As technology evolves, the quest for perfection is also evolving. Building robust, scalable, and efficient software solutions becomes ever more challenging. We need to have some solid starting points right?
In today’s article you and I going to discuss some of the modern software architecture patterns which are being used in big tech companies. We will see their use cases, advantages, and drawbacks as well.
Without waiting any further time, let’s start.
In Short
- 📝 Layered/n-tier architecture pattern separates an app into horizontal layers for presentation, business logic, and data access. This allows for modular development and maintenance.
- 🤝 The client-server pattern has client components that communicate with server components for data and services. Allows centralized resources.
- 💬 Event-driven architecture focuses on events as triggers to handle requests asynchronously. Enables highly responsive systems.
- 🧩 Microkernel architecture separates core systems from plugins. Allows extensibility without altering core code.
- 🗂 Microservices divide an app into independent services by capability. Promotes scalability and rapid development.
- 📨 Broker architecture uses a central broker for communication between distributed components. Decouples components.
- 🌌 Space-based architecture organizes systems into isolated spaces for easy scaling and isolation.
- 👨💻 Master-slave pattern delegates tasks from one master to multiple slaves. Allows distributed processing.
- 📦 Pipe-filter chains data through sequential filters for processing. Enables reusable data transformation.
- 🤝 Peer-to-peer nodes share resources directly without a centralized server. Enables decentralization.
- 🧠 Blackboard pattern has specialized agents collaborate to analyze data. Allows modular problem-solving.
- 📦 Component-based builds apps from reusable components. Promotes encapsulation and modularity.
- 🌐 Service-oriented architecture provides services with defined capabilities. Enables business agility.
- 🏰 Monolithic architecture consolidates functionality into a unified codebase. Simplifies development.
- 🧩 Hybrid combines multiple architectures. Provides flexibility to optimize for requirements.
Table of Contents
Layered / N-tier Architecture Pattern
The Layered Architecture Design, which is also known as N-tier Architecture Design, organizes into several layers horizontally. This arrangement allows the application’s functionalities and responsibilities to separate, hence better maintainability. As I was saying, this pattern is built of multiple layers and these layers or in a group are often referred to as the following types:
- Presentation Layer: Responsible for user interface interactions, responsible for data input and output.
- Business Layer: Executes business logic, mostly responsible for incoming/outgoing requests and data processing.
- Application Layer: Acts as a communication medium between the presentation layer and the data layer.
- Data Layer: Manages data storage within a database.
- Service Layer: Layered design can have variations in its layering based on the application’s needs. In some scenarios, you may find a most common additional layer “Service Layer” or “Data processing Layer” which is responsible for communicating with the underlying database and providing the data into the Application Layer.
As per my experience, wherever I have found a “Service Layer” in the application architecture, aside from its purpose, it is preferred by most software architects to achieve generalized access to any underlying database.
Apart from the discussed layers, some N-layered architectures may have additional layers to accomplish certain functionalities in different scenarios, making the architecture design much more adaptive and flexible.
Use Cases
- E-commerce Platforms: Efficiently manage and update user interface, business logic, and data storage.
- Banking Applications: Maintain clear separation between customer interactions, transaction processing, and data storage.
- Content Management Systems (CMS): Facilitate easier content updates by segregating content presentation, management, and storage.
Advantages
- Modular Development: Development of independent layers, allows modifications without affecting other layers.
- Scalability and Maintainability: Facilitates easier maintenance and updates due to distinct layer responsibilities.
- Popular Framework Integration: Widely supported by frameworks like Java EE, Node.js, Dot NET, Python, etc.
Drawbacks
- Communication Overhead: Performance may be impacted due to communication between layers.
- Potential Complexity: Multiple layers might lead to complexity if not well-defined, increasing the risk of tight coupling and application complications.
We can say that the Layered Architecture Design or N-tier Architecture Design is one of the most robust and common design patterns, amongst other modern software architecture patterns used by software architects. It offers clear division, modularity, and wide adaptability throughout various programming languages! However, caution needs to be taken when planning the layer definitions otherwise you may find yourself in a tight-coupled system very quickly.
Client-Server Architecture Pattern
So in our next pattern, we are going to discuss the Client-Server Architecture Pattern.
It’s a distributed application structure. Here we have two main components: client and server. The server component typically resides in the company’s cloud space or on-premise systems. The client component, on the other hand, sits on the end-user side and communicates with the server component to get the required pieces of information.
