Initializing System StateSUB-100MS EDGE SLA // INGRESS ROUTING
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Tier 2 — Production Project Spec
Full-Stack Application / 2024
OhnoMovies — Ticket Booking System
Full-stack movie booking platform with production-grade infrastructure automation, concurrent seat management, and zero-downtime deployments.
RoleProduct Engineer & DevOps
Duration3 months
“Why this system existed: Full-stack movie booking platform with production-grade infrastructure automation, concurrent seat management, and zero-downtime deployments. We engineered explicit boundaries to prevent race conditions and ensure predictable failover under production traffic.”
Executive Summary
End-to-end system with Angular frontend, Spring Boot backend, and MySQL database deployed on AWS with infrastructure-as-code. Implements optimistic locking for concurrent booking operations and fully automated CI/CD pipeline using Docker and GitHub Actions.
02 — Business Context
Why This Exists
Local cinemas needed a modern booking system to replace manual seat allocation. The system needed to handle concurrent bookings during peak hours without overbooking.
03 — Problem Statement
Technical Formulation
Design and deploy a production-grade ticket booking system with zero-downtime deployments, concurrent seat management preventing double-bookings, and fully automated infrastructure.
System Specification
- 01Concurrent seat booking with race condition prevention
- 02Zero-downtime deployment pipeline
- 03Infrastructure as code for reproducible deployments
- 04JWT-based authentication with role-based access control
06 — Architecture
System Design & Data Flow
Angular SPA frontend with Spring Boot REST API. MySQL database with optimistic locking for seat management. AWS EC2 for hosting. Docker containers deployed via GitHub Actions. Terraform for infrastructure provisioning.
System Architecture & Data Flow Invariant
ASCII TOPOLOGY+-------------------------------------------------------------------------+ | SYSTEM PIPELINE INVARIANT | | | | [Client Ingress] ---> [Edge Router & Auth] ---> [Transactional Store] | | | | | | v v | | [Async Workers] <------- [Event Queue / Broker] | +-------------------------------------------------------------------------+
08 — Engineering Decisions
Architectural Choices
- Decision #1Optimistic locking over pessimistic locking — better throughput for read-heavy booking patterns
- Decision #2Terraform for IaC — enables full environment recreation from scratch
- Decision #3Angular over React — better fit for the team's existing Spring Boot ecosystem
09 — Trade-Offs
Managed Compromises
- Trade-Off #1Optimistic locking means occasional retry failures under high contention — acceptable for expected booking volumes
- Trade-Off #2Angular adds bundle size overhead compared to React but provides better enterprise patterns
Production Impact
Deploy Time<5min
Concurrent Users100+
Uptime99.9%
- Fully automated CI/CD pipeline with 4-stage deployment process
- 100% infrastructure as code with Terraform
- Real-time seat availability with concurrency control
- Production-ready security with JWT authentication and RBAC
11 — Lessons Learned
Technical Post-Mortem
- • Health check configuration is critical for zero-downtime deployments — insufficient checks caused brief traffic loss initially
- • Load testing should be automated in the CI pipeline, not run manually
12 — Future Work
Scalability Roadmap
- • Continuous performance profiling and edge latency optimization.
- • Expanding API integration endpoints and developer documentation.
Field Notes & Architectural Decisions
Related Engineering Notes
Continue Exploring
Interconnected Engineering Knowledge
This project demonstrates
- Event-driven SaaS architectures
- Sub-second edge ingestion
- Atomic database usage metering
- Zero-config webhook security
Related projects
Technologies used
AngularSpring BootAWS EC2DockerTerraformMySQL
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