Architecture for high concurrency starts with acknowledging the chaos of real-world events—thousands of users RSVPing, scanning tickets, or reacting to live updates at once. To handle this, the system must follow distributed principles: stateless services that don’t retain information between requests, event queues that process actions (like ticket scans) sequentially, horizontal scaling to distribute load across multiple servers, and consistent data partitioning to keep data organized and quickly accessible.
A layered architecture—composed of a reverse proxy/load balancer (e.g., Envoy), API Gateway, service mesh (e.g., Istio), and asynchronous event backbone (e.g., Kafka or NATS)—enables scalability and fault tolerance. Using circuit breakers, bulkheads, and retries with exponential backoff, where wait time between retries increases exponentially after each failure, ensures the system remains stable under peak loads.
Event-Driven Domain Modeling: From RSVPs to Real-Time Notifications
In event-driven systems, domain modeling requires decoupling intention from action. RSVP creation is a command; notification dispatch is a reaction. Use Domain-Driven Design (DDD) patterns such as aggregates, bounded contexts, and domain events to capture invariants and maintain transactional boundaries.
Model RSVP as a stateful entity with transitions (PENDING → CONFIRMED → CANCELLED). Notifications become ephemeral, derived artifacts, powered by a pub/sub pattern. Event stores (e.g., EventStoreDB) can serve as the source of truth, enabling time-travel debugging and eventual consistency in distributed scenarios.
Backend Services and API Design: CQRS, BFF Patterns & GraphQL/REST Trade-offs
Command Query Responsibility Segregation (CQRS)
CQRS stands for Command Query Responsibility Segregation, a pattern that separates how your system handles commands (write operations like “buy a ticket” or “register for an event”) from how it handles queries (read operations like “show upcoming events” or “list attendees”).
- Commands update the system’s state by changing data, often processed asynchronously through a message bus or queue (e.g., Kafka, RabbitMQ). This improves resilience and allows high-throughput writes without blocking user requests.
- Queries pull data from read-optimized, often denormalized stores such as Elasticsearch, Redis, or read replicas of the database. This separation ensures fast, scalable reads—critical when thousands of users are browsing event listings in real time.
By decoupling the read and write sides, CQRS allows your app to scale independently based on demand (e.g., lots of reads vs fewer writes), which is ideal for real-time event apps with heavy read traffic.
Backend Services and API Design: CQRS, BFF Patterns & GraphQL/REST Trade-offs
Command Query Responsibility Segregation (CQRS)
CQRS stands for Command Query Responsibility Segregation, a pattern that separates how your system handles commands (write operations like “buy a ticket” or “register for an event”) from how it handles queries (read operations like “show upcoming events” or “list attendees”).
Commands update the system’s state by changing data, often processed asynchronously through a message bus or queue (e.g., Kafka, RabbitMQ, or PubNub). PubNub’s real-time event distribution capabilities enable high-throughput writes and asynchronous processing without blocking user requests.
Queries pull data from read-optimized, often denormalized stores such as Elasticsearch, Redis, or read database replicas. This separation ensures fast, scalable reads, critical when thousands of users browsing event listings in real time.
By decoupling the read and write sides, CQRS allows your app to scale independently based on demand (e.g., lots of reads vs fewer writes), which is ideal for real-time event apps with heavy read traffic.
GraphQL vs REST: Choosing the Right API Strategy
GraphQL and REST are two popular ways to expose backend data to frontends, each with distinct advantages and challenges
GraphQL allows clients to request the data they need, offering fine-grained data access, strong type safety, and fewer round trips. However, it requires more complex caching strategies, can face resolver performance issues at scale, and is harder to debug in production.
REST uses predictable HTTP endpoints (e.g., /events, /users/{id}) and is simpler to implement and monitor. It’s easier to cache at the HTTP layer (CDNs, reverse proxies) and is battle-tested for internal APIs.
Best practice for event platforms
Use a hybrid approach—GraphQL for public APIs where flexibility is key, and REST internally for service-to-service communication, focusing on simplicity and caching.
For a production event app handling live ticketing, registrations, and real-time updates, use CQRS to scale reads vs. writes, implement BFFs for tailored responses to mobile and web users, and choose GraphQL for flexibility and REST for simplicity.
Data Layer Strategy: Temporal Data Models, Multi-Tenant Sharding, and Audit Trails
Temporal data models use bitemporal schemas to track not just what happened, when it was known to the system. Every record includes valid_time and transaction_time. This is vital for resolving event timelines in audits and legal disputes.
For multi-tenant architectures, sharding by tenant (subdomain or organization ID) ensures isolation and scalability. Use hash or range-based sharding in distributed SQL databases like CockroachDB or Yugabyte. Ensure tenant-aware query optimization and security fencing.
