Dimensions of Software Architecture

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Call these “Architectural Concern Categories” or “Dimensions of Software Architecture” or “Axes of Architectural Design” it is your choice. Here I am referring them as Dimensions of Software Architecture. They are extremely important for any software solution creator or software product maker. In this article we are trying to understand these dimensions. I am aware there is some overlapping between these terms.

Execution Models

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How your code runs: in sequence, in parallel, or in reaction to events.

• Synchronous: Step-by-step, blocking (print(read_file()))
• Asynchronous: Non-blocking (await fetch())
• Concurrent: Tasks progress together (threading, asyncio)
• Parallel: Truly simultaneous (multiprocessing)
• Event-driven: React to events (onclick, Node.js)
• Reactive : A model where the system reacts to inputs (data/events) as they come in.
• Event-driven : System behavior is determined by events or messages.
• Blocking / Non-blocking : Whether a thread waits (blocks) or not when performing an operation (like I/O).
• Multi-threaded : Uses multiple threads of execution.
• Single-threaded : Only one thread handles all operations (e.g., Node.js with event loop).
• Actor model : Independent actors communicate via message passing (e.g., Erlang, Akka).
• Coroutine-based : Lightweight cooperative threads (e.g., Python asyncio, Go routines).

Communication Patterns

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How components talk to each other.

• Request-Response: Client-server (HTTP REST)
• Pub-Sub: Send and forget (Kafka, MQTT)
• Message Queue: Tasks queued (RabbitMQ)
• Streaming: Continuous flow (WebSocket, gRPC stream)
• RESTful APIs : HTTP-based stateless communication.
• gRPC : High-performance RPC using Protocol Buffers.
• WebSockets : Bi-directional communication over a single TCP connection.
• Polling / Long Polling : Repeated or extended HTTP requests to simulate real-time communication.
• Shared Memory : Threads/processes access common memory space (risky but fast).
• Remote Procedure Call (RPC) : Directly invoke methods across networks.
• Signal / Slot : Observer-based pattern used in GUIs or reactive systems.

Architectural Styles

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High-level design structure of a system.

• Monolithic: All-in-one (Django app)
• Layered: Separate tiers (MVC, 3-tier)
• Microservices: Small independent services (Netflix)
• Event-driven architecture: Events trigger behavior (e-commerce order placed)
• Service-Oriented Architecture (SOA) : Services communicate over a network, often with an enterprise service bus (ESB).
• Serverless : Logic is deployed as short-lived functions (e.g., AWS Lambda).
• Client-Server : Traditional model with a requesting client and responding server.
• Peer-to-Peer (P2P) : Every node is both a client and server.
• Hexagonal (Ports & Adapters) : Decouples core logic from external systems via interfaces.
• Clean Architecture : Emphasizes separation of concerns and independence of frameworks/UI.
• Onion Architecture : Similar to Clean Architecture, with concentric layers.
• Microkernel / Plugin : Core system extended via plugins (e.g., IDEs).
• Pipeline / Filter : Data passes through a sequence of processing steps.
• Blackboard Architecture : Components contribute to and draw from a shared data structure.
• CQRS (Command Query Responsibility Segregation) : Separate read and write models.
• Event Sourcing : Store changes as a sequence of events.
• Saga Pattern : Handle long-running distributed transactions.
• Circuit Breaker : Prevent failure propagation in microservices.
• Strangler Fig Pattern : Gradually replace a legacy system.
• Bulkhead Pattern : Isolate failures in one part of a system.
• Sidecar Pattern : Attach helper services to main service (common in service mesh).

Deployment & Runtime Models

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How and where the system runs.

• Containers: Lightweight runtime (Docker)
• Serverless: No server management (AWS Lambda)
• Orchestrated/Cloud-native: Designed for cloud (Kubernetes)
• Edge Computing : Computation close to the data source (IoT, CDN).

Scalability & Availability

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How the system handles load and stays online.

• Load balancing: Distribute requests (Nginx)
• Replication: Data copies (MongoDB replica set)
• Sharding: Split data (Cassandra)
• Auto-scaling: Elastic capacity (AWS EC2 Auto Scaling)
• Horizontal Scaling : Add more machines.
• Vertical Scaling : Add more resources to existing machine.
• High Availability : System remains available despite failures.

Data Handling & Consistency

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How data is stored and kept accurate.

• ACID: Reliable DB transactions (PostgreSQL)
• BASE: Eventually consistent (Cassandra)
• CQRS: Read/write separation
• Event Sourcing: Store change events (Kafka log)

Resilience & Fault Tolerance

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How the system recovers from failure.

• Retry: Try again on failure
• Circuit Breaker: Stop failing requests (Netflix Hystrix)
• Fallback: Backup response logic
• Failover : Automatic switching to backup systems.

Security Boundaries

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How the system protects itself and data.

• Authentication: Identity check (OAuth)
• Authorization: Access control (JWT, RBAC)
• TLS/SSL: Encryption in transit

Modularity & Extensibility

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How easily the system can evolve.

• Plugin system: Add-on features
• Inversion of Control (IoC): Framework calls your code (Spring)
• Dependency Injection: Inject components

Observability & Monitoring

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How the system is monitored and debugged.

• Logging: App logs (loguru, winston)
• Metrics: Numbers to track (Prometheus)
• Tracing: Flow tracking (OpenTelemetry)

Dev & Release Lifecycle

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How the system is built, tested, and released.

• CI/CD: Automated build/deploy (GitHub Actions)
• Blue-Green Deployment: Zero downtime switch
• Canary Release: Small rollout

Orthogonal Concepts

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• Scalability : Ability to grow with load.
• Resilience : Ability to recover from failure.
• Observability : System visibility through logging, tracing, and metrics.
• Maintainability : Ease of making changes over time.
• Security-by-Design : Security considered from the outset.
• Idempotency : Repeated operations produce the same result.
• Stateless vs Stateful : Whether or not a component retains memory of previous interactions.