoso is a powerful multi model, Distributed database management system. It has the support of multiple models and flexible data integration capabilities, which can efficiently manage and analyze data in different application scenarios. Although using Virtuoso requires a certain learning cost and may affect performance when processing large-scale data, it provides a comprehensive solution that meets the diverse needs of users in data management and querying.

Amazon Neptune is a fast and scalable graphical database service developed and managed by Amazon Corporation. It is a fully hosted service specifically designed for storing and querying graphic data. Amazon Neptune is part of the remaining Amazon Web Services (AWS) product portfolio and can be seamlessly integrated into AWS's ecosystem. -Database Introduction: Amazon Neptune is a highly scalable and secure graphical database service suitable for applications, social networks, recommendation engines, etc. that need to store and query highly connected datasets. It provides high-performance, reliable, and secure database solutions with its native graphical database model and hosted cloud services advantages. -Founder or Company: Amazon Neptune was launched in November 2017 and is fully developed and managed by Amazon. -Applicable scenarios: Amazon Neptune is applicable to application scenarios that need to process complex relational data, such as social networks, Knowledge graph, recommendation engines, fraud detection, etc. The advantage of graphical databases lies in their ability to quickly and efficiently query highly correlated data and support complex query operations. -Advantages: 1. Powerful performance: Amazon Neptune can provide extremely high performance and throughput through automatic scaling. It can handle large-scale graphical queries with millisecond response time. 2. Fully hosted services: Amazon Neptune is a fully hosted service that AWS will be responsible for database management, backup, failover, and continuous monitoring, allowing developers to focus on application development without worrying about the underlying infrastructure. 3. High reliability: Amazon Neptune provides multiple replication nodes to ensure high availability and persistence of data. It can automatically detect faults and quickly fail over to minimize service interruption. 4. Security Protection: Amazon Neptune supports multiple security features such as network isolation, data encryption, and access control to protect data security. 5. Complete graphic database function: Amazon Neptune supports complete graphic database function, including advanced graphic Query language, index, graphic traversal and graphic analysis. -Disadvantage: Due to Amazon Neptune being specifically designed for graphical data, it may be cumbersome in scenarios where non graphical data is processed. In addition, due to its fully managed nature, the cost may be higher than that of a self built graphics database. -Technical principle: Amazon Neptune is built based on open source graphic databases such as Apache TinkerPop and OpenTracing, as well as distributed tracking libraries. It uses a distributed architecture with multiple replicas and a highly optimized graphical query engine to provide high performance and scalability. -Performance analysis: Amazon Neptune has very high performance and can handle large-scale graphic query operations. It can process a large amount of graphic data and complex graphic queries within a millisecond response time. -Official website: The official website of Amazon Neptune is https://aws.amazon.com/neptune/ -Summary: Amazon Neptune is a hosted graphical database service that provides high performance, reliability, and security for applications handling complex relational data. It is suitable for application scenarios that require processing large-scale graphic data and provides a series of powerful graphic database functions. Although the cost may be high, its fully managed nature allows developers to focus more on application development without worrying about underlying infrastructure management.