Audit trails are immutable logs of all state changes, persisted in append-only structures (e.g., via Kafka topics or ledger-style stores). Ensure traceability with structured logs and correlated request IDs.
Authentication and Authorization: Multi-Role Access Control with OAuth2 and JWT
In multi-role systems (organizers, attendees, staff), access control must be both coarse (role-based) and fine-grained (resource-based). OAuth2 provides delegated identity via authorization flows (e.g., PKCE for mobile). JWTs encode user claims and scopes but must be short-lived and cryptographically signed (RS256 preferred).
Introduce a policy engine like OPA (Open Policy Agent) to evaluate dynamic conditions (e.g., time-bound ticket access). Use refresh tokens and session lifecycles with rotating secrets to mitigate token replay.
Real-Time Capabilities: WebSockets, Push Notifications, and Live Event Feeds
For live updates—seat availability, schedule changes, or Q&A—WebSockets provide full-duplex, persistent connections. Use a broker like Socket.IO or a managed pub/sub system like PubNub or Ably to fan-out messages to thousands of clients.
Push notifications (APNs, FCM) require managing device tokens, delivery receipts, and localized payloads. Use a queue-based buffer to decouple real-time engines from upstream changes. In live feeds, consider DeltaSync patterns or CRDTs (conflict-free replicated data types) to manage shared state without locks.
Mobile and Web Frontend Engineering: Offline Support, Sync Strategies, and PWA Readiness
Mobile and PWA apps must gracefully degrade. Offline-first design involves local-first writes (using SQLite or IndexedDB), conflict resolution strategies (e.g., LWW - Last Write Wins), and sync protocols (e.g., Operational Transforms or CRDTs).
PWA readiness includes Service Workers, cache invalidation strategies, and background sync. Employ periodic sync patterns with backoff timers and connectivity listeners to handle edge cases. For state sync, Apollo Client or Redux Toolkit Query can manage client-side caching efficiently.
Observability: Structured Logging, Distributed Tracing, and Incident-First Monitoring
Structured logging (e.g., JSON with trace IDs) feeds into centralized platforms like ELK or Loki. Enforce log hygiene with consistent schemas and redact PII before persistence.
Distributed tracing (OpenTelemetry, Jaeger) is essential for tracking cross-service requests. Inject traceparent headers in all APIs and capture spans for high-latency calls. Use service graphs to visualize bottlenecks.
Incident-first monitoring involves SLOs, SLIs, and alerting tied to business KPIs (e.g., "95% of ticket scans must complete in <1s"). Integrate with on-call rotation tools (PagerDuty, Opsgenie) and annotate alerts with recent deploys via CI/CD webhooks.
Infrastructure-as-Code: Kubernetes Deployments, Secret Management, and Blue-Green Strategies
Infrastructure must be reproducible. Use tools like Helm or Kustomize atop Kubernetes to template manifests. Leverage GitOps (ArgoCD or Flux) for declarative sync.
Secrets must never be hardcoded—store them in Vault, AWS Secrets Manager, or Kubernetes sealed-secrets. Use RBAC to limit access to secret volumes.
Blue-green deployments minimize downtime: deploy new versions alongside live ones, redirect traffic via Istio/Envoy routing, then promote after smoke tests. Canary releases and progressive rollouts add extra safety with live metrics gating.
Third-Party Integrations: Calendar Sync, Payment Gateways, and Ticket Scanning SDKs
Calendar sync (Google, Outlook) requires OAuth scopes and webhook-based calendar watching. Normalize time zones, recurrence rules (RFC 5545), and daylight savings behavior to avoid scheduling issues.
Payments require PCI-DSS compliance. Use tokenized flows with Stripe, Adyen, or Braintree. Webhooks must be idempotent and secured via HMAC headers.
For ticket scanning, SDKs (e.g., QR/barcode via Scandit or Dynamsoft) need offline fallback, low-light optimization, and validation logic synced via background jobs.
Compliance and Security: GDPR, SOC2 Readiness, and Secure Guest Data Workflows
GDPR compliance involves data minimization, right to erasure, and explicit consent. Architect systems to support data subject requests via asynchronous workflows.
SOC2 readiness includes strict change management, least-privilege access control, and evidence logging. Automate compliance evidence collection (e.g., CI logs, audit trails, access logs).
Guest workflows (e.g., anonymous RSVP or on-site check-ins) must sandbox personal data, encrypt at rest (AES-256), and purge expired sessions. Apply DLP (Data Loss Prevention) for exports, and track consent timestamps.
