Overview:

In this blog post, we will delve into various software design and architectural patterns, including Layered, Microservices, Event-Driven, Cloud-native, CQRS, and Clean architecture.

We will also explore different viewpoints on architectural patterns, such as By connection (Rest, BFF, RPC, P2P, SOA), By composition (Microservices, Monolith, Layers, etc.), and By events (Pub/Sub, CQRS, Event Sourcing, reactive).

Additionally, we will discuss design strategies used in software architecture, including architectural styles (Microservices, Monolith, Event-Driven) and architectural patterns (MVP, MVC, MVVM, Pub/Sub).

The focus will then shift to Microservices Architecture, examining the critical elements of Microservices Architecture (Load Balancer, CDN, API gateway, Monitoring/Management) and the typical Microservices Architecture.

Lastly, we will explore success stories of Microservices and delve into case studies of failed Microservices.

Architectural Patterns:

Top 10 Architectural Patterns – by Dr Milan Milanović (techworld-with-milan.com)

Software design defines a software system’s architecture, components, modules, and interfaces to meet specified requirements. During the software design phase, we create a high-level view of the system, including its structure, behavior, and interactions with other systems. This involves making decisions about the software’s internal structure and organization.

The output of the software design process is a set of design documents or diagrams that serve as a blueprint for the software development team to follow. By following the software design plan, developers can ensure that the final product meets the stakeholders’ requirements and is high quality.

Over time we saw some common software architecture patterns emerge. They are reusable solutions to common design problems. They are often used as a starting point for designing software systems and provide a set of best practices for solving specific architectural issues. They enable us to reduce design complexity, increase system maintainability and reduce development time. In addition, they capture best practices and proven solutions for designing reliable, scalable, maintainable, and extensible software systems.

There are many software architecture design patterns to know, but some of the most important ones are:

1. Layered Architecture: This pattern is based on dividing the application into logical layers, where each layer has a specific responsibility and interacts with the layers above and below it. A typical application could have Presentation, Business, and Data Access Layers.
2. Microservices Architecture: This pattern is based on decomposing the application into small, independent services that communicate with each other through well-defined APIs. Each service is self-contained and should implement a single business capability. The services may communicate with each other via APIs and can be independently deployed.
3. Event-Driven Architecture: This pattern uses events to communicate between different components or services, where events trigger actions or reactions in the system. Events are processed asynchronously, meaning the system does not stop to wait for the handling of an event, increasing overall efficiency. This pattern is handy for applications that require real-time responsiveness and scalability.
4. Space-based Architecture: is a software design method that centers the system’s structure around the idea of “spaces,” independent and autonomous units. High-volume distributed systems often use it to overcome data bottlenecks, network latency, and grid failure. It divides the processing and storage between multiple servers, eliminating the need for a central server and, thus, the problem of a single point of failure.
5. Microkernel Architecture (plug-in architecture): this is an approach where the kernel provides minimal functionality, and services are implemented as separate modules outside the kernel (plug-ins). This separation allows developers to add, modify, or remove system components without impacting the core system functionality.
6. Peer to Peer Architecture: this is a decentralized model where nodes in a network can act as both clients and servers, allowing for distributed sharing of resources and information without the need for a central authority. P2P is used in blockchain technologies or file-sharing networks like BitTorrent.
7. Cloud-native Architecture: this is a pattern where applications are developed and deployed to run on cloud platforms, leveraging cloud services and infrastructure for scalability, reliability, and agility. It’s characterized by containerizing services, allowing for maximum portability and resilience; microservices, a design that separates functionality into modular components; orchestration, managing these containers and services; and declarative APIs, which express the system’s desired state.
8. Command-Query Responsibility Segregation Architecture (CQRS): This pattern separates the command and query responsibilities of an application’s model, making it easier to scale and optimize the application. The command side handles creating, updating, and deleting operations, while the query side handles queries. This can significantly improve performance, simplicity, and scalability.
9. Hexagonal Architecture (Ports and Adapters): This pattern separates the application into an inner and outer layer, where the inner layer contains the business logic and the outer layer includes the interfaces with the outside world. It allows an application to equally be driven by users, programs, automated test or batch scripts, and to be developed and tested in isolation from its eventual run-time devices and databases.
10. Clean Architecture: This pattern emphasizes separating concerns and decoupling components, making it easier to maintain and change an application over time.

