Software Architecture Styles ​

Layered (n-tier) ​

Layered, aka N-tier, is one of the most recognized and standard architecture patterns due to its easy development and maintainability. As the name implies, it follows a tiered method where one logical layer offers services to another layer. This is quite a traditional approach for software development life cycle methodologies where the purpose is to interconnect all the components without any dependability.

For example, take a look at the following figure of a layered architecture pattern. The database layer is at the bottom and delivers services to the subsequent upper layers.

The MVC (Model-View-Controller) pattern is another common instance of this architecture pattern as it uses similar components and a three-layer approach.

Pros ​

• This is a perfect pattern if you want to experiment with less overboard and follow a well-established method.
• As it’s a tiered approach, applications created with this pattern have a layered format as well.
• Interdependency is negligible in this pattern, and hence, unit testing or functional testing becomes easier.

Cons ​

• Large projects using layered architecture patterns are usually resource-consuming and less flexible.
• Using this type of pattern leads to the development of monolithic systems that require complete redeployment for even minute changes.

Use Cases ​

• Applications that require separation of concerns and maintainability as primary architectural pillars.
• You need to build an app quickly with a limited number of engineers.
• Your business needs the traditional IT infrastructure and processes.

Event-Driven ​

This is another modern approach to architectural patterns that revolve around software components asynchronously identifying, processing, and responding to certain ‘events’ like keystrokes, scrolling, mouse click, etc.

Unlike the layered architecture, where data flows from one point to another with e predefined order, the event-driven architecture enables app modules to work as per occurring events. This pattern usually falls into two different categories – broker topology (linking events collectively without a central mediator) and mediator topology (demonstrating multiple operations in an event bus with a central mediator).

In simple words, this type of pattern oriented software architecture includes publishers, subscribers, sources, and sinks – publishers fetch and hoard data in the event store, subscribers render and respond to the event, sources generate data triggered by UI, and sinks are where the data ends up.

Pros ​

• Software of this kind adds to the response time of the architecture, resulting in higher scalability and better business outcomes.
• If you want real-time updates in your app with asymmetric data flow, event-driven architecture is the best bet for you.
• IoT devices that need to exchange data between consumers and service providers in real-time are most suitable for it,

Cons ​

• If multiple modules are responsible for a single event, developers may face challenges in managing errors.
• The transaction-based mechanism for consistency tends to be complicated with the decoupling and mutually-dependent components.

Use Cases ​

• Event-driven software architecture design patterns are perfect for live streaming app development.
• Scalable and extensible applications requiring seamless data communication.
• If you want to develop an app with highly interactive and responsive user interfaces.

Space-Based ​

Space-based architecture pattern stands out from the rest for its ability to overcome high loading problems. Most of the patterns deal with databases and usually lead to app crashes when the database can’t handle the load. Contrastingly, this particular pattern brings forth the concept of tuple space – the distributed shared memory.

For example, the above figure showcases the core attributes of a space-based pattern. The processing unit consists of back-end business logic and web-based components. Whereas you can deploy smaller apps as a single unit, large ones can be broken down into different processing units. Additionally, the virtualized-middleware module contains components to manage data synchronization and handle requests.

The most significant feature of event-driven architectural styles in patterns in software engineering is that it works without a central database. The software receives a request and processes the bid with a timestamp. After that, it upgrades the bid’s data and sends it back to the browser. This is an effective example of how modern web development stacks work.

Pros ​

• It's the best architectural pattern to mitigate scalability and concurrency issues in your application.
• If you work with low-value data that you can occasionally afford to lose without having to face the consequences, this pattern might prove to be useful.
• Apps that deal with changing and unpredictable concurrent user loads will seamlessly work with this architecture.

Cons ​

• There's a chance of corrupting multiple copies if you need to cache data for speed.
• Generating the needed load to test the app may sometimes be arduous.

Use Cases ​

• Event-driven architecture works superiorly with social or eCommerce websites.
• Software and apps that work with a huge userbase or constant volume of requests.
• Ideal for website monitoring, payment processing, real-time marketing, and fraud detection.

CQRS (Command Query Responsibility Segregation) ​

The Command Query Responsibility Segregation (CQRS) pattern refers to an alternate model for storing and querying information. Rather than our having a single model for how we both manipulate and retrieve data, as is common, responsibilities for reads and writes are instead handled by separate models.

These separate read and write models, implemented in code, could be deployed as separate units, giving us the ability to scale reads and writes independently. CQRS is often, though not always, used in conjunction with event sourcing, where—rather than storing the current state of an entity as a single record—we instead project the state of an entity by looking at the history of events related to that entity.

Use Cases ​

• You implemented the database-per-service pattern and want to join data from multiple microservices.
• Your read and write workloads have separate requirements for scaling, latency, and consistency.
• Eventual consistency is acceptable for the read queries.

Domain Driven Design (DDD) ​

If you are building or designing APIs, Microservices or integrating systems then Domain Driven Design (DDD) offers a valuable design technique for mapping business domains to build software services of value.

Using DDD is incredibly useful when designing services because it helps you rationalise the granularity of your software, the ownership boundaries and model interactions need to build decoupled and reactive distributed systems.

Pros ​

• Domain Driven Design uses patterns and principles for solving difficult problems in software and it helps in improving communication. When a common and ubiquitous language is established which is related to the domain model of the project, their teams will find communication to be easier throughout the development life cycle. Moreover Domain Driven Design will need less jargon when the aspects of application will be discussed as ubiquitous language will define simpler terms for more technical aspects.
• It helps in writing a testable and clear code that represents the domain. Emphasis will be placed on Domain over Interface since Domain Driven Design will produce applications which are which are suited accurately and are representative of the domain at hand. The focus is on domain which means that Domain Driven Design approach will produce a product that works well with the audience associated with that domain.
• It improves flexibility and better understanding as Domain Driven Design is based on the concept of object-oriented analysis and design and everything within the domain model can be based on an object and will, and can be quite encapsulated and modular.

Cons ​

• The use of Domain Driven Design is suitable for only when the domain is complex where business logic is complex and convoluted. However it is not suitable for applications which have marginal complexity of domain but have technical complexity.
• The use of Domain Driven Design is time consuming and requires robust domain expertise. Sometimes, an integration of outside members of the team is required who can act as domain experts throughout the development life cycle.
• The practices of Domain Driven Design are relied on constant iteration and integration continuously for building a malleable project which can adjust itself whenever necessary.