MongoDB is an open source document database that adopts a distributed file storage method and features high reliability, scalability, and performance. It was developed and promoted by 10gen company (now MongoDB company). The company was established in 2007 and is headquartered in New York, USA. MongoDB is suitable for large-scale data storage and processing scenarios, especially for applications that require fast iteration and flexible data models. The advantages of MongoDB include: 1. High performance: MongoDB uses Memory-mapped file to store data, which can ensure high-speed read and write operations. 2. Flexible data model: MongoDB adopts a document storage model, which does not require defining data structures in advance and can easily adapt to changing data models. 3. Distributed architecture: MongoDB supports horizontal expansion of data and can handle larger workloads by adding more servers. 4. Rich Query language: MongoDB supports rich Query language, including nested query, range query, regular expression query, etc. 5. Data replication and fault recovery: MongoDB supports automatic data replication and fault recovery, improving data reliability and availability. The drawbacks of MongoDB include: 1. Storage space consumption: Compared to traditional relational databases, MongoDB consumes more storage space when storing data. 2. Limited query performance: Due to MongoDB's flexibility and rich Query language, complex queries may lead to performance degradation. 3. Lack of transaction support: MongoDB does not support transactions, which can complicate the development of applications that require transaction management. The technical principles of MongoDB: 1. Storage Engine: MongoDB uses B-tree indexing to efficiently store and search for data. 2. Fragmentation technology: MongoDB distributes data across multiple servers through data sharding, achieving horizontal expansion of data. 3. Replica Set: MongoDB supports automatic replication and fault recovery of data, providing high availability data access through replica sets. For performance analysis of MongoDB, monitoring tools can be used to obtain various indicator information, such as monitoring response time, query performance, replication latency, etc. through MMS (MongoDB Monitoring Service) or using third-party monitoring tools. The official website of MongoDB is: https://www.mongodb.com/ In summary, MongoDB is a high-performance, scalable, and flexible document database suitable for large-scale data storage and processing scenarios. It has advantages such as flexible data models, high availability, and distributed architecture, but also has disadvantages such as storage space consumption, limited query performance, and lack of transaction support.

Couchbase is a document oriented distributed NoSQL database designed to provide developers with scalable, high-performance, and strongly consistent data storage solutions. -Database Introduction: The Couchbase database was originally merged from two open source projects, Membase and CouchDB, and was released in 2011. It is a NoSQL database based on storing and processing JSON documents. The Couchbase database provides multiple functions, including caching, data persistence, event processing, and mobile device synchronization. -Founded in 2008, Couchbase was founded by Damien Katz, Steve Yen, and Dustin Salings. Damien Katz is the original author of CouchDB, while Steve Yen and Dustin Salings are senior engineers in some other distributed system projects. -Applicable scenarios: Couchbase is suitable for application scenarios that require high performance and scalability, such as real-time analysis, personalized user recommendations, the Internet of Things, advertising technology, and session storage. It is widely used in the back end of Internet and mobile applications, Big data analysis, real-time processing and other fields. -Advantages: 1. High performance: Couchbase has the characteristics of low latency and high throughput, which can meet application scenarios with high requirements for read and write performance. 2. Scalability: Couchbase supports horizontal scaling, which can expand storage and processing capabilities by adding nodes to cope with the constantly growing volume of data and user requests. 3. Strong consistency: Couchbase provides a consistency model to ensure high reliability and consistency in data synchronization between multiple replicas. 4. Flexible data model: Couchbase uses a document oriented data model that does not require strict structural definitions and can adapt to common data changes and evolution. 5. Global distribution: Couchbase supports replication and deployment of multiple data centers, providing low latency and high availability access globally. -Disadvantages: 1. High learning threshold: Compared to traditional relational databases, NoSQL databases require developers to have a certain level of knowledge in distributed systems and unstructured data models. 2. Does not support complex queries: Couchbase does not have the complete query syntax and complex query optimization capabilities of traditional relational databases, and its support for complex queries is limited. 3. Lack of mature ecosystem: Compared to some other NoSQL databases, Couchbase's ecosystem is relatively small and lacks some mature tools and libraries. -Technical principle: Couchbase adopts a distributed architecture and a document based storage model. The data is stored in the form of JSON documents on nodes, and each document has a unique key for access. Couchbase uses the Memcached protocol for cache operations and uses distributed hash algorithms to distribute data across multiple nodes. Couchbase provides high availability and data consistency through replica based failover and automatic data balancing. -Performance analysis: Couchbase achieves high performance by storing hotspot data in memory and asynchronously writing to disk. It utilizes multiple nodes in the cluster to provide parallel processing, addressing the needs of large-scale data and high concurrency access through horizontal scaling and load balancing. Couchbase also provides various monitoring and diagnostic tools for performance analysis and troubleshooting. -Official website: Couchbase's official website is https://www.couchbase.com/ -Summary: Couchbase is a scalable, high-performance, and highly consistent document oriented NoSQL database suitable for various scenarios with high read and write performance requirements. It uses a document storage model that supports horizontal scaling and global distribution, with flexible data models and multi copy synchronization. However, it requires a high learning threshold, limited query functionality, and is relatively small in terms of ecosystem.