Architecture Patterns from different points of View

We identified some architectural styles and patterns in different categories based on a view of them:

1. By Connections – a way how to connect devices and applications.
   1. REST (Representational State Transfer)
   1. BFF (Backend for Frontend)
   1. RPC (Remote Procedure Call)
   1. P2P (Peer to Peer)
   1. SOA (Service-Oriented Architecture)
2. By Composition – how we compose internal structures.
   1. Microservices
   1. Monolith
   1. Microkernel
   1. Layers
   1. Plugins
   1. Components
   1. Nano services
3. By Events – use it to decouple software components in an event-driven manner.
   1. Publish/Subscribe
   1. CQRS (Command Query Responsibility Segregation)
   1. ES (Event Sourcing)
   1. Reactive
4. By Stream – how data flow through our system.
   1. Pipe and Filters
   1. Message Brokers
   1. Fast Data
5. By Data – for data-oriented systems.
   1. Data Mesh
   1. Data Warehouse
   1. Data Fabric
   1. Data-centric Business Logic
   1. Data Lake
6. By Purpose
   1. API Platform
   1. Integration Hub
   1. Developer Portal
   1. Developer Platform

Architecture Styles, Patterns, and Design Patterns

We can frequently see the need for clarification between architectural styles, patterns, and design patterns. These terms often need clarification, and different people give them different meanings. Let’s define it a bit more precisely.

Architectural Styles

Architectural Styles and Architectural Patterns are both design strategies used in software architecture. They provide a means of expressing high-level design solutions, but they do so at slightly different levels of abstraction.

Architecture Styles are the highest level of abstraction where architectural designs instruct us on how to structure our code. They provide a set of constraints and guidelines that can help define the system’s overall structure and high-level modules and how they relate to and interact with one another. Examples of architectural styles include:

• Monolith
• Layered
• Event-driven
• Self-contained Systems
• Microservices
• Space-Based

Architectural Patterns

Architectural Patterns represent a way to implement an architectural style so we can do this regularly. Some examples are how to separate the user interface (UI) and data, how internal modules interact, and what layers we will use. Patterns answer these types of questions. In addition, they usually impact the code base and how to structure the code inside. Examples of architectural patterns include:

• Model-View-Presenter (MVP)
• Model-View-Controller (MVC)
• Model–View–ViewModel (MVVM)
• Repository Pattern
• Publish-Subscribe Pattern

Design Patterns

Design Patterns are different from architectural patterns in that they focus on a smaller area of the code base and have a minor influence (focus on a local problem). Nevertheless, they are specific solutions to common problems in software design. These might include limiting the creation of a class to only one object or notifying all dependent things when the internal state of an object is changed.

We have three groups of patterns:

• Creational: Factory Method, Builder, Singleton, etc.
• Structural: Adapter, Bridge, Decorator, etc.
• Behavioral: Command, Iterator, State, Strategy, etc.

Check the complete list of patterns here: Design Patterns by Refactoring Guru.

Architecture Styles, Patterns, and Design Patterns

The Architecture Of Open Source Applications (Free E-book)

The authors of 40 open-source apps explain the structure of their software and its purpose in these two publications. What are the main elements of each program? How do they communicate? And what did their creators discover as they evolved? The authors of these works offer fascinating insights into their thought processes in their responses to these questions.

You can learn here about the architecture of:

• Firefox Release Engineering
• Git
• Ngnix
• Puppet
• ZeroMQ
• CMake
• The Hadoop Distributed File System
• The NoSQL Ecosystem
• Selenium WebDriver
• High-Performance Networking in Chrome

Check the complete book here.

The Architecture of Open-Source Applications

What is Microservice Architecture?

Have you ever wondered why companies like Netflix and Amazon seem to roll out features at the speed of light? The secret might be hidden in their tech stack based on Microservice architecture.

