SQL Series...2

What Is a Database?

The term database has fallen into loose use lately, losing much of its original meaning. To some people, a database is any collection of data items (phone books, laundry lists, parchment scrolls . . . whatever). Other people define the term more strictly.

I define a database as a self-describing collection of integrated records. And yes, that does imply computer technology, complete with lan-guages such as SQL.

A record is a representation of some physical or conceptual object. Say, for example, that you want to keep track of a business’s customers. You assign a record for each customer. Each record has multiple attributes, such as name, address, and telephone number. Individual names, addresses, and so on are the data.

A database consists of both data and metadata. Metadata is the data that describes the data’s structure within a database. If you know how your data is arranged, then you can retrieve it. Because the database contains a descrip-tion of its own structure, it’s self-describing. The database is integrated because it includes not only data items but also the relationships among data items.

The database stores metadata in an area called the data dictionary, which describes the tables, columns, indexes, constraints, and other items that make up the database.

Because a flat file system has no metadata, applications written to work with flat files must contain the equivalent of the metadata as part of the application program.

Database Size and Complexity

Databases come in all sizes, from simple collections of a few records to mam-moth systems holding millions of records.

A personal database is designed for use by a single person on a single com-puter. Such a database usually has a rather simple structure and a relatively small size. A departmental or workgroup database is used by the members of a single department or workgroup within an organization. This type of database

is generally larger than a personal database and is necessarily more complex; such a database must handle multiple users trying to access the same data at the same time. An enterprise database can be huge. Enterprise databases may model the critical information flow of entire large organizations.

What Is a Database Management System?

Glad you asked. A database management system (DBMS) is a set of programs used to define, administer, and process databases and their associated appli-cations. The database being “managed” is, in essence, a structure that you build to hold valuable data. A DBMS is the tool you use to build that structure and operate on the data contained within the database.

Many DBMS programs are on the market today. Some run only on mainframe computers, some only on minicomputers, and some only on personal com-puters. A strong trend, however, is for such products to work on multiple platforms or on networks that contain all three classes of machines.

A DBMS that runs on platforms of multiple classes, large and small, is called scalable.

Whatever the size of the computer that hosts the database — and regardless of whether the machine is connected to a network — the flow of information between database and user is the same. Figure 1-1 shows that the user com-municates with the database through the DBMS. The DBMS masks the physi-cal details of the database storage so that the application need only concern itself with the logical characteristics of the data, not how the data is stored.

Flat Files

Where structured data is concerned, the flat file is as simple as it gets. No, a flat file isn’t a folder that’s been squashed under a stack of books. Flat files are so called because they have minimal structure. If they were buildings, they’d barely stick up from the ground. A flat file is simply a collection of one data record after another in a specified format — the data, the whole data, and nothing but the data — in effect, a list. In computer terms, a flat file is simple. Because the file doesn’t store structural information (metadata), its overhead (stuff in the file that is not data) is minimal.

Say that you want to keep track of the names and addresses of your com-pany’s customers in a flat file system. The system may have a structure some-thing like this:

Harold Percival26262		S. Howards Mill Rd	Westminster CA92683
Jerry Appel	32323	S. River Lane Rd	Santa Ana	CA92705
Adrian Hansen	232	Glenwood Court	Anaheim	CA92640
John Baker	2222	Lafayette St	Garden GroveCA92643
Michael Pens	77730	S. New Era Rd	Irvine	CA92715
Bob Michimoto	25252	S. Kelmsley Dr	Stanton	CA92610
Linda Smith	444	S.E. Seventh St	Costa Mesa	CA92635
Robert Funnell	2424	Sheri Court	Anaheim	CA92640
Bill Checkal	9595	Curry Dr	Stanton	CA92610
Jed Style	3535	Randall St	Santa Ana	CA92705

As you can see, the file contains nothing but data. Each field has a fixed length (the Name field, for example, is always exactly 15 characters long), and no structure separates one field from another. The person who created the database assigned field positions and lengths. Any program using this file must “know” how each field was assigned, because that information is not contained in the database itself.

