Software Requirements Specification (SRS)
Bus Ticket Booking System
1. Introduction
1.1 Purpose
This document rigorously outlines the functional and non-functional requirements for a Real-Time High-Load Bus Ticket Booking System. The system is engineered to concurrently serve over 10,000 users with stringent performance targets: sub-second end-to-end latency (p99), a minimum 99.99% monthly uptime, and a firm commitment to zero overbooking. This specification serves as the definitive guide for the development, testing, and deployment of the system.
1.2 Scope
The system will provide the following core capabilities:
- Real-time seat inventory management: Accurate and up-to-the-second tracking of seat availability.
- Secure payments: Integration with PayPal for processing online transactions.
- Automated scaling: Dynamic adjustment of resources to handle varying loads.
- Fault tolerance: Mechanisms to ensure system resilience and minimize downtime.
- Centralized observability: Comprehensive monitoring and logging for system health and performance analysis.
Explicitly Excluded from Scope:
- Multi-region active-active deployment: Distribution of the system across multiple geographically dispersed data centers for enhanced availability.
- AI-driven demand forecasting: Predictive analytics to anticipate future booking trends and optimize resource allocation.
2. System Overview
2.1 Key Features
- Real-Time Seat Booking: Ensures atomic reservation of seats utilizing optimistic locking mechanisms to maintain data consistency under high concurrency.
- Resilient Payments: Implements idempotent payment transactions through PayPal integration, preventing duplicate charges and ensuring reliable processing.
- Horizontal Autoscaling: Leverages Kubernetes to automatically scale the number of application instances based on demand.
- Comprehensive Observability: Integrates a robust observability stack including:
- Logs: ELK (Elasticsearch, Logstash, Kibana) for centralized log aggregation and analysis.
- Metrics: Prometheus and Grafana for collecting and visualizing key performance indicators.
- Tracing: Zipkin for distributed transaction tracing to identify performance bottlenecks.
2.2 User Roles
| Role | Description |
|---|---|
| Traveler | End-user who can browse routes, view seat availability, book seats, make online payments, and manage their tickets. |
| Bus Operator | System user responsible for managing bus routes, defining seat configurations, setting pricing, and viewing booking statistics. |
| Administrator | System operator with privileges to monitor overall system health, configure security policies, and manage user roles and permissions. |
3. Functional Requirements
3.1 Seat Reservation
| ID | Requirement | Priority | Notes |
|---|---|---|---|
| FR1 | Travelers shall be able to reserve seats in real-time via a low-latency gRPC API. | High | The gRPC API should be designed for high performance and efficiency to handle concurrent booking requests. |
| FR2 | The system shall maintain a near real-time cache of seat inventory in Redis with a Time-To-Live (TTL) of 5 seconds to minimize direct load on the PostgreSQL database. | High | The caching strategy should balance data freshness with read performance. Consider cache invalidation strategies for critical updates. |
| FR3 | The system shall prevent overbooking of seats using PostgreSQL row-level locks to ensure data integrity during concurrent reservations. | Critical | Row-level locks should be implemented efficiently to minimize contention and impact on overall performance. |
3.2 Payment Processing
| ID | Requirement | Priority | Notes |
|---|---|---|---|
| FR4 | Travelers shall be able to make payments securely via PayPal. All payment transactions must be idempotent, with idempotency keys stored in Redis to prevent duplicate charges. | High | The PayPal integration should adhere to security best practices. Idempotency keys should have an appropriate expiration policy. |
| FR5 | In the event of a failed payment, the system shall automatically initiate a refund process using the Saga pattern to ensure eventual consistency across involved services (e.g., booking and payment). | Medium | The Saga pattern implementation should handle potential compensation transactions and failure scenarios gracefully. Define clear steps and error handling within the Saga. |
3.3 System Administration
| ID | Requirement | Priority | Notes |
|---|---|---|---|
| FR6 | Administrators shall be able to manage Role-Based Access Control (RBAC) roles and permissions for system users through integration with Keycloak using OAuth2/OIDC protocols. | High | Keycloak integration should be seamless and secure. Define the specific roles and their associated permissions. |
| FR7 | Administrators shall have the ability to monitor key system metrics (e.g., API latency, Kafka consumer lag, CPU/memory utilization) in Grafana to gain insights into system performance and health. | Medium | Define the specific metrics that need to be monitored and the thresholds for alerts. Grafana dashboards should be intuitive and provide actionable information. |
4. Non-Functional Requirements
4.1 Performance
