Solution architecture is the practice of designing and implementing software solutions that meet the functional and non-functional requirements of a system. It involves choosing the right technologies, components, interfaces, and patterns to achieve the desired outcomes. In this article, we will explore some common solution architecture patterns and when to use them.
The layered pattern is one of the most widely used solution architecture patterns. It organizes the software components into separate layers that have specific responsibilities and communicate through well-defined interfaces. For example, a typical web application may have a presentation layer, a business logic layer, a data access layer, and a database layer. The layered pattern allows for modularity, reusability, maintainability, and testability of the software components. However, it also introduces some drawbacks, such as performance overhead, complexity, and coupling between layers.
Microservices Architecture Pattern: Microservices break down a monolithic system into small, independently deployable services. This is ideal when you need agility and scalability. It's especially suited for large organizations with complex systems. However, it requires a robust DevOps setup Event-Driven Architecture Pattern: Event-driven architecture is all about handling events and messages. It's excellent for real-time systems, IoT, or any scenario where different parts of the system need to communicate asynchronously. Use it when you want to decouple components and improve responsiveness.
I agree in principle on the benefits of microservices architecture but in my experience it also needs to be taken with a bit of care. Complete decomposition will lead to more complex interconnected architectures which are actually hard to wield and manage, also introducing overhead through added communication through event and message stacks and corresponding security overhead when taking zero-trust principles into account. A middle ground between decomposition and monolith needs to be struck.
In software architecture everything comes with a cost and there is no perfect solution to a software problem. The answer is always it-depends. Coming to the layered architecture, depending on the complexity of the project it might be a good solution as long as it does not introduce Performance overhead or rigidity (flexibility to change a system for future needs). A balance should be struck when migrating a legacy stack to modern technology stack.
It is a traditional approach for segregation in terms of technologies (vs functionality) such as front-end and back-end. Its one more drawback is on security side, a layer compromise may result in entire system compromise, as layers may have interdependencies to constitute to a functional solution.
Streaming Pattern: The crucial idea is to keep data constantly flowing between platforms, using concepts like Enterprise Data/Service Bus. This approach also encourages streaming for batch data flow with a defined start and ending sequence. Kafka is taking a prominent role in many organizations as a standardized EDB platform. It integrates seamlessly with Cloud vendor products such as Azure Event Bus, AWS MSK, or GCP Pub/Sub.
The event-driven pattern is a solution architecture pattern that relies on the production and consumption of events to trigger actions and reactions in the software components. Events are messages that represent changes in the state or condition of the system or its environment. For example, a user clicking a button, a sensor detecting a motion, or a timer expiring are all events. The event-driven pattern enables the software components to be loosely coupled, scalable, responsive, and resilient. However, it also poses some challenges, such as event consistency, reliability, and ordering.
Event driven pattern to be used on the following scenarios 1. Fire and Forget. You publish an event and you dont have to understand the need of the consumer 2. Asynchronous request reply 3. Ordering of the events is not very important 4. Systems requiring high throughput 5. Scalability and Loose coupling 6. Auditing, replay capabilities etc.
Event Sourcing is particularly useful in situations where you need to maintain a full history of changes to a system's state, enabling features like auditing, replaying events, and supporting temporal queries. Instead of storing the current state of an entity, you capture every change or event that occurs in the system and store these events as a log. This approach is valuable when you need to maintain an immutable history of changes for legal or compliance reasons, or when you want to provide features like undo/redo functionality in applications. It's best suited for scenarios where you have a need for a comprehensive historical record of events and the ability to query and analyze data over time.
Event-driven architecture (EDA) is utilized by organizations to coordinate implementations across diverse regions and contexts. It enables efficient data replication, parallel processing, real-time monitoring, interoperability, redundancy, and integration with microservices. Despite its advantages, organizations need to be aware of performance considerations, challenges related to eventual consistency, and the increased complexity in implementation. A strategic approach to data information is essential for effective interactions within an event-driven system. In conclusion, while EDA provides valuable solutions for system coordination, careful consideration of potential challenges in performance, consistency, and complexity is required.
The microservices pattern is a solution architecture pattern that decomposes the software system into small, independent, and self-contained services that communicate through lightweight protocols. Each service is responsible for a single business capability or domain and can be developed, deployed, and scaled independently. The microservices pattern allows for agility, flexibility, innovation, and fault tolerance of the software system. However, it also requires more coordination, monitoring, security, and infrastructure management than a monolithic system.
It also provides flexibility to replace certain microservices technology debts with minimal impact on entire system. There are options to manage overhead for Infrastructure management, security and observability, with maturing orchestration layer solutions such as Kubernetes. Nevertheless great flexibility comes with great responsibility.
Microservices pattern to be used when we have clearly defined the boundaries of a service. This is awesome for future extensibility etc and can scale at ease with the container related technology stacks offered by cloud providers. This can become real pain where 1. There is no infrastructure supporting the needs of hosting. 2. Overhead of security when invoking 100s of services 3. trouble shooting and monitoring the failure when billions of messages are processed etc. 4. Not having a proper API management layer for self discovery, API registration and security token generation. This pattern should be carefully chosen. The cost of infrastructure can easily shoot-up when used incorrectly.