Such low overhead means that operating on flat files can be very fast. On the minus side, however, application programs must include logic that manipu-lates the file’s data at a very low level of complexity. The application must know exactly where and how the file stores its data. Thus, for small systems, flat files work fine. The larger a system is, however, the more cumbersome a flat file system becomes. Using a database instead of a flat file system elimi-nates duplication of effort. Although database files themselves may have more overhead, the applications can be more portable across various hard-ware platforms and operating systems. A database also makes writing appli-cation programs easier because the programmer doesn’t need to know the physical details of where and how the files store their data.

Databases eliminate duplication of effort, because the DBMS handles the data-manipulation details. Applications written to operate on flat files must include those details in the application code. If multiple applications all access the same flat file data, these applications must all (redundantly) include that data manipulation code. By using a DBMS, you don’t need to include such code in the applications at all.

Clearly, if a flat file-based application includes data-manipulation code that only runs on a particular hardware platform, then migrating the application to a new platform is a headache waiting to happen. You have to change all the hardware-specific code — and that’s just for openers. Migrating a similar DBMS-based application to another platform is much simpler — fewer com-plicated steps, fewer aspirin consumed.

Database Models

Different as databases may be in size, they are generally always structured according to one of three database models:

Relational: Nowadays, new installations of database management sys-tems are almost exclusively of the relational type. Organizations that already have a major investment in hierarchical or network technology may add to the existing model, but groups that have no need to maintain compatibility with “legacy systems” nearly always choose the relational model for their databases.

Hierarchical: Hierarchical databases are aptly named because they have a simple hierarchical structure that allows fast data access. They suffer from redundancy problems and a structural inflexibility that makes data-base modification difficult.

Network: Network databases have minimal redundancy but pay for that advantage with structural complexity.

The first databases to see wide use were large organizational databases that today would be called enterprise databases, built according to either the

hierarchical or the network model. Systems built according to the relational model followed several years later. SQL is a strictly modern language; it applies only to the relational model and its descendant, the object-relational model. So here’s where this article says, “So long, it’s been good to know ya,” to the hierarchical and network models.

New database management systems that are not based on the relational model probably conform to the newer object model or the hybrid object-relational model.

Relational model

Dr. E. F. Codd of IBM first formulated the relational database model in 1970, and this model started appearing in products about a decade later. Ironically, IBM did not deliver the first relational DBMS. That distinction went to a small start-up company, which named its product Oracle.

Relational databases have replaced databases built according to earlier models because the relational type has valuable attributes that distinguish relational databases from those other database types. Probably the most important of these attributes is that, in a relational database, you can change the database structure without requiring changes to applications that were based on the old structures. Suppose, for example, that you add one or more new columns to a database table. You don’t need to change any previously written applications that will continue to process that table, unless you alter one or more of the columns used by those applications.

Of course, if you remove a column that an existing application references, you experience problems no matter what database model you follow. One of the best ways to make a database application crash is to ask it to retrieve a kind of data that your database doesn’t contain.

Why relational is better

In applications written with DBMSs that follow the hierarchical or network model, database structure is hard-coded into the application — that is, the application is dependent on the specific physical implementation of the data-base. If you add a new attribute to the database, you must change your appli-cation to accommodate the change, whether or not the application uses the new attribute.

Relational databases offer structural flexibility; applications written for those databases are easier to maintain than similar applications written for hierar-chical or network databases. That same structural flexibility enables you to retrieve combinations of data that you may not have anticipated needing at the time of the database’s design.

Components of a relational database

Relational databases gain their flexibility because their data resides in tables that are largely independent of each other. You can add, delete, or change data in a table without affecting the data in the other tables, provided that the affected table is not a parent of any of the other tables. In this section, I show what these tables consist of and how they relate to the other parts of a relational database.

Guess who’s coming to dinner?

At holiday time, many of my relatives come to my house and sit down at my table. Databases have relations, too, but each of their relations has its own table. A relational database is made up of one or more relations.

A relation is a two-dimensional array of rows and columns, containing single-valued entries and no duplicate rows. Each cell in the array can have only one value, and no two rows may be identical.

Most people are familiar with two-dimensional arrays of rows and columns, in the form of electronic spreadsheets such as Microsoft Excel. The offensive statistics listed on the back of a major-league baseball player’s baseball card are another example of such an array. On the baseball card are columns for year, team, games played, at-bats, hits, runs scored, runs batted in, doubles, triples, home runs, bases on balls, steals, and batting average. A row covers each year that the player has played in the major leagues. You can also store this data in a relation (a table), which has the same basic structure. Figure 1-2 shows a relational database table holding the offensive statistics for a single major-league player. In practice, such a table would hold the statistics for an entire team or perhaps the whole league.

