Improve the UI and training
This commit is contained in:
Urtzi Alfaro
@@ -4,7 +4,8 @@ Forecasting Operations API - Business operations for forecast generation and pre
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"""
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import structlog
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from fastapi import APIRouter, Depends, HTTPException, status, Query, Path, Request
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from fastapi import APIRouter, Depends, HTTPException, status, Query, Path, Request, BackgroundTasks
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from fastapi.responses import JSONResponse
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from typing import List, Dict, Any, Optional
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from datetime import date, datetime, timezone
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import uuid
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@@ -202,6 +203,97 @@ async def generate_multi_day_forecast(
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)
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async def execute_batch_forecast_background(
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tenant_id: str,
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batch_id: str,
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inventory_product_ids: List[str],
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forecast_days: int,
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batch_name: str
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):
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"""
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Background task for batch forecast generation.
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Prevents blocking the API thread for long-running batch operations.
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"""
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logger.info("Starting background batch forecast",
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batch_id=batch_id,
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tenant_id=tenant_id,
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product_count=len(inventory_product_ids))
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database_manager = create_database_manager(settings.DATABASE_URL, "forecasting-service")
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forecasting_service = EnhancedForecastingService(database_manager)
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try:
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# Update batch status to running
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async with database_manager.get_session() as session:
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from app.repositories import PredictionBatchRepository
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batch_repo = PredictionBatchRepository(session)
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await batch_repo.update(
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batch_id,
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{"status": "processing", "completed_products": 0}
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)
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await session.commit()
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# Generate forecasts for all products
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from app.schemas.forecasts import BatchForecastRequest
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batch_request = BatchForecastRequest(
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tenant_id=tenant_id,
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batch_name=batch_name,
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inventory_product_ids=inventory_product_ids,
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forecast_days=forecast_days
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)
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result = await forecasting_service.generate_batch_forecasts(
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tenant_id=tenant_id,
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request=batch_request
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)
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# Update batch status to completed
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async with database_manager.get_session() as session:
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from app.repositories import PredictionBatchRepository
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batch_repo = PredictionBatchRepository(session)
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await batch_repo.update(
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batch_id,
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{
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"status": "completed",
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"completed_at": datetime.now(timezone.utc),
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"completed_products": result.get("successful_forecasts", 0),
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"failed_products": result.get("failed_forecasts", 0)
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}
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)
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await session.commit()
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logger.info("Background batch forecast completed",
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batch_id=batch_id,
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successful=result.get("successful_forecasts", 0),
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failed=result.get("failed_forecasts", 0))
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except Exception as e:
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logger.error("Background batch forecast failed",
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batch_id=batch_id,
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error=str(e))
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try:
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async with database_manager.get_session() as session:
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from app.repositories import PredictionBatchRepository
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batch_repo = PredictionBatchRepository(session)
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await batch_repo.update(
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batch_id,
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{
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"status": "failed",
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"completed_at": datetime.now(timezone.utc),
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"error_message": str(e)
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}
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)
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await session.commit()
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except Exception as update_error:
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logger.error("Failed to update batch status after error",
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batch_id=batch_id,
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error=str(update_error))
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@router.post(
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route_builder.build_operations_route("batch"),
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response_model=BatchForecastResponse
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@@ -211,11 +303,17 @@ async def generate_multi_day_forecast(
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async def generate_batch_forecast(
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request: BatchForecastRequest,
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tenant_id: str = Path(..., description="Tenant ID"),
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background_tasks: BackgroundTasks = BackgroundTasks(),
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request_obj: Request = None,
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current_user: dict = Depends(get_current_user_dep),
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enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
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):
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"""Generate forecasts for multiple products in batch (Admin+ only, quota enforced)"""
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"""
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Generate forecasts for multiple products in batch (Admin+ only, quota enforced).
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IMPROVEMENT: Now uses background tasks for large batches to prevent API timeouts.
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Returns immediately with batch_id for status tracking.
