This commit consolidates the fragmented orchestration service migrations
into a single, well-structured initial schema version file.
Changes:
- Created 001_initial_schema.py consolidating all table definitions
- Merged fields from 2 previous migrations into one comprehensive file
- Added SCHEMA_DOCUMENTATION.md with complete schema reference
- Added MIGRATION_GUIDE.md for deployment instructions
Schema includes:
- orchestration_runs table (47 columns)
- orchestrationstatus enum type
- 15 optimized indexes for query performance
- Full step tracking (forecasting, production, procurement, notifications, AI insights)
- Saga pattern support
- Performance metrics tracking
- Error handling and retry logic
Benefits:
- Better organization and documentation
- Fixes revision ID inconsistencies from old migrations
- Eliminates duplicate index definitions
- Logically categorizes fields by purpose
- Easier to understand and maintain
- Comprehensive documentation for developers
The consolidated migration provides the same final schema as the
original migration chain but in a cleaner, more maintainable format.
This commit addresses all 15 issues identified in the orchestration scheduler analysis:
HIGH PRIORITY FIXES:
1. ✅ Database update methods already in orchestrator service (not in saga)
2. ✅ Add null check for training_client before using it
3. ✅ Fix cron schedule config from "0 5" to "30 5" (5:30 AM)
4. ✅ Standardize on timezone-aware datetime (datetime.now(timezone.utc))
5. ✅ Implement saga compensation logic with actual deletion calls
6. ✅ Extract actual counts from saga results (no placeholders)
MEDIUM PRIORITY FIXES:
7. ✅ Add circuit breakers for inventory/suppliers/recipes clients
8. ✅ Pass circuit breakers to saga and use them in all service calls
9. ✅ Add calling_service_name to AI Insights client
10. ✅ Add database indexes on (tenant_id, started_at) and (status, started_at)
11. ✅ Handle empty shared data gracefully (fail if all 3 fetches fail)
LOW PRIORITY IMPROVEMENTS:
12. ✅ Make notification/validation failures more visible with explicit logging
13. ✅ Track AI insights status in orchestration_runs table
14. ✅ Improve run number generation atomicity using MAX() approach
15. ✅ Optimize tenant ID handling (consistent UUID usage)
CHANGES:
- services/orchestrator/app/core/config.py: Fix cron schedule to 30 5 * * *
- services/orchestrator/app/models/orchestration_run.py: Add AI insights & saga tracking columns
- services/orchestrator/app/repositories/orchestration_run_repository.py: Atomic run number generation
- services/orchestrator/app/services/orchestration_saga.py: Circuit breakers, compensation, error handling
- services/orchestrator/app/services/orchestrator_service.py: Circuit breakers, actual counts, AI tracking
- services/orchestrator/migrations/versions/20251105_add_ai_insights_tracking.py: New migration
All issues resolved. No backwards compatibility. No TODOs. Production-ready.
Critical fixes for training session logging:
1. Training log race condition fix:
- Add explicit session commits after creating training logs
- Handle duplicate key errors gracefully when multiple sessions
try to create the same log simultaneously
- Implement retry logic to query for existing logs after
duplicate key violations
- Prevents "Training log not found" errors during training
2. Audit event async generator error fix:
- Replace incorrect next(get_db()) usage with proper
async context manager (database_manager.get_session())
- Fixes "'async_generator' object is not an iterator" error
- Ensures audit logging works correctly
These changes address race conditions in concurrent database
sessions and ensure training logs are properly synchronized
across the training pipeline.
This commit addresses all identified bugs and issues in the training code path:
## Critical Fixes:
- Add get_start_time() method to TrainingLogRepository and fix non-existent method call
- Remove duplicate training.started event from API endpoint (trainer publishes the accurate one)
- Add missing progress events for 80-100% range (85%, 92%, 94%) to eliminate progress "dead zone"
## High Priority Fixes:
- Fix division by zero risk in time estimation with double-check and max() safety
- Remove unreachable exception handler in training_operations.py
- Simplify WebSocket token refresh logic to only reconnect on actual user session changes
## Medium Priority Fixes:
- Fix auto-start training effect with useRef to prevent duplicate starts
- Add HTTP polling debounce delay (5s) to prevent race conditions with WebSocket
- Extract all magic numbers to centralized constants files:
- Backend: services/training/app/core/training_constants.py
- Frontend: frontend/src/constants/training.ts
- Standardize error logging with exc_info=True on critical errors
## Code Quality Improvements:
- All progress percentages now use named constants
- All timeouts and intervals now use named constants
- Improved code maintainability and readability
- Better separation of concerns
## Files Changed:
- Backend: training_service.py, trainer.py, training_events.py, progress_tracker.py
- Backend: training_operations.py, training_log_repository.py, training_constants.py (new)
- Frontend: training.ts (hooks), MLTrainingStep.tsx, training.ts (constants, new)
All training progress events now properly flow from 0% to 100% with no gaps.
Root Cause:
- Multiple parallel training tasks (3 at a time) were sharing the same database session
- This caused SQLAlchemy session state conflicts: "Session is already flushing" and "rollback() is already in progress"
- Additionally, duplicate model records were being created by both trainer and training_service
Fixes:
1. Separated model training from database writes:
- Training happens in parallel (CPU-intensive)
- Database writes happen sequentially after training completes
- This eliminates concurrent session access
2. Removed duplicate database writes:
- Trainer now writes all model records sequentially after parallel training
- Training service now retrieves models instead of creating duplicates
- Performance metrics are also created by trainer (no duplicates)
3. Added proper data flow:
- _train_single_product: Only trains models, stores results
- _write_training_results_to_database: Sequential DB writes after training
- _store_trained_models: Changed to retrieve existing models
- _create_performance_metrics: Changed to verify existing metrics
Benefits:
- Eliminates database session conflicts
- Prevents duplicate model records
- Maintains parallel training performance
- Ensures data consistency
Files Modified:
- services/training/app/ml/trainer.py
- services/training/app/services/training_service.py
Resolves: Onboarding training job database session conflicts
