turash/concept/11_technical_architecture_implementation.md

9. Technical Architecture & Implementation

Architecture Decision Records (ADRs)

Recommendation: Adopt Architecture Decision Records (ADRs) using the MADR (Markdown Architectural Decision Records) template format.

Implementation:

• Create docs/adr/ directory structure
• Document each major architectural decision in separate ADR files
• Each ADR should include:
  • Status: Proposed | Accepted | Deprecated | Superseded
  • Context: Problem statement
  • Decision: What was decided
  • Consequences: Pros and cons
  • Alternatives Considered: Other options evaluated
  • Rationale: Reasoning behind the decision
  • Date: When decision was made

Example ADR Topics:

1. Graph database selection (Neo4j vs ArangoDB vs Memgraph vs TigerGraph)
2. Go HTTP framework selection (Gin vs Fiber vs Echo vs net/http)
3. Event-driven vs request-response architecture
4. Multi-tenant data isolation strategy
5. Real-time vs batch matching engine
6. Microservices vs modular monolith
7. Go 1.25 experimental features adoption (JSON v2, GreenTea GC)
8. Frontend framework and architecture (React Server Components consideration)
9. Message Queue Selection (MVP): NATS vs Redis Streams vs Kafka
10. Open Standards Integration: NGSI-LD API adoption for smart city interoperability
11. Knowledge Graph Integration: Phase 2 priority for semantic matching
12. Layered Architecture Pattern: Device/Edge → Ingestion → Analytics → Application → Governance

Event-Driven Architecture (EDA)

Recommendation: Adopt event-driven architecture with CQRS (Command Query Responsibility Segregation) for the matching engine.

Rationale:

• Graph updates and match computations are inherently asynchronous
• Real-time matching requires event-driven updates
• Scalability: decouple matching computation from data ingestion
• Resilience: event sourcing provides audit trail and replay capability

Implementation (Phased Approach):

MVP Phase:
Data Ingestion → NATS/Redis Streams → Event Processors → Graph Updates → Match Computation → Match Results Cache

Scale Phase (1000+ businesses):
Data Ingestion → Kafka → Event Processors → Graph Updates → Match Computation → Match Results Cache

Components:

• Event Store (MVP): NATS or Redis Streams for event types (ResourceFlowCreated, SiteUpdated, MatchComputed)
  • NATS: Go-native messaging (nats.go), 60-70% complexity reduction vs Kafka
  • Redis Streams: Simple pub/sub, suitable for initial real-time features
  • Use github.com/nats-io/nats.go or github.com/redis/go-redis/v9 for Go clients
• Event Store (Scale): Kafka topics for high-throughput scenarios
  • Use confluent-kafka-go or shopify/sarama for Go clients
  • Migration path: NATS/Redis Streams → Kafka at 1000+ business scale
• Command Handlers: Process write operations (create/update ResourceFlow)
  • Go HTTP handlers with context support
  • Transaction management with Neo4j driver
• Query Handlers: Serve read operations (get matches, retrieve graph data)
  • Read models cached in Redis
  • Graph queries using Neo4j Go driver
• Event Handlers: React to events (recompute matches when resource flows change)
  • NATS subscribers or Redis Streams consumers with Go workers
  • Channel-based event processing
  • Migration: Upgrade to Kafka consumer groups at scale

Benefits:

• Horizontal scalability for matching computation
• Better separation of concerns
• Event sourcing provides complete audit trail
• Can replay events for debugging or recovery

Caching Strategy

Recommendation: Implement multi-tier caching strategy.

Layers:

1. Application-level cache (Redis):

   • Match results (TTL: 5-15 minutes based on data volatility)
   • Graph metadata (businesses, sites)
   • Economic calculations
   • Geospatial indexes

2. CDN cache (CloudFront/Cloudflare):

   • Static frontend assets
   • Public API responses (non-sensitive match summaries)

3. Graph query cache (Neo4j query cache):

   • Frequently executed Cypher queries
   • Common traversal patterns

Cache Invalidation Strategy:

• Event-driven invalidation on ResourceFlow updates
• Time-based TTL for match results
• Cache warming for popular queries

Real-Time Matching Architecture

Recommendation: Implement incremental matching with streaming updates.

Architecture:

ResourceFlow Change Event → Stream Processor → Graph Delta Update → Incremental Match Computation → WebSocket Notification

Components:

• Stream Processor (MVP): NATS subscribers or Redis Streams consumers
  • Go-native event processing with goroutines
  • Channel-based message processing
  • Scale: Migrate to Kafka consumer groups at 1000+ business scale
• Graph Delta Updates: Only recompute affected subgraphs
• Incremental Matching: Update matches only for changed resource flows
  • Use Go channels for match result pipelines
• WebSocket Server: Push match updates to connected clients
  • Use gorilla/websocket or nhooyr.io/websocket
  • Goroutine per connection model (Go's strength)

Optimization:

• Batch small updates (debounce window: 30-60 seconds)
• Prioritize high-value matches for immediate computation
• Use background jobs for full graph re-computation (nightly)

Query Optimization

Recommendations:

1. Materialized Views:

   • Pre-compute common match combinations
   • Refresh on ResourceFlow changes (event-driven)

2. Query Result Caching:

   • Cache frequent queries (geographic area + resource type combinations)
   • Invalidate on data changes

3. Progressive Query Enhancement:

   • Return quick approximate results immediately
   • Enhance with more details in background
   • Notify user when enhanced results ready

4. Database Connection Pooling:

   • Optimize connection pools for graph database
   • Separate pools for read-heavy vs. write operations

Layered Architecture Pattern

Recommendation: Adopt layered, modular architecture for scalability and maintainability.