CouchDB is an open source NoSQL document oriented Database management system. It uses JSON format to store data and performs queries based on JavaScript. Here is a detailed introduction to CouchDB: Database Introduction: CouchDB is a strong consistency, Distributed database focusing on scalability and high performance. It enables application developers to handle large-scale datasets without worrying about the complexity of traditional relational databases. Date of establishment, founder or company: CouchDB was developed by the Apache Software Foundation (ASF) and began as an open source project in 2005. The main developer of this project is Damien Katz, who hopes to create a consistent and checked document database. Applicable scenario: CouchDB is suitable for scenarios that require reliable and strongly consistent data storage. It achieves high availability and fault tolerance by providing horizontal scalability and distributed replication. CouchDB also supports the development of offline applications, allowing for data processing during network outages and automatic synchronization during network recovery. Advantages: 1. Flexible data model: CouchDB uses a document oriented data model, allowing developers to store data in an unstructured manner. This means that new fields or structures can be easily added to the document without changing the schema. 2. Distributed replication: CouchDB supports distributed replication of databases and can replicate data on multiple nodes. This provides great flexibility for achieving high reliability and fault tolerance. 3. Offline application support: CouchDB allows developers to build offline applications. The application can continue to work in the event of a network outage and automatically synchronize data when the network connection is restored. 4. Strong consistency: CouchDB provides a strong consistency data model, so developers can be confident that data conflicts will not occur during read and write operations. Disadvantages: 1. The Learning curve is steep: because CouchDB adopts a concept and Query language different from traditional relational databases, it may take some time for inexperienced developers to learn and use CouchDB. 2. Lack of mature tool ecosystem: Compared to some mainstream database systems, CouchDB's tool ecosystem is relatively small. This may result in the need to write custom tools and scripts for specific development and operation tasks. Technical principles: CouchDB uses a B-tree as the index structure, which can efficiently perform range queries. It also uses Multiversion concurrency control (MVCC) to handle concurrent access and updates. The data is stored in the form of a document in a database file and serialized using JSON. Performance analysis: CouchDB performs well in write operations, achieving high throughput and low latency. However, for complex queries and highly concurrent read operations, performance may decrease. Performance is also affected by the size of the database and replication factors. Official website: The official website of CouchDB is: https://couchdb.apache.org/ Summary: CouchDB is a document oriented NoSQL database known for its flexible data model, distributed replication, and offline application support. It is suitable for application scenarios that require reliability, scalability, and strong consistency. Although there may be some challenges in terms of learning and tool ecosystems, CouchDB is still a powerful database choice.