The microservices pattern also allows for microsegmentation by enforcing separation of duties, and by reducing the blast radius for each microservice.
Microservices excel in scenarios requiring high scalability, agile updates, and zero-downtime deployments. Adoption demands clear reasons, starting with small changes. They benefit independent deployment, scaling, data partitioning, technology diversity, and autonomous teams. Challenges include communication complexity and resource demands, particularly impractical for small applications. Avoiding anti-patterns, such as neglecting clear reasons for adoption or overlooking communication challenges is crucial for successful adoption, emphasizing thoughtful consideration of benefits and pitfalls.
The serverless pattern is a solution architecture pattern that leverages the cloud computing services to run the software components without managing the underlying servers or infrastructure. The software components are executed on demand by the cloud provider in response to events or requests. The serverless pattern reduces the operational complexity, cost, and overhead of the software system. However, it also introduces some limitations, such as vendor lock-in, cold start, statelessness, and debugging difficulties.
I do agree that serverless is a solution architecture pattern and has many, many benefits. I would caution about enumerating limitations as we are always innovating and improving. Having worked at GCP, MS Azure, and now AWS, multi-cloud is increasingly common and therefore is being addressed by a more agnostic management layer to give visibility into all clouds. At AWS we have also engineered our Lambda functions to no longer have a cold start, and there are many tools to do debugging. There are also platforms running on cloud that totally abstract the vendor cloud and enable seamless migrations of your data at any time to any cloud. In summation, going serverless will truly accelerate cost savings and the ability to innovate.
Serverless in action Suppose a company wants to build a new e-commerce website to sell their products online. They decide to use a serverless architecture. The company starts by creating a static website that is hosted on a CDN. They use HTML, CSS, and JavaScript to create the user interface of the website. Next, they decide to implement a serverless function using AWS Lambda to handle the checkout process. They use Node.js to write the function, which handles the user's order and payment details. They then integrate the Lambda function with an API Gateway to expose it as a REST API. The company also needs to store customer orders and product information, so it chooses to use a serverless database like Amazon DynamoDB.
Serverless (architectural approach not pattern) computing provides scalable, flexible, and cost-effective solutions, freeing organizations from backend server concerns. While suitable for various scenarios like variable workloads and frequent updates, it's not universally applicable and depends on project specifics. Serverless architecture simplifies DevOps processes and is effective for applications with specific requirements. However, in cases of consistent workloads, opting for dedicated servers may be more cost-effective. Migrating legacy applications to a new infrastructure with different architecture can pose challenges in certain scenarios.
The CQRS pattern is a solution architecture pattern that separates the read and write operations of the software system into different models and components. CQRS stands for Command Query Responsibility Segregation. The commands are used to modify the state of the system and the queries are used to retrieve the state of the system. The CQRS pattern enables the software system to handle high volumes of read and write operations with different performance, scalability, and consistency requirements. However, it also adds some complexity, synchronization, and consistency issues to the software system.
CQRS (Command Query Responsibility Segregation) is an architectural approach beneficial for web and cloud applications, emphasizing scalability by separating data handling for writing and reading. Suited for scenarios where reading is predominant, especially when coupled with event sourcing and DDD, CQRS offers a strong foundation. However, caution is advised for small applications or those are mission critical and/or impacting life/health, where alternative approaches may be better. Anti-pattern scenarios involve potential over-engineering, increased maintenance, and performance issues, demanding careful consideration for suitability.
This pattern also takes care some of the security concerns in terms of segregation of models/components, essentially providing a way to reduce attack surface. E.g. Read external calls and write internal calls to the system.
In my projects I have found that the CQRS pattern proves most helpful for separating read/write access as a security measure. This also brings the security decision along in the project and eases development considerations for security. As you can't write data with functionalities designed to be read-only one can be confident and rely on the segregation after this approach has been selected. No need to always stop and think about it from the ground up for every component separately.
In my experience not one solution fit all, it can be combination of solution mentioned above. The end goal should be a solution which is self sustain, self healing and resilient. Which is easy to maintain and able to adopt new changes quickly
Mature enterprise architecture (EA) practices have likely implemented an enterprise architecture repository, to model and store artifacts and relationships for data, application, services, and technology architectures. Solution architectures and their reference patterns should be modeled in the EA repository as well in an effort to drive consistency and reusability across the enterprise.
N-Tier Architecture Pattern: In N-Tier, the application is divided into logical tiers like presentation, business logic, and data access. It's suitable for scenarios where you want to maintain a clear separation of concerns. Big Data Architecture Pattern: This pattern is designed for handling large volumes of data. Use it when dealing with analytics, machine learning, or large-scale data processing. Components like Hadoop and Spark are often part of such architectures.
Facade pattern is another useful architecture style to go through phased modernization journeys. It involves abstracting the complex and legacy architecture/components behind a facade layer.
The CUPID (Configure, Upgrade, Prototype, Integrate, Develop) solution architecture methodology prioritizes off the shelf and pre-built technologies to reduce cost of solution implementation and ownership, lower solution time to completion, and benefits from platform improvements over time.