11 Best Software Architecture Patterns
What is an architectural pattern?
Just like the architecture of a building, software architecture describes the design and collection of components into systems that make up the building blocks of software. Software architecture explains the structural composition of the software program and the interactions between the elements. The principle that defines the software organization schema for these software systems is called an architectural pattern.
The architectural pattern captures the design structures of various systems and elements of software so that they can be reused. During the process of writing software code, developers encounter similar problems multiple times within a project, within the company, and within their careers. One way to address this is to create design patterns that give engineers a reusable way to solve these problems, allowing software engineers to achieve the same output structurally for a given project
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From an engineer’s perspective, software architecture patterns are important because they drive efficiency and productivity. Developers can join an existing project at any point with limited onboarding since they already understand the architecture pattern used in the project. New features can also be added to the project without any difficulty, and common application problems can be solved easily.
From the client’s perspective, architecture patterns optimize development costs, speed up the project’s timeline, and allow the engineer to deliver a high-quality product. Cost estimates are based on an understanding of the architecture systems, so product managers have a more accurate project cost, allowing for early planning and budgeting. Further, a clearly-defined architecture means the system has been validated and scoped. This helps engineers focus on the essentials of the product and also allows managers to plan adequately for project completion.
There are different types of software architecture patterns, but in this article, we shall be exploring nine of them.
1. Model-view-controller pattern
The model-view-controller (MVC) pattern divides an application into three components: A model, a view, and a controller.
The model, which is the central component of the pattern, contains the application data and core functionality. It is the dynamic data structure of the software application, and it controls the data and logic of the application. However, it does not contain the logic that describes how the data is presented to a user.
The view displays application data and interacts with the user. It can access data in the model but cannot understand the data, nor does it understand how the data can be manipulated.
The controller handles the input from the user and mediates between the model and the view. It listens to external inputs from the view or from a user and creates appropriate outputs. The controller interacts with the model by calling a method on it to generate appropriate responses.
These three components interact via some form of notification, such as an event or a callback. These notifications contain state information, such as state changes, which are communicated to update these components. For instance, an external event from the user may be transmitted to the controller to update the view. Therefore, the MVC pattern decouples software components and allows the codes to be reused easily.
One advantage of the MVC pattern is that multiple engineers can work on all three components simultaneously without conflict. Further, the MVC allows the logical grouping of related outputs to generate numerous views from the model. However, one drawback is that navigating the framework could be complex as it introduces several layers of abstraction.
2. Layered pattern (n-tier)
The layered architecture pattern is the most common among developers. It is useful for programs that comprise several groups of subtasks, each of which is at a different level of abstraction. Each of these subtasks is represented by a layer in the software — a unit of modules that produces a cohesive set of services — and each layer provides services to the next higher layer in a unidirectional pattern.
Each layer has a specific role within the application that is connected to the roles of other layers. For instance, a presentation layer, also called the UL layer, would handle all the UI and browser communication logic while a business logic layer would execute certain business requests.
Other types of layers include the application layer and the data access or persistence layer, which then accesses the database layer. This allows the database layer to be separated and enables you to switch from an Oracle server to a SQL server without impacting the other layers — it allows for lower transition costs.
These layers interact in a unidirectional pattern, such that when a user initiates an input, such as clicking a button, the presentation layer sends messages to the lower layer, the application layer, which, in turn, calls the business layer, then the data access layer that accesses the database. So calls in a layered pattern flow downwards, from a higher layer to a lower one.
Layered architecture patterns are found in many e-commerce web applications and desktop applications. It is also helpful for applications that need to be built quickly and enterprise applications that need to adopt traditional IT processes. Furthermore, a layered pattern is ideal for applications that require strict standards of testability.
A key advantage of this pattern is that it allows for an easy way to write a well-organized application. Since it is a popular architecture pattern, developers already have an understanding of how it is used. However, it does have two major drawbacks: Complexity and the cost of adding more layers. These layers may eventually be hard to split up.
Usage:
.Applications that are needed to be built quickly.
.lEnterprise applications that require traditional IT departments and processes.
.Appropriate for teams with inexperienced developers and limited knowledge of architecture patterns.
.Applications that require strict standards of maintainability and testability.
Shortcomings:
.Unorganized source codes and modules with no definite roles can become a problem for the application.
