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Payroll System Design ​

1. Business Requirements ​

Functional Requirements ​

  • User registration and authentication (employees, HR, payroll admins)
  • Employee profile management (personal, tax, and bank details)
  • Payroll processing (salary calculation, deductions, bonuses)
  • Payslip generation and distribution
  • Tax calculation and compliance
  • Leave and attendance integration
  • Alerts/notifications (urgent payroll issues, payment status, compliance deadlines)
  • Mobile-ready responsive UI and API
  • Role-based access control
  • Audit logging for all payroll actions
  • Analytics and reporting (payroll trends, cost breakdowns)

Non-Functional Requirements ​

  • 99.9% availability (max ~8.76 hours downtime/year)
  • Scalability to support large organizations and payroll cycles
  • Secure data storage and access control (PII, financial data)
  • Fast response times (<300ms for most requests)
  • Audit logging and monitoring
  • Backup and disaster recovery
  • GDPR/data privacy compliance
  • Mobile responsiveness

Out of Scope ​

  • Integration with external ERP/accounting systems (unless specified)
  • Direct payment processing to banks (unless specified)
  • Benefits management (unless specified)

2. Estimation & Back-of-the-Envelope Calculations ​

  • Users: 10,000 (employees, HR, admins)
  • Payroll cycles/year: 12 (monthly) × 10,000 = 120,000
  • Payslips/year: 120,000 × 1 KB ≈ 120 MB
  • Employee data: 10,000 × 2 KB ≈ 20 MB
  • Audit logs: 1M × 0.2 KB ≈ 200 MB
  • Reports/analytics: 10,000 × 0.5 KB ≈ 5 MB
  • Total DB size: ~350 MB (excluding logs, backups, attachments)
  • Availability:
    • 99.9% = 8.76 hours/year downtime max
    • Use managed DB, multi-AZ deployment, health checks, auto-scaling

3. High Level Design (Mermaid Diagrams) ​

Component Diagram ​

mermaid
flowchart LR
  User[User (Web/Mobile)]
  LB[Load Balancer]
  App[Application Server]
  DB[(Database)]
  Cache[Cache (Redis)]
  Alert[Alert/Notification Service]
  Analytics[Analytics Engine]

  User --> LB --> App
  App --> DB
  App --> Cache
  App --> Alert
  App --> Analytics
  Analytics --> DB

Data Flow Diagram ​

mermaid
sequenceDiagram
  participant U as User
  participant A as App Server
  participant D as Database
  participant C as Cache
  participant L as Alert Service

  U->>A: Submit Payroll Data
  A->>C: Check Employee/Payroll State
  C-->>A: Hit/Miss
  A->>D: Create/Update Payroll Record
  D-->>A: Success/Fail
  A->>L: Send Urgent Alert (if issue)
  A-->>U: Response

Key Design Decisions ​

  • Database: Relational DB (e.g., PostgreSQL) for transactional data, strong consistency
  • Cache: Redis for fast lookups (sessions, payroll state)
  • Alerting/Notifications: Email/SMS/push via third-party service (e.g., Twilio, Firebase)
  • Analytics: Batch or streaming (e.g., Kafka + Spark, or managed cloud analytics)
  • Deployment: Cloud-based, multi-AZ, managed services for high availability
  • API: REST/GraphQL for mobile and web clients

4. Conceptual Design ​

Entities ​

  • User: id, name, email, password_hash, role, department, registration_date, status
  • EmployeeProfile: id, user_id, personal_info, tax_info, bank_info, status
  • Payroll: id, employee_id, period, gross_salary, deductions, net_salary, status, processed_at
  • Payslip: id, payroll_id, file_url, generated_at
  • LeaveRecord: id, employee_id, period, leave_days, status
  • Alert: id, user_id, payroll_id, type (urgent/compliance/payment), message, created_at, status
  • AuditLog: id, user_id, action, entity, entity_id, timestamp

Key Flows ​

  • Payroll Processing:
    1. HR/admin submits payroll data
    2. App checks payroll state (cache, then DB)
    3. Stores payroll record, generates payslip
    4. Triggers urgent alert if issue/compliance deadline
  • Payslip Distribution:
    1. Payslip generated and distributed to employee
    2. Alert sent for new payslip/payment
  • Alerts:
    • System triggers urgent alerts for payroll issues, compliance, or payment status
  • Analytics:
    • Periodic jobs aggregate payroll, cost, and trend data

Security ​

  • Role-based access control (RBAC)
  • Input validation, rate limiting
  • Encrypted connections (HTTPS)
  • Regular backups and audit logs
  • GDPR/data privacy compliance

5. Bottlenecks and Refinement ​

Potential Bottlenecks ​

  • Payroll calculation at scale:
    • Use batch processing, parallelization, and caching
  • Payslip generation/distribution:
    • Use async queues and object storage for payslips
  • Database contention:
    • Use read replicas, caching, and DB connection pooling
  • Alert delivery:
    • Use async queues for urgent notifications
  • Single region failure:
    • Deploy across multiple availability zones/regions

Refinement ​

  • Monitor system metrics and auto-scale app servers
  • Regularly test failover and backup restores
  • Optimize queries and indexes for frequent operations
  • Consider sharding if user/payroll volume grows significantly

This design provides a scalable, highly available, and mobile-ready payroll system with robust urgent alerts, analytics, and operational best practices.