The Client-Server Architecture typically has the following functionalities:
- Components: As I was discussing it has a client and a server. The client component communicates with the server from the end-user system, and the server component processes the incoming requests, manages the database underneath, and serves data to all the connected clients.
- Connectivity: Clients can access resources from the server which could be data, files, services, etc. Connectivity can be established via various methods like HTTP calls, socket connections, polling services, push notifications, etc.
- Flexibility: One server can serve multiple clients and one client can connect to multiple servers. So architects can segregate the servers based on their functionality and concerns and ship one client to the end user.
Use Cases
- Email Systems: We can send and receive emails through a central server, like Gmail, Outlook, etc.
- Web Apps: Any basic eCommerce website could be an example. Like Amazon.com or Flipkart.com. These are big giants of eCommerce so they do have multiple setups but yes, they are using Client-Server Architecture for sure.
- Online Gaming: Real-time communication among players through a central server for multiplayer gameplay. We all know about PUBG right? It’s one such example.
- Remote File Storage: Allows clients to securely store or retrieve files from a remote server location. An example is Google Drive.
- Applications in general: In the days of modern software architecture patterns whichever application I see, each one of them is using Client-Server Architecture. Whether it be a simple Web Application or typical software, all would prefer using client software for the user interface. It will have a backend server for lifting most of the heavy tasks so that the application doesn’t need to rely on the end-user’s CPU powers entirely.
Advantages
- Versatility: Client-server architecture is applicable across various types of applications may it be email hosting, real-time gaming servers, simple web services, or secure banking systems.
- Real-time Services: This architecture setup is quite ideal for real-time interactions like gaming, or distributed/decoupled systems.
- Centralized Resources: It is effective for centralized resources distributed across multiple servers.
Drawbacks
- Scalability Challenges: Server scalability might be tricky during heavy traffic periods.
- Single Point of Failure: Another big issue is the single point of failure if not handled properly. But nowadays architects are smart enough to set up parallel nodes, like Kubernetes so that the system will never go to a complete stop.
- Complex Maintenance: Changing the architecture or server maintenance can be intricate and resource-intensive.
Client-server architecture serves as a fundamental base for modern software architecture patterns. It is applicable across various industries. You can achieve effective communication and resource access between the client and the server, but with some of the drawbacks as we already discussed for this reason sometimes maintenance of such projects is hard and you need to carefully consideration.
Event-driven / Event-Bus / Sub-Pub Architecture Pattern
Modern Software Architecture Patterns like Event-Driven Architecture (EDA) mainly focus on responsiveness and adaptability to various scenarios. But what do I mean by events here?
Events can be a user action like a button click or keystroke on the keyboard, data change, or maybe some webhook or notification from some 3rd party system. It could be anything!
Event-driven architecture reacts to these events in a dynamic environment asynchronously. EDA allows better responsiveness due to its asynchronous nature but the efforts are all worthy where user experience matters the most like OTT platforms, media streaming platforms, gaming, etc.
Focusing on events as triggers, EDA shows how modern software architecture patterns achieve resilient and fine-tuned on-demand systems in complex contexts.
EDA Topologies
- Mediator Topology: Organizes multiple events within an event bus through a central mediator.
- Broker Topology: Using broker topology events get chained together without relying on a central mediator.
Use Cases
- Social Media Platforms: User events like posting social media stories, liking your friend’s post, or commenting on something trigger real-time events, and it updates the feed of the relevant users across the platform.
- Stock Trading Platforms: Quick response to market changes and executing transactions based on real-time market prices is challenging but can be achieved using Event-Driven Architecture.
- Smart Home Systems: Nowadays our homes are getting stuffed with more and more devices right? These devices react to various inputs, like voice, gesture, distance, wireless frequencies, etc.
- Email Services: Email services like Gmail, Outlook, Yahoo, etc. use event-based architecture to some extent. This way they make sure events with huge mails to processed, and their system works as smoothly as possible.
- eCommerce Sites: eCommerce sites like Amazon, Flipkart, etc, use event-based architecture patterns so that they can provide the smoothest user experience even during high-demand periods and prevent application crashes or resource over-provisioning.
Advantages
- Highly Performant: Agile and performant, enabling systems to react swiftly to various sources, especially during high-demand scenarios.
- Decoupled Components: Comprised of decoupled, single-purpose event processing components that asynchronously receive and process events.
Drawbacks
- Testing Challenges: As we see systems, which are based on event-based architecture, do have multiple modules to handle different events. Testing of these interconnected modules requires a fully functional system with all the modules up and running at the time of testing.