Columns in the array are self-consistent, in that a column has the same mean-ing in every row. If a column contains a player’s last name in one row, the column must contain a player’s last name in all rows. The order in which the rows and columns appear in the array has no significance. As far as the DBMS is concerned, it doesn’t matter which column is first, which is next, and which is last. The DBMS processes the table the same way regardless of the order of the columns. The same is true of rows. The order of the rows simply doesn’t matter to the DBMS.

Every column in a database table embodies a single attribute of the table. The column’s meaning is the same for every row of the table. A table may, for example, contain the names, addresses, and telephone numbers of all an organization’s customers. Each row in the table (also called a record, or a tuple) holds the data for a single customer. Each column holds a single attribute, such as customer number, customer name, customer street, cus-tomer city, customer state, customer postal code, or customer telephone number. Figure 1-3 shows some of the rows and columns of such a table.

The things called relations in a database model correspond to tables in a data-base based on the model. Try to say that ten times fast.

Enjoy the view

One of my favorite views is the Yosemite Valley viewed from the mouth of the Wawona Tunnel, late on a spring afternoon. Golden light bathes the sheer face of El Capitan, Half Dome glistens in the distance, and Bridal Veil Falls forms a silver cascade of sparkling water, while a trace of wispy clouds weaves a tapestry across the sky. Databases have views as well — even if they’re not quite that picturesque. The beauty of database views is their sheer usefulness when you’re working with your data.

Tables can contain many columns and rows. Sometimes all of that data inter-ests you, and sometimes it doesn’t. Only some columns of a table may interest you or only rows that satisfy a certain condition. Some columns of one table and some other columns of a related table may interest you. To eliminate data

that is not relevant to your current needs, you can create a view. A view is a subset of a database that an application can process. It may contain parts of one or more tables.

Views are sometimes called virtual tables. To the application or the user, views behave the same as tables. Views, however, have no independent exis-tence. Views allow you to look at data, but views are not part of the data.

Say, for example, that you’re working with a database that has a CUSTOMER table and an INVOICE table. The CUSTOMER table has the columns CustomerID, FirstName, LastName, Street, City, State, Zipcode, and Phone. The INVOICE table has the columns InvoiceNumber, CustomerID, Date, TotalSale, TotalRemitted, and FormOfPayment.

A national sales manager wants to look at a screen that contains only the customer’s first name, last name, and telephone number. Creating from the CUSTOMER table a view that contains only those three columns enables the manager to view only the needed information without having to see all the unwanted data in the other columns. Figure 1-4 shows the derivation of the national sales manager’s view.

A branch manager may want to look at the names and phone numbers of all customers whose zip codes fall between 90000 and 93999 (Southern and Central California). A view that places a restriction on the rows it retrieves as well as the columns it displays does the job. Figure 1-5 shows the sources for the branch manager’s views columns.

									SALES_MGR View
		Customer ID
										FirstName
		FirstName
		LastName								LastName
		Street								Phone
		City
		State
		Zipcode
		Phone
		INVOICE Table
The sales
		InvoiceNumber
manager’s
view derives		CustomerID
		Date
from the
		TotalSale
CUSTOMER
		TotalRemitted
table.
		FormOfPayment

CUSTOMER Table

ACCTS_PAY View

Customer ID

FirstName

FirstName

LastName

LastName

Street

Date

City

Total Sale

State

TotalRemitted

Zipcode

FormOfPayment

Phone

INVOICE Table

InvoiceNumber

CustomerID

Date

TotalSale

TotalRemitted

FormOfPayment

Views are useful because they enable you to extract and format database data without physically altering the stored data. 

Schemas, domains, and constraints

A database is more than a collection of tables. Additional structures, on sev-eral levels, help to maintain the data’s integrity. A database’s schema provides an overall organization to the tables. The domain of a table column tells you what values you may store in the column. You can apply constraints to a data-base table to prevent anyone (including yourself) from storing invalid data in the table.

