docs/agent/implementation-notes/PAYROLL_IMPLEMENTATION_COMPLETE.md

Payroll System Implementation Summary

Overview

Complete implementation of a robust weekly payroll generation system for the SwiftOps field service management platform. The system automatically aggregates worker attendance (timesheets) and work completed (ticket assignments) to generate accurate weekly compensation records.

Architecture Decision

Approach: Two separate background jobs (Weekly Batch Generation)

• Daily Job: Generate timesheets from ticket_assignments (already implemented)
• Weekly Job: Aggregate timesheets → Generate payroll records (newly implemented)

Rationale:

• Clean separation of concerns
• No race conditions (timesheets finalized before payroll generation)
• Easy to debug and maintain
• Idempotent operations (safe to re-run)

Implementation Components

1. Data Model (src/app/models/user_payroll.py)

Complete SQLAlchemy model matching schema.sql

Key Fields:

• user_id, project_id: Links to worker and project
• period_start_date, period_end_date: Weekly period (Monday-Sunday)
• tickets_closed: Count from ticket_assignments
• hours_worked, days_worked: Aggregated from timesheets
• flat_rate_amount: Weekly base rate from project_role
• ticket_earnings: Calculated earnings from tickets (base + commission)
• bonus_amount, deductions: Manual adjustments
• total_amount: Calculated total compensation
• is_paid: Payment status flag
• version: Optimistic locking for concurrent updates

Business Methods:

• calculate_total(): Computes total_amount from components
• mark_as_paid(): Records payment details
• recalculate_from_data(): Recalculates from fresh data (timesheet corrections)
• can_recalculate: Property to check if recalculation is allowed

Constraints:

• Unique constraint: (user_id, project_id, period_start_date, period_end_date)
• All amounts must be non-negative
• Period end >= period start
• Cannot recalculate paid payrolls

2. Pydantic Schemas (src/app/schemas/payroll.py)

Complete request/response models with validators

Request Schemas:

• PayrollGenerateRequest: Generate for specific user/period (validates Monday-Sunday)
• PayrollBatchGenerateRequest: Generate for all workers in a week
• PayrollUpdateRequest: Update amounts before payment
• PayrollMarkPaidRequest: Mark as paid with payment details

Response Schemas:

• PayrollResponse: Complete payroll with computed fields (can_recalculate, net_earnings, etc.)
• PayrollListResponse: Paginated list
• PayrollGenerateResponse: Generation result with success/skip reason
• PayrollBatchGenerateResponse: Batch summary with errors

Validators:

• period_start_date must be Monday (weekday 0)
• period_end_date must be Sunday (weekday 6)
• Period must be exactly 7 days
• payment_method must be valid (mobile_money, bank_transfer, cash, cheque)

3. Business Logic (src/app/services/payroll_service.py)

Comprehensive service layer with robust error handling

Core Methods:

generate_payroll_for_period()

• Generates payroll for specific user and period
• Checks for existing payroll (idempotent)
• Aggregates data from timesheets and ticket_assignments
• Gets compensation rates from project_roles
• Calculates earnings: flat_rate + (base_amount * tickets * (1 + commission%))
• Creates payroll record with detailed calculation_notes
• Returns success/skip reason

generate_weekly_payroll_batch()

• Generates payroll for all active project_team members
• Processes each worker independently (continues on error)
• Collects errors for review
• Returns comprehensive summary
• Safe for background job (no user context required)

recalculate_payroll()

• Recalculates payroll from fresh timesheet/ticket data
• Only works on unpaid payrolls
• Preserves manually added bonuses/deductions
• Increments version (optimistic locking)
• Updates calculation_notes with timestamp

mark_as_paid()

• Marks payroll as paid
• Records payment method, reference, paid_by user
• Prevents recalculation after payment
• Permanent audit trail

Helper Methods:

• get_week_bounds(): Calculates Monday-Sunday for any date
• get_previous_week_bounds(): Gets last completed week
• aggregate_timesheet_data(): Sums hours_worked, counts days_present
• aggregate_ticket_data(): Counts completed ticket assignments
• get_compensation_rates(): Gets rates from project_team + project_role
• calculate_ticket_earnings(): Formula: base * count + commission

Authorization:

• Platform admins: Full access
• Project managers: Full access to their projects
• Dispatchers: View only
• Workers: View their own payroll only

Error Handling:

• Transaction safety (commit per operation, rollback on error)
• Comprehensive logging
• HTTPException with appropriate status codes
• Graceful degradation (batch continues on individual errors)

4. REST API (src/app/api/v1/payroll.py)

Complete API endpoints with comprehensive documentation

Endpoints:

POST /payroll/generate

• Generate payroll for specific user/period
• Validates Monday-Sunday period
• Returns PayrollGenerateResponse with success/skip reason
• Audit logging

POST /payroll/generate-batch

• Generate payroll for all workers in a week
• Optional project_id filter
• Returns batch summary with errors
• Audit logging

GET /payroll/{payroll_id}

• Get payroll by ID
• Authorization: admins/managers view all, workers view own
• Returns complete payroll with joined data

GET /payroll

• List payrolls with filtering and pagination
• Filters: user_id, project_id, is_paid, period dates
• Sorting: period_start_date, total_amount, created_at, paid_at
• Authorization: workers auto-filtered to own records

POST /payroll/{payroll_id}/recalculate

• Manually trigger recalculation
• Only works on unpaid payrolls
• Returns updated payroll
• Audit logging

POST /payroll/{payroll_id}/mark-paid

• Mark payroll as paid
• Requires payment_method and optional payment_reference
• Returns updated payroll
• Audit logging

All endpoints include:

• Comprehensive OpenAPI documentation
• Authorization via @require_permission("manage_payroll")
• Error handling with appropriate HTTP status codes
• Audit logging via AuditService

5. Router Registration (src/app/api/v1/router.py)

Integrated into main API router

• Imported payroll module
• Registered with prefix="/payroll" and tags=["Payroll"]
• Positioned after timesheets router (logical flow)

6. Timesheet Integration (src/app/services/timesheet_service.py)

Auto-recalculation on timesheet correction

Implementation in update_timesheet():

1. Update timesheet fields
2. Commit changes
3. Find payroll for this period (if exists and unpaid)
4. Trigger recalculation
5. Continue even if recalculation fails (logged warning)

Benefits:

• Payroll stays in sync with attendance data
• No manual recalculation needed
• Only affects unpaid payrolls (safe)
• Non-blocking (timesheet update succeeds even if payroll recalc fails)

7. Background Job (src/app/tasks/payroll_tasks.py)

Simple cron-based approach (no Redis/Celery)

Main Function: generate_weekly_payroll_job()

• Runs Monday morning (e.g., 6 AM)
• Processes previous week (Monday-Sunday)
• Uses PayrollService.generate_weekly_payroll_batch()
• Comprehensive logging to file + console
• Dry-run mode for testing
• Returns summary dict

Schedule Options:

Cron (Linux/Mac):

# Every Monday at 6 AM
0 6 * * 1 cd /path/to/project && python -m app.tasks.payroll_tasks

Windows Task Scheduler:

• Trigger: Weekly, Monday, 6:00 AM
• Action: python -m app.tasks.payroll_tasks
• Working Directory: Project root

CLI Interface:

# Generate for previous week (default)
python -m app.tasks.payroll_tasks

# Dry run (test without committing)
python -m app.tasks.payroll_tasks --dry-run

# Specific date
python -m app.tasks.payroll_tasks --date 2024-12-09

# Specific project
python -m app.tasks.payroll_tasks --project-id <uuid>

# Ad-hoc week (catch-up)
python -m app.tasks.payroll_tasks --start 2024-12-02 --end 2024-12-08

Safety Features:

• Transaction-safe (commits per user, rollback on error)
• Idempotent (safe to run multiple times)
• Continues on error (doesn't fail entire batch)
• Comprehensive logging to file
• Dry-run mode for testing
• Ad-hoc mode for catch-up

Data Flow

Weekly Payroll Generation (Automated)

Monday 6 AM: Background Job Runs
    ↓
Calculate Previous Week (Mon-Sun)
    ↓
For Each Active Project Team Member:
    ↓
    1. Aggregate Timesheets
       - Sum hours_worked (PRESENT days)
       - Count days_worked (PRESENT count)
    ↓
    2. Aggregate Ticket Assignments
       - Count completed assignments in period
    ↓
    3. Get Compensation Rates
       - flat_rate_amount from project_role
       - base_amount, commission_percentage from project_role
    ↓
    4. Calculate Earnings
       - flat_rate (weekly base)
       - ticket_earnings = (base * tickets) + commission
       - total_amount = flat_rate + ticket_earnings + bonus - deductions
    ↓
    5. Create/Update Payroll Record
       - Skip if already paid
       - Update if unpaid (recalculate)
       - Create if doesn't exist
    ↓
Summary: X generated, Y skipped, Z errors

