Definition

RDBMS (relational database management system)

Contributor(s): Jack Vaughan, Shawn Chin and Orjan Timan
This definition is part of our Essential Guide: Guide to NoSQL databases: How they can help users meet big data needs

A relational database management system (RDBMS) is a collection of programs and capabilities that enable IT teams and others to create, update, administer and otherwise interact with a relational database. Most commercial RDBMSes use Structured Query Language (SQL) to access the database, although SQL was invented after the initial development of the relational model and is not necessary for its use.

RDBMS vs. DBMS

In general, databases store sets of data that can be queried for use in other applications. A database management system supports the development, administration and use of database platforms.

An RDBMS is a type of DBMS with a row-based table structure that connects related data elements and includes functions that maintain the security, accuracy, integrity and consistency of the data.

Functions of relational database management systems

Elements of the relational database management system that overarch the basic relational database are so intrinsic to operations that it is hard to dissociate the two in practice.

The most basic RDBMS functions are related to create, read, update and delete operations, collectively known as CRUD. They form the foundation of a well-organized system that promotes consistent treatment of data.

What is the difference between DBMS and RDBMS
DBMSes and RDBMSes both add programmatic, management and other facilities to basic database structures. RDBMSes are particularly tailored to support SQL data access.

The RDBMS typically provides data dictionaries and metadata collections useful in data handling. These programmatically support well-defined data structures and relationships. Data storage management is a common capability of the RDBMS, and this has come to be defined by data objects that range from binary large object (blob) strings to stored procedures. Data objects like this extend the scope of basic relational database operations and can be handled in a variety of ways in different RDBMSes.

The most common means of data access for the RDBMS is via SQL. Its main language components comprise data manipulation language (DML) and data definition language (DDL) statements. Extensions are available for development efforts that pair SQL use with common programming languages, such as COBOL (Common Business-Oriented Language), Java and .NET.

RDBMSes use complex algorithms that support multiple concurrent user access to the database, while maintaining data integrity. Security management, which enforces policy-based access, is yet another overlay service that the RDBMS provides for the basic database as it is used in enterprise settings.

RDBMSes support the work of database administrators (DBAs) who must manage and monitor database activity. Utilities help automate data loading and database backup. RDBMSes manage log files that track system performance based on selected operational parameters. This enables measurement of database usage, capacity and performance, particularly query performance. RDBMSes provide graphical interfaces that help DBAs visualize database activity.

While not limited solely to the RDBMS, ACID compliance is an attribute of relational technology that has proved important in enterprise computing. Standing for atomicity, consistency, isolation and durability, these capabilities have particularly suited RDBMSes for handling business transactions.

Relational database management systems are central to key applications, such as banking ledgers, travel reservation systems and online retailing. As RDBMSes have matured, they have achieved increasingly higher levels of query optimization, and they have become key parts of reporting, analytics and data warehousing applications for businesses as well. RDBMSes are intrinsic to operations of a variety of enterprise applications and are at the center of most master data management (MDM) systems.

RDBMS product history

Many vying relational database management systems arose as news spread in the early 1970s of the relational data model. This and related methods were originally theorized by IBM researcher E.F. Codd, who proposed a database schema, or logical organization, that was not directly associated with physical organization, as was common at the time.

Codd's work was based around a concept of data normalization, which saved file space on storage disk drives at a time when such machinery could be prohibitively expensive for businesses.

File systems and database management systems preceded what could be called the RDBMS era. Such systems ran primarily on mainframe computers. While RDBMSes also ran on mainframes -- IBM's DB2 being a pointed example -- much of their ascendance in the enterprise was in UNIX midrange computer deployments. The RDBMS was a linchpin in the distributed architecture of client/server computing, which connected pools of stand-alone personal computers to file and database servers.

Numerous RDBMSes arose along with the use of client/server computing. Among the competitors were Oracle, Ingres, Informix, Sybase, Unify, Progress and others. Over time, three RDBMSes came to dominate in commercial implementations. Oracle, IBM's DB2 and Microsoft's SQL Server, which was based on a design originally licensed from Sybase, found considerable favor throughout the client/server computing era, despite repeated challenges by competing technologies.

As the 20th century drew to an end, lower-cost, open source versions of RDBMSes began to find use, particularly in web applications. Such systems included MySQL and PostgreSQL.

Eventually, as distributed computing took greater hold and as cloud architecture became more prominently employed, RDBMSes met competition in the form of NoSQL systems. Such systems were often especially designed for massive distribution and high scalability in the cloud, sometimes forgoing SQL-style full consistency for so-called eventual consistency of data. But, even in the most diverse and complex cloud systems, the need for some guaranteed data consistency requires RDBMSes to appear in some way, shape or form. Moreover, versions of RDBMSes have been significantly restructured for cloud parallelization and replication.

This was last updated in April 2018

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