Schemas

The structure of an entire database is its schema, or conceptual view. This structure is sometimes also called the complete logical view of the database. The schema is metadata — as such, it’s part of the database. The metadata itself, which describes the database’s structure, is stored in tables that are just like the tables that store the regular data. Even metadata is data; that’s the beauty of it.

Domains

 

An attribute of a relation (that is, a column of a table) can assume some finite number of values. The set of all such values is the domain of the attribute.

Say, for example, that you’re an automobile dealer who handles the newly introduced Curarri GT 4000 sports coupe. You keep track of the cars you have in stock in a database table that you name INVENTORY. You name one of the table columns Color, which holds the exterior color of each car. The GT 4000 comes in only four colors: blazing crimson, midnight black, snowflake white, and metallic gray. Those four colors are the domain of the Color attribute.

Constraints

Constraints are an important, although often overlooked, component of a database. Constraints are rules that determine what values the table attrib-utes can assume.

By applying tight constraints to a column, you can prevent people from enter-ing invalid data into that column. Of course, every value that is legitimately in the domain of the column must satisfy all the column’s constraints. As I men-tion in the preceding section, a column’s domain is the set of all values that the column can contain. A constraint is a restriction on what a column may contain. The characteristics of a table column, plus the constraints that apply to that column, determine the column’s domain. By applying constraints, you can prevent the entry into a column of data that falls outside the column’s domain.

In the auto dealership example, you can constrain the database to accept only those four values in the Color column. If a data entry operator then tries to enter in the Color column a value of, for example, forest green, the system refuses to accept the entry. Data entry can’t proceed until the operator enters a valid value into the Color field.

The object model challenges the relational model

The relational model has been fantastically successful in a wide variety of application areas. However, it is not problem free. The problems have been made more visible by the rise in popularity of object-oriented programming languages such as C++, Java, and C#. Such languages are capable of handling more complex problems than traditional languages due to such features as user-extensible type systems, encapsulation, inheritance, dynamic binding of methods, complex and composite objects, and object identity. I am not going to explain what all those things mean in this series (although I do touch on some of them later). Suffice it to say that the classic relational model does not mesh well with many of these features. As a result, database management systems based on the object model have been developed and are available on the market. As yet their market share is relatively small.

Object-relational model

Database designers, like everyone else, are constantly searching for the best of all possible worlds. They mused, “Wouldn’t it be great if we could have the advantages of an object-oriented database system, and still retain compatibil-ity with the relational system that we have come to know and love?” This kind of thinking led to the hybrid object-relational model. Object-relational DBMSs extend the relational model to include support for object-oriented data modeling. Object-oriented features have been added to the international SQL standard, allowing relational DBMS vendors to transform their products into object-relational DBMSs, while retaining compatibility with the standard. Thus, whereas the SQL-92 standard describes a purely relational database model, SQL:1999 describes an object-relational database model. SQL:2003 has even more object-oriented features.

I describe ISO/IEC international standard SQL. This is primarily a relational database model. I also include the object-oriented extensions to the standard that were introduced in SQL:1999, and the additional extensions included in SQL:2003. The object-oriented features of the new standard allow developers to apply SQL databases to problems that are too complex to address with the older, purely relational, paradigm. (Ah, paradigms. Ya gotta love ’em.)

Database Design Considerations

A database is a representation of a physical or conceptual structure, such as an organization, an automobile assembly, or the performance statistics of all the major-league baseball clubs. The accuracy of the representation depends on the level of detail of the database design. The amount of effort that you put into database design should depend on the type of information you want to get out of the database. Too much detail is a waste of effort, time, and hard drive space. Too little detail may render the database worthless. Decide how much detail you need now and how much you may need in the future — and then provide exactly that level of detail in your design (no more and no less) — but don’t be surprised if you have to adjust it to meet real-world needs.

Today’s database management systems, complete with attractive graphical user interfaces and intuitive design tools, can give the would-be database designer a false sense of security. These systems make designing a database seem comparable to building a spreadsheet or engaging in some other rela-tively straightforward task. No such luck. Database design is difficult. If you do it incorrectly, you get a database that becomes gradually more corrupt as time goes on. Often the problem doesn’t turn up until after you devote a great deal of effort to data entry. By the time you know that you have a problem, it’s already serious. In many cases, the only solution is to completely redesign the database and reenter all the data. The up side is that you get better at it.