At its core, Microservice architecture is about breaking down an application into a collection of small, loosely coupled services. Each service runs a unique process and communicates through a well-defined API. Each service is a separate codebase, which can be managed by a small development team and deployed independently.

Sam Newman defined Microservices in his book “Building Microservices” as:

Microservices are small, autonomous services that work together.

Microservices architecture is the best fit when we have applications with high scalability needs, many subdomains, and possibly multiple cross-functional development teams. Take into account that organizational shape influences your architecture.

Critical Elements of Microservice Architecture:

1. Load Balancer: Ensures even distribution of incoming network traffic across various servers.
2. CDN (Content Delivery Network): A distributed server system that delivers web content based on the user’s location. It’s about bringing content closer to the end-user, making page loads faster.
3. API Gateway: This is a single entry point for all clients, and it directs requests to the appropriate microservice using REST API or other protocols.
4. Management: Monitoring and coordinating the microservices, ensuring they run efficiently and communicate effectively.
5. Microservices: Each microservice handles a distinct functionality, allowing for focused development and easier troubleshooting. They can talk with each other using RPC (Remote Procedure Call). Services are responsible for persisting their own data or external state.

When building such architectures, we should strive for every service to have a single responsibility with clear boundaries. The services should communicate asynchronously, have a separate database, and build+deployment per microservice.

Microservice architecture

Benefits of Microservice Architecture

• Scalability: Scale up specific parts of an app without affecting others.
• Flexibility: Each microservice can be developed, deployed, and scaled independently.
• Resilience: If one microservice fails, it doesn’t affect the entire system.
• Faster Deployments: Smaller codebases mean quicker feature rollouts.

Drawbacks of Microservice Architecture

• Complexity: More services can lead to a more complex system.
• Eventual Consistency: Maintaining consistency across services can be challenging.
• Network Latency: Inter-service communication can introduce delays.
• Error handling: When an error happens, it’s hard to debug why and where it happened.
• Wrong decomposition: If you do a bad decomposition of your monolith, you can develop a Distributed Monolith.
• If you want to learn more about microservices, check out the book “Building Microservices” by Sam Newman:
• “Building Microservices” by Sam Newman
• And the free book for .NET Microservice Architectures by Cesar de la Torre, Bill Wagner, and Mike Rousos:
• “.NET Microservices: Architecture for Containerized .NET Applications” by Cesar de la Torre, Bill Wagner, and Mike Rousos:
• Also, to handle different trade-offs when creating microservice architecture, we need to know patterns such as Circuit Breaker, Saga, etc. Check Design patterns for Microservices and monolith decomposition strategies.

What Are Microservices Really All About? (And When Not To Use It) (youtube.com)

What are Self-contained Systems (SCS)?

Self-contained Systems (SCS) are a software architecture approach that prioritizes the decentralization of applications into independent systems, each with its domain logic, UI, and data storage. Unlike Microservices, smaller services focused solely on business logic, SCS are larger and encompass a broader scope within a specific domain.

SCS are systems that represent autonomous web applications. They include web UI, business logic, and database and might have a service API. A single team usually owns them.

The main advantages of such systems are:

1. Autonomy: Each SCS operates independently with its database, business logic, and user interface.
2. Domain-aligned: SCS is structured around specific business domains, ensuring each unit represents a coherent and meaningful set of functionalities.
3. Decentralized Data Management: Individual databases per SCS ensure data consistency within its boundary, reducing cross-service dependencies.
4. Technology Diversity: Allows for different technology stacks to be used across other SCS, suiting the specific needs of each domain.
5. Explicitly Published Interface: Well-defined interfaces for interactions with other systems, maintaining a clear contract while preserving encapsulation.
6. Independent Deployability: Each SCS can be deployed, scaled, and updated independently without affecting other systems.