- Error Handling Complexity: Error handling is a very essential part of any software development. However, with multiple interconnected modules, error handling may become very hard.
- Complexity in Data Structures: As per my experience, I have seen developers likely to start the data-structuring part of the application. However as the development progresses with all the different modules, it gets harder to maintain the system-wide data structure.
- Complex Debugging: Non-linear event flows can complicate debugging processes.
- Event Timing Issues: Event order and timing might lead to unexpected behavior.
- Architectural Complexity: Overuse of events might result in convoluted architectures.
This architectural pattern is also known as the Event-Bus Architecture Pattern, as well as the Publisher-Subscriber (Pub-Sub) Architecture Pattern. In Pub-Sub terminology, whenever a node creates a new event, it’s called publishing, and listening to that event publish and eventually reacting accordingly, is called subscribing.
The event-driven architecture pattern is known for its high performance. It allows systems to react promptly to user actions or external events. However, things can become quite complex in terms of system design, inter-module communications, and event processing during heavy load. So wherever you go with EDA, be prepared with a solid plan.
Microkernel Architecture Pattern
The Microkernel Architecture Pattern comprises two primary components, one is the core application and the other one is pluggable modules. The core system is the heart of the application where the essential operations happen, keeping the system functional. On the other hand, plugins are extensions of the core system without altering the core itself. Plugins provide additional features to the application.
The microkernel architecture is suitable where you need to create a software ecosystem but you want others to contribute to your application without letting them change the core system.
For example, if you see WordPress, it follows microkernel architecture. You see, WordPress has WordPress core and WordPress plugin. If you want to add some new feature or functionality you don’t need to modify the WordPress Core but you can build your feature as a plugin on top of the existing core. That’s the beauty of it.
Use Cases
- WordPress: As we were already discussing WordPress has its core which provides its basic functionality. Now, to get extra or custom functionality we have WordPress Plugins which use the WordPress Core only to function properly.
- VSCode: We all know about VSCode right? It’s a lightweight code editing tool from Microsoft. By default, you will get the basic editing tool nothing fancy. But it comes with a fancy plugin library where you get plugins for SSH connector, code highlighter, debugger, git helper, etc.
- Web Browser: Nowadays most web browsers come with plugin support. For example, almost all of us have used extensions like ad-blockers, VPN plugins, password managers, etc. These plugins are nothing but add-on functionality built on top of the core browser functionalities and APIs.
Advantages
- Extensibility: Microkernel Architecture provides to easiest expansion capability to an application through independent development, testing, and deployment of the plugin modules.
- Customization: Modern software architecture patterns always promote customization on top of anything else. Microkernel Architecture is an awesome example of this. But plugins not only get functionality. But can override the existing functionality without even touching the core source codes.
Drawbacks
- Communication Overhead: Your plugin may bring new amazing functionality to the system but to work it needs to be compatible with the core system right? This means you need to know certain things about the core system you are working on. This communication overhead might feel a little overwhelming from time to time.
- Kernel Modifications: Change is the only constant right? Similarly, core systems also tend to evolve or change, the reason could be a vulnerability fix or feature enrichment, but sometimes it may or may not stay compatible with the old core. This may cause any plugins to stop working unless they are also getting modified.
Microkernel Architecture Pattern, amongst the other Modern Software Architecture Patterns, is the only model that separates core system functionalities from optional features.
Even if we are unsure about the future scopes of an application, microkernel architecture supports the application development very efficiently. Despite potential drawbacks, this architecture can support the ever-changing needs of any modern-day software development.
Microservice Architecture Pattern
Microservices Architecture Pattern divides the application features into small and independent deployable codebases. It allows better scalability and rapid development. Microservices work on the principle of independent services. These services can be segregated based on the feature or the type of operation that needs to be performed. More the services you have, the more loosely coupled your system is. Loose coupling brings better scalability, maintainability, and better documentation capability to a system.
Use Cases
- E-commerce Marketplace: Big e-commerce platforms use microservices architecture patterns to handle separate actions like user log-in, product catalog, payment order processing, etc.
- Ride-Sharing Application: Similar to the eCommerce platforms, ride-sharing applications do the log-in, ride requests, driver tracking, payments, etc. in separate microservices.
- Streaming Platform: Streaming platforms like YouTube, Netflix, Hotstar, etc. use this architecture for better user experience and seamless streaming.
Advantages
- Scalability and Modularity: Loose coupling allows microservices to work independently and with lower dependency on each other. As a result, you will find your system is more scalable and modular.
