The Rust port at v2/ has been the primary codebase since the rename in #427. The Python implementation at v1/ is no longer the active target; the only load-bearing path is the deterministic proof bundle at v1/data/proof/ (per ADR-011 / ADR-028 witness verification). Move the whole Python tree into archive/v1/ and document the policy in archive/README.md: no new features, bug fixes only when they affect a still-load-bearing path (currently just the proof), CI continues to verify the proof on every push and PR. Path references updated in 26 files via path-pattern sed (only matches v1/<known-child> patterns, never bare v1 or API URLs like /api/v1/). Two double-prefix typos (archive/archive/v1/) caught and hand-fixed in verify-pipeline.yml and ADR-011. Validated: - Python proof verify.py imports cleanly at archive/v1/data/proof/ (numpy/scipy still required; CI installs requirements-lock.txt from archive/v1/ now) - cargo test --workspace --no-default-features → 1,539 passed, 0 failed, 8 ignored (unaffected by Python tree relocation) - ESP32-S3 on COM7 untouched (no firmware paths changed) After-merge: contributors should re-run any local `python v1/...` commands as `python archive/v1/...` (CLAUDE.md and CHANGELOG already updated).
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WiFi-DensePose Database Operations Review
Summary
Comprehensive testing of the WiFi-DensePose database operations has been completed. The system demonstrates robust database functionality with both PostgreSQL and SQLite support, automatic failover mechanisms, and comprehensive data persistence capabilities.
Test Results
Overall Statistics
- Total Tests: 28
- Passed: 27
- Failed: 1
- Success Rate: 96.4%
Testing Scope
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Database Initialization and Migrations ✓
- Successfully initializes database connections
- Supports both PostgreSQL and SQLite
- Automatic failback to SQLite when PostgreSQL unavailable
- Tables created successfully with proper schema
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Connection Handling and Pooling ✓
- Connection pool management working correctly
- Supports concurrent connections (tested with 10 simultaneous connections)
- Connection recovery after failure
- Pool statistics available for monitoring
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Model Operations (CRUD) ✓
- Device model: Full CRUD operations successful
- Session model: Full CRUD operations with relationships
- CSI Data model: CRUD operations with proper constraints
- Pose Detection model: CRUD with confidence validation
- System Metrics model: Metrics storage and retrieval
- Audit Log model: Event tracking functionality
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Data Persistence ✓
- CSI data persistence verified
- Pose detection data storage working
- Session-device relationships maintained
- Data integrity preserved across operations
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Failsafe Mechanism ✓
- Automatic PostgreSQL to SQLite fallback implemented
- Health check reports degraded status when using failback
- Operations continue seamlessly on SQLite
- No data loss during failover
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Query Performance ✓
- Bulk insert operations: 100 records in < 0.5s
- Indexed queries: < 0.1s response time
- Aggregation queries: < 0.1s for count/avg/min/max
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Cleanup Tasks ✓
- Old data cleanup working for all models
- Batch processing to avoid overwhelming database
- Configurable retention periods
- Invalid data cleanup functional
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Configuration ✓
- All database settings properly configured
- Connection pooling parameters appropriate
- Directory creation automated
- Environment-specific configurations
Key Findings
Strengths
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Robust Architecture
- Well-structured models with proper relationships
- Comprehensive validation and constraints
- Good separation of concerns
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Database Compatibility
- Custom ArrayType implementation handles PostgreSQL arrays and SQLite JSON
- All models work seamlessly with both databases
- No feature loss when using SQLite fallback
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Failsafe Implementation
- Automatic detection of database availability
- Smooth transition to SQLite when PostgreSQL unavailable
- Health monitoring includes failsafe status
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Performance
- Efficient indexing on frequently queried columns
- Batch processing for large operations
- Connection pooling optimized
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Data Integrity
- Proper constraints on all models
- UUID primary keys prevent conflicts
- Timestamp tracking on all records
Issues Found
- CSI Data Unique Constraint (Minor)
- The unique constraint on (device_id, sequence_number, timestamp_ns) may need adjustment
- Current implementation uses nanosecond precision which might allow duplicates
- Recommendation: Review constraint logic or add additional validation
Database Schema
The database includes 6 main tables:
- devices - WiFi routers and sensors
- sessions - Data collection sessions
- csi_data - Channel State Information measurements
- pose_detections - Human pose detection results
- system_metrics - System performance metrics
- audit_logs - System event tracking
All tables include:
- UUID primary keys
- Created/updated timestamps
- Proper foreign key relationships
- Comprehensive indexes
Cleanup Configuration
Default retention periods:
- CSI Data: 30 days
- Pose Detections: 30 days
- System Metrics: 7 days
- Audit Logs: 90 days
- Orphaned Sessions: 7 days
Recommendations
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Production Deployment
- Enable PostgreSQL as primary database
- Configure appropriate connection pool sizes based on load
- Set up regular database backups
- Monitor connection pool usage
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Performance Optimization
- Consider partitioning for large CSI data tables
- Implement database connection caching
- Add composite indexes for complex queries
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Monitoring
- Set up alerts for failover events
- Monitor cleanup task performance
- Track database growth trends
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Security
- Ensure database credentials are properly secured
- Implement database-level encryption for sensitive data
- Regular security audits of database access
Test Scripts
Two test scripts were created:
initialize_database.py- Creates database tablestest_database_operations.py- Comprehensive database testing
Both scripts support async and sync operations and work with the failsafe mechanism.
Conclusion
The WiFi-DensePose database operations are production-ready with excellent reliability, performance, and maintainability. The failsafe mechanism ensures high availability, and the comprehensive test coverage provides confidence in the system's robustness.