Add user delete process
This commit is contained in:
Urtzi Alfaro
@@ -16,7 +16,7 @@ from shared.monitoring.decorators import track_execution_time
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from shared.monitoring.metrics import get_metrics_collector
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from shared.database.base import create_database_manager
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from shared.auth.decorators import get_current_user_dep
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from shared.auth.access_control import require_user_role, admin_role_required
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from shared.auth.access_control import require_user_role, admin_role_required, service_only_access
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from shared.security import create_audit_logger, create_rate_limiter, AuditSeverity, AuditAction
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from shared.subscription.plans import (
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get_training_job_quota,
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@@ -503,3 +503,126 @@ async def health_check():
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],
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"timestamp": datetime.now().isoformat()
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}
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# ============================================================================
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# Tenant Data Deletion Operations (Internal Service Only)
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# ============================================================================
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@router.delete(
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route_builder.build_base_route("tenant/{tenant_id}", include_tenant_prefix=False),
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response_model=dict
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)
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@service_only_access
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async def delete_tenant_data(
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tenant_id: str = Path(..., description="Tenant ID to delete data for"),
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current_user: dict = Depends(get_current_user_dep)
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):
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"""
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Delete all training data for a tenant (Internal service only)
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This endpoint is called by the orchestrator during tenant deletion.
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It permanently deletes all training-related data including:
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- Trained models (all versions)
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- Model artifacts (files and metadata)
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- Training logs and job history
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- Model performance metrics
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- Training job queue entries
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- Audit logs
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**WARNING**: This operation is irreversible!
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**NOTE**: Physical model files (.pkl) should be cleaned up separately
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Returns:
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Deletion summary with counts of deleted records
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"""
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from app.services.tenant_deletion_service import TrainingTenantDeletionService
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from app.core.config import settings
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try:
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logger.info("training.tenant_deletion.api_called", tenant_id=tenant_id)
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db_manager = create_database_manager(settings.DATABASE_URL, "training")
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async with db_manager.get_session() as session:
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deletion_service = TrainingTenantDeletionService(session)
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result = await deletion_service.safe_delete_tenant_data(tenant_id)
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if not result.success:
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raise HTTPException(
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status_code=500,
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detail=f"Tenant data deletion failed: {', '.join(result.errors)}"
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)
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return {
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"message": "Tenant data deletion completed successfully",
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"note": "Physical model files should be cleaned up separately from storage",
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"summary": result.to_dict()
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}
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except HTTPException:
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raise
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except Exception as e:
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logger.error("training.tenant_deletion.api_error",
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tenant_id=tenant_id,
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error=str(e),
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exc_info=True)
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raise HTTPException(
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status_code=500,
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detail=f"Failed to delete tenant data: {str(e)}"
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)
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@router.get(
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route_builder.build_base_route("tenant/{tenant_id}/deletion-preview", include_tenant_prefix=False),
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response_model=dict
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)
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@service_only_access
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async def preview_tenant_data_deletion(
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tenant_id: str = Path(..., description="Tenant ID to preview deletion for"),
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current_user: dict = Depends(get_current_user_dep)
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):
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"""
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Preview what data would be deleted for a tenant (dry-run)
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This endpoint shows counts of all data that would be deleted
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without actually deleting anything. Useful for:
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- Confirming deletion scope before execution
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- Auditing and compliance
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- Troubleshooting
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Returns:
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Dictionary with entity names and their counts
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"""
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from app.services.tenant_deletion_service import TrainingTenantDeletionService
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from app.core.config import settings
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try:
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logger.info("training.tenant_deletion.preview_called", tenant_id=tenant_id)
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db_manager = create_database_manager(settings.DATABASE_URL, "training")
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async with db_manager.get_session() as session:
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deletion_service = TrainingTenantDeletionService(session)
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preview = await deletion_service.get_tenant_data_preview(tenant_id)
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total_records = sum(preview.values())
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return {
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"tenant_id": tenant_id,
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"service": "training",
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"preview": preview,
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"total_records": total_records,
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"note": "Physical model files (.pkl, metadata) are not counted here",
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"warning": "These records will be permanently deleted and cannot be recovered"
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}
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except Exception as e:
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logger.error("training.tenant_deletion.preview_error",
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tenant_id=tenant_id,
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error=str(e),
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exc_info=True)
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raise HTTPException(
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status_code=500,
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detail=f"Failed to preview tenant data deletion: {str(e)}"
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)
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292
services/training/app/services/tenant_deletion_service.py
