1. 🚀 Executive Architecture Summary System Purpose Build a multi-user customer support platform where customers can create support tickets and communicate with support agents. Support agents can: View assigned tickets Change priority and status Reply to customers Assign tickets Resolve tickets Administrators can: Manage users and roles View support performance Review audit activity Primary Users Customers Support Agents Administrators Recommended Architecture Modular monolith + asynchronous background workers Core application: React Web App ↓ API / Application Server ↓ Modular Business Logic ↓ PostgreSQL Supporting infrastructure: ┌──────────────┐ │ React Client │ └──────┬───────┘ ↓ ┌──────────────┐ │ API Server │ └──────┬───────┘ ↓ ┌───────────────┴───────────────┐ ↓ ↓ ┌──────────────┐ ┌──────────────┐ │ PostgreSQL │ │ Object Store │ └──────────────┘ └──────────────┘ ↑ │ ┌───────┴────────┐ │ Background │ │ Worker / Queue │ └───────┬────────┘ ↓ ┌──────────────┐ │ Email Service│ └──────────────┘ Key Design Decisions Start with a modular monolith, not microservices. PostgreSQL is the primary system of record. Use object storage for attachments. Use asynchronous workers for email and non-critical background operations. Keep ticket creation and updates synchronous. Use RBAC for authorization. Design APIs to be idempotent where duplicate operations are possible. Introduce caching only for proven read-heavy workloads. Assumptions The initial application serves one logical platform with multiple customers. Exact tenant/isolation requirements are not specified. Email is the only external integration currently required. No specific compliance certification is required by the provided requirements. Main Risks Ticket search may become database-intensive as ticket volume grows. Email-provider failures can delay notifications. Attachment storage could grow significantly. 500 requests/sec peak requires load testing rather than assumption-based capacity claims. --- 2. 🎯 Functional Requirements Customer Register/login Create support ticket Add description Upload attachment View own tickets Add replies View ticket status Receive notifications Support Agent Login View ticket queue Search tickets Filter tickets Assign tickets Change priority Change status Reply to customers Resolve tickets Administrator Manage users Manage roles View system activity Review audit logs View support metrics System Authentication Authorization Ticket management Notification processing Attachment handling Audit logging Search/filtering Operational monitoring --- 3. 🧭 Non-Functional Requirements Requirement Target Peak traffic 500 requests/sec API latency p95 < 300 ms Availability 99.9% Users 50,000 Backup Daily RPO 24 hours RTO 4 hours Architecture Modular monolith Primary DB PostgreSQL Frontend React Backend Node.js + TypeScript Important The latency, availability and capacity targets must be validated through testing. They are requirements, not guaranteed architectural outcomes. --- 4. 🏗️ High-Level Architecture Client Layer React application provides: Login Ticket dashboard Ticket creation Ticket details Agent queue Admin dashboard API Layer Node.js/TypeScript API handles: Authentication Request validation Authorization Business logic invocation API responses Application Modules Recommended internal modules: Auth Users Tickets Comments Assignments Attachments Notifications Search Audit Reporting These remain inside one deployable application initially. Persistence PostgreSQL stores transactional application data. Object storage stores uploaded files. Background Processing A queue and worker process: Email notifications Notification retries Potentially report generation Other long-running operations --- 5. 🧩 Component Architecture Component Responsibility Dependencies Data Ownership Scaling Model React Client User interface API None CDN/static scaling API Server HTTP/API processing DB, object storage None directly Horizontal Auth Module Login/session handling DB User credentials/session data Horizontal Ticket Module Ticket lifecycle DB Tickets Horizontal Comment Module Ticket conversations DB Comments Horizontal Attachment Module File metadata/upload authorization Object storage, DB Attachment metadata Horizontal Notification Worker Async notifications Queue, email provider Job state Horizontal Audit Module Security/business audit records DB Audit records Horizontal PostgreSQL Transactional persistence Infrastructure Core application data Vertical + read scaling if required Object Storage Attachments Infrastructure Uploaded files Managed scaling Queue Async job delivery Infrastructure Pending jobs Managed scaling Communication Synchronous Client → API API → PostgreSQL API → object-storage authorization/upload flow Asynchronous API → Queue Worker → Email provider --- 6. 🔄 Critical Data Flows Flow A — Create Ticket Customer ↓ React ↓ POST /tickets ↓ Authentication ↓ Authorization ↓ Input validation ↓ Ticket Service ↓ PostgreSQL transaction ↓ Ticket created ↓ Notification job ↓ Queue ↓ Worker ↓ Email provider Processing 1. Customer submits ticket. 