Why Self-contained Systems (SCS) has the edge over microservices:

• Broader scope: SCS has a broader scope encompassing the UI, business logic, and data storage within a bounded context
• Reduced Operational Complexity: Microservices can lead to high operational complexity due to the management of many smaller, interdependent services, while SCS is more significant and more autonomous.
• Data Consistency: SCS manages its data, which can lead to better data consistency within each system, while Microservices often rely on a shared data store.
• Reduced Inter-service Communication: SCS, by encapsulating more functionality, requires less inter-service communication than microservices.
• Better Fit for Certain Domain Complexities: In cases where domain complexities are high and domain boundaries are clear, SCS might provide a better architectural fit due to their domain-aligned nature.

Self-contained Systems (SCS)

Such systems go well along with Domain-Driven Design (DDD). The first step in creating such systems is domain analysis, which can be conducted by identifying bounded contexts that align with specific business domains. Each bounded context is then encapsulated within an SCS, which comprises its own data management, business logic, and user interface, ensuring each system is autonomous yet able to interact with others through well-defined APIs when necessary.

If you want to learn more about Self-contained Systems, check these resources:

• Self-contained Systems by INNOQ.
• Self-contained Systems YT Playlist.
• “Self-Contained Systems (SCS): Microservices Done Right,” InfoQ article.
• “Our journey from Microservices towards Self Contained Systems,” Swissquote article.
• Self-contained service at Microservices.io.

Monolithic vs Microservices?

The architecture of your application can have a big impact on how it works and how easy it is to manage.

In the world of software development projects, one of the critical decisions to make is choosing the right software architecture.

The architecture you choose will significantly impact your project’s scalability, maintainability, and overall success.

Two common ways to structure software are called monolithic and microservice architectures.

Monolithic Architecture:

A monolithic architecture is a traditional model of a software program that is built as a unified unit that is self-contained and independent from other applications.

The word “monolith” is often attributed to something large and glacial, which isn’t far from the truth of a monolith architecture for software design.

In this architecture, all the different components of the application, such as the user interface, business logic, and data access layer, are tightly integrated and deployed together.

This means that any changes or updates to the application require modifying and redeploying the entire monolith.


Microservices Architecture:
In a microservices architecture, an application is built as a collection of small, independent services, each representing a specific business capability.

These services are loosely coupled and have their own business logic and database with a specific goal. Updating, testing, deployment, and scaling occur within each service.

Microservices communicate with each other over a network, often using lightweight protocols like HTTP or messaging queues.

In summary, if you are building a small project, monolithic is good to go, but for a larger project with multiple different services for users, each doing a separate job, microservices work.

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Typical microservice architecture:

What does a typical microservice architecture look like?


The diagram below shows a typical microservice architecture.

🔹Load Balancer: This distributes incoming traffic across multiple backend services.

🔹CDN (Content Delivery Network): CDN is a group of geographically distributed servers that hold static content for faster delivery. The clients look for content in CDN first, then progress to backend services.

🔹API Gateway: This handles incoming requests and routes them to the relevant services. It talks to the identity provider and service discovery.

🔹Identity Provider: This handles authentication and authorization for users.

🔹Service Registry & Discovery: Microservice registration and discovery happen in this component, and the API gateway looks for relevant services in this component to talk to.

🔹Management: This component is responsible for monitoring the services.

🔹Microservices: Microservices are designed and deployed in different domains. Each domain has its own database. The API gateway talks to the microservices via REST API or other protocols, and the microservices within the same domain talk to each other using RPC (Remote Procedure Call).

Benefits of microservices:
– They can be quickly designed, deployed, and horizontally scaled.
– Each domain can be independently maintained by a dedicated team.
– Business requirements can be customized in each domain and better supported, as a result.

Over to you: 1). What are the drawbacks of the microservice architecture?
2). Have you seen a monolithic system be transformed into microservice architecture? How long does it take?
 

Activate to view larger image,

Microservices reference Architecture:

Microservices architecture on AKS – Rajeev Singh | Coder, Blogger, YouTuber (singhrajeev.com)

Advanced Azure Kubernetes Service (AKS) microservices architecture

Advanced Azure Kubernetes Service (AKS) microservices architecture – Azure Architecture Center | Microsoft Learn

DevOps for Microservices Architecture

CI/CD for microservices architectures – Rajeev Singh | Coder, Blogger, YouTuber (singhrajeev.com)

Microservices use case:

Evolution of the Netflix API Architecture

Evolution of the Netflix API Architecture (techworld-with-milan.com)

The highly scalable and loosely linked microservice architecture used by Netflix is well known. Independent services provide independent scaling and varied rates of evolution. Yet, they increase the complexity of use cases involving several services. Netflix provides a uniform API aggregation layer at the edge rather than making hundreds of microservices available to UI developers.

