A relational database is a database that conforms to relational model theory. The software used in a relational database is called a relational database management system (RDBMS). Colloquial use of the term "relational database" may refer to the RDBMS software, or the relational database itself. A relational database is the predominant choice in storing data, over other models like the hierarchical database model or the network model.
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The term relational database was originally defined by and is attributed to Edgar Codd at IBM Almaden Research Center in 1970.[1]
Relational database theory uses a set of mathematical terms, which are roughly equivalent to SQL database terminology. The table below summarizes some of the most important relational database terms and their SQL database equivalents.
| Relational term | SQL equivalent |
|---|---|
| relation, base relvar | table |
| derived relvar | view, query result, result set |
| tuple | row |
| attribute | column |
A relation is defined as a set of tuples that have the same attributes. A tuple usually represents an object and information about that object. Objects are typically physical objects or concepts. A relation is usually described as a table, which is organized into rows and columns. All the data referenced by an attribute are in the same domain and conform to the same constraints.
The relational model specifies that the tuples of a relation have no specific order and that the tuples, in turn, impose no order on the attributes. Applications access data by specifying queries, which use operations such as select to identify tuples, project to identify attributes, and join to combine relations. Relations can be modified using the insert, delete, and update operators. New tuples can supply explicit values or be derived from a query. Similarly, queries identify tuples for updating or deleting. It is necessary for each tuple of a relation to be uniquely identifiable by some combination (one or more) of its attribute values. This combination is referred to as the primary key.
In a relational database, all data are stored and accessed via relations. Relations that store data are called "base relations", and in implementations are called "tables". Other relations do not store data, but are computed by applying relational operations to other relations. These relations are sometimes called "derived relations". In implementations these are called "views" or "queries". Derived relations are convenient in that though they may grab information from several relations, they act as a single relation. Also, derived relations can be used as an abstraction layer.
A domain describes the set of possible values for a given attribute, and can be considered a constraint on the value of the attribute. Mathematically, attaching a domain to an attribute means that any value for the attribute must be an element of the specified set.
The character data value 'ABC', for instance, is not in the integer domain. The integer value 123, satisfies the domain constraint.
Constraints make it possible to further restrict the domain of an attribute. For instance, a constraint can restrict a given integer attribute to values between 1 and 10. Constraints provide one method of implementing business rules in the database. SQL implements constraint functionality in the form of check constraints.
Constraints restrict the data that can be stored in relations. These are usually defined using expressions that result in a boolean value, indicating whether or not the data satisfies the constraint. Constraints can apply to single attributes, to a tuple (restricting combinations of attributes) or to an entire relation.
Since every attribute has an associated domain, there are constraints (domain constraints). The two principal rules for the relational model are known as entity integrity and referential integrity. ('"Referential integrity is the state in which all values of all foreign keys are valid. Referential integrity is based on entity integrity. Entity integrity requires that each entity have a unique key. For example, if every row in a table represents relationships for a unique entity, the table should have one column or a set of columns that provides a unique identifier for the rows of the table. This column (or set of columns) is called the parent key of the table. To ensure that the parent key does not contain duplicate values, a unique index must be defined on the column or columns that constitute the parent key. Defining the parent key is called entity integrity"')
A primary key uniquely defines a relationship within a database. In order for an attribute to be a good primary key it must not repeat. While natural attributes are sometimes good primary keys, surrogate keys are often used instead. A surrogate key is an artificial attribute assigned to an object which uniquely identifies it (for instance, in a table of information about students at a school they might all be assigned a student ID in order to differentiate them). The surrogate key has no intrinsic (inherent) meaning, but rather is useful through its ability to uniquely identify a tuple.
Another common occurrence, especially in regards to N:M cardinality is the composite key. A composite key is a key made up of two or more attributes within a table that (together) uniquely identify a record. (For example, in a database relating students, teachers, and classes. Classes could be uniquely identified by a composite key of their room number and time slot, since no other class could have exactly the same combination of attributes. In fact, use of a composite key such as this can be a form of data verification, albeit a weak one.)
