Files
bakery-ia/services/forecasting/app/services/tenant_deletion_service.py
2025-10-31 11:54:19 +01:00

241 lines
8.1 KiB
Python

# services/forecasting/app/services/tenant_deletion_service.py
"""
Tenant Data Deletion Service for Forecasting Service
Handles deletion of all forecasting-related data for a tenant
"""
from typing import Dict
from sqlalchemy import select, func, delete
from sqlalchemy.ext.asyncio import AsyncSession
import structlog
from shared.services.tenant_deletion import (
BaseTenantDataDeletionService,
TenantDataDeletionResult
)
from app.models import (
Forecast,
PredictionBatch,
ModelPerformanceMetric,
PredictionCache,
AuditLog
)
logger = structlog.get_logger(__name__)
class ForecastingTenantDeletionService(BaseTenantDataDeletionService):
"""Service for deleting all forecasting-related data for a tenant"""
def __init__(self, db: AsyncSession):
self.db = db
self.service_name = "forecasting"
async def get_tenant_data_preview(self, tenant_id: str) -> Dict[str, int]:
"""
Get counts of what would be deleted for a tenant (dry-run)
Args:
tenant_id: The tenant ID to preview deletion for
Returns:
Dictionary with entity names and their counts
"""
logger.info("forecasting.tenant_deletion.preview", tenant_id=tenant_id)
preview = {}
try:
# Count forecasts
forecast_count = await self.db.scalar(
select(func.count(Forecast.id)).where(
Forecast.tenant_id == tenant_id
)
)
preview["forecasts"] = forecast_count or 0
# Count prediction batches
batch_count = await self.db.scalar(
select(func.count(PredictionBatch.id)).where(
PredictionBatch.tenant_id == tenant_id
)
)
preview["prediction_batches"] = batch_count or 0
# Count model performance metrics
metric_count = await self.db.scalar(
select(func.count(ModelPerformanceMetric.id)).where(
ModelPerformanceMetric.tenant_id == tenant_id
)
)
preview["model_performance_metrics"] = metric_count or 0
# Count prediction cache entries
cache_count = await self.db.scalar(
select(func.count(PredictionCache.id)).where(
PredictionCache.tenant_id == tenant_id
)
)
preview["prediction_cache"] = cache_count or 0
# Count audit logs
audit_count = await self.db.scalar(
select(func.count(AuditLog.id)).where(
AuditLog.tenant_id == tenant_id
)
)
preview["audit_logs"] = audit_count or 0
logger.info(
"forecasting.tenant_deletion.preview_complete",
tenant_id=tenant_id,
preview=preview
)
except Exception as e:
logger.error(
"forecasting.tenant_deletion.preview_error",
tenant_id=tenant_id,
error=str(e),
exc_info=True
)
raise
return preview
async def delete_tenant_data(self, tenant_id: str) -> TenantDataDeletionResult:
"""
Permanently delete all forecasting data for a tenant
Deletion order:
1. PredictionCache (independent)
2. ModelPerformanceMetric (independent)
3. PredictionBatch (independent)
4. Forecast (independent)
5. AuditLog (independent)
Note: All tables are independent with no foreign key relationships
Args:
tenant_id: The tenant ID to delete data for
Returns:
TenantDataDeletionResult with deletion counts and any errors
"""
logger.info("forecasting.tenant_deletion.started", tenant_id=tenant_id)
result = TenantDataDeletionResult(tenant_id=tenant_id, service_name=self.service_name)
try:
# Step 1: Delete prediction cache
logger.info("forecasting.tenant_deletion.deleting_cache", tenant_id=tenant_id)
cache_result = await self.db.execute(
delete(PredictionCache).where(
PredictionCache.tenant_id == tenant_id
)
)
result.deleted_counts["prediction_cache"] = cache_result.rowcount
logger.info(
"forecasting.tenant_deletion.cache_deleted",
tenant_id=tenant_id,
count=cache_result.rowcount
)
# Step 2: Delete model performance metrics
logger.info("forecasting.tenant_deletion.deleting_metrics", tenant_id=tenant_id)
metrics_result = await self.db.execute(
delete(ModelPerformanceMetric).where(
ModelPerformanceMetric.tenant_id == tenant_id
)
)
result.deleted_counts["model_performance_metrics"] = metrics_result.rowcount
logger.info(
"forecasting.tenant_deletion.metrics_deleted",
tenant_id=tenant_id,
count=metrics_result.rowcount
)
# Step 3: Delete prediction batches
logger.info("forecasting.tenant_deletion.deleting_batches", tenant_id=tenant_id)
batches_result = await self.db.execute(
delete(PredictionBatch).where(
PredictionBatch.tenant_id == tenant_id
)
)
result.deleted_counts["prediction_batches"] = batches_result.rowcount
logger.info(
"forecasting.tenant_deletion.batches_deleted",
tenant_id=tenant_id,
count=batches_result.rowcount
)
# Step 4: Delete forecasts
logger.info("forecasting.tenant_deletion.deleting_forecasts", tenant_id=tenant_id)
forecasts_result = await self.db.execute(
delete(Forecast).where(
Forecast.tenant_id == tenant_id
)
)
result.deleted_counts["forecasts"] = forecasts_result.rowcount
logger.info(
"forecasting.tenant_deletion.forecasts_deleted",
tenant_id=tenant_id,
count=forecasts_result.rowcount
)
# Step 5: Delete audit logs
logger.info("forecasting.tenant_deletion.deleting_audit_logs", tenant_id=tenant_id)
audit_result = await self.db.execute(
delete(AuditLog).where(
AuditLog.tenant_id == tenant_id
)
)
result.deleted_counts["audit_logs"] = audit_result.rowcount
logger.info(
"forecasting.tenant_deletion.audit_logs_deleted",
tenant_id=tenant_id,
count=audit_result.rowcount
)
# Commit the transaction
await self.db.commit()
# Calculate total deleted
total_deleted = sum(result.deleted_counts.values())
logger.info(
"forecasting.tenant_deletion.completed",
tenant_id=tenant_id,
total_deleted=total_deleted,
breakdown=result.deleted_counts
)
result.success = True
except Exception as e:
await self.db.rollback()
error_msg = f"Failed to delete forecasting data for tenant {tenant_id}: {str(e)}"
logger.error(
"forecasting.tenant_deletion.failed",
tenant_id=tenant_id,
error=str(e),
exc_info=True
)
result.errors.append(error_msg)
result.success = False
return result
def get_forecasting_tenant_deletion_service(
db: AsyncSession
) -> ForecastingTenantDeletionService:
"""
Factory function to create ForecastingTenantDeletionService instance
Args:
db: AsyncSession database session
Returns:
ForecastingTenantDeletionService instance
"""
return ForecastingTenantDeletionService(db)