- Flexibility and Maintainability: Due to its independent nature, it allows developers to work, deploy, or test the microservices freely on separate concerns without needing to know the entire application.
Drawbacks
- Complexity in Data Consistency: As you that in microservice architecture you will find yourself in the middle of several microservices. And it is very much possible to lose track of data across the services.
- Granularity: As we see, in this architecture, we create new services for different concerns but one extra service is one extra overhead on the system. You need to have an understanding of when to create a new service and when to not.
- Performance Impact: One extra service is one extra overhead on the system. Meaning, we need to manage, the intercommunications and everything. So, if overdo the segregation and create unnecessary services, it might cause performance issues.
Microservices Architecture Pattern presents a very clever approach to software development. Modular, independent, and separation of concerns make the services easily deployable, scalable, flexible, and more maintainable. Also, you should keep this in mind and do not go for this pattern blindly as soon as your project starts. Remember for simple and small applications the setup and efforts of a microservices pattern are simply not worthy.
Broker Architecture Pattern
Broker Architecture Patterns mainly focus on a central broker facilitating communication between multiple distributed and independent modules. Using this pattern you can make your system more decoupled and efficient at operation. This pattern is most common in messaging systems where a common broker is responsible for sending messages among multiple functional modules.
Use Cases
- Financial Market Data: While discussing with architects I found that they like to use the broker architecture module to distribute real-time stock market data for trade analysis and decision-making.
- Message Queues: In broker architecture patterns, the brokers manage the message distribution among the various modules, and provide smooth processing but providing asynchronous communication.
- IoT Hub: I have seen, broker architecture is used to facilitate communication between IoT devices and cloud services using centralized or decentralized brokers.
Advantages
- Decoupling and Coordination: Using a broker architecture pattern, you can achieve interaction through remote services achieving decoupling, but at the same time you can keep the coordination and communication centralized and monitored.
- Layered Communication: If you choose this architecture, it improves the flexibility of the system by segregating the communications into single or multiple broker layers.
Drawbacks
- Single Point of Failure: If you are using a central/single broker setup, it is pretty vulnerable to single-point failure.
- Latency and Scalability: In this setup, the broker routes the messages to various commands, but it may cause potential latency and during high demands, it may trigger broker capacity and limit scalability.
- Fault Tolerance and Performance: So, you might have already figured this out, having hidden layers as brokers will surely have some impact on the system performance in the long run.
As per my experience, the broker architecture pattern is very common with distributed systems with decoupled components. It generally has a central broker for inter-component communications. It allows a higher degree of decoupling, dynamic operations management, and layered communication facility amongst the components.
But as we were discussing, we saw the potential risk of a single point of failure, scalability bottleneck, and performance compromization. However, it is an unbeatable choice, for messaging systems and IoT hubs within other modern software architecture patterns.
Space-based Architecture Pattern
The concept of Space-based Architecture (SBA) is very unique, it organizes the systems into isolated spaces. Each of the spaces would be functioning with its own data, business logic, and user interface if any. These spaces communicate with each other by message-passing protocols.
SBA offers a hassle-free setup for distributed systems, high scalability options, operational data segregation, and better processing of data using distributed compute nodes in a space-like grid.
Use Cases
- High-Performance Computing: Space-based Architecture (SBA) enables us to offload the computational tasks across multiple nodes or clusters. As a result, we get higher performance in terms of resources.
- Real-Time Analytics: If we configure inter-node communications along with data streams we can get amazing results in real-time analytics or real-time data transmissions.
- Multiplayer Online Games: It’s an awesome solution for the online multiplayer environment, where various groups can get segregated spaces automatically, and with segregated nodes, we get better CPU performance for massive concurrent players.
Advantages
- Isolation and Autonomy: Each node provides isolated space, which makes the testing deployment and system evaluation easier for the testing team.
- Scalability and Performance: As we know so far, each of the nodes is separated by isolated space, it gives us better control over scalability and its performance because we can upscale or downscale each node as per the requirement and based on the compute power required for any specific node.
- Better compute power: With the ability to have scalability at individual nodes, we can fine-tune specific nodes with higher or lower CPU configuration achieving better and much optimised CPU power.
Drawbacks
- Complexity: You can already sense that more the spaces we have more complex it becomes. But projects, tend to have multiple spaces and if not planned properly it could be challenging in terms of communication also, you might find yourself in potential coupling between nodes.
- Data Management Challenges: The more complex your system gets, you will get hard times, synchronizing the data between spaces.
