Overview
As sole backend owner of a pre-seed social application with 500-1,000 active users, I inherited a system running on a single EC2 instance with no architectural discipline. I executed a phased rescue that eliminated all single points of failure, cut response times by 80-90%, and scaled the platform to 5,000+ users—all while maintaining zero downtime and continuous feature delivery for fundraising.
Context
- Stage: Pre–pre-seed → pre-seed
- Product: Live, real-time social application
- Team: Solo backend owner (technical co-founder)
- Constraints: Live production system, zero downtime tolerance, continuous feature delivery required
The Challenge
The system had validated product-market fit but faced existential technical risks:
Codebase
- No separation of concerns—API routes directly executed database queries
- Business logic scattered across route handlers
- High regression risk with no test coverage
- Feature changes regularly broke unrelated flows
Infrastructure
- 100% of production stack on a single EC2 instance
- Application and MongoDB co-located
- No backups, replication, or failover
- Manual deployments with no CI/CD
Performance
- Home page load times exceeded 10 seconds at ~5,000 users
- APIs returned unbounded datasets (entire rooms, messages, user lists)
- No pagination or query limits
- AWS costs increased without proportional performance gains
Any serious outage would have killed fundraising momentum and user trust.
Technical Approach
I designed a phased rescue that prioritized stability while progressively removing systemic risk.
Phase 1: Backend Re-architecture
Enforced architectural discipline forward-only—all new code followed proper patterns, legacy code refactored opportunistically.
4-Layer Architecture:
- Validation Layer: Schema-based validation, early rejection of malformed requests
- Controller Layer: HTTP handling only, centralized error handling, zero business logic
- Service Layer: Stateless domain logic, testable and reusable
- Data Access Layer: All database queries isolated, consistent patterns
Result: 90-95% of endpoints migrated while maintaining feature velocity.
Phase 2: Performance Optimization
Query & Response:
- Implemented pagination (20-50 items per page) with hard limits
- Refactored "load everything" anti-patterns across all endpoints
- Reduced API payload sizes by orders of magnitude
- Added field selection to return only required data
Database:
- Added indexes for frequently queried fields
- Optimized aggregation pipelines
- Implemented query monitoring and slow query logging
- Removed N+1 patterns through strategic denormalization
Caching:
- Added Redis for frequently accessed data
- Reduced database load by 40-50% for read-heavy operations
Result: Response times reduced 70-85%, home page load from >10s to ~1-2s.
Phase 3: Infrastructure Modernization
Following a major downtime incident, prioritized eliminating single points of failure.
Immediate:
- Migrated MongoDB to Atlas dedicated cluster with automated backups
- Decoupled database failure from application uptime
Application:
- Configured PM2 for multiple backend processes per instance
- Added health checks and load balancing
Long-term:
- Dockerized backend with multi-stage builds
- Migrated to AWS ECS with horizontal scaling and auto-scaling policies
- Implemented CI/CD with automated testing and deployments
- Blue-green deployments with automated rollback
- Deployment time: 30+ minutes → 5 minutes
Challenges & Solutions
Refactoring Without Feature Freeze
- Implemented "architectural ratchet": all new code follows new patterns, refactor legacy opportunistically
- Tracked migration progress (90-95% migrated over 6 months)
Zero-Downtime Database Migration
- Set up Atlas cluster with replication from local instance
- Gradually routed read traffic, then write traffic during low-traffic period
- Zero user-facing impact
Performance Debugging Under Load
- Implemented comprehensive APM and query logging
- Analyzed patterns offline and reproduced in staging
- Data-driven optimization without production experiments
Solo Owner Scalability
- Created comprehensive documentation and runbooks
- Established clear architectural patterns
- Implemented guardrails to prevent common mistakes
- Set up monitoring to catch issues early
Results & Impact
Performance Metrics
| Metric | Before | After | Improvement | | --- | --- | --- | --- | | Median API Response Time | 1-2 seconds | 200-300ms | ~75-85% reduction | | Home Page Load Time | >10 seconds | ~1-2 seconds | ~80-90% reduction | | Concurrent Users | ~1,000 | 5,000+ | 5x increase | | Deployment Time | 30+ minutes | ~5 minutes | 83% reduction |
Business Impact
- Successfully supported 5x user growth without infrastructure crises
- Zero critical outages during fundraising period
- System became credible under investor technical due diligence
- AWS costs stabilized despite 5x user growth
- 90-95% of backend migrated to clean patterns
- Zero production downtime during entire transformation
Key Lessons
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Phased Migration > Big Bang Rewrite: Incremental approach maintained feature velocity while reducing risk through smaller, reversible changes.
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Address Infrastructure Risks Early: Single instance vulnerability was a ticking time bomb. Don't wait for the crisis.
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Architecture Enables Performance: Proper separation of concerns made it possible to identify bottlenecks systematically and implement optimizations cleanly.
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Monitoring Is Not Optional: Data-driven decision making required comprehensive observability from day one.
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Solo Ownership Requires Multipliers: Clear patterns, documentation, automation, and monitoring scaled my impact beyond linear capacity.
Technologies
Backend: Node.js, Express.js
Database: MongoDB, MongoDB Atlas, Redis
Infrastructure: AWS (EC2, ECS), Docker, PM2
DevOps: GitHub Actions, CloudWatch
Development: TypeScript, Jest, Git
Conclusion
By executing a disciplined, phased approach to architectural improvement, I transformed a fragile prototype into a scalable, reliable platform that supported 5x user growth with dramatically better performance and zero critical incidents. The transformation proved that with the right strategy, you can rebuild the plane while flying it—without sacrificing feature velocity or requiring downtime.
Completed over 8 months while maintaining zero production downtime and supporting company growth from pre–pre-seed to pre-seed stage.