During that time, Netflix API architecture went through 4 main stages:

1. Monolith: A complete application is packaged as a single deployment unit.
2. Direct Access: This architecture enables client apps to hit the microservices, which is unsuitable for many clients.
3. Gateway Aggregation Layer: As they observed much duplicative data fetching, Netflix built graph API to provide unified abstraction on top of data and relationships.
4. Federated Gateway: As the number of consumers and the amount of data in the graph increased and the API team was disconnected from the domain expertise, they introduced a federated gateway to provide a unified API for consumers while giving backend developers flexibility and service isolation.
   
   Their GraphQL Gateway is based on Apollo’s reference implementation and is written in Kotlin.

Evolution of an API Architecture (Credits: Netflix)

Read more in link 1 and link 2.

Airbnb Microservice Architecture

Airbnb microservice architecture went through the following phases:

1. Monolith (2008 – 2017)

In the early years, from its inception in 2008 until around 2017, Airbnb operated on a monolithic architecture, primarily using a Ruby on Rails monolith. This setup initially served the company well, accommodating the full-stack engineers capable of handling end-to-end features within the single repository. However, as Airbnb experienced rapid growth and entered a hypergrowth phase, the monolith’s limitations became increasingly evident. The application grew tightly coupled and complex, leading to significant scaling challenges (such as drawing team boundaries over the codebase). Also, one of the most critical issues was the slow deployments, which escalated from taking minutes to several hours or even a day, severely damaging developer productivity. All of this influenced Airbnb’s decision to move to a Microservice architecture.

2. Microservices (2017 – 2020)

Categorizing services marked this shift into four distinct types:

• Data fetching service for data read/write
• Business logic service to combine data from multiple sources
• Workflow for orchestration of various services
• UI aggregation services to bring all of this to UI

A key strategy in this transition was the clear ownership of each service, with specific teams responsible for individual services. This restructuring led to a more specialized team setup, moving away from the full-stack approach to teams focused on the backend and specific data services. Despite the benefits of improved scalability and development speed, Airbnb faced new challenges in managing the complexity and dependencies of many services after a few years. The main issue was that to build an end-to-end feature, they needed to know different services, so multiple teams would need to be involved, and those teams needed to have the same priorities, which took time to manage.

Micro + Macroservices (2020 – Present)

In response to these emerging challenges, around 2020, Airbnb evolved its strategy to a hybrid model combining micro and macroservices. This approach centered on unifying APIs through a GraphQL interface, with a central data aggregator at its core (macroservice). The backend services were restructured to interact through this GraphQL interface, streamlining data flow and service interactions. This means that their backend services get data from the aggregator service (called service block), which then communicates with other microservices to gain data. Check the presentation in the comments.

Current Airbnb Architecture

With the microservice approach, we have a few disadvantages, too. The system is becoming complex to maintain and diagnose issues (logging and tracing). This is very important when dealing with microservices. Yet, we also saw something called “microservice bloatware,” even on Twitter:

But there are also many more examples where the microservices approach fails; if you’re not solving a problem at Netflix size, you probably don’t need microservices.

On the other hand, we have monoliths with a lot of lousy wording about them. But building monoliths doesn’t mean something better per se. In the last years, we often saw the identification of monolith with a big ball of mud architecture or purely building legacy code, which doesn’t mean to be. Yes, monoliths cannot scale or release independent pieces of the system separately, but those are mainly the most significant downsides. Still, you can create tremendous and high-quality code inside. Monolith brings us much less complexity, reduced network calls, more detailed logging, etc. Most subsystems of an entire application or system are stored in the monolith container. It is called self-contained since every system component is housed in a single container.

