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Database normalization is the process of organizing the fields and tables of a relational database to minimize redundancy. Normalization usually involves dividing large tables into smaller (and less redundant) tables and defining relationships between them. The objective is to isolate data so that additions, deletions, and modifications of a field can be made in just one table and then propagated through the rest of the database using the defined relationships.
Edgar F. Codd, the inventor of the relational model, introduced the concept of normalization and what we now know 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 table is often described as "normalized" if it is in the Third Normal Form. Most 3NF tables are free of insertion, update, and deletion anomalies.
A typical example of normalization is that a unique ID is stored everywhere in the system but its name is held in only one table. The name can be updated more easily in one row of one table. A typical update in such an example would be the RIM company changing its name to BlackBerry. That update would be done in one place and immediately the correct "BlackBerry" name would be displayed throughout the system.
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"
The sections below give details of each of these objectives.
When an attempt is made to modify (update, insert into, or delete from) a table, undesired side-effects may follow. Not all tables can suffer from these side-effects; rather, the side-effects can only arise in tables that have not been sufficiently normalized. An insufficiently normalized table might have one or more of the following characteristics:
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 tables, and the relationship between one normalized table and another, mirror real-world concepts and their interrelationships.
Normalized tables are suitable for general-purpose querying. This means any queries against these tables, including future queries whose details cannot be anticipated, are supported. In contrast, tables that are not normalized lend themselves to some types of queries, but not others.
For example, consider an online bookseller whose customers maintain wishlists of books they'd like to have. For the obvious, anticipated query—what books does this customer want?—it's enough to store the customer's wishlist in the table as, say, a homogeneous string of authors and titles.
With this design, though, the database can answer only that one single query. It cannot by itself answer interesting but unanticipated queries: What is the most-wished-for book? Which customers are interested in WWII espionage? How does Lord Byron stack up against his contemporary poets? Answers to these questions must come from special adaptive tools completely separate from the database. One tool might be software written especially to handle such queries. This special adaptive software has just one single purpose: in effect to normalize the non-normalized field.
Unforeseen queries can be answered trivially, and entirely within the database framework, with a normalized table.
Querying and manipulating the data within a data structure which is not normalized, such as the following non-1NF representation of customers' credit card transactions, involves more complexity than is really necessary:
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:
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 this structural complexity could always be removed completely, leading to much greater power and flexibility in the way queries could be formulated (by users and applications) and evaluated (by the DBMS). The normalized equivalent of the structure above would look like this:
|Cust. ID||Tr. ID||Date||Amount|
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.
|This section does not cite any references or sources. (June 2013)|
Example: A table with the fields <Name>, <Age>, <SSN> and <Phone Extension> has many possible superkeys. Three of these are <SSN>, <Phone Extension, Name> and <SSN, Name>. Of those, only <SSN> is a candidate key as the others contain information not necessary to uniquely identify records ('SSN' here refers to Social Security Number, which is unique to each person).
The normal forms (abbrev. NF) of relational database theory provide criteria for determining a table's degree of immunity against logical inconsistencies and anomalies. The higher the normal form applicable to a table, the less vulnerable it is. Each table has a "highest normal form" (HNF): by definition, a table always meets the requirements of its HNF and of all normal forms lower than its HNF; also by definition, a table fails to meet the requirements of any normal form higher than its HNF.
The normal forms are applicable to individual tables; to say that an entire database is in normal form n is to say that all of its tables are in normal form n.
Newcomers to database design sometimes suppose that normalization proceeds in an iterative fashion, i.e. a 1NF design is first normalized to 2NF, then to 3NF, and so on. This is not an accurate description of how normalization typically works. A sensibly designed table is likely to be in 3NF on the first attempt; furthermore, if it is 3NF, it is overwhelmingly likely to have an HNF of 5NF. Achieving the "higher" normal forms (above 3NF) does not usually require an extra expenditure of effort on the part of the designer, because 3NF tables usually need no modification to meet the requirements of these higher normal forms.
