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Enterprise Application Architecture: Mapping to Relational Databases

The role of the data source layer is to communicate with the various pieces of infrastructure that an application needs to do its job. Learn how to talk to a database—which means a relational database for the majority of systems built today.
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The role of the data source layer is to communicate with the various pieces of infrastructure that an application needs to do its job. A dominant part of this problem is talking to a database, which, for the majority of systems built today, means a relational database. Certainly there's still a lot of data in older data storage formats, such as mainframe ISAM and VSAM files, but most people building systems today worry about working with a relational database.

One the biggest reasons for the success of relational databases is the presence of SQL, a mostly standard language for database communication. Although SQL is full of annoying and complicated vendor-specific enhancements, its core syntax is common and well understood.

Architectural Patterns

The first set of patterns comprises the architectural patterns, which drive the way in which the domain logic talks to the database. The choice you make here is far-reaching for your design and thus difficult to refactor, so it's one that you should pay some attention to. It's also a choice that's strongly affected by how you design your domain logic.

Despite SQL's widespread use in enterprise software, there are still pitfalls in using it. Many application developers don't understand SQL well and, as a result, have problems defining effective queries and commands. Although various techniques exist for embedding SQL in a programming language, they're all somewhat awkward. It would be better to access data using mechanisms that fit in with the application development langauge. Database administrations (DBAs) also like to get at the SQL that accesses a table so that they can understand how best to tune it and how to arrange indexes.

For these reasons, it's wise to separate SQL access from the domain logic and place it in separate classes. A good way of organizing these classes is to base them on the table structure of the database so that you have one class per database table. These classes then form a Gateway (466) to the table. The rest of the application needs to know nothing about SQL, and all the SQL that accesses the database is easy to find. Developers who specialize in the database have a clear place to go.

There are two main ways in which you can use a Gateway (466). The most obvious is to have an instance of it for each row that's returned by a query (Figure 3.1). This Row Data Gateway (152) is an approach that naturally fits an object-oriented way of thinking about the data.

Figure 3.1Figure 3.1 A Row Data Gateway (152) has one instance per row returned by a query.

Many environments provide a Record Set (508)—that is, a generic data structure of tables and rows that mimics the tabular nature of a database. Because a Record Set (508) is a generic data structure, environments can use it in many parts of an application. It's quite common for GUI tools to have controls that work with a Record Set (508). If you use a Record Set (508), you only need a single class for each table in the database. This Table Data Gateway (144) (see Figure 3.2) provides methods to query the database that return a Record Set (508)

Figure 3.2Figure 3.2 A Table Data Gateway (144) has one instance per table.

Even for simple applications I tend to use one of the gateway patterns. A glance at my Ruby and Python scripts will confirm this. I find the clear separation of SQL and domain logic to be very helpful.

The fact that Table Data Gateway (144) fits very nicely with Record Set (508) makes it the obvious choice if you are using Table Module (125). It's also a pattern you can use to think about organizing stored procedures. Many designers like to do all of their database access through stored procedures rather than through explicit SQL. In this case you can think of the collection of stored procedures as defining a Table Data Gateway (144) for a table. I would still have an in-memory Table Data Gateway (144) to wrap the calls to the stored procedures, since that keeps the mechanics of the stored procedure call encapsulated.

If you're using Domain Model (116), some further options come into play. Certainly you can use a Row Data Gateway (152) or a Table Data Gateway (144) with a Domain Model (116). For my taste, however, that can be either too much indirection or not enough.

In simple applications the Domain Model (116) is an uncomplicated structure that actually corresponds pretty closely to the database structure, with one domain class per database table. Such domain objects often have only moderately complex business logic. In this case it makes sense to have each domain object be responsible for loading and saving from the database, which is Active Record (160) (see Figure 3.3). Another way to think of the Active Record (160) is that you start with a Row Data Gateway (152) and then add domain logic to the class, particularly when you see repetitive code in multiple Transaction Scripts (110).

Figure 3.3Figure 3.3 In the Active Record (160) a customer domain object knows how to interact with database tables.

In this kind of situation the added indirection of a Gateway (466) doesn't provide a great deal of value. As the domain logic gets more complicated and you begin moving toward a rich Domain Model (116), the simple approach of an Active Record (160) starts to break down. The one-to-one match of domain classes to tables starts to fail as you factor domain logic into smaller classes. Relational databases don't handle inheritance, so it becomes difficult to use strategies [Gang of Four] and other neat OO patterns. As the domain logic gets feisty, you want to be able to test it without having to talk to the database all the time.