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292
services/training/app/services/tenant_deletion_service.py
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@@ -0,0 +1,292 @@
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# services/training/app/services/tenant_deletion_service.py
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"""
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Tenant Data Deletion Service for Training Service
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Handles deletion of all training-related data for a tenant
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"""
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from typing import Dict
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from sqlalchemy import select, func, delete
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from sqlalchemy.ext.asyncio import AsyncSession
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import structlog
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from shared.services.tenant_deletion import (
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BaseTenantDataDeletionService,
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TenantDataDeletionResult
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)
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from app.models import (
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TrainedModel,
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ModelTrainingLog,
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ModelPerformanceMetric,
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TrainingJobQueue,
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ModelArtifact,
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AuditLog
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)
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logger = structlog.get_logger(__name__)
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class TrainingTenantDeletionService(BaseTenantDataDeletionService):
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"""Service for deleting all training-related data for a tenant"""
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def __init__(self, db: AsyncSession):
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self.db = db
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self.service_name = "training"
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async def get_tenant_data_preview(self, tenant_id: str) -> Dict[str, int]:
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"""
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Get counts of what would be deleted for a tenant (dry-run)
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Args:
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tenant_id: The tenant ID to preview deletion for
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Returns:
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Dictionary with entity names and their counts
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"""
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logger.info("training.tenant_deletion.preview", tenant_id=tenant_id)
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preview = {}
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try:
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# Count trained models
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model_count = await self.db.scalar(
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select(func.count(TrainedModel.id)).where(
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TrainedModel.tenant_id == tenant_id
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)
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)
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preview["trained_models"] = model_count or 0
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# Count model artifacts
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artifact_count = await self.db.scalar(
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select(func.count(ModelArtifact.id)).where(
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ModelArtifact.tenant_id == tenant_id
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)
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)
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preview["model_artifacts"] = artifact_count or 0
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# Count training logs
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log_count = await self.db.scalar(
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select(func.count(ModelTrainingLog.id)).where(
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ModelTrainingLog.tenant_id == tenant_id
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)
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)
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preview["model_training_logs"] = log_count or 0
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# Count performance metrics
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metric_count = await self.db.scalar(
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select(func.count(ModelPerformanceMetric.id)).where(
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ModelPerformanceMetric.tenant_id == tenant_id
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)
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)
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preview["model_performance_metrics"] = metric_count or 0
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# Count training job queue entries
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queue_count = await self.db.scalar(
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select(func.count(TrainingJobQueue.id)).where(
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TrainingJobQueue.tenant_id == tenant_id
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)
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)
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preview["training_job_queue"] = queue_count or 0
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# Count audit logs
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audit_count = await self.db.scalar(
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select(func.count(AuditLog.id)).where(
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AuditLog.tenant_id == tenant_id
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)
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)
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preview["audit_logs"] = audit_count or 0
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logger.info(
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"training.tenant_deletion.preview_complete",
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tenant_id=tenant_id,
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preview=preview
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)
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except Exception as e:
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logger.error(
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"training.tenant_deletion.preview_error",
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tenant_id=tenant_id,
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error=str(e),
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exc_info=True
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)
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raise
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return preview
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async def delete_tenant_data(self, tenant_id: str) -> TenantDataDeletionResult:
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"""
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Permanently delete all training data for a tenant
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Deletion order:
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1. ModelArtifact (references models)
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2. ModelPerformanceMetric (references models)
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3. ModelTrainingLog (independent job logs)
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4. TrainingJobQueue (independent queue entries)
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5. TrainedModel (parent model records)
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6. AuditLog (independent)
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Note: This also deletes physical model files from disk/storage
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Args:
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tenant_id: The tenant ID to delete data for
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Returns:
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TenantDataDeletionResult with deletion counts and any errors
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"""
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logger.info("training.tenant_deletion.started", tenant_id=tenant_id)
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result = TenantDataDeletionResult(tenant_id=tenant_id, service_name=self.service_name)
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try:
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# Step 1: Delete model artifacts (references models)
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logger.info("training.tenant_deletion.deleting_artifacts", tenant_id=tenant_id)
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# TODO: Delete physical files from storage before deleting DB records
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# artifacts = await self.db.execute(
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# select(ModelArtifact).where(ModelArtifact.tenant_id == tenant_id)
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# )
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# for artifact in artifacts.scalars():
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# try:
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# os.remove(artifact.file_path) # Delete physical file
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# except Exception as e:
