By Diego Liascovich
Full-Stack Developer | Microservices | Angular | Node.js
title: "📐 20+ Essential Architecture Patterns for Scalable Systems"
published: true
description: "A deep dive into core software architecture patterns including CQRS, Saga, Event Sourcing, Sidecar, BFF, Circuit Breaker, Canary Releases, and more — with real-world examples."
tags: architecture, backend, microservices, devops, scalability
In modern system design, there’s no one-size-fits-all solution. Choosing the right architecture and deployment patterns is essential for building scalable, resilient, and maintainable systems.
This guide explores 20+ key patterns, including structural, behavioral, and DevOps practices — each with real-world use cases, pros, and cons.
🧱 1. Sidecar Pattern
Type: Infrastructure
Description: A helper process runs alongside your main service, typically in the same pod or VM. Handles logging, proxying, config updates.
When to Use: You want to separate cross-cutting concerns (like TLS or logging) from business logic.
✅ Benefits: Language agnostic, modular, enhances observability
❌ Drawbacks: Resource overhead, complex orchestration
💡 Example: Envoy proxy sidecar in Istio (service mesh)
🔁 2. Saga Pattern
Type: Transaction Management
Description: Breaks distributed transactions into local ones, each with compensating logic for rollback.
When to Use: Long-running workflows involving multiple services
✅ Benefits: Maintains eventual consistency, decouples services
❌ Drawbacks: Complex rollback logic, debugging challenges
💡 Example: Order → Inventory → Payment → Cancel on failure
📤 3. Outbox Pattern
Type: Reliable Messaging
Description: Events are written to a DB outbox table in the same transaction as business data, then published asynchronously.
When to Use: When you need reliable event publishing tied to DB operations
✅ Benefits: Avoids lost events, strong consistency
❌ Drawbacks: Requires polling/CDC, delays in event delivery
💡 Example: Store order in DB → insert event → publish to Kafka
🧠 4. CQRS
Type: Data Modeling
Description: Separates command (write) and query (read) models for better scalability and complexity management.
When to Use: Apps with complex reads/writes or audit requirements
✅ Benefits: Optimized performance, flexibility, read model scaling
❌ Drawbacks: Complexity, event syncing challenges
💡 Example: Write to PostgreSQL, read from Redis or Mongo projection
🔀 5. Scatter-Gather
Type: Data Aggregation
Description: Sends requests in parallel to multiple sources, gathers and combines responses.
When to Use: You need data from multiple systems simultaneously
✅ Benefits: High performance, real-time aggregation
❌ Drawbacks: Timeout management, inconsistent failures
💡 Example: Flight search: query 5 airlines in parallel
🎼 6. Choreography Pattern
Type: Workflow Coordination
Description: Services react to events without centralized control — event-driven flow.
When to Use: Loose coupling, microservices autonomy
✅ Benefits: Flexible, scalable, no central dependency
❌ Drawbacks: Difficult traceability, uncontrolled event flow
💡 Example: UserSignedUp → EmailService & CRMService
🧑✈️ 7. Orchestrator Pattern
Type: Workflow Management
Description: A central orchestrator invokes services step by step and controls the workflow.
When to Use: When visibility and central control are needed
✅ Benefits: Traceability, error handling, flow visibility
❌ Drawbacks: Central point of failure, increased coupling
💡 Example: OrderService calls Inventory → Billing → Notifications
🔄 8. Pipes and Filters
Type: Stream/Data Processing
Description: Data flows through a pipeline, where each filter transforms it.
When to Use: ETL, image/audio processing, or data streams
✅ Benefits: Modular, testable components
❌ Drawbacks: Debugging and latency buildup
💡 Example: ETL: Extract → Clean → Normalize → Store
🕒 9. Event Sourcing
Type: State Management
Description: Store all events instead of the current state. Rebuild state by replaying events.
When to Use: You need full audit history or rollback
✅ Benefits: Full traceability, undo/redo, time-travel queries
❌ Drawbacks: Hard to query, event versioning
💡 Example: Account balance rebuilt from: Deposited, Withdrawn events
🌐 10. Ambassador Pattern
Type: Network Proxy
Description: A sidecar proxy handles outbound/inbound communication, authentication, TLS, etc.
When to Use: For consistent service communication and observability
✅ Benefits: Decouples app from infra, adds resilience
❌ Drawbacks: Extra network hop, resource usage
💡 Example: Envoy sidecar for outbound requests with retries
🧩 11. Backend for Frontend (BFF)
Type: API Gateway Pattern
Description: Each client (mobile/web) has a tailored backend optimized for its needs.
When to Use: When different clients need different formats or aggregations
✅ Benefits: Faster, cleaner frontends; avoids overfetching
❌ Drawbacks: More backend services to maintain
💡 Example: Mobile BFF combines user, cart, and product APIs
⚡ 12. Circuit Breaker
Type: Resilience/Fault Tolerance
Description: If a service fails too often, calls to it are stopped temporarily.