A foreign key is a reference to a key in another relation, meaning that the referencing table has, as one of its attributes, the values of a key in the referenced table. Foreign keys need not have unique values in the referencing relation. Foreign keys effectively use the values of attributes in the referenced relation to restrict the domain of one or more attributes in the referencing relation.
A foreign key could be described formally as: "For all tuples in the referencing relation projected over the referencing attributes, there must exist a tuple in the referenced relation projected over those same attributes such that the values in each of the referencing attributes match the corresponding values in the referenced attributes."
A stored procedure is executable code that is associated with, and generally stored in, the database. Stored procedures usually collect and customize common operations, like inserting a tuple into a relation, gathering statistical information about usage patterns, or encapsulating complex business logic and calculations. Frequently they are used as an application programming interface (API) for security or simplicity. Implementations of stored procedures on SQL DBMSs often allow developers to take advantage of procedural extensions (often vendor-specific) to the standard declarative SQL syntax.
Stored procedures are not part of the relational database model, but all commercial implementations include them.
An index is one way of providing quicker access to data. Indices can be created on any combination of attributes on a relation. Queries that filter using those attributes can find matching tuples randomly using the index, without having to check each tuple in turn. This is analogous to using the index of a book to go directly to the page on which the information you are looking for is found i.e. you do not have to read the entire book to find what you are looking for. Relational databases typically supply multiple indexing techniques, each of which is optimal for some combination of data distribution, relation size, and typical access pattern. B+ trees, R-trees, and bitmaps, hash index.
Indices are usually not considered part of the database, as they are considered an implementation detail, though indices are usually maintained by the same group that maintains the other parts of the database. It should be noted that use of efficient indexes on both primary and foreign keys can dramatically improve query performance. This is because B-tree indexes result in query times proportional to log(n) where N is the number of rows in a table and hash indexes result in constant time queries (no size dependency so long as the relevant part of the index fits into memory).
Queries made against the relational database, and the derived relvars in the database are expressed in a relational calculus or a relational algebra. In his original relational algebra, Codd introduced eight relational operators in two groups of four operators each. The first four operators were based on the traditional mathematical set operations:
The remaining operators proposed by Codd involve special operations specific to relational databases:
Other operators have been introduced or proposed since Codd's introduction of the original eight including relational comparison operators and extensions that offer support for nesting and hierarchical data, among others.
Normalization was first proposed by Codd as an integral part of the relational model. It encompasses a set of procedures designed to eliminate nonsimple domains (non-atomic values) and the redundancy (duplication) of data, which in turn prevents data manipulation anomalies and loss of data integrity. The most common forms of normalization applied to databases are called the normal forms. Normalization trades reducing redundancy for increased information entropy. Normalization is criticised because it increases complexity and processing overhead required to join multiple tables representing what are conceptually a single item .
Relational databases, as implemented in relational database management systems, have become a predominant choice for the storage of information in new databases used for financial records, manufacturing and logistical information, personnel data and much more. Relational databases have often replaced legacy hierarchical databases and network databases because they are easier to understand and use, even though they are much less efficient. As computer power has increased, the inefficiencies of relational databases, which made them impractical in earlier times, have been outweighed by their ease of use. However, relational databases have been challenged by Object Databases, which were introduced in an attempt to address the object-relational impedance mismatch in relational database, and XML databases.
The three leading commercial relational database vendors are Oracle, Microsoft, and IBM.[2] The three leading open source implementations are MySQL, PostgreSQL, and SQLite. Amazon Relational Database Service is a database as a service offering MySQL and Oracle database engines.
Digital watermarking for relational databases emerged as a candidate solution to provide copyright protection, tamper detection, traitor tracing, maintaining integrity of relational data. Many watermarking techniques have been proposed in the literature to address these purposes.
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