241 lines
8.1 KiB
Python
241 lines
8.1 KiB
Python
# services/forecasting/app/services/tenant_deletion_service.py
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"""
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Tenant Data Deletion Service for Forecasting Service
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Handles deletion of all forecasting-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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Forecast,
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PredictionBatch,
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ModelPerformanceMetric,
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PredictionCache,
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AuditLog
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)
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logger = structlog.get_logger(__name__)
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class ForecastingTenantDeletionService(BaseTenantDataDeletionService):
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"""Service for deleting all forecasting-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 = "forecasting"
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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("forecasting.tenant_deletion.preview", tenant_id=tenant_id)
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preview = {}
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try:
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# Count forecasts
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forecast_count = await self.db.scalar(
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select(func.count(Forecast.id)).where(
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Forecast.tenant_id == tenant_id
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)
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)
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preview["forecasts"] = forecast_count or 0
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# Count prediction batches
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batch_count = await self.db.scalar(
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select(func.count(PredictionBatch.id)).where(
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PredictionBatch.tenant_id == tenant_id
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)
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)
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preview["prediction_batches"] = batch_count or 0
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# Count model 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 prediction cache entries
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cache_count = await self.db.scalar(
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select(func.count(PredictionCache.id)).where(
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PredictionCache.tenant_id == tenant_id
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)
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)
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preview["prediction_cache"] = cache_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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"forecasting.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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"forecasting.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 forecasting data for a tenant
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Deletion order:
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1. PredictionCache (independent)
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2. ModelPerformanceMetric (independent)
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3. PredictionBatch (independent)
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4. Forecast (independent)
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5. AuditLog (independent)
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Note: All tables are independent with no foreign key relationships
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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("forecasting.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 prediction cache
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logger.info("forecasting.tenant_deletion.deleting_cache", tenant_id=tenant_id)
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cache_result = await self.db.execute(
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delete(PredictionCache).where(
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PredictionCache.tenant_id == tenant_id
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)
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)
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result.deleted_counts["prediction_cache"] = cache_result.rowcount
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logger.info(
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"forecasting.tenant_deletion.cache_deleted",
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tenant_id=tenant_id,
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count=cache_result.rowcount
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)
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# Step 2: Delete model performance metrics
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logger.info("forecasting.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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"forecasting.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 prediction batches
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logger.info("forecasting.tenant_deletion.deleting_batches", tenant_id=tenant_id)
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batches_result = await self.db.execute(
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delete(PredictionBatch).where(
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PredictionBatch.tenant_id == tenant_id
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)
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)
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result.deleted_counts["prediction_batches"] = batches_result.rowcount
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logger.info(
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"forecasting.tenant_deletion.batches_deleted",
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tenant_id=tenant_id,
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count=batches_result.rowcount
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)
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# Step 4: Delete forecasts
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logger.info("forecasting.tenant_deletion.deleting_forecasts", tenant_id=tenant_id)
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forecasts_result = await self.db.execute(
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delete(Forecast).where(
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Forecast.tenant_id == tenant_id
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)
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)
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result.deleted_counts["forecasts"] = forecasts_result.rowcount
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logger.info(
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"forecasting.tenant_deletion.forecasts_deleted",
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tenant_id=tenant_id,
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count=forecasts_result.rowcount
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)
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# Step 5: Delete audit logs
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logger.info("forecasting.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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"forecasting.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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"forecasting.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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)
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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 forecasting data for tenant {tenant_id}: {str(e)}"
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logger.error(
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"forecasting.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_forecasting_tenant_deletion_service(
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db: AsyncSession
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) -> ForecastingTenantDeletionService:
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"""
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Factory function to create ForecastingTenantDeletionService instance
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Args:
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db: AsyncSession database session
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Returns:
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ForecastingTenantDeletionService instance
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"""
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return ForecastingTenantDeletionService(db)
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