- Network Latency Impact: As the spaces need to communicate with each other, for data retrieval it might get costly sometimes in terms of latency which will directly affect overall system performance.
The Space-based Architecture Pattern is a strong candidate for setups needing easy isolation and higher scalability. It provides better business logic and a better grip on resource usage. Even though the sweet aspects, it can be harder to maintain and can get complex to understand and modify stuff. But in terms of scalability, it stands out from the other Modern Software Architecture Patterns.
Master-slave Architecture Pattern
The master-slave architecture pattern is a popular distributed pattern that has a central master node and multiple slave nodes. The master node is responsible for arranging and organizing the task execution and slave nodes simply follow the instructions given by the master node.
This architecture pattern can offload the workload efficiently among the slave nodes. The master node serves as the entry point where all the requests/tasks come in for execution and delegates tasks to the slave nodes based on several parameters. On completion, slaves inform the master node along with the task result, and the master aggregates and generates the final result.
Advantages
- Data-Intensive Processing: As we were discussing the master-slave architecture delegates tasks among the slave nodes sort of like distributed processing. This is why it has been adopted by some popular frameworks like Hadoop, Spark, etc for efficient and optimized analysis of huge datasets.
- Enhanced Availability Systems: The master node can have multiple slave nodes right? Now suppose one of the slave nodes got offline. Still, it won’t be a show-stopper for it. Master can understand the situation and will get the tasks done, using other slave nodes without any noticeable issues.
- Real-Time Analytics: You might have already guessed it, the more slave nodes you have more CPU power you possess. It can be beneficial when you need to process huge datasets requiring intensive CPU power.
- Scalability & Load Balancing: The beauty of this architectural pattern is you can get quick and easy horizontal scalability just by adding mode slave nodes. And load balancing was already there and provided by the master node.
- Fault Tolerance & Redundancy: As we already discussed by leveraging multiple nodes we have to get things going even if we lose some of the slave nodes. This increases the fault tolerance of the system.
Drawbacks
- Synchronization Challenges: Having multiple nodes is awesome but synchronizing and communicating with the master node may get a little tricky sometimes.
- Physical Bottleneck: Theoretically we can have an unlimited number of slave nodes but in reality, one master node will be able to handle a certain number of nodes. It varies based on master node CPU power, communication bandwidth available, etc.
In distributed computing, in many cases, architects prefer master-slave architecture patterns for scalability, fault tolerance, and better CPU power. I have seen this pattern being used most in data-intensive applications like analytics, classifications, predictions, etc.
Pipe-filter Architecture Pattern
In the Pipe-Filter Architecture Pattern, data flows through a series of filters arranged within a pipeline. These filters are nothing but business logic for data transformation or data processing in stages. You can think of these filters as components, which are designed for data processing. The communication channels between the filters are represented as pipes here. In this architectural pattern, we get to see a sequential flow of the data, from one filer to another.
Use Cases
- Image Processing: Image processing is a CPU-intensive task. And most of the image processing algorithms you pick, require sequential processing of the image data. May it be JPEG to PNG conversion, image background removal, etc. As the Pipe-Filter Architecture Pattern has the inbuilt property of sequential data flow, it becomes the best fit for such a workload.
- Data ETL (Extract, Transform, Load): When you need to transform datasets for a certain format, and you want to feed the intermediate transformations to other systems in those cases you can choose this pattern.
- Audio Signal Processing: Similar to image processing, audio is also very delicate to process and needs exact steps to follow to get the desired result.
Advantages
- Loose & Flexible Coupling: You can develop or maintain the filters without bothering about the other filter you have in the pipe giving you the power to design a loosely coupled and flexible system.
- Parallel Processing: One filter processes the data and passes it on to the next filter. However, the first filter can be utilized to process the next data. Hence we can process data parallelly.
- Reusability & Modularity: Filters are concise code base that works independently right? We can reuse the filters in any place wherever required.
Drawbacks
- Performance Overhead: If you create new filters for every small operation, in that case, the flexibility of the system may become a performance and computational obstacle.
- Inapt for Interactive Systems: Pipe-based architecture pattern is not generally used for interactive systems, as data needs to pass through multiple filters.
- Long-Running Computations: This architecture pattern is focused on an optimized data process. It is generally seen that every filter does significant log running jobs, and as a whole, it takes time to get the final output from such systems.
