Web site template - . NOT NULL This is the simplest of field
. NOT NULL This is the simplest of field level constraints, making sure that a value must always be entered into a field when a record is added or changed. . Validation check Similar to a NOT NULL constraint, a validation checking type of constraint restricts values in fields when a record is added or changed in a table. A check validation constraint can be as simple as making sure a field allowing only M for Male or F for Female, will only ever contain those two possible values. Otherwise, check validation constraints can become fairly complex in some relational databases, perhaps allowing inclusion of user written functions running SQL scripting. . Keys Key constraints include primary keys, foreign keys, and unique keys. All these key types are discussed briefly later on in this chapter and further in later chapters in this book. Key constraints allow the checking and validation of values between fields in different tables. Primary and foreign keys are essentially the implementation of relationships between parent and child tables. Those relationships or relations are the source of the term relational database. Some relational databases allow constraints to be specified at both the field level or for an entire table as a whole, depending on the type of constraint. Understanding Relations for Normalization By dictionary definition, the term normalization means to make normal in terms of causing something to conform to a standard, or to introduce consistency with respect to style and content. In terms of relational database modeling, that consistency becomes a process of removing duplication in data, among other factors. Removal of duplication tends to minimize redundancy. Minimization of redundancy implies getting rid of unneeded data present in particular places, or tables. In reality, normalization usually manages to divide information into smaller, more manageable parts, preferably not too small. The most obvious redundancies can usually be removed without getting too deeply mathematical about everything. Commercially speaking, primary objectives are usually to save space and organize data for usability and manageability, without sacrificing performance. All this is often a juggling act and commonly partially ironed out by trial and error. Additionally the demands of intensely busy applications and end-user needs can tend to necessitate breaking the rules of normalization in many ways to meet performance requirements. Rules are usually broken simply by not applying every possible layer of normalization. Normal Forms beyond 3rd Normal Form are often ignored and sometimes even 3rd Normal Form itself is discounted. Normalization can be described as being one of introduction of granularity, removal of duplication, or minimizing of redundancy, or simply the introduction of tables, all of which place data into a better organized state. Normalization is an incremental process. In other words, each Normal Form layer adds to whatever Normal Forms have already been applied. For example, 2nd Normal Form can only be applied to tables in 1st Normal Form, and 3rd Normal Form only applied to tables in 2nd Normal Form, and so on. Each Normal Form is a refinement of the previous Normal Form. Similarly 3rd Normal cannot be applied to tables in 4th Normal Form because by definition tables in 4th Normal Form are cumulatively already in 3rd Normal Form. 48 Chapter 3
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