The main normal forms are summarized below.
|Normal form||Defined by||In||Brief definition|
|1NF||First normal form||Two versions: E.F. Codd (1970), C.J. Date (2003)||1970 and 2003||The domain of each attribute contains only atomic values, and the value of each attribute contains only a single value from that domain.|
|2NF||Second normal form||E.F. Codd||1971||No non-prime attribute in the table is functionally dependent on a proper subset of any candidate key|
|3NF||Third normal form||Two versions: E.F. Codd (1971), C. Zaniolo (1982)||1971 and 1982||Every non-prime attribute is non-transitively dependent on every candidate key in the table. The attributes that do not contribute to the description of the primary key are removed from the table. In other words, no transitive dependency is allowed.|
|EKNF||Elementary Key Normal Form||C. Zaniolo||1982||Every non-trivial functional dependency in the table is either the dependency of an elementary key attribute or a dependency on a superkey|
|BCNF||Boyce–Codd normal form||Raymond F. Boyce and E.F. Codd||1974||Every non-trivial functional dependency in the table is a dependency on a superkey|
|4NF||Fourth normal form||Ronald Fagin||1977||Every non-trivial multivalued dependency in the table is a dependency on a superkey|
|5NF||Fifth normal form||Ronald Fagin||1979||Every non-trivial join dependency in the table is implied by the superkeys of the table|
|DKNF||Domain/key normal form||Ronald Fagin||1981||Every constraint on the table is a logical consequence of the table's domain constraints and key constraints|
|6NF||Sixth normal form||C.J. Date, Hugh Darwen, and Nikos Lorentzos||2002||Table features no non-trivial join dependencies at all (with reference to generalized join operator)|
|This section does not cite any references or sources. (December 2014)|
Databases intended for online transaction processing (OLTP) are typically more normalized than databases intended for online analytical processing (OLAP). OLTP applications are characterized by a high volume of small transactions such as updating a sales record at a supermarket checkout counter. The expectation is that each transaction will leave the database in a consistent state. By contrast, databases intended for OLAP operations are primarily "read mostly" databases. OLAP applications tend to extract historical data that has accumulated over a long period of time. For such databases, redundant or "denormalized" data may facilitate business intelligence applications. Specifically, dimensional tables in a star schema often contain denormalized data. The denormalized or redundant data must be carefully controlled during extract, transform, load (ETL) processing, and users should not be permitted to see the data until it is in a consistent state. The normalized alternative to the star schema is the snowflake schema. In many cases, the need for denormalization has waned as computers and RDBMS software have become more powerful, but since data volumes have generally increased along with hardware and software performance, OLAP databases often still use denormalized schemas.
Denormalization is also used to improve performance on smaller computers as in computerized cash-registers and mobile devices, since these may use the data for look-up only (e.g. price lookups). Denormalization may also be used when no RDBMS exists for a platform (such as Palm), or no changes are to be made to the data and a swift response is crucial.
Denormalization is the opposite of normalization. In recognition that denormalization can be deliberate and useful, the non-first normal form is a definition of database designs which do not conform to first normal form, by allowing "sets and sets of sets to be attribute domains" (Schek 1982). The languages used to query and manipulate data in the model must be extended accordingly to support such values.
One way of looking at this is to consider such structured values as being specialized types of values (domains), with their own domain-specific languages. However, what is usually meant by non-1NF models is the approach in which the relational model and the languages used to query it are extended with a general mechanism for such structure; for instance, the nested relational model supports the use of relations as domain values, by adding two additional operators (nest and unnest) to the relational algebra that can create and flatten nested relations, respectively.
Consider the following table:
Assume a person has several favourite colours. Obviously, favourite colours consist of a set of colours modeled by the given table. To transform a 1NF into an NF² table a "nest" operator is required which extends the relational algebra of the higher normal forms. Applying the "nest" operator to the 1NF table yields the following NF² table:
To transform this NF² table back into a 1NF an "unnest" operator is required which extends the relational algebra of the higher normal forms. The unnest, in this case, would make "colours" into its own table.
Although "unnest" is the mathematical inverse to "nest", the operator "nest" is not always the mathematical inverse of "unnest". Another constraint required is for the operators to be bijective, which is covered by the Partitioned Normal Form (PNF).
A relational schema such that every table is either 1) unary or 2) a binary function between unary tables is a presentation of a freely generated category. An instance on such a schema is (a presentation of) a functor from the free category to the category of sets. If the schema further imposes path equivalence constraints, then the schema denotes a finitely presented category (which is intuitively the quotient of the underlying free category by the path equivalence constraints). A schema denoting a finitely presented category is said to be in categorical normal form (see Functorial Data Migration).
It states that the domain of an attribute must include only atomic (simple, indivisible) values and that the value of any attribute in a tuple must be a single value from the domain of that attribute.