TigerGraph is an efficient distributed Graph database, which aims to process large-scale directed and undirected graph data to support complex graph analysis and graph computing tasks. The following is a detailed introduction to the TigerGraph database: -Database Introduction: The TigerGraph database is a new generation NoSQL database based on distributed graph storage and computation. The TigerGraph database adopts a distributed computing and storage architecture that can quickly process and analyze massive graph data. -Founded in 2012, TigerGraph was founded by a technology team in Silicon Valley, USA. The founder and chief technology officer is Dr. Yu Xu, an expert in bitmap database field. -Applicable scenarios: The TigerGraph database can be applied to multiple practical scenarios, including social network analysis, recommendation systems, network threat detection, intelligent traffic management, etc. It is suitable for tasks that require high-performance and complex graph analysis. -Advantages: The TigerGraph database has the following advantages: 1. High performance: TigerGraph uses a distributed computing and storage architecture to quickly process and analyze large-scale graph data. 2. Powerful graph computing power: TigerGraph provides a powerful graph computing engine that can support complex graph algorithms and query operations. 3. Flexibility: TigerGraph supports dynamic graph architecture and can quickly adapt to different data patterns and query requirements. 4. Scalability: TigerGraph adopts a distributed architecture that can easily scale to hundreds of servers to support large-scale data processing. 5. Easy to use: TigerGraph provides intuitive visualization tools and easy-to-use development interfaces, allowing users to quickly get started and develop. -Disadvantages: The main drawbacks of the TigerGraph database are relatively high learning costs and deployment costs. Due to its complex distributed computing and storage architecture, it requires a certain learning and configuration cost to fully utilize its functionality and performance. -Technical principle: The TigerGraph database adopts an architecture based on distributed graph computing. It uses a graph storage model and a distributed computing framework to distribute graph data on multiple servers, and processes graph computing tasks through parallel computing and distributed scheduling. -Performance analysis: The TigerGraph database has excellent performance. Compared with traditional relational databases, TigerGraph has higher performance and scalability when processing large-scale graph data and graph analysis tasks. -Official website: The official website of TigerGraph is https://www.tigergraph.com/ -Summary: TigerGraph is a high-performance distributed Graph database, which is suitable for processing large-scale graph data and complex graph computing tasks. It has powerful graph computing power, flexibility, and scalability, making it a powerful tool for solving complex data analysis and graph algorithm problems.

DataStax Enterprise Graph is a high-performance, distributed graphical database designed to meet complex graphical data models and query requirements. It was launched by DataStar in 2016. DataStar itself is a company focusing on Apache Cassandra open source Distributed database. Applicable scenario: -Datasets with complex connections: DataStar Enterprise Graph is particularly suitable for data with multiple connections, such as social networks, recommendation systems, Knowledge graph, etc. -Need for high performance and scalability: This database achieves high performance and seamless scalability through distributed architecture and the advantages of Cassandra, making it suitable for processing large-scale datasets. -High availability and fault tolerance requirements: DataStax Enterprise Graph ensures high availability and fault tolerance through multiple replica replication and partitioning, and can continue to provide services even in the event of node failure. Advantages: 1. Powerful graphic data model: support complex graphic structure, and provide flexible Query language to process graphic data. 2. High performance and scalability: Through Cassandra's distributed architecture and optimized graphical query engine, excellent performance and scalability have been achieved. 3. High availability and fault tolerance: Utilizing Cassandra's multi copy replication and partitioning mechanism, it ensures high availability and fault tolerance. 4. Comprehensive tool ecosystem: DataStax Enterprise Graph provides rich tools and APIs for developers to operate and manage data. Disadvantages: 1. The Learning curve is steep: Because DataStar Enterprise Graph has a complex data model and Query language, the Learning curve is steep for developers who are not familiar with graphic databases. 2. Dependency on Cassandra: DataStax Enterprise Graph is built on Cassandra, so it is necessary to understand and master Cassandra's knowledge when using it. Technical principles: The underlying technology of DataStax Enterprise Graph is based on Apache Cassandra, which adopts a distributed and decentralized architecture. Each node is symmetrical, and the data is distributed and stored on different nodes in a partitioned manner. DataStar Enterprise Graph uses Gremlin Query language to process graph data, and implements graph related extensions on Cassandra's data model. Performance analysis: DataStax Enterprise Graph improves performance by: 1. Distributed storage and computing: Data is stored on multiple nodes in a partitioned manner, and queries can be executed in parallel, improving overall performance. 2. Horizontal scalability: The database can be expanded by adding nodes, and it has the function of automatic data sharding and load balancing. Official website: https://www.datastax.com/products/datastax-enterprise-graph Summary: DataStax Enterprise Graph is a powerful and high-performance distributed graphics database suitable for processing data with complex connectivity relationships, providing high availability and scalability. It is built on Cassandra and provides a rich set of tools and APIs to help developers process and manage graphic data. Although the Learning curve is steep, developers can easily handle large-scale graphic data sets with its functional and performance advantages.

