Database normalization

Database normalization, or simply normalization, is the process of organizing the columns (attributes) and tables (relations) of a relational database to reduce data redundancy and improve data integrity. Normalization is also the process of simplifying the design of a database so that it achieves the optimal structure composed of atomic elements. It was first proposed by Edgar F. Codd, as an integral part of a relational model.

Normalization involves arranging attributes in relations based on dependencies between attributes, ensuring that the dependencies are properly enforced by database integrity constraints. Normalization is accomplished by applying some formal rules either by a process of synthesis or decomposition. Synthesis creates a normalized database design based on a known set of dependencies. Decomposition takes an existing (insufficiently normalized) database design and improves it based on the known set of dependencies.

Edgar F. Codd, the inventor of the relational model (RM), introduced the concept of normalization and what is now known as the first normal form (1NF) in 1970. Codd went on to define the second normal form (2NF) and third normal form (3NF) in 1971, and Codd and Raymond F. Boyce defined the Boyce-Codd normal form (BCNF) in 1974. Informally, a relational database relation is often described as "normalized" if it meets third normal form. Most 3NF relations are free of insertion, update, and deletion anomalies.

Objectives

A basic objective of the first normal form defined by Codd in 1970 was to permit data to be queried and manipulated using a "universal data sub-language" grounded in first-order logic. (SQL is an example of such a data sub-language, albeit one that Codd regarded as seriously flawed.)

The objectives of normalization beyond 1NF (first normal form) were stated as follows by Codd:

1. To free the collection of relations from undesirable insertion, update and deletion dependencies;
2. To reduce the need for restructuring the collection of relations, as new types of data are introduced, and thus increase the life span of application programs;
3. To make the relational model more informative to users;
4. To make the collection of relations neutral to the query statistics, where these statistics are liable to change as time goes by.

— E.F. Codd, "Further Normalization of the Data Base Relational Model"

An update anomaly. Employee 519 is shown as having different addresses on different records.

An insertion anomaly. Until the new faculty member, Dr. Newsome, is assigned to teach at least one course, his details cannot be recorded.

A deletion anomaly. All information about Dr. Giddens is lost if he temporarily ceases to be assigned to any courses.

When an attempt is made to modify (update, insert into, or delete from) a relation, the following undesirable side-effects may arise in relations that have not been sufficiently normalized:

• Update anomaly. The same information can be expressed on multiple rows; therefore updates to the relation may result in logical inconsistencies. For example, each record in an "Employees' Skills" relation might contain an Employee ID, Employee Address, and Skill; thus a change of address for a particular employee may need to be applied to multiple records (one for each skill). If the update is only partially successful – the employee's address is updated on some records but not others – then the relation is left in an inconsistent state. Specifically, the relation provides conflicting answers to the question of what this particular employee's address is. This phenomenon is known as an update anomaly.
• Insertion anomaly. There are circumstances in which certain facts cannot be recorded at all. For example, each record in a "Faculty and Their Courses" relation might contain a Faculty ID, Faculty Name, Faculty Hire Date, and Course Code. Therefore we can record the details of any faculty member who teaches at least one course, but we cannot record a newly hired faculty member who has not yet been assigned to teach any courses, except by setting the Course Code to null. This phenomenon is known as an insertion anomaly.
• Deletion anomaly. Under certain circumstances, deletion of data representing certain facts necessitates deletion of data representing completely different facts. The "Faculty and Their Courses" relation described in the previous example suffers from this type of anomaly, for if a faculty member temporarily ceases to be assigned to any courses, we must delete the last of the records on which that faculty member appears, effectively also deleting the faculty member, unless we set the Course Code to null. This phenomenon is known as a deletion anomaly.

Minimize redesign when extending the database structure

When a fully normalized database structure is extended to allow it to accommodate new types of data, the pre-existing aspects of the database structure can remain largely or entirely unchanged. As a result, applications interacting with the database are minimally affected.

Normalized relations, and the relationship between one normalized relation and another, mirror real-world concepts and their interrelationships.

Example

Querying and manipulating the data within a data structure that is not normalized, such as the following non-1NF representation of customers, credit card transactions, involves more complexity than is really necessary:

Customer	Cust. ID	Transactions
Jones	1
Wilkins	2	Tr. ID Date Amount 12898 14-Oct-2003 −21 1234 12-Nov-2003 -12
Stevens	3	Tr. ID Date Amount 12907 15-Oct-2003 −18 14920 20-Nov-2003 −70 15003 27-Nov-2003 −60

To each customer corresponds a 'repeating group' of transactions. The automated evaluation of any query relating to customers' transactions, therefore, would broadly involve two stages:

1. Unpacking one or more customers' groups of transactions allowing the individual transactions in a group to be examined, and
2. Deriving a query result based on the results of the first stage

For example, in order to find out the monetary sum of all transactions that occurred in October 2003 for all customers, the system would have to know that it must first unpack the Transactions group of each customer, then sum the Amounts of all transactions thus obtained where the Date of the transaction falls in October 2003.

One of Codd's important insights was that structural complexity can be reduced, leading to much greater power and flexibility in the way queries could be formulated (by users and applications) and evaluated (by the DBMS). A more normalized equivalent of the structure above might look like this:

Customer	Cust. ID
Jones	1
Wilkins	2
Stevens	3

Cust. ID	Tr. ID	Date	Amount
1	12890	14-Oct-2003	−87
1	12904	15-Oct-2003	−50
2	12898	14-Oct-2003	−21
3	12907	15-Oct-2003	−18
3	14920	20-Nov-2003	−70
3	15003	27-Nov-2003	−60

In the modified structure, the key is {Cust. ID} in the first relation, {Cust. ID, Tr ID} in the second relation.

Now each row represents an individual credit card transaction, and the DBMS can obtain the answer of interest, simply by finding all rows with a Date falling in October, and summing their Amounts. The data structure places all of the values on an equal footing, exposing each to the DBMS directly, so each can potentially participate directly in queries; whereas in the previous situation some values were embedded in lower-level structures that had to be handled specially. Accordingly, the normalized design lends itself to general-purpose query processing, whereas the unnormalized design does not. The normalized version also allows the user to change the customer name in one place and guards against errors that arise if the customer name is misspelled on some records.

See also

• UNF – Unnormalized form
• 1NF – First normal form
• 2NF – Second normal form
• 3NF – Third normal form
• EKNF – Elementary key normal form
• BCNF – Boyce–Codd normal form
• 4NF – Fourth normal form
• ETNF – Essential tuple normal form
• 5NF – Fifth normal form
• 6NF – Sixth normal form
• DKNF – Domain/key normal form
• Denormalization
• Refactoring

Further reading

• Date, C. J. (1999), An Introduction to Database Systems (8th ed.). Addison-Wesley Longman. ISBN 0-321-19784-4.
• Kent, W. (1983) A Simple Guide to Five Normal Forms in Relational Database Theory, Communications of the ACM, vol. 26, pp. 120–125
• H.-J. Schek, P. Pistor Data Structures for an Integrated Data Base Management and Information Retrieval System

External links

• Database Normalization Basics by Mike Chapple (About.com)
• Database Normalization Intro, Part 2
• An Introduction to Database Normalization by Mike Hillyer.
• A tutorial on the first 3 normal forms by Fred Coulson
• DB Normalization Examples
• Description of the database normalization basics by Microsoft
• Database Normalization and Design Techniques by Barry Wise, recommended reading for the Harvard MIS.
• A Simple Guide to Five Normal Forms in Relational Database Theory
• Normalization in DBMS by Chaitanya (beginnersbook.com)
• A Step-by-Step Guide to Database Normalization