When to Use: Unstable external dependencies
✅ Benefits: Prevents cascading failure, protects resources
❌ Drawbacks: Adds complexity and tuning challenges
💡 Example: Use opossum or resilience4j to wrap outbound calls
🚀 13. Rolling Deployment
Type: Deployment Strategy
Description: Gradually replace old app versions with new ones without downtime.
When to Use: Production updates with low risk
✅ Benefits: Zero-downtime
❌ Drawbacks: Rollback may be partial or slower
💡 Example: Kubernetes rolling updates
💚 14. Blue-Green Deployment
Type: Deployment Strategy
Description: Deploy to a staging environment (green), test, then switch live traffic from blue.
When to Use: For safe, instant rollback
✅ Benefits: Fast recovery, easy switch
❌ Drawbacks: Requires double infrastructure
💡 Example: Switch load balancer to green environment after test pass
🐤 15. Canary Release
Type: Progressive Delivery
Description: Roll out new code to a small segment of users first.
When to Use: To test changes in production with low risk
✅ Benefits: Real user feedback, safer deployment
❌ Drawbacks: Monitoring and traffic routing complexity
💡 Example: Deploy to 5% of traffic, monitor, then expand
💥 16. Chaos Engineering
Type: Resilience Testing
Description: Intentionally introduce failures to test system response.
When to Use: To proactively test fault tolerance
✅ Benefits: Reveals hidden risks, improves recovery
❌ Drawbacks: Requires safeguards; risky in prod
💡 Example: Netflix’s Chaos Monkey kills instances randomly
🔁 17. Retry Pattern
Type: Fault Tolerance
Description: Retry transient failures, often with exponential backoff
When to Use: For flaky or unstable services
✅ Benefits: Handles temporary glitches
❌ Drawbacks: Retry storms, latency buildup
💡 Example: Retry payment API call 3 times with backoff
🪦 18. Dead Letter Queue (DLQ)
Type: Messaging Reliability
Description: Failed messages are sent to a DLQ for later analysis or reprocessing
When to Use: When you can’t afford message loss
✅ Benefits: Ensures recoverability
❌ Drawbacks: Requires monitoring and cleanup logic
💡 Example: Unprocessable Kafka/RabbitMQ messages redirected to DLQ
🚦 19. Throttling
Type: API Protection
Description: Controls the rate of operations per user/requester
When to Use: Prevent resource exhaustion or DoS abuse
✅ Benefits: Maintains system stability
❌ Drawbacks: Can degrade user experience
💡 Example: 5 requests per second per IP
⏱️ 20. Rate Limiting
Type: Usage Control
Description: Set hard request caps over time windows
When to Use: Prevent DDoS or ensure fair usage
✅ Benefits: Avoid abuse, preserve performance
❌ Drawbacks: Legit users might be blocked
💡 Example: 1000 requests/hour API key limit
📊 Comparison Table of Architecture Patterns
| Pattern | Type | Main Benefits | Main Drawbacks |
|---|---|---|---|
| Sidecar | Infrastructure | Modularity, language agnostic | Extra resources, operational complexity |
| Saga | Transaction | Eventual consistency, decoupling | Complex rollback, debugging |
| Outbox | Reliable Messaging | Guarantees event delivery | Requires polling or CDC |
| CQRS | Data Modeling | Scalability, performance | Increased complexity |
| Scatter-Gather | Data Aggregation | Parallelism, speed | Timeout and failure handling |
| Choreography | Workflow | Loose coupling, extensible | Hard to trace |
| Orchestrator | Workflow | Centralized control, traceable | Single point of failure |
| Pipes and Filters | Data Processing | Modular, testable | Latency, debugging difficulty |
| Event Sourcing | State Management | Full audit trail | Query complexity, event versioning |
| Ambassador | Proxy | Decouples networking logic | Adds network hop |
| Backend for Frontend | API Composition | Optimized UX | More services to maintain |
| Circuit Breaker | Fault Tolerance | Prevents cascading failures | Configuration complexity |
| Rolling Deployment | Deployment | Zero downtime | Slow rollback |
| Blue-Green Deployment | Deployment | Instant rollback | Double resource use |
| Canary Release | Deployment | Safer releases | Complex traffic routing |
| Chaos Engineering | Resilience Testing | Improves robustness | Risky without safeguards |
| Retry Pattern | Fault Tolerance | Handles transient failures | Potential retry storms |
| Dead Letter Queue | Messaging | Prevents data loss | Requires manual handling |
| Throttling | Rate Control | Protects backend | May degrade user experience |
| Rate Limiting | Rate Control | Fair usage |
📘 Final Thoughts
Architecture is all about tradeoffs. The right pattern depends on your scale, goals, and team structure.
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