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"""
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metrics = get_metrics_collector(request_obj)
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try:
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@@ -258,48 +356,104 @@ async def generate_batch_forecast(
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error_message=None
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)
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# Skip rate limiting for service-to-service calls (orchestrator)
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# Rate limiting is handled at the gateway level for user requests
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# IMPROVEMENT: For large batches (>5 products), use background task
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# For small batches, execute synchronously for immediate results
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batch_name = getattr(request, 'batch_name', f"batch-{datetime.now().strftime('%Y%m%d_%H%M%S')}")
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forecast_days = getattr(request, 'forecast_days', 7)
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# Create a copy of the request with the actual list of product IDs to forecast
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# (whether originally provided or fetched from inventory service)
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from app.schemas.forecasts import BatchForecastRequest
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updated_request = BatchForecastRequest(
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tenant_id=tenant_id, # Use the tenant_id from the path parameter
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batch_name=getattr(request, 'batch_name', f"orchestrator-batch-{datetime.now().strftime('%Y%m%d')}"),
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inventory_product_ids=inventory_product_ids,
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forecast_days=getattr(request, 'forecast_days', 7)
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)
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batch_result = await enhanced_forecasting_service.generate_batch_forecasts(
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tenant_id=tenant_id,
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request=updated_request
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)
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if metrics:
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metrics.increment_counter("batch_forecasts_success_total")
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logger.info("Batch forecast generated successfully",
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tenant_id=tenant_id,
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total_forecasts=batch_result.get('total_forecasts', 0))
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# Convert the service result to BatchForecastResponse format
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from app.schemas.forecasts import BatchForecastResponse
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# Create batch record first
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batch_id = str(uuid.uuid4())
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now = datetime.now(timezone.utc)
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return BatchForecastResponse(
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id=batch_result.get('id', str(uuid.uuid4())), # Use 'id' field (UUID) instead of 'batch_id' (string)
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tenant_id=tenant_id,
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batch_name=updated_request.batch_name,
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status="completed",
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total_products=batch_result.get('total_forecasts', 0),
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completed_products=batch_result.get('successful_forecasts', 0),
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failed_products=batch_result.get('failed_forecasts', 0),
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requested_at=now,
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completed_at=now,
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processing_time_ms=0,
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forecasts=[],
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error_message=None
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)
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async with enhanced_forecasting_service.database_manager.get_session() as session:
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from app.repositories import PredictionBatchRepository
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batch_repo = PredictionBatchRepository(session)
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batch_data = {
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"tenant_id": tenant_id,
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"batch_name": batch_name,
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"total_products": len(inventory_product_ids),
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"forecast_days": forecast_days,
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"status": "pending"
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}
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batch = await batch_repo.create_batch(batch_data)
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batch_id = str(batch.id)
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await session.commit()
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# Use background task for large batches to prevent API timeout
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use_background = len(inventory_product_ids) > 5
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if use_background:
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# Queue background task
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background_tasks.add_task(
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execute_batch_forecast_background,
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tenant_id=tenant_id,
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batch_id=batch_id,
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inventory_product_ids=inventory_product_ids,
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forecast_days=forecast_days,
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batch_name=batch_name
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)
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logger.info("Batch forecast queued for background processing",
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tenant_id=tenant_id,
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batch_id=batch_id,
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product_count=len(inventory_product_ids))
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# Return immediately with pending status
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from app.schemas.forecasts import BatchForecastResponse
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return BatchForecastResponse(
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id=batch_id,
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tenant_id=tenant_id,
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batch_name=batch_name,
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status="pending",
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total_products=len(inventory_product_ids),
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completed_products=0,
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failed_products=0,
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requested_at=now,
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completed_at=None,
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processing_time_ms=0,
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forecasts=None,
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error_message=None
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)
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else:
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# Small batch - execute synchronously
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from app.schemas.forecasts import BatchForecastRequest
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updated_request = BatchForecastRequest(
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tenant_id=tenant_id,
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batch_name=batch_name,
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inventory_product_ids=inventory_product_ids,
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forecast_days=forecast_days
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)
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batch_result = await enhanced_forecasting_service.generate_batch_forecasts(
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tenant_id=tenant_id,
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request=updated_request
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)
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if metrics:
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metrics.increment_counter("batch_forecasts_success_total")
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logger.info("Batch forecast completed synchronously",
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tenant_id=tenant_id,
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total_forecasts=batch_result.get('total_forecasts', 0))
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# Convert the service result to BatchForecastResponse format
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from app.schemas.forecasts import BatchForecastResponse
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return BatchForecastResponse(
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id=batch_id,
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tenant_id=tenant_id,
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batch_name=batch_name,
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status="completed",
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total_products=batch_result.get('total_forecasts', 0),
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completed_products=batch_result.get('successful_forecasts', 0),
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failed_products=batch_result.get('failed_forecasts', 0),
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requested_at=now,
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completed_at=datetime.now(timezone.utc),
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processing_time_ms=0,
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forecasts=[],
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error_message=None
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)
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except ValueError as e:
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if metrics:
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@@ -806,3 +960,50 @@ async def preview_tenant_data_deletion(
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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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@router.get("/health/database")
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async def database_health():
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"""
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Database health check endpoint with connection pool monitoring.
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Returns detailed connection pool statistics for monitoring and alerting.
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Useful for detecting connection pool exhaustion before it causes issues.
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"""
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from app.core.database import get_db_health, get_connection_pool_stats
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from datetime import datetime
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try:
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# Check database connectivity
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db_healthy = await get_db_health()
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# Get connection pool statistics
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pool_stats = await get_connection_pool_stats()
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response = {
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"service": "forecasting",
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"timestamp": datetime.now(timezone.utc).isoformat(),
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"database_connected": db_healthy,
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"connection_pool": pool_stats,
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"overall_status": "healthy" if db_healthy and pool_stats.get("status") == "healthy" else "degraded"
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}
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# Return appropriate status code based on health
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if not db_healthy or pool_stats.get("status") == "critical":
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return JSONResponse(status_code=503, content=response)
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elif pool_stats.get("status") == "warning":
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return JSONResponse(status_code=200, content=response)
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else:
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return response
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except Exception as e:
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logger.error("Health check failed", error=str(e))
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return JSONResponse(
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status_code=503,
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content={
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"service": "forecasting",
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"timestamp": datetime.now(timezone.utc).isoformat(),
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"overall_status": "unhealthy",
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"error": str(e)
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}
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)
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