Almost all applications today deal with persisted data. The only rare exceptions are usually not very complicated such as a calculator, unit conversion tool, or minor utility. Let us assume that any application we’re working on is going to have some sort of persistant state that must be kept between executions of the application. Typically the state is stored in either a file or a database. File access is useful for many desktop applications used by a single user as the format can be created very easily from the data model, the data is easy to transfer to another user via email or some other file transfer process, and backups involve just storing the file somewhere safe.
Many applications that involve multiple users, including web applications, will instead employ a database. Even single user desktop applications sometimes use a database. Database access is often performed by a section of the code converting objects or structures into database tupils and storing them in tables. When the data is required again, select statements are made that call the data and turn it back into a structure. Usually this storage and retrieval of data is done in the same code base as the rest of the application.
There are a few problems with this approach however. For one, the database code can sometimes become tightly coupled with the business logic of the application itself. The application knows about the data being stored in a database and will often be changed accordingly to facilitate database access. Switching to a different database system can involve very large rewrites of not only the database code, but also application code as it can rely on database specific functionality. Think of functions that pass along snippets of SQL in order to construct the correct objects. Often that SQL will be database specific.
Besides being tied to a single database, the same coupling also ties applications to databases in general. If your application is expecting the data to be stored in a relational database, assumptions in the business logic will be made regarding that. This inhibits your ability to transition to a file store or other NoSQL solution if required. Because of these concerns, my topic for today is about abstraction and remote data sources.
If you code your database logic in with your application code, coupling can form. However if you separate them completely your application doesn’t need to know where the data comes from or where it goes when its saved. All the application has to do is follow a simple generic interface for storing and retrieving data. This not only elements coupling, but has some other scalability benefits that I’ll discuss a bit later.
To really appreciate interfaced based design and loose coupling, try removing your data access code from your application code base completely. Instead, use a language neutral format such as JSON or XML to communicate between your application and your data source, itself a small application. The application will send a request along with perhaps some data if it needs something stored. The data source will then transform that data into a format that can be stored and place it either in a database, file, or any other storage medium that you desire. Later if you want to modify how your data is stored, maybe a different DB system is more suited or you’re moving from a file-based system to a database, you can just modify the implementation of the data source application while not changing a line of code in your actual application. The loose coupling means that as long as the data source maintains a solid interface that the application can interact with, the application can be developed independently of the data source.
If each of your interactions between your application and datasource are atomic and independent of each other, stateless, you can horizontally scale your datasource using replication. Each data source can access a different replicated read-only data store for request information. Meanwhile write access will all go to a master data store that can be replicated to the read-only slaves. Read requests from an application can be distributed to multiple data sources, especially useful if the data source does any CPU intensive filtering or processing.
The main benefit however remains that by separating your data source from your application as a remote service, you can ensure that your code is loosely coupled and that your business logic will not rely upon the underlying method which your data is stored. This will lead to greater flexibility in your code, and a more agile platform on which to build.Databases, Design, Scalability comment below, or link to this permanent URL from your own site.