The Pipe-Filter Architecture Pattern performs well in transforming and processing data by passing it through several sequential filters to get the desired output. However, do not pick this pattern for interactive systems. If you have some background tasks to complete or image processing to perform, then you go with this pattern.
Peer-to-peer Architecture Pattern
Peer-to-Peer (P2P) Architecture Pettern is a decentralized concept where each node acts as a client and server at the same time. P2P nodes directly exchange resources without any need for a centralized server. One of the big names in the tech world “BlockChain” is based on the P2P principle.
Use Cases
- Decentralized File Sharing: Peer-to-Peer (P2P) Architecture Pettern can allow users to share files directly without a need for a conventional central server. Examples are BitTorrents or Skype-like services.
- Blockchain Networks: Cryptocurrency is widely accepted tech around the world due to its decentralized nature in transactions and maintaining distributed ledges across various nodes around the globe. The transactional data is neither owned by the node owner nor the government. Nobody can temper any record, and even it the data gets modified on one node after getting votes from other nodes error gets solved automatically.
Advantages
- Decentralization and Resilience: As we do not need any centralized server in P2P architecture we can achieve easy decentralization, fault-tolerance, and network resilience.
- Resource Efficiency: The P2P node can share the hardware resources over the network, which has the potential to boost task efficiency and cost-effectiveness.
- Distributed Computing: P2P utilizes CPU resources from all the available nodes which enables parallel processing and better CPU efficiency.
- Enhanced Data Privacy: P2P nodes are hosted by users like you and me! It does not rely on a central server making it harder to penetrate. Every node has the data but they do not own it. Making it super secure and private.
Drawbacks
- Service Quality Variations: As the nodes are not owned by some company or authority we often find difficulties getting a consistent performance sometime.
- Security Challenges: P2P nodes communicate with each other through various ports and protocols. If not audited properly P2P systems may become vulnerable to various cyber threats.
- Interface Dependencies: Accessing the shared resource is great but accessing different resources might require specific interfaces or drivers. So some cases we may find it hard times to connect due to a lack of drivers.
Peer-to-peer systems are the most suitable solutions for collaborative and distributed services or networks. Because of its decentralized nature, it provides better scalability, privacy, and resource optimization options. But you need to keep this in mind, that building a P2P service from the ground up is hard. You won’t be getting enough individuals who want to plug their system into your network at the beginning. Otherwise, you need to rely on other 3rd party P2P providers. Along with the potential flaws you need to decide whether you need P2P architecture or not.
Blackboard Architecture Pattern
The Blackboard Architecture Pattern is not very common in normal applications but it is used in AI systems to solve problems. In this pattern, multiple agents, specialized in different things, collaborate within and share a knowledge base, and provide insights on the given problem trying to solve it together.
Use Cases
- Medical Diagnosis: In the medical field, we are witnessing AI systems. We can make them solve certain problems like analyzing CT scan reports, X-ray reports, etc. Having multiple agents specialized in different things can provide input about the reports and by aggregating the insights we can get a prediagnosis of any report.
- Scientific Data Analysis: We can use such systems and find patterns, analyze, and forecast insights from real-life datasets.
- Natural Language Processing: In NLP systems, we aggregate several agents capable of various things, like audio processing, intent classification, linguistic knowledge, etc.
Advantages
- Modular Problem Solving: AI agents can give different insights from their training knowledge and by having multiple agents the system can mimic human-like analysis.
- Decentralized Information Flow: This architecture pattern facilitates agent-to-agent communication through various communication channels providing information flow.
- Adaptability and Flexibility: We can allocate roles to different agents and get output from them from different perspectives.
Drawbacks
- Resource Inefficiency: Agents and nodes can be situated in distant places and may be difficult to transmit data from/to them introducing latency in the system.
- Potential Information Overload: In some cases, we may find these agents give unnecessary information resulting in unreliable and out-of-context analysis.
- Dependency on Network Structure: In this pattern, agents communicate with each other the share various information but this communication is random and essential at the same time for generating conclusions, hence effective network connectivity and communication channels can be a performance bottleneck.
Dynamic and collaborative agents with specialized knowledge bases can get to meaningful conclusions for a dataset. To achieve such abilities we don’t need to develop a specific algorithm but it gets identified by these agents on its own.
Nowadays we are witnessing AI chatbots like OpenAI’s ChatGPT, Antorpic’s Claud, Google’s Gemini, etc. can process images, audio, files, etc. along with the normal text. They are multi-model GPTs, right? What are these? These are examples of Blackboard Architecture Patterns only.
