Aniworld/tests/performance/README.md

Performance Testing Suite

This directory contains performance tests for the Aniworld API and download system.

Test Categories

API Load Testing (test_api_load.py)

Tests API endpoints under concurrent load to ensure acceptable performance:

• Load Testing: Concurrent requests to endpoints
• Sustained Load: Long-running load scenarios
• Concurrency Limits: Maximum connection handling
• Response Times: Performance benchmarks

Key Metrics:

• Requests per second (RPS)
• Average response time
• Success rate under load
• Graceful degradation behavior

Download Stress Testing (test_download_stress.py)

Tests the download queue and management system under stress:

• Queue Operations: Concurrent add/remove operations
• Capacity Testing: Queue behavior at limits
• Memory Usage: Memory leak detection
• Concurrency: Multiple simultaneous downloads
• Error Handling: Recovery from failures

Key Metrics:

• Queue operation success rate
• Concurrent download capacity
• Memory stability
• Error recovery time

Running Performance Tests

Run all performance tests:

conda run -n AniWorld python -m pytest tests/performance/ -v -m performance

Run specific test file:

conda run -n AniWorld python -m pytest tests/performance/test_api_load.py -v

Run with detailed output:

conda run -n AniWorld python -m pytest tests/performance/ -vv -s

Run specific test class:

conda run -n AniWorld python -m pytest \
    tests/performance/test_api_load.py::TestAPILoadTesting -v

Performance Benchmarks

Expected Results

Health Endpoint:

• RPS: ≥ 50 requests/second
• Avg Response Time: < 0.1s
• Success Rate: ≥ 95%

Anime List Endpoint:

• Avg Response Time: < 1.0s
• Success Rate: ≥ 90%

Search Endpoint:

• Avg Response Time: < 2.0s
• Success Rate: ≥ 85%

Download Queue:

• Concurrent Additions: Handle 100+ simultaneous adds
• Queue Capacity: Support 1000+ queued items
• Operation Success Rate: ≥ 90%

Adding New Performance Tests

When adding new performance tests:

1. Mark tests with @pytest.mark.performance decorator
2. Use @pytest.mark.asyncio for async tests
3. Include clear performance expectations in assertions
4. Document expected metrics in docstrings
5. Use fixtures for setup/teardown

Example:

@pytest.mark.performance
class TestMyFeature:
    @pytest.mark.asyncio
    async def test_under_load(self, client):
        \"\"\"Test feature under load.\"\"\"
        # Your test implementation
        metrics = await measure_performance(...)
        assert metrics["success_rate"] >= 95.0

Continuous Performance Monitoring

These tests should be run:

• Before each release
• After significant changes to API or download system
• As part of CI/CD pipeline (if resources permit)
• Weekly as part of regression testing

Troubleshooting

Tests timeout:

• Increase timeout in pytest.ini
• Check system resources (CPU, memory)
• Verify no other heavy processes running

Low success rates:

• Check application logs for errors
• Verify database connectivity
• Ensure sufficient system resources
• Check for rate limiting issues

Inconsistent results:

• Run tests multiple times
• Check for background processes
• Verify stable network connection
• Consider running on dedicated test hardware

Performance Optimization Tips

Based on test results, consider:

1. Caching: Add caching for frequently accessed data
2. Connection Pooling: Optimize database connections
3. Async Processing: Use async/await for I/O operations
4. Load Balancing: Distribute load across multiple workers
5. Rate Limiting: Implement rate limiting to prevent overload
6. Query Optimization: Optimize database queries
7. Resource Limits: Set appropriate resource limits

Integration with CI/CD

To include in CI/CD pipeline:

# Example GitHub Actions workflow
- name: Run Performance Tests
  run: |
      conda run -n AniWorld python -m pytest \
        tests/performance/ \
        -v \
        -m performance \
        --tb=short

References

• Pytest Documentation
• HTTPX Async Client
• Performance Testing Best Practices