We can have architected monoliths that will fulfill all our use cases and requested architecture attributes in the system without dealing with the complexity of microservice architecture. The best example is Shopify, with over 3 million lines of code, one of the giant monoliths in the world. Instead of rewriting its entire monolith as microservices, Shopify chose modularization as the solution, while they served 1.27 million requests per second during Black Friday. But there are also more examples of monoliths, such as StackOverflow, Basecamp, or Istio. Also, recently, we saw that one team in Amazon (Prime Video) abandoned microservice architecture in favor of monolith.

We want to have separate modules and work on them but maintain simplicity to build a modular monolith. An adequately produced modular monolith can be a good step that can be more or less transformed into a microservice solution tomorrow if needed. So, the recommended path is Monolith > apps > services > microservices.

“You shouldn’t start a new project with microservices, even if you’re sure your application will be big enough to make it worthwhile.​” — Martin Fowler.

When we want to build a modular monolith, it is crucial to divide the system into manageable modules before assembling them into a monolith for deployment. As all communication between the modules might result in a cross-network call if you decide to break it into services in the future, high cohesion and low coupling are crucial in this situation. This means that all inter-module communication must be abstracted, asynchronous, or based on messaging for the modules to handle calls that travel across the network in the future.

How can we put in place such a concept? First, we create separate modules; each has its architecture, and those modules are pulled together into a single API gateway. This allows us to deploy the whole system as a monolith, but it will enable us to pull out separate modules into services if needed in the future.

What is a Distributed Monolith?

There are three types of monoliths:

1. Traditional Monoliths
   
   These are the most common monolith types, where everything is bundled together. Usually, we have some user interface, business logic, and data access layers in a single tier and deployment. There are no clear boundaries between domains, and the code will have shared libraries.
2. Modular Monoliths
   
   With modular monoliths, we have defined precise functional slices and dependencies, meaning we can have each module independent of the other. Yet, there will still be a single deployment unit and a single database. This is what we want to achieve.
3. Distributed Monoliths
   
   Here, we have one modern monolith variant, where our system is deployed like microservice architecture but built with monolith principles in mind. We usually get to this point while attempting to create a microservice architecture without considering some architectural and process changes needed. For example, we know we have a distributed monolith when some services cannot be deployed separately, are chatty, and cannot scale and share the same data source.

This is an obvious example of an anti-pattern. These systems are built like monoliths but deployed similarly to microservices. Here, we have the worst of both worlds: tightly coupled services with the complexity of microservices. Unfortunately, we usually get here by developing microservice architecture wrongly.

Monolith Decomposition Strategy

If we are stuck with traditional or distributed monoliths, we need to do some monolith decomposition. There are a few approaches that can help here:

1. Strangler Fig Pattern
   
   The Strangler Fig Pattern (Coined by Martin Fowler) comes from a collection of plants that grow by “strangling” their hosts. This pattern enables the replacement of specific functionality with new services. Here, we create a façade that intercepts requests going to the monolith and routes these requests to the monolith or new services. And we gradually migrate old functionality to new services, yet consumers always hit the façade.
   
   Here, you can also use Domain-Driven Design (DDD) to incrementally refactor the application into more minor services, where you first find ubiquitous languages (common vocabulary) between all stakeholders, then identify relevant modules to apply this vocabulary to them and define domain models of the monolithic application. In the last step, you define bounded contexts for the models, which are boundaries within a domain.
2. Branch by abstraction
   
   With this approach, we create an abstraction layer over our original component so that we can replace it step by step. Client requests are directed to this layer, allowing us to change everything behind it. The client will hit only the new component when we finish the changes. With this pattern, we can coexist two implementations of the same functionality, the true Liskov substitution principle. Although, like the Strangler Fig pattern, with this one, we are working on a bit lower level of abstraction, where our focus is more on components than the systems.

If you’re interested in this approach, I recommend the following book: “Monolith to Microservices,” by Sam Newman.

When Microservices are not good fit?

