Tale of Software Architect(ure): Part 4 (Things Should Consider When Design/Architect a Software System)

Story:

In a busy town, there’s a popular online bakery run by a system of various components working together to ensure smooth operations. Each component has a special job, but they must interact each other to keep the bakery running efficiently.

At the heart of the bakery is data, like the ingredients for the bakery’s recipes. There’s data about customers, orders, inventory (flour, sugar, etc.), and payments. The bakery’s system stores this data in neat databases: one for customers, one for products, and one for orders. These databases are like the pantry, where everything is well-organized so the bakers can grab what they need quickly.

Whenever a customer places an order, the system must follow a precise recipe, where all steps must succeed together. This is a transaction, just like baking a cake where you can’t skip steps.

Behind the scenes, sometimes the interactions are synchronous—like when the baker asks the delivery driver, “Is the van ready?” and waits for a “Yes” or “No.” Other times, they are asynchronous—like when the baker sends a note to the kitchen to start baking and doesn’t wait for an immediate response, knowing the kitchen will get to it when they can.

Key Factor Consideration when Start Designing/Architecting a Software System:

1. Data

Data is often the most critical asset in a software system, as most applications revolve around storing, processing, and retrieving data. Managing how data is structured, stored, accessed, and transmitted plays a central role in the architecture.

Key Considerations for Data:

• Data Models: Choosing the right data model (relational vs. non-relational) is essential for meeting the system's needs. For example, relational databases (SQL) are great for structured data and transactions, while NoSQL databases are more suited for unstructured or semi-structured data.
• Data Integrity: Ensuring the consistency and correctness of data across the system is critical. This includes maintaining referential integrity in databases, data validation, and ensuring atomicity in transactions.
• Data Flow: Understanding how data moves through the system is crucial. This includes how data is created, read, updated, and deleted (CRUD operations), as well as how data transformations occur between different layers or services.
• Data Security: Sensitive data must be encrypted both at rest and in transit. Role-based access controls (RBAC) and proper data masking techniques should also be used to limit exposure.
• Data Scalability: As data grows in volume, the architecture should be able to scale effectively. This could mean introducing techniques like database sharding, partitioning, or using distributed databases.

Types of Data Need to Handle:

• Operational Data: refers to real-time data that supports day-to-day business operations. It is typically transactional, used in processes like order processing, inventory management, and customer interactions. It is frequently updated and used to run the business efficiently.
• Analytical Data: is used for analysis, reporting, and decision-making. It is historical or aggregated data, often extracted from operational data sources, and stored in data warehouses or analytical systems to uncover trends, patterns, and insights for strategic planning.

2. Transactions

Transactions are essential for maintaining data integrity, especially in systems where multiple operations are performed on the same set of data. In distributed systems or microservices architectures, managing transactions becomes even more challenging.

Key Considerations for Transactions:

• Atomicity: Transactions should be atomic, meaning they are all-or-nothing. Either all parts of the transaction succeed, or none do. This is critical to prevent data corruption or inconsistency in cases of failures.
• Consistency: After a transaction, the system should move from one valid state to another. This ensures that data adheres to predefined rules, constraints, and integrity.
• Isolation: Transactions should be isolated, meaning concurrent transactions should not interfere with each other. Isolation prevents issues like dirty reads, lost updates, and race conditions.
• Durability: Once a transaction has been committed, it should be permanently stored, even in the event of a system crash.
• Distributed Transactions: In a microservices or distributed system, different components might need to work together to complete a transaction. Coordinating transactions across these components requires mechanisms like two-phase commit (2PC) or eventual consistency models like Saga patterns.

3. Components

In software architecture, a component is a modular, self-contained unit that performs a specific function within a larger system. It is like a building block, with a defined role and a clear boundary, often encapsulating a set of related functionalities. Components interact with other components through well-defined interfaces or APIs to contribute to the overall functionality of the system.

Key Considerations of Components:

• Encapsulation: A component hides its internal logic and exposes only the necessary interfaces, making it easier to manage and reuse.
• Separation of Concerns: Each component is responsible for a distinct part of the system's functionality, reducing complexity by dividing tasks into smaller, manageable pieces.
• Reusability: Components are designed to be reusable across different parts of a system or even in other systems.
• Interchangeability: Because of clear interfaces, components can often be replaced or upgraded without affecting the rest of the system.

4. Component Boundaries

Component boundaries refer to the clear lines of separation between individual components in a software system. These boundaries define what is inside a component (its internal logic, data, and functionality) and what is exposed to the outside world (its interfaces or APIs). Component boundaries are critical because they enforce encapsulation, manage interactions, and ensure that components remain modular and maintainable.