Amazon DocumentDB is a MongoDB based managed database service launched by Amazon. It provides users with a flexible, scalable, and easy-to-use document database solution. The following will provide a detailed introduction to various aspects of Amazon DocumentDB: 1. Database Introduction: Amazon DocumentDB is a fully hosted document database service that is compatible with MongoDB and has high availability, scalability, and performance. It supports document database models and provides a wide range of functions and feature sets, including indexing, querying, replication, backup, and recovery. 2. Founder or Company: Amazon DocumentDB was launched on January 9, 2019, created and launched by Amazon Corporation. 3. Applicable scenario: Amazon DocumentDB is suitable for enterprises and applications that need to store and process large amounts of data in cloud environments. It can accommodate various workloads, including Web applications, Content management system, logs and event logging, mobile applications, and so on. 4. Advantages: -Compatibility: Seamless integration with MongoDB based applications, making it easy to migrate existing MongoDB workloads. -Scalability: It is easy to expand the Amazon DocumentDB cluster by adding nodes according to the requirements of the application. -Reliability: Amazon DocumentDB provides high availability and persistence, and supports automatic fault recovery and backup. -Performance: Due to Amazon DocumentDB's SSD based storage and some optimization measures, it can provide low latency and high throughput read and write performance. 5. Disadvantages: Due to Amazon DocumentDB being a proprietary product of Amazon, it cannot be used in other cloud providers or private data centers. In addition, it currently only supports some MongoDB features and does not support certain advanced features. 6. Technical Principle: Amazon DocumentDB combines the advantages of MongoDB's API with AWS infrastructure to provide high-performance and highly available document database services. It uses a distributed storage architecture, where data is stored on multiple physical servers to provide fault tolerance and scalability. It also uses technologies such as multi replica replication and automatic failover to ensure high availability and data protection. 7. Performance analysis: Amazon DocumentDB has excellent performance. According to data provided by Amazon, it can provide hundreds of thousands of read and write operations per second, with low latency and high throughput. 8. Official website: The official website of Amazon DocumentDB is https://aws.amazon.com/documentdb/ 9. Summary: Amazon DocumentDB is a MongoDB based managed database service that provides high availability, scalability, and performance. It is compatible with MongoDB and has integration advantages with Amazon AWS infrastructure. It is suitable for various workloads and has good compatibility, reliability, and performance. However, it should be noted that it is a proprietary product of Amazon and can only be used in AWS at present.

RavenDB is an open source, document based Database management system. Database Introduction: RavenDB is a document based database that uses JSON format to store data. It manages data in a document oriented manner, using key value pairs to organize data. RavenDB provides functions such as document storage, querying, indexing, transactions, and replication, allowing users to store and retrieve data in a simple and intuitive manner. Founding time and founder or company: RavenDB was first launched by Ayende Rahien (also known as Oren Eini) in 2009, and was initially developed and maintained by Hibernating Rhinos, a company founded by Ayende Rahien. Applicable scenario: RavenDB is suitable for various scenarios, especially in applications that require real-time data synchronization, high-performance queries, and transaction support. It is also useful for applications that require high scalability and flexible data models. Advantages: 1. High performance: RavenDB uses in memory indexing and a B+tree based database architecture, providing very high query performance and throughput. 2. Powerful query function: RavenDB supports flexible query syntax and can perform complex query operations. 3. Distributed architecture: RavenDB supports distributed deployment and can easily achieve data synchronization and load balancing. 4. Transaction Support: RavenDB provides powerful transaction support, allowing users to perform atomic operations on multiple documents. 5. Easy to use: RavenDB provides a simple and intuitive API, allowing developers to easily interact with the database. Disadvantages: 1. Relatively small community support: Compared to other database systems, RavenDB is relatively new, so its community support is relatively small. 2. The Learning curve is steep: RavenDB has a unique data model and query syntax, which may take some time for inexperienced developers to adapt and learn. Technical principles: RavenDB uses some technical principles based on logs and B+tree indexing to achieve high performance and data consistency. It stores data in memory, and uses a lock free Concurrency control mechanism to ensure the reliability and consistency of data. Performance analysis: RavenDB performs well in high concurrency read write scenarios and has low latency. Its memory indexing and B+tree based storage engine make query performance very efficient. Official website: The official website of RavenDB is https://ravendb.net/ On this website, you can find detailed information, documentation, examples, and support resources about RavenDB. Summary: RavenDB is an open source document based Database management system with high performance, powerful query function, distributed architecture and transaction support. It is suitable for applications that require real-time data synchronization, high-performance queries, and transaction support, and provides a simple and easy-to-use API. However, the community support for this database is relatively small, and for inexperienced developers, it may take some time to adapt and learn.

