Architecture

Architecture Patterns Quick Reference

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Architecture Design Patterns Best Practices Reference Guide
  1. Pattern Selection Quick Reference
  2. Pattern Categories
  3. Decision Flowchart: Choosing the Right Pattern
  4. Pattern Comparison Matrix
  5. Pattern Combinations
  6. Pattern Selection Criteria
  7. Common Anti-Patterns
  8. Getting Started
  9. Pattern Maturity Model
  10. Complete Pattern Index
  11. Additional Resources
  12. Conclusion
  13. Next Steps

Building resilient, scalable distributed systems requires choosing the right architectural patterns for your specific challenges. This guide provides a quick reference to help you select the most appropriate pattern based on your problem domain, with links to detailed explanations of each pattern.

Pattern Selection Quick Reference

Use this table to quickly identify which pattern addresses your specific challenge:

Your Challenge Recommended Pattern When to Use
Service calls timing out Asynchronous Request-Reply Operations take longer than HTTP timeout limits
Service keeps failing Circuit Breaker Prevent cascading failures from unavailable services
Temporary network glitches Retry Handle transient failures that resolve quickly
One service affecting others Bulkhead Isolate resources to contain failures
API throttling errors Rate Limiting Control request rate to throttled services
Legacy system integration Anti-Corruption Layer Protect clean architecture from legacy systems
Slow query performance Materialized View Pre-compute complex queries for faster reads
Large message payloads Claim Check Reduce message size by storing data externally
Migrating legacy systems Strangler Fig Gradually replace legacy with modern systems
Cross-cutting concerns Sidecar Add functionality without modifying applications
Database scalability Sharding Distribute data across multiple databases
Multiple API calls Gateway Aggregation Combine multiple backend calls into one
Event distribution Publisher-Subscriber Decouple event producers from consumers
Service health monitoring Health Endpoint Monitoring Proactively detect service failures
Authentication across services Federated Identity Centralize authentication and authorization

Pattern Categories

Architecture patterns can be grouped by the problems they solve:

🛡️ Resilience Patterns

Patterns that help systems handle failures gracefully:

Circuit Breaker: Prevents cascading failures by temporarily blocking calls to failing services. Like an electrical circuit breaker, it “trips” when failures exceed a threshold, allowing the system to fail fast and recover gracefully.

Retry: Automatically retries failed operations to handle transient failures. Uses strategies like exponential backoff to avoid overwhelming already-stressed services.

Bulkhead: Isolates resources into separate pools to prevent one failing component from consuming all resources. Named after ship compartments that contain flooding.

💡 Combining Resilience Patterns

These patterns work best together: Retry handles transient failures, Circuit Breaker prevents overwhelming failing services, and Bulkhead contains the blast radius of failures.

⚡ Performance Patterns

Patterns that optimize system performance and responsiveness:

Asynchronous Request-Reply: Decouples long-running operations from immediate responses, preventing timeouts and improving user experience.

Materialized View: Pre-computes and stores query results to avoid expensive computations at read time. Ideal for complex aggregations and reports.

Claim Check: Reduces message payload size by storing large data externally and passing only a reference. Improves messaging system performance and reduces costs.

Sharding: Distributes data across multiple databases to improve scalability and performance. Each shard handles a subset of the total data.

🔄 Integration Patterns

Patterns that facilitate communication between systems:

Anti-Corruption Layer: Provides a translation layer between systems with different semantics, protecting your clean architecture from legacy system quirks.

Gateway Aggregation: Combines multiple backend service calls into a single request, reducing client complexity and network overhead.

Publisher-Subscriber: Enables asynchronous event-driven communication where publishers don’t need to know about subscribers.

Federated Identity: Delegates authentication to external identity providers, enabling single sign-on across multiple systems.

🎯 Operational Patterns

Patterns that improve system operations and management:

Rate Limiting: Controls the rate of requests sent to services to avoid throttling errors and optimize throughput.

Health Endpoint Monitoring: Exposes health check endpoints for proactive monitoring and automated recovery.

Sidecar: Deploys helper components alongside applications to handle cross-cutting concerns like logging, monitoring, and configuration.

🏗️ Migration Patterns

Patterns that support system modernization:

Strangler Fig: Gradually replaces legacy systems by incrementally migrating functionality to new implementations. Named after a fig tree that grows around and eventually replaces its host.