Key Considerations of Component Boundaries:

Encapsulation:

• The internal details of a component, such as its data structures and algorithms, are hidden behind its boundary.
• Only the public interface or API is exposed, meaning other components interact with it without needing to know its internal workings.

Defined Interfaces:

• Components communicate across boundaries using well-defined interfaces, such as REST APIs, message queues, or method calls.
• These interfaces specify what services the component offers and how other components can access them.

Loose Coupling:

• Component boundaries help maintain loose coupling between components, meaning that one component can be modified or replaced without heavily impacting others, as long as the interface remains consistent.

Responsibility Separation:

• Each component focuses on its specific responsibility within its boundary, adhering to the principle of separation of concerns.
• For example, in a system, a User Authentication component only deals with login and authentication, while an Order Processing component focuses on managing orders.

Boundary as a Contract:

• A component boundary acts as a contract that defines what the component provides and what it expects. It ensures that interactions between components follow strict rules, making the system more predictable and robust.

5. Interaction Between Components

In any complex software system, different components must interact efficiently and reliably. These components may be individual modules, microservices, databases, or external APIs.

Key Considerations for Component Interaction:

Coupling and Cohesion: One of the principles of good architecture is to reduce coupling (interdependence) between components while increasing cohesion (internal consistency within a component). Loosely coupled components are easier to maintain and extend, as changes to one component have minimal impact on others.

Communication Patterns: How components communicate with each other can greatly affect system performance and reliability:

1. Synchronous Communication: In synchronous communication (e.g., HTTP calls, REST APIs), one component waits for a response from another before proceeding. This is simple but can lead to bottlenecks if a service is slow or fails.
2. Asynchronous Communication: In asynchronous communication (e.g., message queues, event-driven architectures), components can send messages and continue processing without waiting for a response. This is better for scalability and fault tolerance.
3. Messaging Systems: Tools like RabbitMQ, Kafka, and others can be used to decouple services, allowing for event-driven architectures and more resilient interactions between components.

State Management: If components share state, it’s important to ensure consistency. In distributed systems, eventual consistency models may be required where state synchronization happens over time.

APIs and Contracts: Each component should expose a clear API or interface that defines how other components can interact with it. These contracts should be stable to ensure that changes in one component do not break others.

Service Discovery: In a dynamic environment (e.g., microservices), components may be distributed across multiple servers or containers. Service discovery mechanisms allow components to find and communicate with each other without hard-coding addresses.

Load Balancing and Fault Tolerance: If one component needs to interact with another, load balancing ensures that requests are spread out to avoid overloading any single instance. Fault tolerance ensures that if a component fails, the system can recover gracefully or reroute requests to a backup instance.

Others Important Thinking Parts:

Keeping the above key factors in mind we should consider or drive the following things while start designing a software system.

1. Understand Requirements (Functional and Non-Functional)

• Functional Requirements: These are the specific features and functionalities that the software must provide to fulfill user needs (e.g., user authentication, data processing, report generation).
• Non-Functional Requirements: These include performance, scalability, security, maintainability, and usability. Understanding these early will help you design a system that meets both immediate and future needs.

Key Considerations:

• Engage with stakeholders to clarify business requirements.
• Capture both the short-term and long-term goals of the system.
• Balance functional features with non-functional attributes like speed, reliability, and scalability.

2. Modularity and Separation of Concerns

• A well-architected system should divide responsibilities into smaller, distinct components. This makes the system easier to develop, maintain, and scale.
• Separation of Concerns: Different aspects of the system should be handled by separate modules. For example, data access logic should be separate from business logic.

Key Considerations:

• Ensure each component has a single responsibility.
• Use modular design patterns (e.g., microservices, monoliths with clear boundaries).
• Avoid tight coupling between modules, which can make changes difficult.

3. Scalability

• Vertical Scaling (scaling up) involves increasing the capacity of a single server, while Horizontal Scaling (scaling out) involves adding more servers to handle increased load.
• Consider how the system will handle increasing amounts of data, users, and requests over time.

Key Considerations:

• Plan for both horizontal and vertical scaling.
• Use load balancers, distributed databases, and caching mechanisms to support growth.
• Choose scalable architectures like cloud-based services, microservices, or serverless approaches.

4. Maintainability and Extensibility

• Software systems evolve over time due to changing business needs or new technology. The architecture should be designed to be maintainable and easy to extend.
• Write clean, understandable code that other developers can modify.

Key Considerations:

• Follow SOLID principles to create a maintainable codebase.
• Use design patterns (e.g., factory pattern, repository pattern) to create extendable systems.
• Avoid over-engineering; build flexible solutions without unnecessary complexity.

5. Performance and Efficiency

• A system should be responsive and efficient under expected workloads. This includes optimizing code execution, database queries, and network communication.

