Book Excerpt

Supertype and subtype tables in SQL Server

Implementing Supertypes and Subtypes

You are reading part 5 from "Physical data storage in SQL Server 2005 and 2008," excerpted from A Developer's Guide to Data Modeling for SQL Server, Covering SQL Server 2005 and 2008, by Eric Johnson and Joshua Jones, copyright 2008, printed with permission from Addison-Wesley Professional.
We discuss supertypes and subtypes in Chapter 2. These are entities that have several kinds of real-world objects being modeled. For example, we might have a supertype called phone with subtypes for corded and cordless phones. We separate objects into a subtype cluster because even though a phone is a phone, different types will require that we track different attributes. For example, on a cordless phone, you need to know the working range of the handset and the frequency on which it operates, and with a corded phone, you could track something like cord length. These differences are tracked in the subtypes, and all the common attributes of phones are held in the supertype.

How do you go about physically implementing a subtype cluster in SQL Server? You have three options. The first is to create a single table that represents the attributes of the supertype and also contains the attributes of all the subtypes. Your second option is to create tables for each of the subtypes, adding the supertype attributes to each of these subtype tables. Third, you can create the supertype table and the subtype tables, effectively implementing the subtype cluster in the same way it was logically modeled.

To determine which method is correct, you must look closely at the data being stored. We will walk through each of these options and look at the reasons you would use them, along with the pros and cons of each.

Supertype Table

You would choose this option when the subtypes contain few or no differences from the data stored in the supertype. For example, let's look at a cluster that stores employee data. While building a model, you discover that the company has salaried as well as hourly employees, and you decide to model this difference using subtypes and supertypes. After hashing out all the requirements, you determine that the only real difference between these types is that you store the annual salary for the salaried employees and you need to store the hourly rate and the number of hours for an hourly employee.

In this example, the subtypes contain very subtle differences, so you could build this subtype cluster by using only the supertype table. For this situation, you would likely create a single employee table that contains all the attributes for employees, including all three of the subtype attributes for salary, hourly rate, and hours. Whenever you insert an hourly employee, you would require that data be in the hourly rate and hour columns and that the salary column be left NULL. For salaried employees, you would do the exact opposite.

Implementing the types in this way makes it easy to find the employee data because all of it is in the same place. The only drawback is that you must implement some logic to look at the columns that are appropriate to the type of employee you are working with. This supertype-only implementation works well only because there are very few additional attributes from the subtype's entities. If there were a lot of differences, you would end up with many of the columns being NULL for any given row, and it would take a great deal of logic to pull the data together in a meaningful way.

Subtype Tables

When the data contained in the subtypes is dissimilar and the number of common attributes from the supertype is small, you would most likely implement the subtype tables by themselves. This is effectively the opposite data layout that would prompt you to use the supertype-only model.

Suppose you're creating a system for a retail store that sells camera equipment. You could build a subtype cluster for the products that the store sells, because the products fall into distinct categories. If you look only at cameras, lenses, and tripods, you have three very different types of product. For each one, you need to store the model number, stock number, and the product's availability, but that is where the similarities end. For cameras you need to know the maximum shutter speed, frames per second, viewfinder size, battery type, and so on. Lenses have a different set of attributes, such as the focal length, focus type, minimum distance to subject, and minimum aperture. And tripods offer a new host of data; you need to store the minimum and maximum height, the planes on which it can pivot, and the type of head. Anyone who has ever bought photography equipment knows that the differences listed here barely scratch the surface; you would need many other attributes on each type to accurately describe all the options.

The sheer number of attributes that are unique for each subtype, and the fact that they have only a few in common, will push you toward implementing only the subtype tables. When you do this, each subtype table will end up storing the common data on its own. In other words, the camera, lens, and tripod tables would have columns to store model numbers, SKU numbers, and availability. When you're querying for data implemented in this way, the logic needs to support looking at the appropriate table for the type of product you need to find.

Supertype and Subtype Tables

You have probably guessed this: When there are a good number of shared attributes and a good number of differences in the subtypes, you will probably implement both the supertype and the subtype tables. A good example is a subtype cluster that stores payment information for your customers. Whether your customer pays with an electronic check, credit card, gift certificate, or cash, you need to know a few things. For any payment, you need to know who made it, the time the payment was received, the amount, and the status of the payment. But each of these payment types also requires you to know the details of the payment. For credit cards, you need the card number, card type, security code, and expiration date. For an electronic check, you need the bank account number, routing number, check number, and maybe even a driver's license number. Gift cards are simple; you need only the card number and the balance. As for cash, you probably don't need to store any additional data.

This situation calls for implementing both the supertype and the subtype tables. A Payment table could contain all the high-level detail, and individually credit card, gift card, and check tables would hold the information pertinent to each payment type. We do not have a cash table, because we do not need to store any additional data on cash payments beyond what we have in the Payment table.

When implementing a subtype cluster in this way, you also need to store the subtype discrimination, usually a short code or a number that is stored as a column in the supertype table to designate the appropriate subtype table. We recommend using a single character when possible, because they are small and offer more meaning to a person than a number does. In this example, you would store CC for credit card, G for a gift card, E for electronic check, and C for cash. (Notice that we used CC for a credit card to distinguish it from cash.) When querying a payment, you can join to the appropriate payment type based on this discriminator.

If you need data only from either the supertype or the subtype, this method offers two benefits: you need go to only one table, and you don't retrieve extraneous data. However, the flip side is that you must determine which subtype table you need to query and then join both tables if you need data from both the supertype and a subtype table. Additionally, you may find yourself needing information from the supertype and multiple subtypes; this will add overhead to your queries because you must join multiple tables.

Supertypes and Subtypes: A Final Word

Implementing supertypes and subtypes can, at times, be tricky. If you take the time to fully understand the data and look at the implications of splitting the data into multiple tables versus keeping it tighter, you should be able to determine the best course of action. Don't be afraid to generate some test data and run various options through performance tests to make sure you make the correct choice. When we get to building the physical model, we look at using subtype clusters as well as other alternatives for especially complex situations.

Summary

In this chapter, we have looked at the available objects inside SQL Server that you will use when implementing your physical model. It's important to understand these objects for many reasons. You must keep all this in mind when you design your logical model so that you design with SQL Server in mind. This also plays a large part later when you build and implement your physical model. You will probably not use every object in SQL Server for every database you build, but you need to know your options. Later, we walk through creating your physical model, and at that time we go over the various ways you can use these physical objects to solve problems.

In the next chapter, we talk about normalization, and then we move on to the meat and potatoes of this book by getting into our sample project and digging into a lot of real-world issues.

 Check out the authors' website where they co-host a podcast show for IT professionals.


TABLE OF CONTENTS
   Part 1: Physical data storage in SQL Server 2005 and 2008
   Part 2: SQL Server 2008 data types: Datetime, string and more
   Part 3: Enforcing data integrity in a SQL Server database
   Part 4: SQL Server and data manipulation in T-SQL
   Part 5: Supertype and subtype tables in SQL Server

Data Modeling for SQL Server This chapter excerpt from A Developer's Guide to Data Modeling for SQL Server, Covering SQL Server 2005 and 2008 by Eric Johnson and Joshua Jones, is printed with permission from Addison-Wesley Professional, Copyright 2008.

Click here for the chapter download or purchase the book here.


This was first published in September 2008

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