MarkLogic is a document oriented Multi-model database designed to help enterprises manage, store and analyze a large number of semi-structured and unstructured data. Below is a detailed introduction to the MarkLogic database: 1. Database Introduction: MarkLogic is an enterprise level database that combines NoSQL and search engine technology, with horizontal scalability and high availability. It can process a large amount of semi-structured and unstructured data, and provide full-text search, semantic search, Transaction processing, complex query and other functions. 2. Date of establishment, founder or company: MarkLogic was founded in 2001 by Christopher Lynn and Frank Cohen. Their goal is to provide enterprises with a scalable database solution by combining NoSQL and search technology. 3. Applicable scenarios: MarkLogic is suitable for enterprise scenarios that require processing large amounts of semi structured and unstructured data. It can be used in various industries, such as finance, healthcare, retail, and media, to help businesses manage, store, and analyze data. 4. Advantages: -Multiple model support: MarkLogic supports multiple data models such as documents, relational data, graphics, and time series data, with strong flexibility. -High availability and horizontal scalability: MarkLogic has high availability and horizontal scalability, which can handle large-scale data and ensure data availability. -Full text search and semantic search: MarkLogic provides powerful full text and semantic search functions to help users quickly find the information they need. -Security: MarkLogic provides fine-grained security controls and authentication mechanisms to protect the security of enterprise data. 5. Disadvantages: -The Learning curve is steep: MarkLogic is relatively new. Compared with traditional relational databases, it may take some time to learn and master it. -High cost: Compared to traditional open source databases, the commercial version of MarkLogic may require relatively high costs. 6. Technical principles: -Multi model storage: MarkLogic uses a document based data storage model, which can store semi structured and unstructured data as documents, and then retrieve and query them through indexes. -Multi core indexing and search engine: MarkLogic uses multi core indexing and search engine technology to respond to complex queries and full-text searches in real-time. -ACID transactions: MarkLogic supports ACID transactions to ensure data consistency and reliability. 7. Performance analysis: MarkLogic's performance analysis involves multiple aspects, including data loading, query response time, and concurrent processing. The specific performance indicators will be determined based on the specific usage and application scenarios. 8. Official website: The official website of MarkLogic is: https://www.marklogic.com/ 9. Summary: MarkLogic is a document oriented Multi-model database, which is suitable for enterprise scenarios that need to process a large amount of semi-structured and unstructured data. It combines NoSQL and search engine technology, with features such as high availability, horizontal scalability, full-text search, and semantic search. Although the Learning curve is steep, MarkLogic can provide a reliable and flexible database solution for enterprises.

ArangoDB is a Multi-model database, which combines the functions of traditional graphic database, document database and key value database. It provides a flexible data model and rich Query language, so that users can use different data models to store and query data. ArangoDB was founded by the German company ArangoDB GmbH in 2012. The founders of the company include Frank Celler, Lucas Dohmen, and Claudius Weinberger. Its headquarters are located in Cologne, Germany. ArangoDB is applicable to various scenarios, including social network applications, Content management system, log analysis, graphic analysis, location data management, etc. Its multi-model characteristics enable users to choose appropriate data models based on different application requirements, thereby improving development efficiency and performance. The advantages of ArangoDB include: 1. Multiple model support: You can use graphics, documents, and key value data models simultaneously to flexibly store and query data. 2. ACID transaction support: Ensure data consistency and reliability. 3. Data replication and sharding: Support data replication and sharding to improve data availability and scalability. 4. Built-in full-text search engine: It can facilitate full-text search operations. 5. Powerful Query language: use AQL (ArangoDB Query Language) to perform complex data query and analysis. However, ArangoDB also has some drawbacks: 1. Relatively small community support: Compared to some mainstream databases, ArangoDB's community support is relatively small. 2. Relatively new databases: Due to its short establishment time, ArangoDB is relatively new compared to some traditional databases. The technical principle of ArangoDB is to store data as a collection of documents, graphics, or key values. It uses a native multi model storage engine that can simultaneously manage data collections from different models. It also supports flexible graphical data models, allowing users to efficiently perform complex graphical analysis operations. Data storage and query operations support ACID transactions, which can ensure the reliability of data. Regarding performance analysis, ArangoDB performs excellently in data reading, writing, and querying. It can handle large-scale datasets and support horizontal scaling, improving concurrent access and throughput. The official website of ArangoDB is: https://www.arangodb.com/ To sum up, ArangoDB is a Multi-model database that combines the functions of graphics, documents and key value databases. It has flexible data model, powerful Query language and ACID transaction support. However, relatively small community support and relatively new databases are some of its drawbacks. Anyway, ArangoDB performs well in storing and querying data, and is suitable for various application scenarios.