Decision Flowchart: Choosing the Right Pattern

Use this flowchart to navigate to the most appropriate pattern for your situation:

graph TD Start[What's your challenge?] --> Q1{Service
availability?} Q1 -->|Failing repeatedly| CB[Circuit Breaker] Q1 -->|Temporary failures| Retry[Retry Pattern] Q1 -->|One affects others| Bulkhead[Bulkhead] Q1 -->|Performance| Q2{What type?} Q2 -->|Long operations| Async[Asynchronous Request-Reply] Q2 -->|Slow queries| MV[Materialized View] Q2 -->|Large messages| CC[Claim Check] Q2 -->|Database scale| Shard[Sharding] Q1 -->|Integration| Q3{What need?} Q3 -->|Legacy system| ACL[Anti-Corruption Layer] Q3 -->|Multiple calls| GA[Gateway Aggregation] Q3 -->|Event distribution| PubSub[Publisher-Subscriber] Q3 -->|Authentication| FI[Federated Identity] Q1 -->|Operations| Q4{What aspect?} Q4 -->|Throttling| RL[Rate Limiting] Q4 -->|Monitoring| HEM[Health Endpoint] Q4 -->|Cross-cutting| Sidecar[Sidecar] Q1 -->|Migration| SF[Strangler Fig] style CB fill:#ff6b6b style Retry fill:#ff6b6b style Bulkhead fill:#ff6b6b style Async fill:#51cf66 style MV fill:#51cf66 style CC fill:#51cf66 style Shard fill:#51cf66 style ACL fill:#4dabf7 style GA fill:#4dabf7 style PubSub fill:#4dabf7 style FI fill:#4dabf7 style RL fill:#ffd43b style HEM fill:#ffd43b style Sidecar fill:#ffd43b style SF fill:#a78bfa

Pattern Comparison Matrix

Compare patterns across key dimensions:

Pattern Combinations

Many real-world systems combine multiple patterns for comprehensive solutions:

Resilient Microservices Stack

Circuit Breaker + Retry + Bulkhead + Health Endpoint
  • Circuit Breaker: Prevents cascading failures
  • Retry: Handles transient failures
  • Bulkhead: Isolates resources
  • Health Endpoint: Enables monitoring

High-Performance API Gateway

Gateway Aggregation + Rate Limiting + Async Request-Reply
  • Gateway Aggregation: Reduces client calls
  • Rate Limiting: Prevents overwhelming backends
  • Async Request-Reply: Handles long operations

Legacy System Modernization

Strangler Fig + Anti-Corruption Layer + Federated Identity
  • Strangler Fig: Gradual migration strategy
  • Anti-Corruption Layer: Protects new code from legacy
  • Federated Identity: Unified authentication

Pattern Selection Criteria

Consider these factors when choosing patterns:

System Requirements

📋 Functional Requirements

  • Availability: How much downtime is acceptable?
  • Performance: What are your latency requirements?
  • Scalability: How much growth do you expect?
  • Consistency: What consistency guarantees do you need?

Technical Constraints

🔧 Technical Factors

  • Existing infrastructure: What systems are already in place?
  • Team expertise: What patterns does your team know?
  • Technology stack: What frameworks and libraries are available?
  • Budget: What resources can you allocate?

Operational Considerations

⚙️ Operations

  • Monitoring: Can you observe the pattern's behavior?
  • Maintenance: How complex is ongoing maintenance?
  • Testing: Can you effectively test the implementation?
  • Documentation: Is the pattern well-documented?

Common Anti-Patterns

Avoid these common mistakes when applying patterns:

⚠️ Pattern Misuse

Over-engineering: Don't apply complex patterns to simple problems. Start simple and add patterns as needed.

Pattern stacking: Avoid combining too many patterns without clear justification. Each pattern adds complexity.

Ignoring trade-offs: Every pattern has costs. Consider performance overhead, operational complexity, and maintenance burden.

Cargo cult implementation: Don't copy patterns without understanding why they work. Adapt patterns to your specific context.

Getting Started

Follow this approach when implementing patterns:

1. Identify the Problem

Clearly define the challenge you’re trying to solve:

  • What symptoms are you experiencing?
  • What are the root causes?
  • What are your success criteria?

