Introduction
An important aspect of most every business is record keeping. In our information society, this has become an important aspect of business, and much of the world's computing power is dedicated to maintaining and using databases.
Databases of all kinds pervade almost every business. All kinds of data, from emails and contact information to financial data and records of sales, are stored in some form of a database. The quest is on for meaningful storage of less-structured information, such as subject knowledge.
This is the first of a two-part article that will provide an introduction to relational databases and the SQL language. This first part describes some of the key elements of the technology with an emphasis on database normalization. The second part will describe a less theoretical approach to database design, as well as provide an introduction to the SQL language.
History
The concept of relational databases was first described by Edgar Frank Codd (almost exclusively referenced as E. F. Codd in technical literature) in the IBM research report RJ599, dated August 19th, 1969.1 However, the article that is usually considered the cornerstone of this technology is "A Relational Model of Data for Large Shared Data Banks," published in Communications of the ACM(Vol. 13, No. 6, June 1970, pp. 377-87). Only the first part of the article is available online.
Additional articles by E. F. Codd throughout the 1970s and 80s are still considered gospel for relational database implementations. His famous "Twelve Rules for Relational Databases"2 were published in two Computerworld articles "Is Your DBMS Really Relational?" and "Does Your DBMS Run By the Rules?" on October 14, 1985, and October 21, 1985, respectively. He has since expanded on the 12 rules, and they now number 333, as published in his book "The Relational Model for Database Management, Version 2" (Addison -Wesley, 1990).
Codd's twelve rules call for a language that can be used to define, manipulate, and query the data in the database, expressed as a string of characters. The language, SQL, was originally developed in the research division of IBM (initially at
SQL has been adopted as an ANSI/ISO standard. Although revised in 1999 (usually referenced as SQL99 or SQL3), most vendors are still not fully compliant with the 1992 version of the standard. The 1992 standard is smaller and simpler to reference for a user, and since only some of the 1999-specific requirements are typically implemented at this time, it may be a better starting point for learning the language.
Need-to-Know for the Database Developer
The Rules of the Game
Codd's Twelve Rules
Many references to the twelve rules include a thirteenth rule - or rule zero:
A relational database management system (DBMS) must manage its stored data using only its relational capabilities.
This is basically a corollary or companion requirement to rule #4
- Information Rule
All information in the database should be represented in one and only one way -- as values in a table. - Guaranteed Access Rule
Each and every datum (atomic value) is guaranteed to be logically accessible by resorting to a combination of table name, primary key value, and column name. - Systematic Treatment of Null Values
Null values (distinct from empty character string or a string of blank characters and distinct from zero or any other number) are supported in the fully relational DBMS for representing missing information in a systematic way, independent of data type. - Dynamic Online Catalog Based on the Relational Model
The database description is represented at the logical level in the same way as ordinary data, so authorized users can apply the same relational language to its interrogation as they apply to regular data. - Comprehensive Data Sublanguage Rule
A relational system may support several languages and various modes of terminal use. However, there must be at least one language whose statements are expressible, per some well-defined syntax, as character strings and whose ability to support all of the following is comprehensible:
a. data definition
b. view definition
c. data manipulation (interactive and by program)
d. integrity constraints
e. authorization
f. transaction boundaries (begin, commit, and rollback). - View Updating Rule
All views that are theoretically updateable are also updateable by the system. - High-Level Insert, Update, and Delete
The capability of handling a base relation or a derived relation as a single operand applies not only to the retrieval of data, but also to the insertion, update, and deletion of data. - Physical Data
Application programs and terminal activities remain logically unimpaired whenever any changes are made in either storage representation or access methods. - Logical Data
Application programs and terminal activities remain logically unimpaired when information preserving changes of any kind that theoretically permit unimpairment are made to the base tables. - Integrity
Integrity constraints specific to a particular relational database must be definable in the relational data sublanguage and storable in the catalog, not in the application programs. - Distribution
The data manipulation sublanguage of a relational DBMS must enable application programs and terminal activities to remain logically unimpaired whether and whenever data are physically centralized or distributed. - Nonsubversion Rule
If a relational system has or supports a low-level (single-record-at-a-time) language, that low-level language cannot be used to subvert or bypass the integrity rules or constraints expressed in the higher-level (multiple-records-at-a-time) relational language.
(Author's Note: I do not have access to E.F. Codd's original text, but the above wording appears to be commonly used in other documents. See source 2.)
The rules primarily address implementation requirements for relational database management system (RDBMS) vendors. However, some of them also have an impact on application design.
In the early days of relational database products, these twelve rules were often used to evaluate RDBMSs. In the academic community, the discussions of full compliance of RDBMS versus the Relational Model continues, as does the discussion about whether the SQL language satisfies all the requirements. But we will stick to more practical matters.