All of these forces push you to in'direction as your Domain Model (116) gets richer. In this case the Gateway (466) can solve some problems, but it still leaves you with the Domain Model (116) coupled to the schema of the database. As a result there's some transformation from the fields of the Gateway (466) to the fields of the domain objects, and this transformation complicates your domain objects.

A better route is to isolate the Domain Model (116) from the database completely, by making your indirection layer entirely responsible for the mapping between domain objects and database tables. This Data Mapper (165) (see Figure 3.4) handles all of the loading and storing between the database and the Domain Model (116) and allows both to vary independently. It's the most complicated of the database mapping architectures, but its benefit is complete isolation of the two layers.

Figure 3.4Figure 3.4 A Data Mapper (165) insulates the domain objects and the database from each other.

I don't recommend using a Gateway (466) as the primary persistence mechanism for a Domain Model (116). If the domain logic is simple and you have a close correspondence between classes and tables, Active Record (160) is the simple way to go. If you have something more complicated, Data Mapper (165) is what you need.

These patterns aren't entirely mutually exclusive. In much of this discussion we're thinking of the primary persistence mechanism, by which we mean how you save the data in some kind of in-memory model to the database. For that you'll pick one of these patterns; you don't want to mix them because that ends up getting very messy. Even if you're using Data Mapper (165) as your primary persistence mechanism, however, you may use a data Gateway (466) to wrap tables or services that are being treated as external interfaces.

In my discussion of these ideas, both here and in the patterns themselves, I tend to use the word "table." However, most of these techniques can apply equally well to views, queries encapsulated through stored procedures, and commonly used dynamic queries. Sadly, there isn't a widely used term for table/view/query/stored procedure, so I use "table" because it represents a tabular data structure. I usually think of views as virtual tables, which is of course how SQL thinks of them too. The same syntax is used for querying views as for querying tables.

Updating obviously is more complicated with views and queries, as you can't always update a view directly but instead have to manipulate the tables that underlie it. In this case encapsulating the view/query with an appropriate pattern is a very good way to implement that update logic in one place, which makes using the views both simpler and more reliable.

One of the problems with using views and queries in this way is that it can lead to inconsistencies that may surprise developers who don't understand how a view is formed. They may perform updates on two different structures, both of which update the same underlying tables where the second update overwrites an update made by the first. Providing that the update logic does proper validation, you shouldn't get inconsistent data this way, but you may surprise your developers.

I should also mention the simplest way of persisting even the most complex Domain Model (116). During the early days of objects many people realized that there was a fundamental "impedance mismatch" between objects and relations. Thus, there followed a spate of effort on object-oriented databases, which essentially brought the OO paradigm to disk storage. With an OO database you don't have to worry about mapping. You work with a large structure of interconnected objects, and the database figures out when to move objects on or off disks. Also, you can use transactions to group together updates and permit sharing of the data store. To programmers this seems like an infinite amount of transactional memory that's transparently backed by disk storage.

The chief advantage of OO databases is that they improve productivity. Although I'm not aware of any controlled tests, anecdotal observations put the effort of mapping to a relational database at around a third of programming effort—a cost that continues during maintenance.

Most projects don't use OO databases, however. The primary reason against them is risk. Relational databases are a well-understood and proven technology backed by big vendors who have been around a long time. SQL provides a relatively standard interface for all sorts of tools. (If you're concerned about performance, all I can say is that I haven't seen any conclusive data comparing the performance of OO against that of relational systems.)

Even if you can't use an OO database, you should seriously consider buying an O/R mapping tool if you have a Domain Model (116). While the patterns in this book will tell you a lot about how to build a Data Mapper (165), it's still a complicated endeavor. Tool vendors have spent many years working on this problem, and commercial O/R mapping tools are much more sophisticated than anything that can reasonably be done by hand. While the tools aren't cheap, you have to compare their price with the considerable cost of writing and maintaining such a layer yourself.

There are moves to provide an OO-database-style layer that can work with relational databases. JDO is such a beast in the Java world, but it's still too early to tell how they'll work out. I haven't had enough experience with them to draw any conclusions for this book.

Even if you do buy a tool, however, it's a good idea to be aware of these patterns. Good O/R tools give you a lot of options in mapping to a database, and these patterns will help you understand when to use the different choices. Don't assume that a tool makes all the effort go away. It makes a big dent, but you'll still find that using and tuning an O/R tool takes a small but significant chunk of work.

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