Vision Document: Bus Ticket Booking System
Scalable, Reliable, and Cost-Efficient Booking for Modern Transit
1. Introduction
The Real-Time Bus Ticket Booking System is a high-performance platform designed to handle 10,000+ concurrent bookings with sub-second latency and 99.99% uptime. Built on a pragmatic tech stack (Java/Spring Boot, Kafka, Kubernetes), it eliminates overbooking risks, simplifies payment integration, and ensures fault tolerance for mid-sized operators and enterprises.
2. Problem Statement
Current Industry Pain Points:
- Peak Load Failures: Systems crash during holidays or flash sales.
- Complex Integrations: Fragmented payment and auth systems.
- Manual Scaling: Inefficient resource usage during off-peak hours.
- Limited Observability: No unified logs or metrics for rapid debugging.
Our Solution:
- Horizontal scaling with Kubernetes (HPA/KEDA).
- Event-driven resilience via Kafka and idempotent gRPC APIs.
- Unified security with Keycloak (OAuth2/OIDC) and RBAC.
- Cost-effective observability using ELK + Prometheus/Grafana.
3. Vision Statement
"To empower bus operators with a battle-tested, scalable booking platform that balances performance and simplicity where every seat is reserved atomically, every payment is guaranteed, and every failure is recoverable within seconds."
4. Key Objectives
| Objective | Metric |
|---|---|
| Scalability | 500 bookings/sec per Kubernetes pod |
| Latency | 300ms API response (p99) |
| Reliability | 99.99% uptime; 30s pod auto-recovery |
| Cost Efficiency | 40% lower cloud costs vs. legacy systems |
5. Target Audience
| Segment | Needs Addressed |
|---|---|
| Travelers | Instant bookings, real-time seat maps, PayPal integration. |
| Bus Operators | Centralized seat inventory, fraud alerts (Redis rate limits). |
| DevOps | Kubernetes-driven scaling, ArgoCD rollbacks, Grafana dashboards. |
6. Core Features
6.1. Real-Time Seat Management
- Optimistic Locking: PostgreSQL transactions with row-level locks.
- Redis Caching: Seat availability cached with TTL to reduce DB load.
- gRPC Services: Low-latency seat holds and releases.
6.2. Resilient Payment Flow
- PayPal Integration: Idempotent API keys stored in Redis to prevent duplicates.
- Saga Pattern: Compensating transactions for refunds on partial failures.
6.3. Proactive Monitoring
- ELK Stack: Centralized logs for rapid incident diagnosis.
- Prometheus/Grafana: Real-time metrics (API latency, Kafka lag).
- Zipkin: Distributed tracing for gRPC/Kafka flows.
7. Technical Strategy
7.1. Architecture
- Microservices: Spring Boot (Java 21) + Spring Cloud, Eureka for service discovery.
- Event Streaming: Kafka for seat-inventory updates and payment triggers.
- CI/CD: GitHub Actions + GHCR + ArgoCD (GitOps).
7.2. Data Layer
- PostgreSQL: ACID-compliant transactions for bookings.
- Liquibase: Version-controlled schema migrations.
- Redis: Cache for seat maps and payment idempotency keys.
7.3. Security
- Keycloak: OAuth2/OIDC with JWT tokens and LDAP integration.
- Secrets: SOPS + Kubernetes Secrets (avoiding Vault costs).
7.4. Deployment
- Kubernetes: Helm-managed pods with HPA/KEDA autoscaling (CPU/Kafka metrics).
- Observability:
- Logs: Filebeat + Elasticsearch + Kibana.
- Metrics: Prometheus exporters + Grafana dashboards.
- Traces: Zipkin for gRPC/Kafka interactions.
8. Non-Functional Requirements
| Category | Requirement | How We Achieve It |
|---|---|---|
| Scalability | Auto-scale from 5 to 50 pods based on load. | KEDA + Kafka consumer lag. |
| Recovery | 30s pod restart; 5 min regional failover. | Kubernetes self-healing. |
| Security | RBAC via Keycloak; encrypted secrets. | SOPS + Kubernetes Sealed Secrets. |
10. Service Level Objectives, Agreements & Error Budgets
| Measure | Target | Rationale |
|---|---|---|
| SLA (Customer-Facing) | 99.99% monthly uptime | Guarantees mission-critical availability (~4.38 min downtime per month). |
| SLO (Engineering-Facing) | - 95th percentile API latency 300ms - 99th percentile availability + 99.9% | Drives internal performance targets while aligning with SLA. |
| Error Budget | 0.1% monthly error budget | ~43.2 minutes of allowable downtime or degraded performance. |
| Burn Rate Policies | - Pause non-critical deploys when >50% budget consumed in 14 days - Emergency rollback if >75% budget consumed | Ensures SLA compliance and controlled release cadence. |
11. Load-Testing & Resilience Scenarios
We validate system behavior under realistic traffic patterns using k6 and Gatling:
| Scenario | Description | Key Metrics |
|---|---|---|
| Soak Test | 24h at 200 req/sec (20% above peak) | Memory/CPU leak detection; sustained latency |
| Spike Test | Sudden jump from 50 to 500 req/sec in 30s | Autoscaling response time; error rate |
| Stress Test | Ramp to 1,000 req/sec until failure | Maximum throughput; bottleneck analysis |
| Chaos Test | Randomly terminate 1-2 pods every 10 min during soak | Recovery time; error budget burn rate |
12. Failover & Retry Mechanisms
PostgreSQL & Redis
- Active-Passive Postgres with automatic failover via Patroni + etcd
- HikariCP health checks route connections to healthy primary/replica
- Retry Policy: Exponential backoff on transient errors (max 5 retries, 200ms - 3s)
Kafka Event Streaming
- Idempotent Producers for exactly-once writes
- Consumer Offset Management: Commit post-processing; dead-letter
failed-eventstopic - Cooperative Rebalancing to minimize partition thrash
- Cross-region DR via MirrorMaker 2.0
13. Rate-Limiting & DDoS Protection
- Spring Cloud Gateway enforces token-bucket limits per tenant:
- 100 req/min for free tier
- 1,000 req/min for enterprise tier
- Distributed Rate Store: Redis cluster with Lua scripts
- WAF Integration: AWS WAF or Cloudflare managed rules for SQLi/XSS protection
- Global Throttling: CDN edge caching + geofencing rules for burst attacks
14. Caching Strategy
| Layer | Purpose | TTL / Eviction | Fallback |
|---|---|---|---|
| Redis Read Cache | Seat availability lookups | 5s TTL, LRU eviction | On miss Postgres read with row lock |
| App-Level Cache | Idempotency keys, rate counters | 1h sliding window | Persist to Redis if local cache unavailable |
| Stale-If-Error | Serve slightly out-of-date seat map if Redis down | Serve stale up to 30s | Trigger background cache refresh |
- Stampede Protection: Redlock mutex per key
- Write-Through & Write-Behind: Idempotency writes sync; booking events async update cache
15. Kafka Schema Evolution & Governance
- Confluent Schema Registry enforces backward/forward compatibility
- Avro serialization with explicit version fields
- Contract-First Development: Shared Avro definitions in central Git repo
- Automated CI Checks:
avro-tools testagainst live registry- Canary deployment via dedicated topic + compatibility tests
16. Conclusion
This comprehensive platform not only meets its functional goals but also adheres to standards ensuring robust performance, seamless scalability, and enterprise-level reliability without over-engineering or excessive cost.
- Document Version: 1.0
- Date: 2025-06-19
- Author: ArturChernets