Multi-model database

Most database management systems are organized around a single data model that determines how data can be organized, stored, and manipulated. In contrast, a multi-model database is designed to support multiple data models against a single, integrated backend.^[1] Document, graph, relational, and key-value models are examples of data models that may be supported by a multi-model database.

Background

The relational data model became popular after its publication by Edgar F. Codd in 1970. Due to increasing requirements for horizontal scalability and fault tolerance, NoSQL databases became prominent after 2009. NoSQL databases use a variety of data models, with document, graph, and key-value models being popular.^[2]

Enterprises and applications that require multiple data models sometimes adopt a strategy of Polyglot Persistence,^[3] using separate data stores for each model. This strategy has two major disadvantages: it leads to a significant increase in operational complexity, and there is no support for maintaining data consistency across the separate data stores.

Multi-model databases are intended to offer the data modeling advantages of Polyglot Persistence,^[3] without its disadvantages. Operational complexity, in particular, is reduced through the use of a single data store.^[2]

The first multi-model database was OrientDB, created in 2010 as an answer to the fragmented NoSQL environment, with the goal of providing one product to replace multiple NoSQL databases.

Architecture

The main difference between the available multi-model databases is related to their architectures. Multi-model databases can support different models either within the engine or via different layers on top of the engine. For example, products like ArangoDB and OrientDB provide an engine, which is handling documents and graphs. While products like FoundationDB and Spanner feature layers on top of a key-key store.^[4] With a layered architecture, each data model is provided via its own component.

User-defined data models

In addition to offering multiple data models in a single data store, some databases allow developers to easily define custom data models. This capability is enabled by ACID transactions with high performance and scalability. In order for a custom data model to support concurrent updates, the database must be able to synchronize updates across multiple keys. ACID transactions, if they are sufficiently performant, allow such synchronization.^[5] JSON documents, graphs, and relational tables can all be implemented in a manner that inherits the horizontal scalability and fault-tolerance of the underlying data store.

See also

• Comparison of multi-model databases
• ACID
• NoSQL
• Comparison of structured storage software
• Database transaction
• Distributed database
• Distributed transaction
• Document-oriented database
• Graph database
• Relational model

References

1. The 451 Group, "Neither Fish Nor Fowl: The Rise of Multi-Model Databases"
2. Infoworld, "The Rise of the Multi-Model Database"
3. Polyglot Persistence
4. "layer"
5. ODBMS, "Polyglot Persistence or Multiple Data Models?"

External links

• OrientDB Document and Graph Model
• ArangoDB Key Features
• FoundationDB Multi-Model Architecture
• Polyglot Persistence
• The 451 Group, "Neither Fish Nor Fowl: The Rise of Multi-Model Databases"
• ODBMS, "On Multi-Model Databases. Interview with Martin Schönert and Frank Celler."
• ODBMS, "Polyglot Persistence or Multiple Data Models?"
• Infoworld, "The Rise of the Multi-Model Database"
• Crate.IO Storage and Consistency