Timesheet Correction Flow

Manager Corrects Timesheet
    ↓
TimesheetService.update_timesheet()
    ↓
Update timesheet fields
    ↓
Commit changes
    ↓
Find payroll for this period (if exists and unpaid)
    ↓
If found: Trigger PayrollService.recalculate_payroll()
    ↓
    - Re-aggregate timesheets
    - Re-aggregate tickets
    - Recalculate earnings
    - Preserve bonuses/deductions
    - Increment version
    - Update calculation_notes
    ↓
Payroll now reflects corrected timesheet

Calculation Formula

Total Compensation

total_amount = flat_rate_amount + ticket_earnings + bonus_amount - deductions

Ticket Earnings

base_earnings = base_amount * tickets_closed
commission = base_earnings * (commission_percentage / 100)
ticket_earnings = base_earnings + commission

Example Calculation

Worker: John Doe
Role: Technician
Period: Dec 09-15, 2024

From project_role:
- flat_rate_amount: KES 5,000 (weekly base)
- base_amount: KES 500 per ticket
- commission_percentage: 10%

From timesheets (Dec 09-15):
- days_worked: 5 (Monday-Friday present)
- hours_worked: 40 hours

From ticket_assignments (Dec 09-15):
- tickets_closed: 12 tickets

Calculation:
flat_rate = 5,000
base_earnings = 500 * 12 = 6,000
commission = 6,000 * 0.10 = 600
ticket_earnings = 6,000 + 600 = 6,600
bonus_amount = 0 (none added)
deductions = 0 (none added)

total_amount = 5,000 + 6,600 + 0 - 0 = KES 11,600

Key Features

1. Robustness

• ✅ Transaction safety (atomic operations)
• ✅ Optimistic locking (version column)
• ✅ Comprehensive error handling
• ✅ Idempotent operations (safe to re-run)
• ✅ Graceful degradation (continues on error)
• ✅ Audit logging (full trail)

2. Data Integrity

• ✅ Unique constraint (no duplicate payrolls)
• ✅ Validation (Monday-Sunday, non-negative amounts)
• ✅ Cannot modify paid payrolls
• ✅ Auto-recalculation on timesheet correction
• ✅ Preserves manual adjustments (bonuses/deductions)

3. Performance

• ✅ Batch processing (all workers at once)
• ✅ Efficient queries (joinedload, filters)
• ✅ Pagination support (API)
• ✅ Indexed columns (period dates, user_id, project_id)

4. Maintainability

• ✅ Clean separation of concerns (Model/Schema/Service/API)
• ✅ Comprehensive documentation (docstrings)
• ✅ Logging (debug, info, error levels)
• ✅ Simple approach (no external dependencies)

5. Flexibility

• ✅ Manual generation (ad-hoc periods)
• ✅ Project-specific filtering
• ✅ Dry-run mode (testing)
• ✅ CLI interface (operations)
• ✅ API endpoints (programmatic access)

Testing Checklist

Unit Tests

• PayrollService.generate_payroll_for_period()
• PayrollService.generate_weekly_payroll_batch()
• PayrollService.recalculate_payroll()
• PayrollService.aggregate_timesheet_data()
• PayrollService.aggregate_ticket_data()
• PayrollService.calculate_ticket_earnings()
• UserPayroll.calculate_total()
• UserPayroll.mark_as_paid()
• UserPayroll.recalculate_from_data()

Integration Tests

• Generate payroll for worker with timesheets and tickets
• Generate batch payroll for multiple workers
• Recalculate payroll after timesheet correction
• Mark payroll as paid
• Prevent recalculation of paid payroll
• Handle worker with no timesheets (0 days worked)
• Handle worker with no tickets (flat rate only)
• Handle worker with no role (0 compensation)
• Idempotency (re-run generation)
• Optimistic locking (concurrent updates)

API Tests

• POST /payroll/generate (success, skip, error)
• POST /payroll/generate-batch (success, errors)
• GET /payroll/{id} (authorization)
• GET /payroll (filtering, pagination, sorting)
• POST /payroll/{id}/recalculate (unpaid only)
• POST /payroll/{id}/mark-paid (once only)