AllegroGraph is a high-performance Graph database for storing, managing and querying graph structure data. It was developed by Franz Inc., an American company founded by Dr. Jans Aasman, who graduated from Amazon and the Department of Computer Science at New York University. The database was first released in 2005 and has been continuously improved and enhanced in subsequent versions. AllegroGraph is suitable for various scenarios, especially those that require complex data analysis and inference based on relationships, connections, and graph structures. It is widely used in Knowledge graph, intelligent map analysis oriented applications, social network analysis, biomedicine and other fields. The advantages of AllegroGraph include: 1. High performance: AllegroGraph uses a mixed storage strategy of memory and hard disk, providing excellent query performance and scalability. 2. Powerful query capability: It supports the flexible SPARQL Query language and can handle complex query and reasoning requirements. 3. Distributed and parallel processing: AllegroGraph can be horizontally expanded to meet the storage needs of massive data and the processing of high concurrency query requests. 4. Built in inference engine: It supports rule-based reasoning and can automatically discover and reveal hidden patterns and associations in data. 5. Support for multiple data models: AllegroGraph not only supports RDF data models, but also supports attribute graphs and relational data models. The drawbacks of AllegroGraph include: 1. High price: Compared with some open source Graph database, AllegroGraph has a higher license fee, which may not be applicable to small projects or teams with limited budgets. 2. More complex management and maintenance: Due to its powerful functionality and flexibility, using and managing the AllegroGraph database may require certain learning and training costs. The technical principle of AllegroGraph is based on the design of the Graph database storage and query engine. It uses a data structure called "Infinite State Graph" (ISG) to represent graph data and divides it into multiple graph slices to achieve horizontal scaling. AllegroGraph also utilizes indexing and caching techniques to accelerate query and data access operations. Regarding performance analysis, AllegroGraph performs well in terms of throughput, response time, and horizontal scalability. It has high connection speed, low query latency, and effective concurrent processing capabilities. In addition, AllegroGraph also provides rich monitoring and analysis tools to help users monitor and tune the performance of the database. You can access AllegroGraph's official website by visiting（ https://allegrograph.com/ ）Learn more details, documentation, and Case study. To sum up, AllegroGraph is a powerful and excellent Graph database, which is suitable for various application scenarios that need to process graph structure data. It has advantages such as high performance, flexible query ability, distributed processing, and built-in inference engine, but its disadvantages are high price and high management complexity. Through in-depth understanding and rational use, AllegroGraph can help users fully utilize and mine the value of graph data.

Neo4j is an open source Graph database that stores and processes data graphically. This article will provide a detailed introduction to the installation process of Neo4j and demonstrate how to create a data table for data insertion, modification, query, and deletion operations. Neo4j installation process: 1. Download and install Neo4j: Visit the official website of Neo4j（ https://neo4j.com/ ）Enter the download page. Choose the version that is suitable for your operating system to download. Then follow the installation program's instructions for installation. 2. Start the Neo4j server: After installation is completed, open the bin folder in the Neo4j installation directory and double-click to start the Neo4j server. The server will listen for connections on the default port 7687. 3. Access the Neo4j management interface: Open a browser and enter http://localhost:7474/ Access Neo4j's web management interface. Create a data table: In Neo4j, data is stored in the form of a graph, which consists of nodes and relationships. The following is an example of creating a data table: 1. Create a node: ``` CREATE (:Person {name: 'Alice', age: 30}) ``` 2. Create a relationship: ``` MATCH (p:Person {name: 'Alice'}), (p2:Person {name: 'Bob'}) CREATE (p)-[:KNOWS]->(p2) ``` Data insertion, modification, query, and deletion: 1. Insert data: ``` CREATE (:Person {name: 'Alice', age: 30}) ``` 2. Modify data: ``` MATCH (p:Person {name: 'Alice'}) SET p.age = 35 ``` 3. Query data: ``` MATCH (p:Person {name: 'Alice'}) RETURN p ``` 4. Delete data: ``` MATCH (p:Person {name: 'Alice'}) DELETE p ``` The above is a simple example of the installation process and data operation of Neo4j. Through Neo4j's graphical database model, complex data structures and relationships can be easily represented and processed. Neo4j also provides a rich Query language and API, making data operation more flexible and efficient.