How to Design the Database
Theoretical Foundation
Purpose
The problem with data is that it changes. Not just its individual items' values change, but their structure and use, especially when kept over extended periods of time. Even for public records that may have been kept for hundreds of years, there are occasionally changes in what data elements are captured and recorded and how.
Therefore, a method to avoid problems due to duplication of data values and modification of structure and content has been developed. This method is called normalization.
You normalize a database in order to ensure data consistency and stability, to minimize data redundancy, and to ensure consistent updatability and maintainability of the data, and avoid update and delete anomalies that result in ambiguous data or inconsistent results.
Some Key Concepts
Before we continue, I would like to show the correlation between the formal names of Tables, Rows, and Columns in Relational Theory and their more common counterparts:
| Formal Name | Common Name | Also Known As |
| Relation | Table | Entity |
| Tuple | Row | Record |
| Attribute | Column | Field |
A Primary Key is one or more columns whose values uniquely identify a row in a table (See rule #2 above).
A Candidate Key is one or more columns whose values could be used to uniquely identify a row in a table. The Primary Key is chosen among a table's Candidate Keys.
Normalization
Normalization is the formalization of the design process of making a database compliant with the concept of a Normal Form. It addresses various ways in which we may look for repeating data values in a table. There are several levels of the Normal Form, and each level requires that the previous level be satisfied. I have used the wording (indicated in italicized text) for each normalization rule from the Handbook of Relational Database Design by Candace C. Fleming and Barbara von Halle.4
The normalization process is based on collecting an exhaustive list of all data items to be maintained in the database and starting the design with a few "superset" tables. Theoretically, it may be possible, although not very practical, to start by placing all the attributes in a single table. For best results, start with a reasonable breakdown.
First
Reduce entities to first normal form (1NF) by removing repeating or multivalued attributes to another, child entity.
Basically, make sure that the data is represented as a (proper) table. While key to the relational principles, this is somewhat a motherhood statement. However, there are six properties of a relational table (the formal name for "table" is "relation"):
Property 1: Entries in columns are single-valued.
Property 2: Entries in columns are of the same kind.
Property 3: Each row is unique.
Property 4: Sequence of columns is insignificant.
Property 5: Sequence of rows is insignificant.
Property 6: Each column has a unique name.
The most common sins against the first normal form (1NF) are the lack of a Primary Key and the use of "repeating columns." This is where multiple values of the same type are stored in multiple columns. Take, for example, a database used by a company's order system. If the order items were implemented as multiple columns in the Orders table, the database would not be 1NF:
| OrderNo | Line1Item | Line1Qty | Line1Price | Line2Item | Line2Qty | Line2Price |
| 245 | PN768 | 1 | $35 | PN656 | 3 | $15 |
To make this first normal form, we would have to create a child entity of Orders (Order Items) where we would store the information about the line items on the order. Each order could then have multiple Order Items related to it.
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Second
Reduce first normal form entities to second normal form (2NF) by removing attributes that are not dependent on the whole primary key.
The purpose here is to make sure that each column is defined in the correct table. Using the more formal names may make this a little clearer. Make sure each attribute is kept with the entity that it describes.
Consider the Order Items table that we established above. If we place Customer reference in the Order Items table (Order Number, Line Item Number, Item, Qty, Price, Customer) and assume that we use Order Number and Line Item Number as the Primary Key, it quickly becomes obvious that the Customer reference becomes repeated in the table because it is only dependent on a portion of the Primary Key - namely the Order Number. Therefore, it is defined as an attribute of the wrong entity. In such an obvious case, it should be immediately clear that the Customer reference should be in the Orders table, not the Order Items table.
So instead of:
| OrderNo | ItemNo | Customer | Item | Qty | Price |
| 245 | 1 | SteelCo | PN768 | 1 | $35 |
| 245 | 2 | SteelCo | PN656 | 3 | $15 |
| 246 | 1 | Acme Corp | PN371 | 1 | $2.99 |
| 246 | 2 | Acme Corp | PN015 | 7 | $5 |
We get:
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Third Normal Form
Reduce second normal form entities to third normal form (3NF) by removing attributes that depend on other, nonkey attributes (other than alternative keys).
This basically means that we shouldn't store any data that can either be derived from other columns or belong in another table. Again, as an example of derived data, if our Order Items table includes both Unit Price, Quantity, and Extended Price, the table would not be 3NF. So we would remove the Extended Price (= Qty * Unit Price), unless, of course, the value saved is a manually modified (rebate) price, but the Unit Price reflects the quoted list price for the items at the time of order.
Also, when we established that the Customer reference did not belong in the Order Items table, we said to move it to the Orders table. Now if we included customer information, such as company name, address, etc., in the Orders table, we would see that this information is dependent not so much on the Order per se, but on the Customer reference, which is a nonkey (not Primary Key) column in the Orders table. Therefore, we need to create another table (Customers) to hold information about the customer. Each Customer could then have multiple Orders related to it.
| OrderNo | Customer | Address | City |
| 245 | SteelCo | | Vinings |
| 246 | Acme Corp | | |
| 247 | SteelCo | | Vinings |
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Why Stop Here?