.Skipping previous layers to create tight coupling can lead to a logical mess full of complex interdependencies.
.Basic modifications can require a complete redeployment of the application.
3. Microservices pattern
The Microservices pattern involves creating multiple applications — or microservices — that can work interdependently. Although each microservice can be developed and deployed independently, its functionality is interwoven with other microservices.
A key concept in the microservices pattern is the separate deployment of units. This creates a streamlined delivery pipeline that allows for easy deployment of microservices and increases application scalability. Another key feature of this pattern is that it is a distributed architecture, meaning that the structure’s components can be fully decoupled and accessed through remote access protocols such as REST, SOAP, or GraphQL. This distributed nature of the pattern allows for its high scalability properties.
The microservices architecture uses several design patterns: Aggregator pattern, API gateway design pattern, chain of responsibility pattern, branch pattern, and asynchronous messaging design pattern. Each approach provides a method to manipulate data to produce services.
A major advantage of microservices architecture is the independent deployment of each microservice. Engineers can write and maintain each microservice independent of the others, potentially increasing its functionality and scalability. Further, because each microservice is small, it is easier to rewrite and update. Microservices architecture is best for web applications and websites with small components. It is also useful for corporate data centers that have well-defined boundaries. Some challenges for microservices come up around complexity, particularly in the network layer. Furthermore, decoupling services to work completely independently of each other requires significant architectural expertise.
The software can be like a baby elephant: It is cute and fun when it’s little, but once it gets big, it is difficult to steer and resistant to change. The microservice architecture is designed to help developers avoid letting their babies grow up to be unwieldy, monolithic, and inflexible. Instead of building one big program, the goal is to create a number of different tiny programs and then create a new little program every time someone wants to add a new feature.
Caveats:
· The services must be largely independent or else interaction can cause the cloud to become imbalanced.
· Not all applications have tasks that can’t be easily split into independent units.
· Performance can suffer when tasks are spread out between different microservices. The communication costs can be significant.
· Too many microservices can confuse users as parts of the web page appear much later than others.
Best for:
· Websites with small components
· Corporate data centers with well-defined boundaries
· Rapidly developing new businesses and web applications
· Development teams that are spread out, often across the globe
4. Event-driven architecture
Many programs spend most of their time waiting for something to happen. This is especially true for computers that work directly with humans, but it’s also common in areas like networks. Sometimes there’s data that needs processing, and other times there isn’t.
The event-driven architecture helps manage this by building a central unit that accepts all data and then delegates it to the separate modules that handle the particular type. This handoff is said to generate an “event,” and it is delegated to the code assigned to that type.
Programming a web page with JavaScript involves writing the small modules that react to events like mouse clicks or keystrokes. The browser itself orchestrates all of the input and makes sure that only the right code sees the right events. Many different types of events are common in the browser, but the modules interact only with the events that concern them. This is very different from the layered architecture where all data will typically pass through all layers. Overall, event-driven architectures:
· Are easily adaptable to complex, often chaotic environments
· Scale easily
· Are easily extendable when new event types appear
Caveats:
· Testing can be complex if the modules can affect each other. While individual modules can be tested independently, the interactions between them can only be tested in a fully functioning system.
· Error handling can be difficult to structure, especially when several modules must handle the same events.
· When modules fail, the central unit must have a backup plan.
· Messaging overhead can slow down processing speed, especially when the central unit must buffer messages that arrive in bursts.
· Developing a systemwide data structure for events can be complex when the events have very different needs.
· Maintaining a transaction-based mechanism for consistency is difficult because the modules are so decoupled and independent.
Best for:
· Asynchronous systems with asynchronous data flow
· Applications where the individual data blocks interact with only a few of the many modules
· User interfaces
5.Client-server pattern
In the client-server architecture patterns, there are two main components: The client, which is the service requester, and the server, which is the service provider. Although both client and server may be located within the same system, they often communicate over a network on separate hardware.
The client component initiates certain interactions with the server to generate the services needed. While the client components have ports that describe the needed services, the servers have ports that describe the services they provide. Both components are linked by request/reply connectors. A classic example of this architectural pattern is the World Wide Web. The client-server pattern is also used for online applications such as file sharing and email.