FaunaDB is a distributed, globally scalable Multi-model database. It provides powerful transaction performance and supports complex queries and multiple data models. FaunaDB was founded in California, USA in 2011 by Evan Weaver, Matt Freels, and Chris Anderson. FaunaDB was originally developed as part of an open source project Federated Social Web. Subsequently, FaunaDB gradually evolved into an independent database project and released its first official version in 2017. FaunaDB is suitable for various scenarios, including web and mobile applications, the Internet of Things, real-time analytics, and more. It provides multiple data models, including document, graphical, relational, and time series, allowing developers to store and query data more flexibly. The advantages of FaunaDB include: 1. Distributed and Global Scalability: FaunaDB adopts a strong consistency replication protocol and can easily scale to multiple geographical locations, supporting global deployment. 2. ACID transaction support: FaunaDB supports atomic, consistent, isolated, and persistent transactions, enabling developers to ensure data integrity and consistency. 3. Multiple model support: FaunaDB supports multiple data models, including document, graphical, relational, and time series, allowing developers to choose appropriate data models based on actual needs. 4. Powerful query capability: FaunaDB supports complex query operations, including nested queries, multi condition queries, full-text searches, etc., allowing developers to flexibly query data. However, FaunaDB also has some drawbacks: 1. The Learning curve is steep: because FaunaDB has complex functions and flexible data models, it may take some time and resources for beginners to learn and use it. 2. High costs: FaunaDB is a commercial database product that has higher usage costs compared to open source solutions. 3. Dependency on cloud service providers: FaunaDB typically runs on the infrastructure of cloud service providers, which means that using FaunaDB depends on the availability and stability of cloud service providers. Technically speaking, FaunaDB adopts a sharable multi copy architecture. It stores data shards on multiple nodes to ensure data reliability and scalability. When querying, FaunaDB uses MVCC (Multiversion concurrency control) to achieve transaction isolation and consistency. In terms of performance analysis, FaunaDB has good performance. It can handle high concurrency read and write operations, and has the characteristics of low latency and high throughput. Meanwhile, FaunaDB also provides flexible caching and indexing mechanisms to support faster queries. The official website of FaunaDB is https://fauna.com/ Developers can obtain more information, documents, and cases about FaunaDB on their official website. To sum up, FaunaDB is a distributed, globally scalable Multi-model database. It has strong transaction performance and flexible query capability, and is suitable for various application scenarios. Although FaunaDB has some Learning curve and cost challenges, it is still a database solution worth considering.