Many database designers stop at 3NF, and those first three levels of normalization do provide the most bang for the buck. Indeed, these were the original normal forms described in E. F. Codd's first papers. However, there are currently four additional levels of normalization, so read on. Be aware of what you don't do, even if you stop with 3NF. In some cases, you may even need to de-normalize some for performance reasons.
Boyce/Codd Normal Form
Reduce third normal form entities to Boyce/Codd normal form (BCNF) by ensuring that they are in third normal form for any feasible choice of candidate key as primary key.
In short, Boyce/Codd normal form (BCNF) addresses dependencies between columns that are part of a Candidate Key.
Some of the normalizations performed above may depend on our choice of the Primary Key. BCNF addresses those cases where applying the normalization rules to a Candidate Key other than the one chosen as the Primary Key would give a different result. In actuality, if we substitute any Candidate Key for Primary Key in 2NF and 3NF, 3NF would be equivalent with BCNF.
In a way, the BCNF is only necessary because the formal definitions center around the Primary Key rather than an entity item abstraction. If we define an entity item as an object or information instance that correlates to a row, and consider the normalization rules to refer to entity items, this normal form would not be required.
In our example for 2NF above, we assumed that we used a composite Primary Key consisting of Order Number and Line Item Number, and we showed that the customer reference was only dependent on a portion of the Primary Key - the Order Number. If we had assigned a unique identifier to every Order Item independent of the Order Number, and used that as a single column Primary Key, the normalization rule itself would not have made it clear that it was necessary to move the Customer reference.
There are some less obvious situations for this normalization rule where a set of data actually contains more than one relation, which the following example should illustrate.
Consider a scenario of a large development organization, where the projects are organized in project groups, each with a team leader acting as a liaison between the overall project and a group of developers in a matrix organization. Assume we have the following situation:
- Each Project can have many Developers.
- Each Developer can have many Projects.
- For a given Project, each Developer only works for one Lead Developer.
- Each Lead Developer only works on one Project.
- A given Project can have many Lead Developers.
In this case, we could theoretically design a table in two different ways:
| ProjectNo | Developer | Lead Developer |
| 20020123 | John Doe | Elmer Fudd |
| 20020123 | Jane Doe | Sylvester |
| 20020123 | Jimbo | Elmer Fudd |
| 20020124 | John Doe | Ms. Depesto |
Case 1: Project Number and Developer as a Candidate Key can be used to determine the Lead Developer. In this case, the Lead Developer depends on both attributes of the key, and the table is 3NF if we consider that our Primary Key.
| Lead Developer | Developer | ProjectNo |
| Elmer Fudd | John Doe | 20020123 |
| Sylvester | Jane Doe | 20020123 |
| Elmer Fudd | Jimbo | 20020123 |
| Ms. Depesto | John Doe | 20020124 |
Case 2: Lead Developer and Developer is another Candidate Key, but in this case, the Project Number is determined by the Lead Developer alone. Thus it would not be 3NF if we consider that our Primary Key.
In reality, these three data items contain more than one relation (Project - Lead Developer and Lead Developer - Developer). To normalize to BCNF, we would remove the second relation and represent it in a second table. (This also illustrates why a table is formally named a relation.)
| ProjectNo | Lead Developer |
| 20020123 | Elmer Fudd |
| 20020123 | Sylvester |
| 20020123 | Elmer Fudd |
| 20020124 | Ms. Depesto |
| Lead Developer | Developer |
| Elmer Fudd | John Doe |
| Elmer Fudd | Jimbo |
| Sylvester | Jane Doe |
| Ms. Depesto | John Doe |
Fourth Normal Form
Reduce Boyce/Codd normal form entities to fourth normal form (4NF) by removing any independently multivalued components of the primary key to two new parent entities. Retain the original (now child) entity only if it contains other, nonkey attributes.
Where BCNF deals with dependents of dependents, 4NF deals with multiple, independent dependents of the Primary Key. This is a bit easier to illustrate.
Let us say we wanted to represent the following data: Manager, Manager Awards, and Direct Reports. Here, a Manager could have multiple Awards, as well as multiple Direct Reports. 4NF requires that these be split into two separate tables, one for Manager - Awards, and one for Manager - Direct Reports. We may need to maintain a Managers table for other Manager attributes.