A simple example is online banking services. When a bank customer accesses online banking services using a web browser, the client initiates a request to the bank’s web server. In this case, the web browser is the client, accessing the bank’s web server for data using the customer’s login details. The application server interprets this data using the bank’s business logic and then provides the appropriate output to the webserver.
One major advantage of this architecture pattern is the central computing of data; all files are stored in a central location for this network. Therefore, the data, as well as the network peripherals, are centrally controlled. A disadvantage, however, is that the server is expensive to purchase and manage.
The client-server model is related to the peer-to-peer architecture pattern and is often described as a subcategory of this pattern. The latter uses a decentralized system in which peers communicate with each other directly.
Usage:
.Applications like emails, online banking services, the World Wide Web, network printing, file sharing applications, gaming apps, etc.
.Applications that focus on real-time services like telecommunication apps are built with a distributed application structure.
.Applications that require controlled access and offer multiple services for a large number of distributed clients.
.An application with centralized resources and services that has to be distributed over multiple servers.
Shortcomings:
.Incompatible server capacity can slow down, causing a performance bottleneck.
.Servers are usually prone to a single point of failure.
.Changing the pattern is a complex and expensive process.
.Server maintenance can be a demanding and expensive task.
6. Microkernel Architecture Pattern
This architecture pattern consists of two types of components — a core system and several plug-in modules. While the core system works on minimal functionality to keep the system operational, the plug-in modules are independent components with specialized processing.
If we take a business application’s perspective, the core system can be defined as general business logic without the custom code for special cases, special rules, or complex conditional processes. On the other hand, the plug-in modules are meant to enhance the core system in order to produce additional business capabilities.
Taking the example of a task scheduler application, the microkernel contains all the logic for scheduling and triggering tasks, while the plug-ins contain specific tasks. As long as the plug-ins adhere to a predefined API, the microkernel can trigger them without having to know the implementation details.
Usage:
.Applications that have a clear segmentation between basic routines and higher-order rules.
.Applications that have a fixed set of core routines and a dynamic set of rule that needs frequent updates.
Shortcoming:
.The plugins must have good handshaking code so that the microkernel is aware of the plugin installation and is ready to work.
.Changing a microkernel is almost impossible if there are multiple plugins dependent on it.
.It is difficult to choose the right granularity for the kernel function in advance and more complex at a later stage.
7. Space-based architecture
Many websites are built around a database, and they function well as long as the database is able to keep up with the load. But when usage peaks and the database can’t keep up with the constant challenge of writing a log of the transactions, the entire website fails.
The space-based architecture is designed to avoid functional collapse under high load by splitting up both the processing and the storage between multiple servers. The data is spread out across the nodes just like the responsibility for servicing calls. Some architects use the more amorphous term “cloud architecture.” The name “space-based” refers to the “tuple space” of the users, which is cut up to partition the work between the nodes. “It’s all in-memory objects,” says Richards. “The space-based architecture supports things that have unpredictable spikes by eliminating the database.”
Storing the information in RAM makes many jobs much faster, and spreading out the storage with the processing can simplify many basic tasks. But the distributed architecture can make some types of analysis more complex. Computations that must be spread out across the entire data set — like finding an average or doing a statistical analysis — must be split up into sub-jobs, spread out across all of the nodes, and then aggregated when it’s done.
Caveats:
· Transactional support is more difficult with RAM databases.
· Generating enough load to test the system can be challenging, but the individual nodes can be tested independently.
· Developing the expertise to cache the data for speed without corrupting multiple copies is difficult.
Best for:
· High-volume data like clickstreams and user logs
· Low-value data that can be lost occasionally without big consequences — in other words, not bank transactions
· Social networks
8.Master-Slave Architecture Pattern
Imagine a single database receiving multiple similar requests at the same time. Naturally, processing every single request at the same time can complicate and slow down the application process. A solution to this problem is a master-slave architecture pattern that functions with the master database launching multiple slave components to process those requests quickly.
As the title suggests, the master-slave architecture pattern can be pictured as a master distributing tasks to its slaves. Once the slave components finish their tasks, the distributed tasks are compiled by the master and displayed as the result.
One must note that the master has absolute control and power over the slave components, determining their communication and functional priorities. What makes this pattern unique is that each slave would process the requests simultaneously, providing the results at the same time. This also means that the slave operations would not be considered complete until every slave has returned the result to the master.