Background Job Tests

• Run with default (previous week)
• Run with specific date
• Run with project filter
• Run in dry-run mode
• Ad-hoc week generation
• Error handling (DB connection, invalid data)

Production Deployment

1. Database Migration

-- Verify table exists
SELECT * FROM user_payroll LIMIT 1;

-- Check indexes
\d user_payroll

-- Verify constraints
SELECT conname, contype FROM pg_constraint WHERE conrelid = 'user_payroll'::regclass;

2. Schedule Background Job

Linux/Mac (Cron):

# Edit crontab
crontab -e

# Add line (Monday 6 AM)
0 6 * * 1 cd /var/www/swiftops-backend && /usr/bin/python3 -m app.tasks.payroll_tasks >> /var/log/payroll_jobs.log 2>&1

Windows (Task Scheduler):

1. Open Task Scheduler
2. Create Task: "Weekly Payroll Generation"
3. Trigger: Weekly, Monday, 6:00 AM
4. Action: Start Program
   • Program: python.exe
   • Arguments: -m app.tasks.payroll_tasks
   • Working Directory: D:\atomio\swiftops-backend
5. Settings: Run whether user is logged on or not

3. Monitoring

• Check logs: payroll_job_YYYYMMDD_HHMMSS.log
• Monitor errors in batch summary
• Alert on job failure (email/SMS)
• Track metrics: generated count, skipped count, errors

4. First Run

# Dry run first (test)
python -m app.tasks.payroll_tasks --dry-run

# Run for specific week (backfill)
python -m app.tasks.payroll_tasks --start 2024-12-02 --end 2024-12-08

# Verify results
# Check database for generated payrolls
# Review logs for errors

Maintenance

Weekly Operations

1. Monday morning: Job runs automatically
2. Review logs for errors
3. Check skipped workers (investigate why)
4. Fix any errors (missing roles, invalid data)
5. Re-run for specific workers if needed

Manual Operations

# Generate for specific worker
curl -X POST http://localhost:8000/api/v1/payroll/generate \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "uuid",
    "project_id": "uuid",
    "period_start_date": "2024-12-09",
    "period_end_date": "2024-12-15"
  }'

# List unpaid payrolls
curl http://localhost:8000/api/v1/payroll?is_paid=false

# Mark as paid
curl -X POST http://localhost:8000/api/v1/payroll/{id}/mark-paid \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "payment_method": "mobile_money",
    "payment_reference": "ABC123XYZ"
  }'

Future Enhancements

Phase 2 (Optional)

• Email notifications to workers (payroll generated)
• PDF payslip generation
• Payment gateway integration (M-Pesa B2C)
• Payroll approval workflow
• Payroll adjustments (corrections after payment)
• Payroll reports (project, period, worker)
• Payroll analytics (cost trends, efficiency)

Phase 3 (Advanced)

• Multi-currency support
• Tax calculations (PAYE, NHIF, NSSF)
• Payroll export (accounting systems)
• Payroll forecasting
• Bonus calculation rules engine
• Overtime tracking and compensation

Summary

Implementation Status: ✅ COMPLETE

Files Created/Modified:

1. ✅ src/app/models/user_payroll.py (NEW)
2. ✅ src/app/schemas/payroll.py (UPDATED)
3. ✅ src/app/services/payroll_service.py (UPDATED)
4. ✅ src/app/api/v1/payroll.py (UPDATED)
5. ✅ src/app/api/v1/router.py (UPDATED)
6. ✅ src/app/services/timesheet_service.py (UPDATED)
7. ✅ src/app/tasks/payroll_tasks.py (UPDATED)
8. ✅ src/app/models/project.py (UPDATED - added payrolls relationship)

Key Achievements:

• ✅ Robust weekly payroll generation from timesheets
• ✅ Automatic recalculation on timesheet correction
• ✅ Simple background job (no Redis/Celery)
• ✅ Comprehensive error handling and logging
• ✅ Idempotent operations (production-safe)
• ✅ Complete REST API with documentation
• ✅ Authorization and audit logging
• ✅ Optimistic locking for concurrency
• ✅ CLI interface for operations

Production-Ready Features:

• Transaction safety
• Data integrity constraints
• Comprehensive logging
• Error recovery
• Dry-run mode
• Manual override capabilities
• Complete audit trail

The system is now ready for deployment and testing! 🚀