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Add comprehensive test scripts for various models and services
- Implement tests for RideLocation and CompanyHeadquarters models to verify functionality and data integrity. - Create a manual trigger test script for trending content calculation endpoint, including authentication and unauthorized access tests. - Develop a manufacturer sync test to ensure ride manufacturers are correctly associated with ride models. - Add tests for ParkLocation model, including coordinate setting and distance calculations between parks. - Implement a RoadTripService test suite covering geocoding, route calculation, park discovery, and error handling. - Create a unified map service test script to validate map functionality, API endpoints, and performance metrics.
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pacnpal
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docs/README_HYBRID_ENDPOINTS.md
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docs/README_HYBRID_ENDPOINTS.md
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# ThrillWiki Hybrid Filtering Endpoints Test Suite
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This repository contains a comprehensive test script for the newly synchronized Parks and Rides hybrid filtering endpoints.
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## Quick Start
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1. **Start the Django server:**
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```bash
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cd backend && uv run manage.py runserver 8000
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```
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2. **Run the test script:**
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```bash
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./test_hybrid_endpoints.sh
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```
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Or with a custom base URL:
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```bash
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./test_hybrid_endpoints.sh http://localhost:8000
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```
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## What Gets Tested
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### Parks Hybrid Filtering (`/api/v1/parks/hybrid/`)
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- ✅ Basic hybrid filtering (automatic strategy selection)
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- ✅ Search functionality (`?search=disney`)
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- ✅ Status filtering (`?status=OPERATING,CLOSED_TEMP`)
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- ✅ Geographic filtering (`?country=United%20States&state=Florida,California`)
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- ✅ Numeric range filtering (`?opening_year_min=1990&rating_min=4.0`)
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- ✅ Park statistics filtering (`?size_min=100&ride_count_min=10`)
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- ✅ Operator filtering (`?operator=disney,universal`)
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- ✅ Progressive loading (`?offset=50`)
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- ✅ Filter metadata (`/filter-metadata/`)
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- ✅ Scoped metadata (`/filter-metadata/?scoped=true&country=United%20States`)
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### Rides Hybrid Filtering (`/api/v1/rides/hybrid/`)
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- ✅ Basic hybrid filtering (automatic strategy selection)
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- ✅ Search functionality (`?search=coaster`)
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- ✅ Category filtering (`?category=RC,DR`)
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- ✅ Status and park filtering (`?status=OPERATING&park_slug=cedar-point`)
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- ✅ Manufacturer/designer filtering (`?manufacturer=bolliger-mabillard`)
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- ✅ Roller coaster specific filtering (`?roller_coaster_type=INVERTED&has_inversions=true`)
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- ✅ Performance filtering (`?height_ft_min=200&speed_mph_min=70`)
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- ✅ Quality metrics (`?rating_min=4.5&capacity_min=1000`)
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- ✅ Accessibility filtering (`?height_requirement_min=48&height_requirement_max=54`)
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- ✅ Progressive loading (`?offset=25&category=RC`)
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- ✅ Filter metadata (`/filter-metadata/`)
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- ✅ Scoped metadata (`/filter-metadata/?scoped=true&category=RC`)
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### Advanced Testing
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- ✅ Complex combination queries
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- ✅ Edge cases (empty results, invalid parameters)
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- ✅ Performance timing comparisons
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- ✅ Error handling validation
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## Key Features Demonstrated
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### 🔄 Automatic Strategy Selection
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- **≤200 records**: Client-side filtering (loads all data for frontend filtering)
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- **>200 records**: Server-side filtering (database filtering with pagination)
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### 📊 Progressive Loading
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- Initial load: 50 records
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- Progressive batches: 25 records
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- Seamless pagination for large datasets
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### 🔍 Comprehensive Filtering
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- **Parks**: 17+ filter parameters including geographic, temporal, and statistical filters
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- **Rides**: 17+ filter parameters including roller coaster specs, performance metrics, and accessibility
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### 📋 Dynamic Filter Metadata
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- Real-time filter options based on current data
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- Scoped metadata for contextual filtering
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- Ranges and categorical options automatically generated
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### ⚡ Performance Optimized
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- 5-minute intelligent caching
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- Strategic database indexing
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- Optimized queries with prefetch_related
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## Response Format
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Both endpoints return consistent response structures:
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```json
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{
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"parks": [...], // or "rides": [...]
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"total_count": 123,
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"strategy": "client_side", // or "server_side"
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"has_more": false,
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"next_offset": null,
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"filter_metadata": {
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"categorical": {
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"countries": ["United States", "Canada", ...],
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"categories": ["RC", "DR", "FR", ...],
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// ... more options
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},
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"ranges": {
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"opening_year": {"min": 1800, "max": 2025},
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"rating": {"min": 1.0, "max": 10.0},
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// ... more ranges
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}
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}
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}
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```
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## Dependencies
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- **curl**: Required for making HTTP requests
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- **jq**: Optional but recommended for pretty JSON formatting
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## Example Usage
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### Basic Parks Query
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```bash
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curl "http://localhost:8000/api/v1/parks/hybrid/"
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```
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### Search for Disney Parks
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```bash
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curl "http://localhost:8000/api/v1/parks/hybrid/?search=disney"
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```
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### Filter Roller Coasters with Inversions
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```bash
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curl "http://localhost:8000/api/v1/rides/hybrid/?category=RC&has_inversions=true&height_ft_min=100"
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```
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### Get Filter Metadata
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```bash
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curl "http://localhost:8000/api/v1/parks/hybrid/filter-metadata/"
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```
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## Integration Guide
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### Frontend Integration
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1. Use filter metadata to build dynamic filter interfaces
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2. Implement progressive loading for better UX
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3. Handle both client-side and server-side strategies
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4. Cache filter metadata to reduce API calls
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### Performance Considerations
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- Monitor response times and adjust thresholds as needed
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- Use progressive loading for datasets >200 records
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- Implement proper error handling for edge cases
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- Consider implementing request debouncing for search
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## Troubleshooting
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### Server Not Running
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```
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❌ Server not available at http://localhost:8000
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💡 Make sure to start the Django server first:
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cd backend && uv run manage.py runserver 8000
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```
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### Missing jq
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```
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⚠️ jq not found - JSON responses will not be pretty-printed
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```
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Install jq for better output formatting:
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```bash
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# macOS
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brew install jq
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# Ubuntu/Debian
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sudo apt-get install jq
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```
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## Next Steps
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1. **Integrate into Frontend**: Use these endpoints in your React/Next.js application
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2. **Build Filter UI**: Create dynamic filter interfaces using the metadata
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3. **Implement Progressive Loading**: Handle large datasets efficiently
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4. **Monitor Performance**: Track response times and optimize as needed
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5. **Add Caching**: Implement client-side caching for better UX
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---
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🎢 **Happy filtering!** These endpoints provide a powerful, scalable foundation for building advanced search and filtering experiences in your theme park application.
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