Architecture Layers:

1. Device/Edge Layer: Local processing for IoT devices

   • Data filtering and aggregation at edge
   • Reduces bandwidth and improves latency
   • Enables offline operation for field devices

2. Ingestion & Context Layer: Data normalization and routing

   • Open APIs (NGSI-LD) or message buses (NATS/Redis Streams)
   • Data normalization and validation
   • Context information brokering

3. Analytics/Service Layer: Business logic and domain services

   • Matching engine services
   • Economic calculation services
   • Domain services (traffic, energy, public safety)

4. Application/Presentation Layer: APIs and user interfaces

   • REST APIs, GraphQL, WebSocket endpoints
   • Frontend applications (React, Mapbox)
   • Mobile PWA

5. Governance/Security/Metadata Layer: Cross-cutting concerns

   • Identity management (OAuth2, JWT)
   • Access control (RBAC)
   • Audit logging and monitoring
   • Data governance and versioning

Benefits:

• Enhanced flexibility and scalability
• Independent development and deployment of layers
• Better separation of concerns
• Easier integration with existing city systems
• Supports edge processing for IoT devices

Implementation:

• Modular microservices architecture
• Containerization (Docker, Kubernetes)
• Service mesh for inter-service communication (optional at scale)

Knowledge Graph Integration

Recommendation: Plan knowledge graph capabilities for Phase 2 implementation.

Market Opportunity:

• Knowledge graph market growing at 36.6% CAGR (fastest-growing segment)
• Neo4j GraphRAG enables AI-enhanced querying and recommendation systems
• Semantic data integration improves match quality by 30-40% in similar platforms

Implementation Phases:

• Phase 1: Property graph model (already designed in data model)
• Phase 2: Enhance with knowledge graph capabilities for semantic matching
  • Semantic relationships between resources
  • Taxonomy integration (EWC, NACE codes)
  • Process compatibility matrices
• Phase 3: Integrate GraphRAG for natural language querying and AI recommendations
  • Neo4j GraphRAG for natural language queries
  • AI-enhanced match recommendations
  • Predictive matching capabilities

Technical Benefits:

• Improved match quality through semantic understanding
• Better resource categorization and classification
• Enhanced recommendation accuracy
• Competitive advantage through AI-enhanced matching

Migration Strategies & Backward Compatibility

Data Migration Framework

Database Migration Strategy:

• Schema Evolution: Use Neo4j schema migration tools for graph structure changes
• Data Transformation: Implement transformation pipelines for data format changes
• Zero-Downtime Migration: Blue-green deployment with gradual data migration
• Rollback Procedures: Maintain backup snapshots for quick rollback capability

Migration Phases:

1. Preparation: Create migration scripts and test data transformation
2. Validation: Run migrations on staging environment with full dataset
3. Execution: Blue-green deployment with traffic switching
4. Verification: Automated tests verify data integrity post-migration
5. Cleanup: Remove old data structures after successful validation

API Versioning Strategy

Semantic Versioning:

• Major Version (X.y.z): Breaking changes, new API endpoints
• Minor Version (x.Y.z): New features, backward-compatible
• Patch Version (x.y.Z): Bug fixes, no API changes

API Evolution:

• Deprecation Headers: Warn clients of deprecated endpoints
• Sunset Periods: 12-month deprecation period for breaking changes
• Version Negotiation: Accept-Version header for client-driven versioning
• Documentation: Version-specific API documentation and migration guides

Feature Flag Management

Progressive Rollout:

• Percentage-Based: Roll out features to X% of users
• User-Segment Based: Target specific user groups for testing
• Geographic Rollout: Roll out by region/country
• Gradual Enablement: Increase feature exposure over time

Flag Management:

• Central Configuration: Redis-backed feature flag service
• Real-time Updates: WebSocket notifications for feature changes
• Audit Trail: Track feature flag changes and user exposure
• A/B Testing: Integrate with experimentation framework

Rollback Procedures

Automated Rollback:

• Health Checks: Automated monitoring for service degradation
• Threshold Triggers: Automatic rollback on error rate thresholds
• Manual Override: Emergency rollback capability for critical issues
• Gradual Rollback: Percentage-based rollback to minimize user impact

Data Rollback:

• Snapshot-Based: Database snapshots for point-in-time recovery
• Incremental Backup: Continuous backup of critical data
• Schema Rollback: Automated schema reversion scripts
• Data Validation: Automated checks for data integrity post-rollback

Testing Strategy for Migrations

Migration Testing:

• Unit Tests: Test individual migration scripts
• Integration Tests: Test end-to-end migration workflows
• Load Tests: Test migration performance under load
• Chaos Testing: Test migration resilience to failures

Compatibility Testing:

• Client Compatibility: Test with various client versions
• Data Compatibility: Verify data transformations preserve integrity
• Performance Compatibility: Ensure migrations don't impact performance
• Functional Compatibility: Verify all features work post-migration

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