2. API authenticates the request. 3. Request schema is validated. 4. Ticket module creates ticket. 5. Database transaction commits. 6. Notification job is queued. 7. API returns successful ticket creation. 8. Worker sends notification independently. Failure If email fails: Ticket remains created. Notification job is retried. Failed jobs eventually enter dead-letter handling. Customer should not lose the ticket because an email provider failed. --- Flow B — Agent Resolves Ticket Agent ↓ React ↓ PATCH /tickets/{id} ↓ Authentication ↓ RBAC ↓ Ticket Service ↓ Database Transaction ↓ Status = RESOLVED ↓ Audit Event ↓ Notification Job The status update should be transactional. --- 7. 🔌 API Architecture Method Endpoint Purpose Auth Input Output Errors POST /auth/login Login No Credentials Session/token 401 GET /tickets List tickets Yes Filters/pagination Ticket list 401/403 POST /tickets Create ticket Yes Ticket data Ticket 400/401 GET /tickets/{id} View ticket Yes ID Ticket 403/404 POST /tickets/{id}/comments Add reply Yes Comment Comment 400/403 PATCH /tickets/{id} Update ticket Yes Status/priority Ticket 400/403/404 POST /tickets/{id}/assign Assign ticket Yes Agent ID Ticket 400/403 POST /attachments Create upload Yes File metadata Upload information 400/413 GET /users/me Current user Yes None User 401 GET /admin/audit-logs Audit logs Admin Filters Audit records 403 Validation API should validate: Required fields String lengths Enumerated status values Priority values File size File type User permissions Idempotency Ticket creation can optionally support: Idempotency-Key: <client-generated-key> This helps prevent duplicate tickets when clients retry requests. Pagination Use cursor-based pagination for large ticket lists if offset pagination becomes inefficient. --- 8. 🗄️ Data Architecture Core Entities User ├── Role └── Ticket Ticket ├── Comments ├── Assignments ├── Attachments └── Audit Events User id email password_hash role status created_at updated_at Ticket id customer_id assigned_agent_id subject description status priority created_at updated_at resolved_at Comment id ticket_id author_id body created_at Attachment id ticket_id uploaded_by object_key filename content_type size created_at Audit Event id actor_id action resource_type resource_id metadata created_at Indexes Likely useful indexes: Ticket(customer_id, created_at) Ticket(assigned_agent_id, status) Ticket(status, priority, created_at) Comment(ticket_id, created_at) AuditEvent(resource_type, resource_id, created_at) Exact indexes should be confirmed using production query patterns and query plans. Transactions Ticket state changes should use database transactions when multiple related records must remain consistent. Example: Update ticket + Create audit event These can be committed atomically where appropriate. Retention Requirements do not specify retention periods. Therefore: Open decision: define ticket, audit-log and attachment retention requirements. --- 9. ⚡ Performance & Caching Strategy Critical Paths Most performance-sensitive operations are likely: Ticket list Ticket search/filtering Ticket detail Agent queue Dashboard queries Initial Strategy Do not introduce distributed caching immediately. Start with: Proper database indexes Pagination Efficient SQL queries Connection pooling Response payload limits Lazy loading for comments/attachments Possible Future Cache If dashboard reads become expensive: Cache Key: dashboard:{tenant}:{date_range} TTL: 30-120 seconds Invalidation: Short TTL + explicit invalidation for critical updates Staleness: Acceptable for dashboard statistics Failure: Bypass cache → PostgreSQL Attachment Optimization Large files should not pass through the application server unnecessarily. Prefer: Client ↓ API requests upload authorization ↓ Object storage signed upload ↓ Client uploads directly This reduces API-server bandwidth. --- 10. 📬 Async & Event Architecture Event: TicketCreated Producer: Ticket Module Event: TicketCreated Consumer: Notification Worker Action: Send notification email Retry Strategy Example: Attempt 1 → immediate Attempt 2 → short delay Attempt 3 → longer delay Attempt 4 → longer delay Failure → Dead Letter Queue Use exponential backoff with a maximum retry count. Idempotency Notification jobs should contain a unique job/event identifier. The worker should safely handle duplicate delivery. Example: event_id = ticket-created-12345 The worker records processed event IDs or otherwise ensures duplicate processing does not create harmful side effects. Ordering Ticket state updates may require ordering if they are asynchronously processed. For the initial architecture, keep state-changing ticket operations synchronous to avoid unnecessary event-ordering complexity. --- 11. 