Before you go with this pattern you need to consider that these models are great but can mess things up quite easily and conclusions may come completely out of context. Considering Blackboard Architecture Pattern should be a strategic choice for the application you are building, it should not be a choice influenced by the trend.
Component-Based Architecture Pattern
Component-Based Architecture Patterns are common in GUI frameworks, SDKs, and service-oriented systems. As per this architecture concept, you create different reusable components that you can use in multiple places to accomplish the task in a unified and controlled manner. The major advantage of this type of concept is this, if you want the system behavior in one place and you have created a component for it, and changed it in the comment itself you get the desired flow in all the places.
For example, you will find such concepts in some front-end frameworks like React, Angular, Vue, etc. Also, in backend systems like Laravel, NextJs, NestJs, etc, we see a component-based approach for better optimization and maintenance.
Use Cases
- Graphic Design Software: Graphic designing tools like drawing, photoshop, etc, or tools like image filters, text editing, etc, typically have similar functionality to perform. In such cases having components for redundant functionalities can make a significant difference in terms of software design and maintainability.
- GUI Library: Simple GUI components like buttons, text fields, header texts, etc, or compound components (multiple simple components bundled together) can provide a faster and more structured development experience.
- Financial Software Suite: In financial software suites, many things need to behave the same wherever they are used. While building such applications having such components helps to developer a lot.
Advantages
- Modular Design: Having reusable components makes code more maintainable and well-structured.
- Extensibility: Simple components can be combined and you get compound components. By doing so you can get a reusable and modular functionality as a complete component. Use it anywhere you get the same flow and effect.
- Encapsulation & Independence: To use the component you don’t need to know every detail of it. You just need to know how to pass the required parameters, and how we get the output out of it, hence providing encapsulation as you know now components are self-contained making them independent as well.
Drawbacks
- Over-fragmentation Challenges: In component-based architecture people tend to create a lot of components, which eventually make things over-fragmented, imposing more complexity and harder manageability.
- Customization Limitations: One of the main reasons for this architecture is reusable components. However reusable components are much more restrictive as they need to be more generic.
- Complexity in Maintenance: Having multiple components in your project can make simple modifications harder and make things a bit more difficult to maintain.
Component-based Pattern allows a modular, reusable approach to your development. This can be very efficient. These components are kind of ready-to-use code blocks, encapsulated in nature. If you create basic components properly in the long run you will get a very stable and extensible system with minimal effort.
Adoption of this pattern is very common in the projects nowadays. Even if it may introduce some degree of complexity, I prefer to go with it as it gives me a more structured and maintained codebase to work on. Also due to the encapsulation of the components, I don’t always need to worry what a junior dev will do with it. Just explain to them the functionality, and how to use it, and relax.
Service-Oriented Architecture (SOA) Pattern
Service-Oriented Architecture (SOA) works on the fundamental principle of discrete business capabilities within individual services. These services have inter-service communication in-build and generally come with a great user interface for management. Such architectures are good if you intend to create your very own software ecosystem.
Use Cases
- Business Process Optimization: By providing common functionalities like log management, user authentication, backup, etc, SOA streamlines the development process and provides the users with a seamless experience.
- Healthcare Systems Unification: In the health sector, you will see the use of SOA is quite common. With such an ecosystem you can have basic things as a package and also with a strong interconnectivity between the systems, doctors can derive much better insights from the data.
Advantages
- Faster Time-to-Market: In this architecture pattern, a service is a full-blown software solution that can do its specified job with great precision. Having such a reliable system is super useful in terms of development time and costs for other developments. You as an organization will be able to move 10x faster than your competitors.
- Efficient Maintenance: The modular and self-contained nature of the services promotes better efficiency in maintenance. Generally, organizations do have a dedicated support team for each of the services.
- Adaptability and Interoperability: Loose coupling is surely one of the major reasons for its adoption but having an eco-system can make things a lot easier and quick to set up.
Drawbacks
- Design Complexity: As we have seen with other patterns, more the independent services you have more harder to maintain. It holds for this one as well. Having too many services may cause issues sometimes,
- Versioning Challenges: Each of the services are independent software solution right? So companies keep on developing the services and releasing new versions. But sometimes with major version releases, it may become incompatible with the older version of the other services in the ecosystem.
SOA is one of the most potent patterns to follow if you want to sell your software as a SaaS product. It provides you the advantage of rapid development, efficient maintenance, and an ecosystem for your customers.