Key Considerations:

• Use performance benchmarks to guide design decisions.
• Optimize algorithms and data structures.
• Consider caching mechanisms and lazy loading to improve performance.
• Profile the system early to identify potential bottlenecks.

6. Security

• Security is a critical aspect of software architecture. A secure system protects sensitive data and guards against potential attacks.

Key Considerations:

• Implement authentication and authorization mechanisms.
• Secure data in transit and at rest (e.g., using SSL/TLS encryption and data masking).
• Follow security best practices such as input validation, data sanitization, and regular vulnerability assessments.
• Use well-established security frameworks and libraries.

7. Data Management and Storage

• Systems often need to store and manage large volumes of data. Choosing the right storage solutions (e.g., SQL vs. NoSQL) and structuring data efficiently are key considerations.

Key Considerations:

• Analyze data patterns and usage to decide between SQL (relational) and NoSQL (non-relational) databases.
• Implement efficient indexing and query strategies to ensure fast data retrieval.
• Plan for data backups, disaster recovery, and data integrity.

8. Reliability and Fault Tolerance

• Systems must be designed to handle failures gracefully, ensuring reliability even in adverse conditions. This includes both hardware and software failures.

Key Considerations:

• Implement redundancy (e.g., replication in databases, multiple servers).
• Use failover strategies and circuit breakers to prevent cascading failures.
• Create a robust monitoring and alerting system to detect issues before they impact users.

9. Concurrency and Parallelism

• Modern software systems often need to handle multiple tasks simultaneously, either on multiple processors or distributed systems.

Key Considerations:

• Design for concurrency if the system will need to handle multiple users or processes simultaneously.
• Use techniques like multithreading, message queues, or distributed computing for parallel execution.
• Ensure thread safety and consistency, particularly with shared resources.

10. User Experience (UX) and Usability

• The architecture should accommodate good UX design, including responsiveness, ease of use, and accessibility.

Key Considerations:

• Design for mobile-first or responsive interfaces if applicable.
• Ensure smooth user interactions, including fast loading times and intuitive navigation.
• Consider accessibility standards to make the system usable by a wide range of users.

11. Technology Stack Selection

• Choosing the right technologies (programming languages, frameworks, libraries) can have a significant impact on both development speed and system performance.

Key Considerations:

• Choose technologies based on the project’s requirements, team expertise, and long-term viability.
• Avoid vendor lock-in and evaluate the community and support available for each technology.
• Use tools and libraries that align with the scalability, maintainability, and performance goals.

12. Testing Strategy

• A good design includes strategies for testing at different levels (unit tests, integration tests, and end-to-end tests) to ensure system reliability.

Key Considerations:

• Automate testing where possible to ensure code stability over time.
• Use continuous integration (CI) and continuous delivery (CD) pipelines to catch errors early.
• Implement monitoring tools to track system behavior in production and detect issues.

13. Deployment Strategy

• Consider how the system will be deployed, updated, and rolled back if necessary.

Key Considerations:

• Use containerization (e.g., Docker, Kubernetes) for consistent deployment across environments.
• Automate deployment pipelines for efficiency.
• Plan for rollback strategies in case of deployment failures.

14. Documentation

• A well-documented system ensures that new developers can quickly onboard, and existing team members can easily modify and maintain the system.

Key Considerations:

• Document the system architecture and key decisions.
• Keep API documentation up to date.
• Ensure the codebase is well-commented, especially in complex areas.

15. Cost Efficiency

• Consider the cost implications of your design, including infrastructure, development, and long-term maintenance costs.

Key Considerations:

• Optimize cloud resources to avoid over-provisioning.
• Use open-source tools where appropriate to minimize licensing fees.
• Plan for future upgrades and maintenance to avoid costly rewrites.

Summary:

In software architecture, several key concepts play a crucial role in building robust systems. Data is the core asset, representing all the information the system processes, while transactions ensure data consistency and integrity by grouping operations into all-or-nothing units. Interactions are the communication between components that enable collaboration to achieve system functionality, either synchronously or asynchronously.

A component is a modular, self-contained unit responsible for a specific task, designed to be reusable, maintainable, and encapsulated. Component boundaries define the separation between components, ensuring that internal logic is hidden and interactions occur only through well-defined interfaces. These boundaries promote loose coupling and clear responsibility separation, making the system more scalable, maintainable, and adaptable.

Previous Parts:

Part 1: Tale of software architect(ure): Part 1 (Software Architecture and Software Design)

Part 2: Tale of software architect(ure): Part 2 (Role of Software Architect and Knowledge To Have)

Part 3: Tale of Software Architect(ure): Part 3 (Characteristics of Software Architecture)