System Architecture & Data Infrastructure - Version Mapping
Version Timeline
| Version | Status | Description | Implementation Phase |
|---|---|---|---|
| MVP.0 | Specification Complete | Cross-cutting infrastructure foundation | Core Platform Launch |
| V1.0 | Planned | Event streaming platform | Post-MVP Enhancement |
| V1.1 | Planned | Advanced caching | Post-MVP Enhancement |
| V1.2 | Planned | Database scaling | Post-MVP Enhancement |
| V2.0 | Planned | Microservices evolution | Major Platform Expansion |
| V2.1 | Planned | Data pipeline modernization | Major Platform Expansion |
MVP.0 (Cross-Cutting Infrastructure Foundation)
Status: Specification Complete First Introduced: MVP.0 Last Updated: 2025-10-25
Core Features
Infrastructure Stack:
- PostgreSQL 15+ (single instance + read replica)
- Redis 7+ (single instance, persistence enabled)
- Redis Streams for message queue (upgrade to Kafka in V1)
- Kubernetes container orchestration (GKE, EKS, or AKS)
- CloudFront or Cloudflare CDN
- S3-compatible object storage (AWS S3, Backblaze B2)
- OpenTelemetry + Prometheus + Grafana monitoring
Enabled Patterns:
- Event publishing via transactional outbox
- Background job processing (iCal import, search reindex)
- Redis caching for permissions and availability
- Basic event bus with at-least-once delivery
Entities Introduced
-
Event - Atomic, immutable record of system actions
- Supports event-driven architecture backbone
- Enables cross-domain messaging and audit trails
- Implements correlation and causation tracking
-
Outbox - Transactional outbox pattern implementation
- Guarantees reliable, exactly-once event publishing
- Survives system failures through database transactions
- Exponential backoff retry strategy with dead letter queue
-
Job - Background task management
- Asynchronous processing for non-blocking operations
- Queue-based prioritization (critical, default, low)
- Retry logic with timeout support
-
CacheEntry - Distributed caching metadata
- Redis-backed performance optimization
- Tag-based invalidation patterns
- TTL strategies per data type
Infrastructure Patterns
-
Event-Driven Architecture
- Domains publish events via transactional outbox
- Consumers react asynchronously via event bus
- Loose coupling between services
-
Database Architecture
- Single PostgreSQL cluster with logical multi-tenancy
- org_id partitioning for tenant isolation
- Primary database + 2 read replicas
- PgBouncer connection pooling
-
Service Layer Architecture
- Modular service-oriented architecture
- Domain-aligned service boundaries
- REST APIs with JWT authentication
- Saga pattern for distributed transactions
-
Caching Architecture
- Multi-layer: Application (Redis), CDN, Database Query Cache
- Event-driven invalidation patterns
- TTL-based expiration strategies
-
Job Queue Architecture
- Multiple queues: default, critical, bulk, scheduled
- Horizontal worker scaling
- Dead letter queue for failures
Performance Targets (MVP)
- API response time: p95 < 200ms
- Event publish latency: < 1s
- Job execution: 95% within 5 minutes
- Cache hit ratio: > 80%
- Database queries: p95 < 50ms
Deferred to V1
- Kafka/PubSub for high-throughput event streaming
- Multi-region database replication
- Advanced caching (multi-level, warming strategies)
- Auto-scaling worker pools
- Event replay and time-travel debugging
V1.0: Event Streaming Platform
Status: Planned Target: Post-MVP Enhancement
Key Enhancements
Event Streaming:
- Migration from Redis Streams to Kafka or Google Pub/Sub
- Event schema registry for versioning
- Stream processing for real-time metric aggregation
- Event sourcing for critical domains
Performance Improvements:
- API response time: p95 < 150ms
- Event publish latency: < 500ms
- Job execution: 95% within 2 minutes
- Cache hit ratio: > 90%
Deployment Evolution:
- Canary deployment with automated rollback
- Enhanced monitoring and observability
- Improved disaster recovery procedures
Infrastructure Additions
- High-throughput message broker (Kafka/PubSub)
- Event schema versioning system
- Real-time stream processing framework
- Enhanced monitoring dashboards
V1.1: Advanced Caching
Status: Planned Target: Post-MVP Enhancement
Key Features
Caching Enhancements:
- Cache warming on data updates
- Predictive caching based on access patterns
- Multi-region cache replication
- Cache hit ratio monitoring and optimization
Benefits:
- Reduced database load
- Improved response times
- Better user experience globally
- Cost optimization through efficiency
V1.2: Database Scaling
Status: Planned Target: Post-MVP Enhancement
Key Features
Scaling Capabilities:
- Horizontal sharding by org_id
- Automated shard rebalancing
- Read replica routing by query type
- Connection pooling optimization
Benefits:
- Support for larger customer base
