kaauh_ats/load_tests

ATS Load Testing Framework

This directory contains a comprehensive load testing framework for the ATS (Applicant Tracking System) application using Locust. The framework provides realistic user simulation, performance monitoring, and detailed reporting capabilities.

📋 Table of Contents

• Overview
• Installation
• Quick Start
• Test Scenarios
• Configuration
• Test Data Generation
• Performance Monitoring
• Reports
• Distributed Testing
• Best Practices
• Troubleshooting

🎯 Overview

The ATS load testing framework includes:

• Multiple User Types: Public users, authenticated users, API clients, file uploaders
• Realistic Scenarios: Job browsing, application submission, dashboard access, API calls
• Performance Monitoring: System metrics, database performance, response times
• Comprehensive Reporting: HTML reports, JSON data, performance charts
• Test Data Generation: Automated creation of realistic test data
• Distributed Testing: Master-worker setup for large-scale tests

🚀 Installation

Prerequisites

# Python 3.8+ required
python --version

# Install required packages
pip install locust faker psutil matplotlib pandas requests

# Optional: For enhanced reporting
pip install jupyter notebook seaborn

Setup

1. Clone the repository and navigate to the project root
2. Install dependencies:

   pip install -r requirements.txt
   pip install locust faker psutil matplotlib pandas
   

3. Set up environment variables:

   export ATS_HOST="http://localhost:8000"
   export TEST_USERNAME="your_test_user"
   export TEST_PASSWORD="your_test_password"
   

⚡ Quick Start

1. List Available Scenarios

python load_tests/run_load_tests.py list

2. Run a Smoke Test

# Interactive mode with web UI
python load_tests/run_load_tests.py run smoke_test

# Headless mode (no web UI)
python load_tests/run_load_tests.py headless smoke_test

3. Generate Test Data

python load_tests/run_load_tests.py generate-data --jobs 100 --users 50 --applications 500

4. View Results

After running tests, check the load_tests/results/ directory for:

• HTML reports
• CSV statistics
• Performance charts
• JSON data

📊 Test Scenarios

Available Scenarios

Scenario	Users	Duration	Description
smoke_test	5	2m	Quick sanity check
light_load	20	5m	Normal daytime traffic
moderate_load	50	10m	Peak traffic periods
heavy_load	100	15m	Stress testing
api_focus	30	10m	API endpoint testing
file_upload_test	15	8m	File upload performance
authenticated_test	25	8m	Authenticated user workflows
endurance_test	30	1h	Long-running stability

User Types

1. PublicUser: Anonymous users browsing jobs and careers
2. AuthenticatedUser: Logged-in users with full access
3. APIUser: REST API clients
4. FileUploadUser: Users uploading resumes and documents

Common Workflows

• Job listing browsing
• Job detail viewing
• Application form access
• Application submission
• Dashboard navigation
• Message viewing
• File uploads
• API endpoint calls

⚙️ Configuration

Environment Variables

# Target application host
export ATS_HOST="http://localhost:8000"

# Test user credentials (for authenticated tests)
export TEST_USERNAME="testuser"
export TEST_PASSWORD="testpass123"

# Database connection (for monitoring)
export DATABASE_URL="postgresql://user:pass@localhost/kaauh_ats"

Custom Scenarios

Create custom scenarios by modifying load_tests/config.py:

"custom_scenario": TestScenario(
    name="Custom Load Test",
    description="Your custom test description",
    users=75,
    spawn_rate=15,
    run_time="20m",
    host="http://your-host.com",
    user_classes=["PublicUser", "AuthenticatedUser"],
    tags=["custom", "specific"]
)

Performance Thresholds

Adjust performance thresholds in load_tests/config.py:

PERFORMANCE_THRESHOLDS = {
    "response_time_p95": 2000,  # 95th percentile under 2s
    "response_time_avg": 1000,  # Average under 1s
    "error_rate": 0.05,         # Error rate under 5%
    "rps_minimum": 10,          # Minimum 10 RPS
}

📝 Test Data Generation

Generate Realistic Data

# Default configuration
python load_tests/run_load_tests.py generate-data

# Custom configuration
python load_tests/run_load_tests.py generate-data \
    --jobs 200 \
    --users 100 \
    --applications 1000

Generated Data Types

• Jobs: Realistic job postings with descriptions, qualifications, benefits
• Users: User profiles with contact information and social links
• Applications: Complete application records with cover letters
• Interviews: Scheduled interviews with various types and statuses
• Messages: User communications and notifications

Test Files

Automatically generated test files for upload testing:

• Text files with realistic content
• Various sizes (configurable)
• Stored in load_tests/test_files/

📈 Performance Monitoring

System Metrics

• CPU Usage: Percentage utilization
• Memory Usage: RAM consumption and usage percentage
• Disk I/O: Read/write operations
• Network I/O: Bytes sent/received, packet counts
• Active Connections: Number of network connections