Apache Cassandra is a highly scalable and distributed NoSQL database system. It was developed by Facebook in 2008 and opened source in 2010, and is currently maintained by the Apache Software Foundation. Cassandra is designed to handle large-scale datasets and applications with high availability requirements, and it has horizontal scalability and fault tolerance. Cassandra is suitable for scenarios that require large capacity and high-performance Distributed database, especially for applications that need to write and read large amounts of data quickly. Common application scenarios include log analysis, time series data storage, social networks, network recommendations, and the Internet of Things. The main advantages of Cassandra include the following: 1. Distributed architecture: Cassandra adopts a distributed data storage and replication mechanism, which can distribute data on hundreds of servers, providing high availability and scalability. 2. Fast read and write: Cassandra adopts a storage engine based on log structure, which can provide high performance in write and read operations. 3. Fault tolerance and high availability: Cassandra supports redundant replication of data and automatic fault recovery, allowing it to continue providing services even in the event of partial node failures. Some of Cassandra's drawbacks include: 1. Data consistency: Cassandra adopts the Eventual consistency model, that is, it does not guarantee strong consistency of data among all nodes. For some applications requiring strong consistency, additional work may be required to solve this problem. 2. High storage space requirements: Cassandra replicates data on multiple servers to provide fault tolerance. This makes Cassandra's storage requirements relatively high. 3. Complexity: Cassandra is relatively complex in terms of configuration and management, requiring some learning and experience. Cassandra's technical principle is based on a distributed Hash table, which distributes data evenly across multiple nodes through a consistent hash algorithm. It adopts a peer-to-peer replication architecture without a central node, and each node can run independently and process read and write requests. Cassandra also supports multiple data centers and cross regional replication, providing flexible data storage and redundancy strategies. For performance analysis, Cassandra has the following key indicators: 1. Throughput: Cassandra can provide high write and read throughput, especially suitable for scenarios that require processing large amounts of data and high concurrency of reads and writes. 2. Latency: Cassandra typically provides low latency read and write operations. The size of latency is influenced by a series of factors, such as data model design, hardware configuration, and load conditions. Cassandra's official website is: https://cassandra.apache.org/ Summary: Apache Cassandra is a highly scalable and distributed NoSQL database system suitable for applications with large-scale datasets and high availability requirements. It has advantages such as distributed architecture, fast read/write, fault tolerance, and high availability, but there are some drawbacks in terms of data consistency, storage space requirements, and complexity. Cassandra's technical principle is based on a distributed Hash table, which uses a consistent hash algorithm to distribute data on multiple nodes. In terms of performance, Cassandra has the characteristics of high throughput and low latency.

IBM Cloudant is a distributed non relational Database management system (NoSQL), which can be deployed and managed through the cloud. It is developed based on Apache CouchDB and has high availability, horizontal scalability, and powerful data replication capabilities. The founder of IBM Cloudant is Damien Katz, who is one of the core developers of the CouchDB project. IBM acquired Cloudant in 2014 and incorporated it into its cloud services product line. Applicable scenario: 1. Web and mobile applications: Cloudant can quickly process large-scale users and data, and provides high availability and powerful query functions, making it very suitable for processing large amounts of data in Web and mobile applications. 2. Internet of Things (IoT) applications: Cloudant's distributed architecture and scalability make it an ideal choice for IoT applications, capable of processing large amounts of sensor data and providing real-time data analysis and queries. 3. Applications that require high scalability and availability: Cloudant can easily scale horizontally to adapt to growing data and user volumes, and has automatic fault recovery and data replication capabilities to ensure that applications are always available. Advantages: 1. High availability and scalability: Cloudant has a distributed architecture that allows for easy horizontal scaling to adapt to large-scale data and user volume. At the same time, it has automatic fault recovery and data replication functions, ensuring high availability of data. 2. Powerful data replication function: Cloudant supports automatic data replication across geographic locations and cloud providers to ensure disaster tolerance and high availability of data. 3. Flexible data models: Cloudant supports multiple data models, including document based, key value storage, and graphical databases, allowing developers to choose the appropriate data model according to the needs of the application. Disadvantages: 1. Complex usage: Compared to traditional relational databases, Cloudant has a high threshold for learning and using, and needs to adapt and adjust to the characteristics of non relational databases. 2. Does not support complex Transaction processing: Compared with relational databases, Cloudant supports relatively weak Transaction processing and does not support complex Transaction processing operations. Technical principles: Cloudant is developed based on Apache CouchDB's open source technology, which uses a distributed architecture, scalable hash algorithms, and database replication technology. Cloudant's data is stored in the form of documents, with each document identified by a unique ID and stored in JSON format. It uses partitioning and hashing techniques to evenly distribute data across multiple nodes, achieving horizontal scalability and high availability of data. Performance analysis: Cloudant has high scalability and powerful data processing capabilities, which can handle large-scale data and user volume. It can provide high throughput and low latency data access performance, and support complex data queries and real-time analysis. Official website: https://www.ibm.com/cloud/cloudant Summary: IBM Cloudant is a distributed non relational Database management system with high availability, scalability and powerful data replication functions. It is suitable for web and mobile applications, IoT applications, and applications that require high scalability and availability. Although complex use and weak Transaction processing support are its disadvantages, Cloudant's flexible data model, distributed architecture and high performance make it an ideal choice for processing large-scale data.