Catalog
concept#Architecture#Software Engineering#Reliability

Architectural Patterns

Reusable solution principles for structural design problems in software systems that standardize component organization, responsibilities, and interactions.

Architectural patterns are reusable solutions to common structural problems in software systems.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Cloud provider platforms (e.g., Azure, AWS)CI/CD pipelines for automated deploymentsObservability tools (logging, metrics, tracing)

Principles & goals

Separation of concerns: clear responsibilities between components.Evaluate trade-offs: explicitly weigh quality attributes against each other.Context sensitivity: apply patterns to context, not as dogma.
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Wrong choice can cause unnecessary complexity or performance issues.
  • Incompatibility with existing platforms and legacy systems.
  • Neglecting non-functional requirements during selection.
  • Document pattern rationale and consequences
  • Adopt in small, measurable steps
  • Continuously validate with metrics and tests

I/O & resources

  • Non-functional requirements (scaling, reliability)
  • Domain models and business goals
  • Existing technical landscape and constraints
  • Recommended architectural pattern(s) with consequences
  • Architecture diagrams and interface descriptions
  • Migration and implementation plan

Description

Architectural patterns are reusable solutions to common structural problems in software systems. They capture proven organization of components, responsibilities, and interactions to address concerns like scalability, reliability, and modifiability. Patterns guide design decisions and trade-offs across projects and teams, and provide a shared vocabulary for architectural communication and evaluation.

✔

  • Faster decision making through proven solution templates.
  • Improved communication via a shared vocabulary.
  • Increased reusability and consistency across projects.

✖

  • Overfitting: patterns may be adopted uncritically.
  • Not every domain fits every pattern; context matters.
  • Adoption costs for restructuring and training.

  • Throughput

    Number of processed requests per unit of time.

  • Mean Time to Recovery (MTTR)

    Average time to restore service after a failure.

  • Latency (95th/99th percentile)

    Measurement of response times for critical paths.

Layered architecture in enterprise software

Separation of presentation, business and persistence layers for clear responsibility separation.

Microservices at an e-commerce provider

Decomposition into domain-specific services for independent deployments and scaling.

Event-driven architecture in a payments platform

Event-based integration reduces coupling and supports asynchronous processing.

1

Capture context and quality requirements

2

Identify and evaluate suitable patterns

3

Develop a proof of concept and measure

4

Incremental adoption with monitoring

⚠️ Technical debt & bottlenecks

  • Ad-hoc interface smoothing for quick delivery
  • Incomplete modularization causing hidden coupling
  • Outdated patterns that no longer fit new requirements
Data accessNetwork latencyDeployment complexity
  • Adopting event streaming without addressing consistency needs
  • Using a distributed pattern for low load with high cost
  • Splitting into microservices without organizational ownership
  • Underestimating integration costs between components
  • Neglecting observability when changing patterns
  • Lack of metrics to validate expected benefits
Software architecture and design patterns knowledgeKnowledge of distributed systemsOperational understanding (deployments, monitoring)
ScalabilityReliabilityMaintainability/Modifiability
  • • Legacy systems with limited integration capability
  • • Regulatory requirements for data residency
  • • Budget and staffing constraints