Multi-model database

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

Contents

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]

A multi-model database is a database that can store, index and query data in more than one model. For some time, databases have primarily supported only one model, such as: relational database, document-oriented database, graph database or triplestore. A database that combines many of these is multi-model.

For some time, it was all but forgotten (or considered irrelevant) that there were any other database models besides relational. The relational model and notion of third normal form were the default standard for all data storage. However, prior to the dominance of relational data modeling, from about 1980 to 2005, the hierarchical database model was commonly used. Since 2000 or 2010, many NoSQL models that are non-relational, including documents, triples, key–value stores and graphs are popular. Arguably, geospatial data, temporal data, and text data are also separate models, though indexed, queryable text data is generally termed a "search engine" rather than a database.[ citation needed ]

The first time the word "multi-model" has been associated to the databases was on May 30, 2012 in Cologne, Germany, during the Luca Garulli's key note "NoSQL Adoption – What’s the Next Step?". [3] [4] Luca Garulli envisioned the evolution of the 1st generation NoSQL products into new products with more features able to be used by multiple use cases.

The idea of multi-model databases can be traced back to Object–Relational Data Management Systems (ORDBMS) in the early 1990s and in a more broader scope even to federated and integrated DBMSs in the early 1980s. An ORDBMS system manages different types of data such as relational, object, text and spatial by plugging domain specific data types, functions and index implementations into the DBMS kernels. A multi-model database is most directly a response to the "polyglot persistence" approach of knitting together multiple database products, each handing a different model, to achieve a multi-model capability as described by Martin Fowler. [5] 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, so multi-model databases have begun to fill in this gap.

Multi-model databases are intended to offer the data modeling advantages of polyglot persistence, [5] without its disadvantages. Operational complexity, in particular, is reduced through the use of a single data store. [2]

Benchmarking multi-model databases

As more and more platforms are proposed to deal with multi-model data, there are a few works on benchmarking multi-model databases. For instance, Pluciennik, [6] Oliveira, [7] and UniBench [8] reviewed existing multi-model databases and made an evaluation effort towards comparing multi-model databases and other SQL and NoSQL databases respectively. They pointed out that the advantages of multi-model databases over single-model databases are as follows :

  1. they are able to ingest a variety of data formats such as CSV (including Graph, Relational), JSON into storage without any additional efforts.
  2. they can employ a unified query language such as AQL, Orient SQL, SQL/XML, SQL/JSON to retrieve correlated multi-model data, such as graph-JSON-key/value, XML-relational, and JSON-relational in a single platform.
  3. they are able to support multi-model ACID transactions in the stand-alone mode.

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. Some products may provide an engine which supports documents and graphs while others provide layers on top of a key-key store. [9] 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. [10] 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

Related Research Articles

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<span class="mw-page-title-main">Database model</span> Type of data model

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A document-oriented database, or document store, is a computer program and data storage system designed for storing, retrieving and managing document-oriented information, also known as semi-structured data.

A triplestore or RDF store is a purpose-built database for the storage and retrieval of triples through semantic queries. A triple is a data entity composed of subject–predicate–object, like "Bob is 35" or "Bob knows Fred".

<span class="mw-page-title-main">MarkLogic Server</span>

MarkLogic Server is a document-oriented database developed by MarkLogic. It is a NoSQL multi-model database that evolved from an XML database to natively store JSON documents and RDF triples, the data model for semantics. MarkLogic is designed to be a data hub for operational and analytical data.

A NoSQL database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but the name "NoSQL" was only coined in the early 21st century, triggered by the needs of Web 2.0 companies. NoSQL databases are increasingly used in big data and real-time web applications. NoSQL systems are also sometimes called Not only SQL to emphasize that they may support SQL-like query languages or sit alongside SQL databases in polyglot-persistent architectures.

A graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. A key concept of the system is the graph. The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation. Graph databases hold the relationships between data as a priority. Querying relationships is fast because they are perpetually stored in the database. Relationships can be intuitively visualized using graph databases, making them useful for heavily inter-connected data.

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<span class="mw-page-title-main">OrientDB</span>

OrientDB is an open source NoSQL database management system written in Java. It is a Multi-model database, supporting graph, document, key/value, and object models, but the relationships are managed as in graph databases with direct connections between records. It supports schema-less, schema-full and schema-mixed modes. It has a strong security profiling system based on users and roles and supports querying with Gremlin along with SQL extended for graph traversal. OrientDB uses several indexing mechanisms based on B-tree and Extendible hashing, the last one is known as "hash index", there are plans to implement LSM-tree and Fractal tree index based indexes. Each record has Surrogate key which indicates position of record inside of Array list, links between records are stored either as single value of record's position stored inside of referrer or as B-tree of record positions which allows fast traversal of one-to-many relationships and fast addition/removal of new links. OrientDB is the fifth most popular graph database according to the DB-Engines graph database ranking, as of December 2021.

The following is provided as an overview of and topical guide to databases:

<span class="mw-page-title-main">Oracle NoSQL Database</span>

Oracle NoSQL Database is a NoSQL-type distributed key-value database from Oracle Corporation. It provides transactional semantics for data manipulation, horizontal scalability, and simple administration and monitoring.

FoundationDB is a free and open-source multi-model distributed NoSQL database developed by Apple Inc. with a shared-nothing architecture. The product was designed around a "core" database, with additional features supplied in "layers." The core database exposes an ordered key–value store with transactions. The transactions are able to read or write multiple keys stored on any machine in the cluster while fully supporting ACID properties. Transactions are used to implement a variety of data models via layers.

SequoiaDB is a multi-model NewSQL database.

ArangoDB is a free and open-source native graph database system developed by ArangoDB Inc. ArangoDB is a multi-model database system since it supports three data models with one database core and a unified query language AQL. AQL is mainly a declarative language and allows the combination of different data access patterns in a single query. ArangoDB is a NoSQL database system but AQL is similar in many ways to SQL.

Polyglot persistence is a term that refers to using multiple data storage technologies within a single system, in order to meet varying data storage needs. Such a system may consist of multiple applications, or it may be a single application with smaller components.

Amazon DocumentDB is a managed proprietary NoSQL database service that supports document data structures, with some compatibility with MongoDB version 3.6 and version 4.0. As a document database, Amazon DocumentDB can store, query, and index JSON data. It is available on Amazon Web Services. As of March 2023, AWS introduced some compliance with MongoDB 5.0 but lacks time series collection support.

References

  1. ↑ The 451 Group, "Neither Fish Nor Fowl: The Rise of Multi-Model Databases"
  2. 1 2 Infoworld, "The Rise of the Multi-Model Database"
  3. ↑ "Multi-Model storage 1/2 one product". 2012-06-01.
  4. ↑ "Nosql Matters Conference 2012 | NoSQL Matters CGN 2012" (PDF). 2012.nosql-matters.org. Retrieved 2017-01-12.
  5. 1 2 Polyglot Persistence
  6. ↑ Ewa Pluciennik and Kamil Zgorzalek. "The Multi-model Databases - A Review". Bdas 2017: 141–152.
  7. ↑ Fábio Roberto Oliveira, Luis del Val Cura. "Performance Evaluation of NoSQL Multi-Model Data Stores in Polyglot Persistence Applications". Ideas '16: 230–235.
  8. ↑ Chao Zhang, Jiaheng Lu, Pengfei Xu, Yuxing Chen. "UniBench: A Benchmark for Multi-Model Database Management Systems" (PDF). TPCTC 2018.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  9. ↑ "layer"
  10. ↑ ODBMS, "Polyglot Persistence or Multiple Data Models?"
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