This pattern is well-suited for applications that can be divided into smaller segments for executing similar requests. An appropriate example would be a database application that requires heavy multitasking as its vital component.
Usage:
.Development of Operating Systems that may require a multiprocessors compatible architecture.
.Advanced applications where larger services have to be decomposed into smaller components.
.Applications process raw data stored in different servers over a distributed network.
.Web browsers that follow multithreading to increase their responsiveness.
Shortcomings:
.Failure of the master component can lead to a loss of data with no backup over the slave components.
.Dependencies within the system can lead to a failure of the slave components.
.There can be an increase in overhead costs due to the isolated nature of the slave components.
9. Pipe-Filter Architecture Pattern
A pipe-filter architecture pattern processes a stream of data in a unidirectional flow where components are referred to as filters, and pipes are those which connect these filters. The chain of processing data takes place where the pipes transmit data to the filters, and the result of one filter becomes the input for the next filter. The function of this architecture is to break down significant components/processes into independent and multiple components that can be processed simultaneously.
The pipe-filter pattern is best suited for applications that process data in a stream using web services and can create simple sequences to complex structures. Compilers can be considered a fitting example of having this architecture pattern since each filter performs lexical analysis, parsing, semantic analysis, and code generation.
Usage:
.It can be used for applications facilitating a simple, one-way data processing and transformation.
.Applications using tools like Electronic Data Interchange and External Dynamic List.
.Development of data compilers used for error-checking and syntax analysis.
.To perform advanced operations in Operating Systems like UNIX, where the output and input of programs are connected in a sequence.
Shortcomings:
· There can be a loss of data in between filters if the infrastructure design is not reliable.
· The slowest filter limits the performance and efficiency of the entire architecture.
· During transmission between filters, the data-transformation overhead costs might increase.
· The continuous transformational character of the architecture makes it less user-friendly for interactional systems.
10.Broker Architecture Pattern
A broker pattern is used for structuring distributed systems with decoupled components. By invoking remote services, components can interact with others in broker architecture patterns. Also, the broker is responsible for all the coordination and communication among the components.
Clients, servers, and brokers are three major components of the broker pattern. Generally, a broker will have access to all the services and characteristics related to a particular server. When clients request a service from the broker, the broker redirects them to a suitable service category for further process.
One of the key benefits of this architecture pattern is how it manages operations, such as change, addition, deletion, or relocation, related to objects in a dynamic manner. Lastly, this architecture pattern separates all communication-related code into layers from the application, allowing applications to run on distributed or single computers. Because of such advantages, broker architecture has been prevalent.
Usage:
.Used in message broker softwares such as Apache ActiveMQ, Apache Kafka, RabbitMQ, and JBoss Messaging.
.For structuring distributed systems that have decoupled components.
Shortcomings:
.Shallow fault tolerance capacity.
.Requires standardization of service description.
.The hidden layer may decrease software performance.
.Higher latency and requires more effort in deployment.
Diagram:
11. Peer-to-Peer Architecture Pattern
In the peer-to-peer architectural pattern, individual components are called peers. A peer can act as a client, a server, or both and change its role dynamically over time. As a client, a peer can request service from other peers, and as a server, a peer can provide services to other peers. The significant difference between peer-to-peer and client-server architecture is that each computer on the network has considerable authority and the absence of a centralized server. Its capacity increases as more and more computers join the network.
An excellent example of a peer-to-peer architecture pattern would be file-sharing networks like Skype, BitTorrent, and Napster. In BitTorrent, peer-to-peer architecture is used for distributing the data and files on the internet in a decentralized fashion. By using this protocol, one can transfer large video and audio files with the utmost ease. In Skype, you use the VoIP P2P architecture pattern to make a voice call and send text messages to another user. In this manner, you can use peer-to-peer architecture for file sharing, messaging, collaboration, etc.
Usage:
. File-sharing networks such as Gnutella and G2.
. Cryptocurrency-based products such as Bitcoin and Blockchain.
. Multimedia products such as P2PTV and PDTP.
Shortcomings:
. No guarantee of high-quality service.
. Achieving robust security is challenging.
. Performance depends on the number of nodes connected to the network.
. No way to backup files or folders.
. Might need a specific interface to read the file.
Diagram:
