Software architecture patterns help create a standard solution to common or frequent software-related problems. Many programmers today can benefit from understanding the unique traits of various types of software architecture design patterns and the specific problems they solve.
To that end, this article reviews five of the major types of software architecture design in use today, the role they play in software development environments and the circumstances that warrant their use.
Imagine you have a collection of shared resources and services that must be shared with various clients and, for business purposes, you can't release every resource for free. How do you control the service quality and access to sensitive content based on your requirements?
A client-server architecture pattern is a distributed application structure that facilitates communication between clients and servers regardless of whether they share the same network. A client requests data or services, and the server responds to the request by sending the resources. It's a highly elastic design pattern given how multiple clients can interact with a single server (and vice versa).
Email is a classic example of the client-server design pattern. When you look for a particular email, the server searches its resource pool. Then, the server returns the requested email back to the client.
The Model-View-Controller (MVC) architecture design is considered a standard software development approach and makes up the backbone of popular development frameworks like ASP.NET and AngularJS. The model layer of MVC sits above the database and stores information about data types and underlying business logic. The view layer contains HMTL, CSS and other collections of front-end interface code. Finally, the controller provides a proxy between the model and the view elements to enforce the rules and methods surrounding data transactions.
MVC is commonly used to build both web and mobile applications, although the relative complexity of MVC may not render it suitable for apps that use a simple UI. Layer isolation can pose a big management challenge for architects, and it can be difficult to understand the architecture without going through every module. Nonetheless, MVC still finds widespread use thanks to its ability to make large applications easy to modify, test and maintain.
Consider a multi-architecture approach
The various types of software architecture design patterns offer standardized solutions to a variety of complex problems across disciplines, and the architecture design you choose will serve as the foundation for all your future transactions and communications. While there are certainly pros and cons to each individual approach, remember that the incorporation of two or more designs might help the application and better business performance.
3. Event driven
Many programs stay on the lookout for certain events to occur to respond accordingly where the event could be triggered by certain data requests, a UI interaction or some other influencing factor. These events can even be generated asynchronously in case the application needs to, for instance, scale up in response to increased workload demands. An event-driven architecture uses these events as the main catalyst for application operations.
As an example, picture a large software ecosystem consisting of multiple distributed systems that caters to streams of data requests in the form of asynchronous messages. In a case like this, an event-driven architecture could help provide a form centralized control that governs data delegation to various modules and recognizes connections between data types. Each transaction serves as an "event trigger" that assigns operational code to that data type to ensure that responses to data requests are accurate.
Given its ability to provide high levels of scalability, this type of software architecture design usually finds appeal within complex, enterprise-level software environments riddled with large collections of both internal and outward-facing business applications. However, application modules should remain as independent as possible in an event-driven architecture, because things like testing can become very complex if software component coupling is too tight and events are constantly triggering unexpected actions.
A microkernel architecture primarily concerns itself with an application's basic routines and standardized actions based on regular data inputs. An IDE, for instance, can open files, edit them and begin background processes. Standard routines like these are the type that would sit in a microkernel.
A microkernel can often compile code at the click of a button, as well as compile code written in different languages through additional plugins. The microkernel architecture is ideal for tools that are used extensively across an organization's various business groups and deliver a routine lineup of core software routines.
Any software can be easy to manage if it's small, but large monolithic applications are often resistant to change and difficult to scale dynamically. To combat this problem, software teams eventually managed to create an architectural pattern consisting of small, containerized services that embody individual modules of application functionality. Eventually, this pattern came to be known as microservice-based architecture -- or, simply, microservices.
Netflix was one of the first to offer the development community a demonstrative glimpse of what organizations could achieve with this architectural approach in action at a massive scale. The main UI Netflix users see is populated by collections of separate services, be it a list of your show history, account details or personalized watch recommendations. In this sense, Netflix's application isn't one program, but rather a "constellation" of many, smaller programs.
While this type of software architecture design might resemble the event-driven or microkernel approaches, a microservices architecture should ideally be reserved for times when the various functional services within an application can be clearly and readily segmented by domain. For instance, if a certain collection of services requires more memory than others on average, it might make sense to group these services into a domain where servers can always scale up or down quickly.