Database Metrics

• Active Connections: Current database connections
• Query Count: Total queries executed
• Average Query Time: Mean query execution time
• Slow Queries: Count of slow-running queries
• Cache Hit Ratio: Database cache effectiveness

Real-time Monitoring

During tests, the framework monitors:

• Response times (avg, median, 95th, 99th percentiles)
• Request rates (current and peak)
• Error rates and types
• System resource utilization

📋 Reports

HTML Reports

Comprehensive web-based reports including:

• Executive summary
• Performance metrics
• Response time distributions
• Error analysis
• System performance graphs
• Recommendations

JSON Reports

Machine-readable reports for:

• CI/CD integration
• Automated analysis
• Historical comparison
• Custom processing

Performance Charts

Visual representations of:

• Response time trends
• System resource usage
• Request rate variations
• Error rate patterns

Report Locations

load_tests/
├── reports/
│   ├── performance_report_20231207_143022.html
│   ├── performance_report_20231207_143022.json
│   └── system_metrics_20231207_143022.png
└── results/
    ├── report_Smoke Test_20231207_143022.html
    ├── stats_Smoke Test_20231207_143022_stats.csv
    └── stats_Smoke Test_20231207_143022_failures.csv

🌐 Distributed Testing

Master-Worker Setup

For large-scale tests, use distributed testing:

Start Master Node

python load_tests/run_load_tests.py master moderate_load --workers 4

Start Worker Nodes

# On each worker machine
python load_tests/run_load_tests.py worker

Configuration

• Master: Coordinates test execution and aggregates results
• Workers: Execute user simulations and report to master
• Network: Ensure all nodes can communicate on port 5557

Best Practices

1. Network: Use low-latency network between nodes
2. Resources: Ensure each worker has sufficient CPU/memory
3. Synchronization: Start workers before master
4. Monitoring: Monitor each node individually

🎯 Best Practices

Test Planning

1. Start Small: Begin with smoke tests
2. Gradual Increase: Progressively increase load
3. Realistic Scenarios: Simulate actual user behavior
4. Baseline Testing: Establish performance baselines
5. Regular Testing: Schedule periodic load tests

Test Execution

1. Warm-up: Allow system to stabilize
2. Duration: Run tests long enough for steady state
3. Monitoring: Watch system resources during tests
4. Documentation: Record test conditions and results
5. Validation: Verify application functionality post-test

Performance Optimization

1. Bottlenecks: Identify and address performance bottlenecks
2. Caching: Implement effective caching strategies
3. Database: Optimize queries and indexing
4. CDN: Use content delivery networks for static assets
5. Load Balancing: Distribute traffic effectively

CI/CD Integration

# Example GitHub Actions workflow
- name: Run Load Tests
  run: |
    python load_tests/run_load_tests.py headless smoke_test
    # Upload results as artifacts

🔧 Troubleshooting

Common Issues

1. Connection Refused

Error: Connection refused

Solution: Ensure the ATS application is running and accessible

# Check if application is running
curl http://localhost:8000/

# Start the application
python manage.py runserver

2. Import Errors

ModuleNotFoundError: No module named 'locust'

Solution: Install missing dependencies

pip install locust faker psutil matplotlib pandas

3. High Memory Usage

Symptoms: System becomes slow during tests

Solutions:

• Reduce number of concurrent users
• Increase system RAM
• Optimize test data generation
• Use distributed testing

4. Database Connection Issues

OperationalError: too many connections

Solutions:

• Increase database connection limit
• Use connection pooling
• Reduce concurrent database users
• Implement database read replicas

Debug Mode

Enable debug logging:

export LOCUST_DEBUG=1
python load_tests/run_load_tests.py run smoke_test

Performance Issues

Slow Response Times

1. Check System Resources: Monitor CPU, memory, disk I/O
2. Database Performance: Analyze slow queries
3. Network Latency: Check network connectivity
4. Application Code: Profile application performance

High Error Rates

1. Application Logs: Check for errors in application logs
2. Database Constraints: Verify database integrity
3. Resource Limits: Check system resource limits
4. Load Balancer: Verify load balancer configuration

Getting Help

1. Check Logs: Review Locust and application logs
2. Reduce Load: Start with smaller user counts
3. Isolate Issues: Test individual components
4. Monitor System: Use system monitoring tools

📚 Additional Resources

• Locust Documentation
• Performance Testing Best Practices
• Django Performance Tips
• PostgreSQL Performance

🤝 Contributing

To contribute to the load testing framework:

1. Add Scenarios: Create new test scenarios in config.py
2. Enhance Users: Improve user behavior in locustfile.py
3. Better Monitoring: Add new metrics to monitoring.py
4. Improve Reports: Enhance report generation
5. Documentation: Update this README

📄 License

This load testing framework is part of the ATS project and follows the same license terms.

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Happy Testing! 🚀

For questions or issues, please contact the development team or create an issue in the project repository.