When microservices are not a good fit – Google Sheets

Code references:

https://github.com/dotnet-architecture/eShopOnWeb

eShop sample applications have been updated and moved to https://github.com/dotnet/eShop. Active development will continue there. We also recommend the Reliable Web App patterns guidance for building web apps with enterprise app patterns.

eShop Reference Application – “AdventureWorks”

dotnet/eShop: A reference .NET application implementing an eCommerce site (github.com)

A reference .NET application implementing an e-commerce website using a services-based architecture.

Recap:

Layered Architecture

One of the fundamental software design patterns is the layered architecture. In this architecture, the application is divided into multiple layers, each with its own responsibility and communication boundaries. The layers may include the presentation layer, business layer, data access layer, and infrastructure layer. Each layer provides a well-defined interface to the layers above it, allowing for efficient and modular development.

Microservices Architecture

The microservices architecture is a architectural style that has gained significant popularity in recent years. In this architecture, the application is decomposed into a large number of small, independent services, each focused on a specific business domain. These services communicate with each other through well-defined APIs, eliminating the need for tightly coupled dependencies. This architecture promotes scalability, flexibility, and easier maintenance.

Event-Driven Architecture

Event-driven architecture is a communication pattern that relies on events to convey information between components. Events can be generated by various sources such as user interactions, system events, or external systems. The events are sent as messages, which can be consumed by interested listeners. This pattern promotes loose coupling and event-driven development, enabling real-time and reactive applications.

Cloud-native Architecture

Cloud-native architecture refers to building applications that are designed to take advantage of the cloud computing services provided by platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). These architectures leverage cloud services, such as serverless functions, containers, and event-driven messaging, to deliver scalable and resilient applications.

CQRS (Command-Query Responsibility Segregation)

CQRS is a software design pattern that separates the data retrieval and modification operations into two different models. The command model handles changes made to the data, while the query model focuses on data retrieval. This pattern enables scalable and performance-oriented applications by allowing different services to process commands and queries independently.

Clean Architecture

Clean architecture is a software architecture pattern that promotes separation of concerns, separation of layers, and a clear definition of boundaries. It emphasizes the importance of dependency injection, interfaces, and interfaces for communication between components. This pattern promotes flexibility and reusability, making it easier to modify and maintain the application in the future.

Architecture Patterns from Different Points of View

Architecture patterns can be classified based on different perspectives. Some of the common classifications are:

1. By Connection: Architecture patterns can be classified based on their communication mechanisms. For example, Rest, BFF (Back-end for Front-end), RPC (Remote Procedure Call), P2P (Peer-to-Peer), and SOA (Service-Oriented Architecture) are architectural patterns that are commonly used to facilitate communication between components.

2. By Composition: Architecture patterns can be classified based on their composition. Microservices, Monolith, Layers, and other components are commonly used to compose an application architecture.

3. By Events: Architecture patterns can also be classified based on their event-driven nature. Pub/Sub (Publish-Subscribe), CQRS (Command-Query Responsibility Segregation), Event Sourcing, and reactive architecture are all examples of patterns that leverage events to drive the application behavior.

Design Strategies in Software Architecture

Design strategies play a crucial role in software architecture. Some commonly used design strategies include:

1. Architectural Styles: Different architectural styles, such as microservices, monolith, and event-driven, provide different approaches for organizing and structuring the software architecture. Each style has its own benefits and trade-offs.

2. Architectural Patterns: Architectural patterns, such as MVP (Model-View-Presenter), MVC (Model-View-Controller), MVVM (Model-View-ViewModel), and Pub/Sub, offer reusable solutions to common software design problems. These patterns help in achieving separation of concerns, maintainability, and scalability.

Conclusion

In conclusion, software design and architectural patterns provide structured approaches to designing and building software applications. By leveraging these patterns, developers can achieve scalability, flexibility, and maintainability in software development. The layered architecture, microservices architecture, event-driven architecture, cloud-native architecture, CQRS, and clean architecture are examples of widely used patterns. By considering different viewpoints, such as connections, composition, and events, developers can choose appropriate patterns based on their specific requirements. Design strategies, such as architectural styles and architectural patterns, further assist developers in designing robust and maintainable software architectures.

References:

What are microservices?