This table:
| Manager | Awards | Direct Reports |
| Scrooge McDuck | Stingy John | Donald Duck |
| Minnie Mouse | Mouse of the Month | Mickey Mouse |
| Minnie Mouse | Mouse of the Year | Pluto |
| Clara | | Goofy |
becomes two tables:
Manager Awards Table
| Manager | Awards |
| Scrooge McDuck | Stingy John |
| Minnie Mouse | Mouse of the Month |
| Minnie Mouse | Mouse of the Year |
| Clara | |
Direct Reports Table
| Manager | Direct Reports |
| Scrooge McDuck | Donald Duck |
| Minnie Mouse | Mickey Mouse |
| Minnie Mouse | Pluto |
| Clara | Goofy |
Fifth
Reduce fourth normal form entities to fifth normal form (5NF) by removing pairwise cyclic dependencies (appearing within composite primary keys with three or more component attributes) to three or more parent entities.
This addresses problems that arise from representing associations between multiple entities with interdependencies. Making it 5NF consists of adding parent tables, one for each meaningful combination that has children in the original table.
A table with such information is 5NF if the information cannot be represented in multiple smaller entities alone.
An example of such a situation may be the representation of Actors, Plays, and Theaters. In order to know who plays what and where, we need the combination of these three attributes. However, they each relate to each other cyclically. So to resolve this, we would need to establish parent tables with Actor - Play, Play - Theater, and Theater - Actor. These would each contain a portion of the Primary Key in the Actor, Play, and Theater table.
| Actor | Play | Theater |
| Billy Bob | Catcher in the | West 42nd |
| Ann | Catcher in the | West 42nd |
| John | Catch-22 | Broadway |
| Lily | Hamlet | Broadway |
| Lisa | Cats | West 42nd |
| Andy | Cats | |
Domain Key
(Domain/key normal form (DKNF) is a normal form used in database normalization which requires that the database contains no constraints other than domain constraints and key constraints.
A domain constraint specifies the permissible values for a given attribute, while a key constraint specifies the attributes that uniquely identify a row in a given table.
The domain/key normal form is the Holy Grail of relational database design,[citation needed] achieved when every constraint on the relation is a logical consequence of the definition of keys and domains, and enforcing key and domain restraints and conditions causes all constraints to be met. Thus, it avoids all non-temporal anomalies.
It's much easier to build a database in domain/key normal form than it is to convert lesser databases which may contain numerous anomalies. However, successfully building a domain/key normal form database remains a difficult task, even for experienced database programmers. Thus, while the domain/key normal form eliminates the problems found in most databases, it tends to be the most costly normal form to achieve. However, failing to achieve the domain/key normal form may carry long-term, hidden costs due to anomalies which appear in databases adhering only to lower normal forms over time.
A violation of DKNF occurs in the following table:
| Wealthy Person | ||
| Wealthy Person | Wealthy Person Type | Net Worth in Dollars |
| Steve | Eccentric Millionaire | 124,543,621 |
| Roderick | Evil Billionaire | 6,553,228,893 |
| Katrina | Eccentric Billionaire | 8,829,462,998 |
| | Evil Millionaire | 495,565,211 |
(Assume that the domain for Wealthy Person consists of the names of all wealthy people in a pre-defined sample of wealthy people; the domain for Wealthy Person Type consists of the values 'Eccentric Millionaire', 'Eccentric Billionaire', 'Evil Millionaire', and 'Evil Billionaire'; and the domain for Net Worth in Dollars consists of all integers greater than or equal to 1,000,000.)
There is a constraint linking Wealthy Person Type to Net Worth in Dollars, even though we cannot deduce one from the other. The constraint dictates that an Eccentric Millionaire or Evil Millionaire will have a net worth of 1,000,000 to 999,999,999 inclusive, while an Eccentric Billionaire or Evil Billionaire will have a net worth of 1,000,000,000 or higher. This constraint is neither a domain constraint nor a key constraint; therefore we cannot rely on domain contraints and key constraints to guarantee that an inconsistent Wealthy Person Type / Net Worth in Dollars combination does not make its way into the database.
The DKNF violation could be eliminated by altering the Wealthy Person Type domain to make it consist of just two values, 'Evil' and 'Eccentric' (the wealthy person's status as a millionaire or billionaire is implicit in their Net Worth in Dollars, so no useful information is lost).
DKNF is frequently difficult to achieve in practice.
Conclusion
While we may not always observe all the rules or normalize our databases to the fifth and domain key normal form, it is important to have a basic understanding of the theoretical principles of database design. It will help us not only design normalized databases, but to build more powerful and flexible applications. Also, it will help us ensure that our data remains usable. Now that we have laid the theoretical foundation and defined the formal database design methods for normalization, it may be time to take a break. I need one anyway. :->
When we resume with Part 2 of this article, I will show how we can design a fairly well normalized database using nothing but some common sense, a few simple rules, and a piece of string, so get ready! Part 2 of this article will address a different approach to designing the database, normalization through synthesis, and will describe the SQL language.
Oh, and never mind the piece of string. :->
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