- Improved query performance
- Better resource utilization
- Scalable multi-tenancy
V2.0: Microservices Evolution
Status: Planned Target: Major Platform Expansion
Key Features
Architectural Evolution:
- Full microservices decomposition
- Service mesh implementation (Istio, Linkerd)
- gRPC for inter-service communication
- API gateway with rate limiting and circuit breaking
Deployment:
- Progressive delivery with feature flags
- Advanced auto-scaling policies
- Enhanced resilience patterns
Benefits
- Independent service deployment
- Technology stack flexibility
- Enhanced fault isolation
- Improved team autonomy
V2.1: Data Pipeline Modernization
Status: Planned Target: Major Platform Expansion
Key Features
Data Infrastructure:
- Real-time streaming to data warehouse
- Change data capture (Debezium) for synchronization
- Data lake for historical event archives
- ML model serving infrastructure
Analytics Capabilities:
- Real-time business intelligence
- Advanced reporting and dashboards
- Predictive analytics support
- Machine learning integration
Benefits
- Enhanced data-driven decision making
- Real-time insights
- Historical data analysis
- AI/ML capability foundation
Version Comparison Matrix
| Feature | MVP.0 | V1.0 | V1.2 | V2.0 | V2.1 |
|---|---|---|---|---|---|
| Message Queue | Redis Streams | Kafka/PubSub | Kafka/PubSub | Kafka/PubSub | Kafka + CDC |
| Database | Single region | Single region | Sharded | Sharded + Multi-region | + Data Lake |
| Caching | Basic Redis | Advanced Redis | Multi-region | Service Mesh Cache | Predictive |
| Service Architecture | Modular SOA | Modular SOA | Modular SOA | Microservices | Microservices |
| Event Processing | At-least-once | Exactly-once | Exactly-once | Event Sourcing | Stream Processing |
| Deployment | Blue/Green | Canary | Canary | Progressive | Progressive |
| Auto-scaling | Manual | Worker pools | Database + Workers | Full stack | ML-driven |
| Observability | Basic metrics | Enhanced | Enhanced | Service mesh | + ML ops |
Migration Paths
MVP.0 → V1.0
Prerequisites:
- Stable MVP.0 deployment in production
- Event volume justifies Kafka investment
- Team training on event streaming platforms
Migration Steps:
- Deploy Kafka cluster alongside Redis Streams
- Implement event schema registry
- Dual-publish events to both systems
- Migrate consumers to Kafka one domain at a time
- Validate event delivery and ordering
- Decommission Redis Streams for events
- Monitor performance improvements
Rollback Plan:
- Keep Redis Streams active during migration
- Ability to switch consumers back to Redis
- Retain dual-publishing for 30 days post-migration
V1.0 → V1.2
Prerequisites:
- Single database reaching capacity limits
- org_id distribution suitable for sharding
- Database operations team trained on sharding
Migration Steps:
- Analyze org_id distribution for shard key effectiveness
- Implement sharding layer (Citus, Vitess, or custom)
- Create initial shards (2-4 shards)
- Migrate data shard by shard (low-activity orgs first)
- Update application connection routing
- Validate data consistency across shards
- Monitor query performance
Rollback Plan:
- Ability to consolidate shards back to single database
- Data backup before each shard migration
- Gradual migration with validation gates
V1.2 → V2.0
Prerequisites:
- Service boundaries well-defined and stable
- Team structure aligned with microservices
- DevOps infrastructure for independent deployments
Migration Steps:
- Select first service to extract (e.g., analytics-service)
- Define service API contracts (gRPC schemas)
- Implement service mesh infrastructure
- Deploy extracted service alongside monolith
- Route traffic gradually to new service
- Repeat for remaining domains
- Decommission monolith components
Rollback Plan:
- Gradual migration allows rollback per service
- Monolith remains operational during transition
- API gateway can route to either implementation
V2.0 → V2.1
Prerequisites:
- Data warehouse infrastructure deployed
- Analytics team ready to consume real-time data
- ML/AI use cases identified
Migration Steps:
- Deploy CDC tooling (Debezium)
- Configure data warehouse connectors
- Implement data lake storage
- Set up streaming pipelines
- Migrate batch analytics to real-time
- Deploy ML model serving infrastructure
- Train teams on new data access patterns
Rollback Plan:
- Batch processes remain available as backup
- Data lake populated from backfill if needed
- Gradual migration of analytics consumers
Related Documents
- System Architecture Specification
- System Architecture Schema
- Platform Specification
- [Data Model](../../TVL Data and Domain Model Specification 2025-10-21 (1).md)
- MVP.0 Overview
- Architecture Decision Records
Document Status: Complete Last Updated: 2025-10-25 Next Review: Post-MVP.0 deployment