| ID | Requirement | Metric | Notes |
|---|---|---|---|
| NFR1 | The 99th percentile (p99) of the API response time for seat reservation and payment initiation endpoints shall be less than 300 milliseconds under a sustained load of 500 requests per second per application pod. | p99 < 300ms @ 500 req/sec/pod | Performance testing should be conducted under realistic load conditions to validate this requirement. |
| NFR2 | End-to-end latency for seat inventory updates propagated via Kafka shall be less than 1 second under normal operating conditions. | E2E Latency < 1s | This includes the time taken for an update to be produced, transmitted, and consumed by relevant services. |
4.2 Scalability
| ID | Requirement | Metric | Notes |
|---|---|---|---|
| NFR3 | The system shall be capable of automatically scaling horizontally from a baseline of 5 pods to a maximum of 50 pods based on CPU utilization and Kafka consumer lag metrics, utilizing KEDA (Kubernetes Event-driven Autoscaling). | Scale from 5 to 50 pods | Define the specific CPU utilization and Kafka lag thresholds that trigger scaling events. Ensure that the scaling process is efficient and does not negatively impact performance during scaling operations. |
4.3 Reliability
| ID | Requirement | Metric | Notes |
|---|---|---|---|
| NFR4 | The system shall achieve a monthly uptime of at least 99.99%, allowing for a maximum of approximately 4.3 minutes of unscheduled downtime per month. | Uptime ≥ 99.99% (≤ 4.3 mins downtime/month) | Implement robust monitoring, alerting, and automated recovery mechanisms to meet this stringent availability target. Consider redundancy at various levels. |
| NFR5 | In the event of a pod failure, the Kubernetes orchestration platform shall automatically restart the failed pod within 30 seconds to minimize service disruption. | Auto-restart within 30s | Configure appropriate liveness and readiness probes for the Kubernetes pods to enable timely detection and recovery from failures. |
4.4 Security
| ID | Requirement | Implementation | Notes |
|---|---|---|---|
| NFR6 | All internal and external APIs shall enforce JWT (JSON Web Token) validation for authentication and authorization, leveraging Keycloak as the identity provider and adhering to OAuth2/OIDC flows. | JWT Validation (Keycloak/OAuth2/OIDC) | Secure all API endpoints to prevent unauthorized access. Implement proper token generation, signing, and verification mechanisms. Regularly review and update security configurations. |
| NFR7 | All sensitive secrets, including database passwords and third-party API keys, shall be securely encrypted at rest using SOPS (Secrets OPerationS) and managed as Kubernetes Secrets. | SOPS + Kubernetes Secrets | Implement a secure workflow for managing and accessing secrets. Ensure that secrets are never hardcoded in the application code or configuration files. Regularly rotate sensitive secrets as a security best practice. |
5. External Interfaces
5.1 User Interfaces
- Traveler Web/Mobile Application: Provides a user-friendly interface for browsing bus routes, viewing interactive seat maps (built with React.js), filling out booking details, and completing payments through an integrated PayPal SDK.
- Administrator Dashboard: A web-based interface (built with Grafana) for visualizing system metrics and dashboards, and Kibana for searching and analyzing aggregated logs.
5.2 APIs
- gRPC Endpoints: High-performance, low-latency endpoints for core functionalities such as real-time seat reservation and initiating payment transactions.
- REST APIs: Standardized HTTP-based endpoints for administrative operations, including user management and bus route configuration.
5.3 Data Storage
- PostgreSQL: A relational database management system providing ACID (Atomicity, Consistency, Isolation, Durability) compliant transactions for managing core booking data and ensuring data integrity.
- Redis: An in-memory data store used as a high-speed cache for seat availability information and for storing idempotency keys to ensure reliable payment processing.
- Kafka: A distributed event streaming platform used as the backbone for asynchronous communication of seat updates and payment events between different services.
6. System Constraints
- Budget: The project aims to minimize the use of paid third-party tools. As an example, Sealed Secrets will be preferred over HashiCorp Vault for managing secrets in Kubernetes.
- Deployment Environment: The initial deployment will be within a single cloud region (either AWS or GCP) to manage complexity during the initial phase.
- Compliance Requirements: The system must adhere to GDPR regulations for handling personal data of EU users and comply with PCI-DSS standards for processing payment information.
7. Assumptions & Dependencies
- Assumption 1: The PayPal API will maintain a minimum availability of 99.95%.
- Dependency 1: A fully managed Kubernetes cluster (e.g., AWS EKS or Google GKE) will be provisioned and maintained by the cloud provider.
- Dependency 2: A properly configured and accessible Keycloak server will be available for identity and access management.
- Document Version: 1.0
- Date: 2025-06-19
- Author: ArturChernets