2. Research Patterns

Use this guide to identify candidate patterns:

  • Review the quick reference table
  • Follow the decision flowchart
  • Read detailed pattern articles

3. Evaluate Options

Compare patterns against your requirements:

  • Implementation complexity
  • Operational overhead
  • Team expertise
  • Budget constraints

4. Start Small

Begin with a pilot implementation:

  • Choose a non-critical component
  • Implement the pattern
  • Monitor and measure results
  • Iterate based on learnings

5. Scale Gradually

Expand successful implementations:

  • Document lessons learned
  • Train team members
  • Apply to additional components
  • Refine based on experience

Pattern Maturity Model

Assess your organization’s pattern adoption maturity:

graph LR L1[Level 1:
Ad-hoc] --> L2[Level 2:
Aware] L2 --> L3[Level 3:
Defined] L3 --> L4[Level 4:
Managed] L4 --> L5[Level 5:
Optimizing] style L1 fill:#ff6b6b style L2 fill:#ffd43b style L3 fill:#4dabf7 style L4 fill:#51cf66 style L5 fill:#a78bfa

Level 1 - Ad-hoc: No consistent pattern usage, reactive problem-solving

Level 2 - Aware: Team knows patterns exist, occasional usage

Level 3 - Defined: Documented pattern guidelines, consistent application

Level 4 - Managed: Metrics-driven pattern selection, regular reviews

Level 5 - Optimizing: Continuous improvement, pattern innovation

Complete Pattern Index

Here’s the complete list of patterns covered in this series:

  1. Rate Limiting Pattern (January) - Control request rates to throttled services
  2. Anti-Corruption Layer Pattern (February) - Protect architecture from legacy systems
  3. Retry Pattern (March) - Handle transient failures gracefully
  4. Claim Check Pattern (April) - Reduce message payload sizes
  5. Materialized View Pattern (May) - Pre-compute complex queries
  6. Strangler Fig Pattern (June) - Gradually migrate legacy systems
  7. Sidecar Pattern (July) - Add functionality via helper components
  8. Sharding Pattern (August) - Distribute data for scalability
  9. Gateway Aggregation Pattern (September) - Combine multiple API calls
  10. Publisher-Subscriber Pattern (October) - Event-driven communication
  11. Health Endpoint Monitoring Pattern (November) - Proactive health checks
  12. Federated Identity Pattern (December) - Centralized authentication
  13. Circuit Breaker Pattern (January) - Prevent cascading failures
  14. Bulkhead Pattern (March) - Isolate resources to contain failures
  15. Asynchronous Request-Reply Pattern (April) - Handle long-running operations

Additional Resources

Books

  • “Cloud Design Patterns” by Microsoft - Comprehensive pattern catalog
  • “Release It!” by Michael Nygard - Production-ready software patterns
  • “Building Microservices” by Sam Newman - Microservices architecture patterns
  • “Domain-Driven Design” by Eric Evans - Strategic design patterns

Online Resources

Practice

💡 Learning by Doing

The best way to learn patterns is through hands-on practice:

  • Build sample applications implementing each pattern
  • Contribute to open-source projects using these patterns
  • Conduct architecture reviews with your team
  • Share knowledge through blog posts and presentations

Conclusion

Architecture patterns are powerful tools for solving common distributed systems challenges. This quick reference guide helps you:

  • Quickly identify the right pattern for your problem
  • Compare patterns across multiple dimensions
  • Understand relationships between patterns
  • Avoid common pitfalls in pattern application
  • Plan your learning journey through the pattern catalog

Remember: patterns are guidelines, not rigid rules. Adapt them to your specific context, measure their impact, and iterate based on results. Start with simple patterns like Retry and Health Endpoint Monitoring, then gradually adopt more complex patterns as your system evolves.

Next Steps

  1. Bookmark this guide for quick reference during architecture discussions
  2. Read detailed articles for patterns relevant to your current challenges
  3. Experiment with implementations in non-critical components
  4. Share knowledge with your team through workshops and code reviews
  5. Measure impact using metrics and monitoring

Building resilient, scalable systems is a journey. These patterns provide a proven roadmap based on collective industry experience. Use them wisely, adapt them thoughtfully, and your systems will be better prepared for the challenges of production environments.

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