Also keep in mind there are already big giants providing SaaS products from basic services like storage, authentication, etc. For being on the win-win side you might not always compete with them but having in-built integration can boost your product’s acceptability. At the same time, you can keep on building a future-ready software ecosystem.
Monolithic Architecture Pattern
Monolithic Architecture is one of the legacy concepts and execution patterns. Most of the very old systems you find, are using monolithic architecture. These types of applications use a single database, and single codebase and are deployed as a unified node.
Use Cases
- Small to Medium Web Applications: Setting up a monolithic application is a lot easier task than going to the ATM and trying to recall your PIN. Small or even medium businesses still prefer to go with Monolithic Architecture for their websites.
- Rapid Prototyping: Developers prefer monolithic architecture for building POCs or testing things, as it does not require a lot of time, and they can concentrate on the actual thing they are working on.
Advantages
- Fast Development: The major advantage is the super fast environment setup.
- Simplified Testing and Debugging: Having just one codebase and no fancy complexity it provides a very simple testing and debugging environment.
Drawbacks
- Limited Scalability: Monolithic applications get deployed in a unified system. If you run it for production, and you feel to scale it up, then there is no other option other than buying bigger resources. Which apparently will cost you more when the resource demand is not much.
- Complex Updates: Updating the software is hard. To update it you need to update that single codebase right? Meaning, you need to know what you are doing and what can potentially break your system.
- Dependency and Rigidity: As we just discussed, having such a monolithic app, is like having a very dependent and rigid system. As no code change can be considered independent. One simple change may cause a completely faulty system.
Whether you should use it or not? If you ask me, I would say, try to avoid it if you are making something big. But in cases like POCs, a portfolio website, a simple blog, or something then you may choose this.
Legacy systems were built using this pattern only. But companies understood the pain points. Most of them are already in the process of getting the thing in more modern software architecture patterns like microservice architecture, SOP, or Microkernel architecture, etc.
Hybrid Architecture Pattern
Hybrid Architecture is not some new concept but, is a combination of multiple architectural patterns, like event-driven architecture, microservice architecture, or maybe pipe-filter architecture whichever provides a better solution to your project’s problem statement.
Use Cases
- Complex Enterprise Systems: In bigger organizations, can see very complex systems they use or build. And we as developers can never ensure that sticking to one architectural pattern will be enough for the system. We typically would have no other option than having multiple architectural patterns.
- Scalable Applications: When you pick the best combination of architectural patterns to solve various challenges, you are doing nothing but making the system much more scalable than any other system.
- Real-Time Analytics: Having multiple architecture options at the same time, you can use different methodologies for various data processing and real-time analytics.
Advantages
- Flexibility and Scalability: Cases I have seen where organizations like to have public clouds (ex. AWS, Azure, GCP, etc.) as well as an on-premise or private cloud at the same time. They can use different architectures and get the most out of such setups.
- Seamless Migration: Based on the system load many a time you may need to offload tasks between several clouds including on-premise or private cloud setups. So you need to plan how to offload your processing with various architecture options as problem solutions.
- Cost Optimization: When companies use private clouds, it is mostly because they need an extra layer of privacy and security. But it is much costlier than getting things done using public clouds. So having this flexibility where you can perform some tasks in the private cloud and some tasks in the public cloud saves a lot of money.
Drawbacks
- Complex Management: Different patterns come with their own set of problems, having multiple patterns simply adds up right?
- Integration Complexity: Interconnectivity and harmony between the different patterns is a tough thing. Need to have a solid plan beforehand.
The Hybrid Architecture Pattern is the most compelling solution among the other modern software architecture patterns. As we see the complex systems nowadays we have no choice other than using more than one pattern. Also, cost-effectiveness and better resource optimization are the absolute outcomes of such a design pattern.
However, keeping all the trade-offs in mind, opting for a Hybrid Architecture Pattern should be guided by specific requirements and not unnecessary.
So, Which of these Modern Software Architecture Patterns you are going to pick?
If you are reading this, let me thank you first for being with me till this point. So based on your project, which architectural pattern resonates the most?
Does the modularity of microservice architecture or Hybrid architecture fit the best, or the comfort of Monolithic Architecture is what you need?
However, the above explanation is a mere scratch on the surface of this topic. Also, let me know if you have encountered any other patterns which are not covered here. Your knowledge and experiences could be extremely beneficial in bringing light to lesser-known but equally influential approaches to software architecture.
Please let me know in the comment section below which architectural design best matches the goals of your project. Also please share your thoughts and ideas.
Happy coding my fellow devs! See you in the next one!