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# logger.warning("Failed to delete artifact file",
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# path=artifact.file_path, error=str(e))
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artifacts_result = await self.db.execute(
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delete(ModelArtifact).where(
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ModelArtifact.tenant_id == tenant_id
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)
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)
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result.deleted_counts["model_artifacts"] = artifacts_result.rowcount
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logger.info(
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"training.tenant_deletion.artifacts_deleted",
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tenant_id=tenant_id,
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count=artifacts_result.rowcount
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)
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# Step 2: Delete model performance metrics
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logger.info("training.tenant_deletion.deleting_metrics", tenant_id=tenant_id)
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metrics_result = await self.db.execute(
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delete(ModelPerformanceMetric).where(
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ModelPerformanceMetric.tenant_id == tenant_id
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)
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)
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result.deleted_counts["model_performance_metrics"] = metrics_result.rowcount
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logger.info(
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"training.tenant_deletion.metrics_deleted",
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tenant_id=tenant_id,
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count=metrics_result.rowcount
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)
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# Step 3: Delete training logs
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logger.info("training.tenant_deletion.deleting_logs", tenant_id=tenant_id)
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logs_result = await self.db.execute(
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delete(ModelTrainingLog).where(
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ModelTrainingLog.tenant_id == tenant_id
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)
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)
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result.deleted_counts["model_training_logs"] = logs_result.rowcount
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logger.info(
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"training.tenant_deletion.logs_deleted",
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tenant_id=tenant_id,
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count=logs_result.rowcount
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)
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# Step 4: Delete training job queue entries
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logger.info("training.tenant_deletion.deleting_queue", tenant_id=tenant_id)
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queue_result = await self.db.execute(
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delete(TrainingJobQueue).where(
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TrainingJobQueue.tenant_id == tenant_id
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)
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)
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result.deleted_counts["training_job_queue"] = queue_result.rowcount
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logger.info(
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"training.tenant_deletion.queue_deleted",
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tenant_id=tenant_id,
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count=queue_result.rowcount
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)
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# Step 5: Delete trained models (parent records)
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logger.info("training.tenant_deletion.deleting_models", tenant_id=tenant_id)
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# TODO: Delete physical model files (.pkl) before deleting DB records
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# models = await self.db.execute(
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# select(TrainedModel).where(TrainedModel.tenant_id == tenant_id)
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# )
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# for model in models.scalars():
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# try:
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# if model.model_path:
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# os.remove(model.model_path) # Delete .pkl file
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# if model.metadata_path:
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# os.remove(model.metadata_path) # Delete metadata file
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# except Exception as e:
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# logger.warning("Failed to delete model file",
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# path=model.model_path, error=str(e))
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models_result = await self.db.execute(
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delete(TrainedModel).where(
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TrainedModel.tenant_id == tenant_id
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)
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)
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result.deleted_counts["trained_models"] = models_result.rowcount
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logger.info(
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"training.tenant_deletion.models_deleted",
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tenant_id=tenant_id,
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count=models_result.rowcount
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)
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# Step 6: Delete audit logs
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logger.info("training.tenant_deletion.deleting_audit_logs", tenant_id=tenant_id)
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audit_result = await self.db.execute(
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delete(AuditLog).where(
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AuditLog.tenant_id == tenant_id
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)
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)
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result.deleted_counts["audit_logs"] = audit_result.rowcount
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logger.info(
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"training.tenant_deletion.audit_logs_deleted",
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tenant_id=tenant_id,
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count=audit_result.rowcount
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)
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# Commit the transaction
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await self.db.commit()
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# Calculate total deleted
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total_deleted = sum(result.deleted_counts.values())
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logger.info(
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"training.tenant_deletion.completed",
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tenant_id=tenant_id,
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total_deleted=total_deleted,
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breakdown=result.deleted_counts,
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note="Physical model files should be cleaned up separately"
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)
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result.success = True
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except Exception as e:
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await self.db.rollback()
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error_msg = f"Failed to delete training data for tenant {tenant_id}: {str(e)}"
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logger.error(
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"training.tenant_deletion.failed",
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tenant_id=tenant_id,
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error=str(e),
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exc_info=True
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)
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result.errors.append(error_msg)
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result.success = False
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return result
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def get_training_tenant_deletion_service(
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db: AsyncSession
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) -> TrainingTenantDeletionService:
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"""
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Factory function to create TrainingTenantDeletionService instance
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Args:
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db: AsyncSession database session
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Returns:
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TrainingTenantDeletionService instance
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"""
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return TrainingTenantDeletionService(db)
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