🔐 Security Architecture Authentication Use: Secure password hashing HTTPS Secure session/token handling Login rate limiting Session expiration Authorization RBAC: CUSTOMER ↓ Own tickets only AGENT ↓ Authorized support tickets ADMIN ↓ Administrative functions Authorization must be checked server-side. Never rely on frontend role checks alone. Trust Boundaries Internet ↓ Frontend/API boundary ↓ Application ↓ Database ↓ External services Sensitive Assets Password hashes User information Ticket conversations Attachments Authentication credentials API secrets Audit records Security Controls TLS Strong password hashing Input validation Parameterized database queries Secure cookies/tokens RBAC Secrets manager Dependency scanning Audit logging Rate limiting File upload restrictions Attachment Security Validate: File size MIME type Extension Upload authorization Consider malware scanning before making files available to other users. --- 12. 🛡️ Reliability Architecture Failure Scenario Detection Recovery User Impact Mitigation API instance failure Health checks Replace instance Temporary request failure Multiple instances DB failure DB monitoring Restore/failover strategy Service degradation Backups + tested recovery Email failure Worker metrics Retry Notification delayed Queue + retry Queue failure Queue monitoring Provider recovery Async jobs delayed Durable queue Object storage failure Upload errors Retry Attachment unavailable Retry + clear error Network timeout Request timeout Controlled retry Delayed response Timeouts Duplicate job Idempotency check Ignore duplicate None Idempotency key Database overload DB metrics Scale/optimize Increased latency Indexing + load testing Retry Rule Never retry indefinitely. Retries must have: Maximum attempts Backoff Timeout Dead-letter handling where applicable --- 13. 📈 Scalability Strategy Current MVP 2+ API instances ↓ Managed PostgreSQL ↓ Object Storage ↓ Queue + Worker API servers should remain stateless so they can scale horizontally. Near-Term Growth If traffic increases: 1. Horizontally scale API instances. 2. Optimize database queries. 3. Improve indexes. 4. Scale workers based on queue depth. 5. Introduce caching for proven hot reads. 6. Consider read replicas if read traffic becomes dominant. Potential Bottlenecks Database Likely first major bottleneck because ticket management is transaction-heavy. Search Simple PostgreSQL search may work initially. If search requirements become significantly more sophisticated, a dedicated search engine could be evaluated. Do not add one before the workload justifies it. Queue Monitor: Queue depth Processing latency Failure rate Retry count --- 14. 📊 Observability Architecture Metrics Track: Request count p50/p95/p99 latency HTTP error rate Database latency Database connection usage Queue depth Worker failures Email delivery failures Attachment upload failures CPU/memory utilization Logs Log: Request correlation ID Authentication events Authorization failures Ticket lifecycle events Worker failures External API failures Do not log: Passwords Authentication tokens API secrets Sensitive ticket content unnecessarily Tracing Distributed tracing is especially useful across: API ↓ Database ↓ Queue ↓ Worker ↓ Email provider Alerts Create alerts for: Sustained API error rate p95 latency breach Database saturation Queue backlog Worker failure spike Backup failure Infrastructure health problems --- 15. ☁️ Deployment Architecture Environments Development ↓ Staging ↓ Production Production Recommended logical setup: Users ↓ CDN / Load Balancer ↓ API Instances ↓ PostgreSQL ↓ Object Storage API ↓ Queue ↓ Worker ↓ Email Provider CI/CD Pipeline: Git Push ↓ Lint ↓ Unit Tests ↓ Build ↓ Security Checks ↓ Integration Tests ↓ Deploy Staging ↓ Smoke Tests ↓ Production Deployment Database Migrations Migrations should be: Version controlled Automated Tested in staging Backward-compatible where rolling deployments require it Deployment Strategy For this system, a rolling deployment is sufficient initially if the application supports backward-compatible releases. Blue/green or canary deployment can be introduced if deployment risk or traffic scale later justifies it. Backup Daily PostgreSQL backups are required by the sample requirement. But backup success alone is insufficient. Perform periodic restore tests. --- 16. ⚖️ Architecture Tradeoffs Decision Chosen Approach Alternative Benefit Tradeoff Application architecture Modular monolith Microservices Lower operational complexity Less independent service deployment Database PostgreSQL Document DB Strong transactions + relational queries Requires careful schema/query design Notifications Async queue Synchronous email Doesn't block ticket operations More infrastructure Attachments Object storage DB blobs Better file scalability Separate storage lifecycle Cache Initially none Redis Simpler MVP Some repeated reads may remain expensive Search PostgreSQL initially Search engine Lower complexity Advanced search may require later migration Deployment Rolling Blue/green Simpler Less isolation during deployment --- 17. ⚠️ Architecture Risk Register Risk Likelihood Impact Mitigation Validation DB becomes bottleneck Medium High Indexing, query optimization Load testing Email provider outage Medium Medium Queue + retries Failure testing Attachment growth High Medium Object storage + lifecycle policy Storage projection Search performance degrades Medium Medium Query optimization Query/load testing Incorrect authorization Low/Medium High Central RBAC checks Security testing Backup cannot restore Low High Restore drills Recovery testing 500 req/s target not achieved Medium High Profiling + horizontal scaling Load test Queue duplicates jobs Medium Medium Idempotent workers Failure testing --- 18. 🛠️ Implementation Roadmap Phase 1 — Foundation Build: Repository structure TypeScript backend React frontend PostgreSQL Configuration management Basic CI pipeline Phase 2 — Core Architecture Build: Authentication User management RBAC Ticket module Comment module Assignment workflow Phase 3 — Data & APIs Build: REST APIs Validation Pagination Database migrations Indexes Attachment metadata API documentation Phase 4 — Security & Reliability Build: Rate limiting Secure sessions Audit logging Error handling Timeouts Retry policies Backup configuration Phase 5 — Performance & Scaling Validate: 500 req/s peak p95 < 300 ms Database capacity Concurrent users Queue throughput Only then introduce additional performance infrastructure where measurements justify it. Phase 6 — Observability Implement: Centralized logs Metrics Tracing Dashboards Alerts Health endpoints Phase 7 — Deployment Implement: Staging Production Infrastructure as code CI/CD Rolling deployments Automated migrations Phase 8 — Production Hardening Perform: Security testing Load testing Failure testing Backup restoration Dependency failure testing Operational runbooks --- 19. 🧪 Architecture Validation Plan Unit Testing Test: Ticket business rules Authorization rules Validation Status transitions Assignment logic Integration Testing Test: API → PostgreSQL API → Object Storage API → Queue Worker → Email Provider End-to-End Testing Example: Customer creates ticket ↓ Agent sees ticket ↓ Agent replies ↓ Customer receives update ↓ Agent resolves ticket Load Testing Validate: 500 requests/sec p95 latency < 300 ms Database behavior under load Worker throughput Concurrent ticket operations Failure Testing Simulate: Database unavailable Email provider timeout Queue unavailable Object storage failure Duplicate jobs Network timeout Security Testing Test: Broken access control Authentication bypass Injection File upload abuse Rate-limit bypass Session security Privilege escalation Recovery Testing Validate: Database restoration Backup integrity RPO RTO Application recovery procedure --- 20. 🏁 FINAL BUILD-READY SPECIFICATION Architecture Modular monolith Node.js + TypeScript React frontend PostgreSQL Object storage Background queue + workers Components Authentication Users Tickets Comments Assignments Attachments Notifications Audit Reporting APIs Authentication Ticket CRUD Comments Assignment Attachments User profile Admin audit Data Stores PostgreSQL for transactional data Object storage for attachments Queue for asynchronous jobs Data Flows Ticket creation is synchronous Notifications are asynchronous Ticket state changes are transactional Attachments use direct object-storage uploads where appropriate Security Controls HTTPS Password hashing RBAC Input validation Rate limiting Secrets management Audit logging File validation Reliability Controls Health checks Timeouts Bounded retries Exponential backoff Idempotent workers Dead-letter handling Database backups Restore testing Scaling Strategy Stateless API instances Horizontal scaling Database optimization Worker scaling Object storage Cache only when measurements justify it Observability Metrics Structured logs Distributed traces Health checks Alerts Operational dashboards Deployment Development ↓ CI Tests ↓ Staging ↓ Smoke Tests ↓ Production Critical Decisions Confirm tenant/isolation model. Confirm authentication/session approach. Define ticket retention. Define attachment limits. Confirm exact email provider. Validate 500 req/s requirement. Validate p95 < 300 ms. Define detailed disaster-recovery procedure. Open Questions 1. Is the platform single-tenant or multi-tenant? 2. What maximum attachment size is allowed? 3. Are customers allowed to communicate through email as well as the web application? 4. How long should tickets and audit logs be retained? 5. Is full-text search required? 6. Are there compliance requirements? 7. What exact availability measurement defines the 99.9% target? Immediate Engineering Actions 1. Confirm unresolved requirements. 2. Create domain model. 3. Design PostgreSQL schema. 4. Define API contracts. 5. Implement authentication and RBAC. 6. Implement ticket lifecycle. 7. Add automated tests. 8. Add queue-based notifications. 9. Add observability. 10. Deploy staging. 11. Run load and failure tests. 12. Harden before production. Result: This design deliberately starts with a modular monolith and only introduces additional infrastructure where the stated workload or reliability requirements justify it.