253 lines
10 KiB
Python
253 lines
10 KiB
Python
"""
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Base Repository for Forecasting Service
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Service-specific repository base class with forecasting utilities
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"""
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from typing import Optional, List, Dict, Any, Type
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import text
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from datetime import datetime, date, timedelta
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import structlog
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from shared.database.repository import BaseRepository
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from shared.database.exceptions import DatabaseError
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logger = structlog.get_logger()
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class ForecastingBaseRepository(BaseRepository):
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"""Base repository for forecasting service with common forecasting operations"""
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def __init__(self, model: Type, session: AsyncSession, cache_ttl: Optional[int] = 600):
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# Forecasting data benefits from medium cache time (10 minutes)
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super().__init__(model, session, cache_ttl)
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async def get_by_tenant_id(self, tenant_id: str, skip: int = 0, limit: int = 100) -> List:
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"""Get records by tenant ID"""
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if hasattr(self.model, 'tenant_id'):
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return await self.get_multi(
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skip=skip,
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limit=limit,
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filters={"tenant_id": tenant_id},
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order_by="created_at",
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order_desc=True
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)
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return await self.get_multi(skip=skip, limit=limit)
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async def get_by_inventory_product_id(
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self,
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tenant_id: str,
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inventory_product_id: str,
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skip: int = 0,
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limit: int = 100
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) -> List:
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"""Get records by tenant and inventory product"""
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if hasattr(self.model, 'inventory_product_id'):
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return await self.get_multi(
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skip=skip,
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limit=limit,
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filters={
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"tenant_id": tenant_id,
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"inventory_product_id": inventory_product_id
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},
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order_by="created_at",
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order_desc=True
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)
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return await self.get_by_tenant_id(tenant_id, skip, limit)
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async def get_by_date_range(
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self,
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tenant_id: str,
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start_date: datetime,
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end_date: datetime,
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skip: int = 0,
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limit: int = 100
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) -> List:
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"""Get records within date range for a tenant"""
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if not hasattr(self.model, 'forecast_date') and not hasattr(self.model, 'created_at'):
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logger.warning(f"Model {self.model.__name__} has no date field for filtering")
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return []
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try:
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table_name = self.model.__tablename__
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date_field = "forecast_date" if hasattr(self.model, 'forecast_date') else "created_at"
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query_text = f"""
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SELECT * FROM {table_name}
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WHERE tenant_id = :tenant_id
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AND {date_field} >= :start_date
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AND {date_field} <= :end_date
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ORDER BY {date_field} DESC
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LIMIT :limit OFFSET :skip
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"""
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result = await self.session.execute(text(query_text), {
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"tenant_id": tenant_id,
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"start_date": start_date,
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"end_date": end_date,
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"limit": limit,
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"skip": skip
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})
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# Convert rows to model objects
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records = []
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for row in result.fetchall():
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record_dict = dict(row._mapping)
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record = self.model(**record_dict)
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records.append(record)
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return records
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except Exception as e:
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logger.error("Failed to get records by date range",
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model=self.model.__name__,
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tenant_id=tenant_id,
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error=str(e))
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raise DatabaseError(f"Date range query failed: {str(e)}")
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async def get_recent_records(
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self,
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tenant_id: str,
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hours: int = 24,
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skip: int = 0,
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limit: int = 100
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) -> List:
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"""Get recent records for a tenant"""
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cutoff_time = datetime.utcnow() - timedelta(hours=hours)
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return await self.get_by_date_range(
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tenant_id, cutoff_time, datetime.utcnow(), skip, limit
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)
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async def cleanup_old_records(self, days_old: int = 90) -> int:
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"""Clean up old forecasting records"""
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try:
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cutoff_date = datetime.utcnow() - timedelta(days=days_old)
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table_name = self.model.__tablename__
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# Use created_at or forecast_date for cleanup
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date_field = "forecast_date" if hasattr(self.model, 'forecast_date') else "created_at"
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query_text = f"""
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DELETE FROM {table_name}
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WHERE {date_field} < :cutoff_date
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"""
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result = await self.session.execute(text(query_text), {"cutoff_date": cutoff_date})
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deleted_count = result.rowcount
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logger.info(f"Cleaned up old {self.model.__name__} records",
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deleted_count=deleted_count,
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days_old=days_old)
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return deleted_count
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except Exception as e:
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logger.error("Failed to cleanup old records",
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model=self.model.__name__,
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error=str(e))
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raise DatabaseError(f"Cleanup failed: {str(e)}")
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async def get_statistics_by_tenant(self, tenant_id: str) -> Dict[str, Any]:
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"""Get statistics for a tenant"""
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try:
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table_name = self.model.__tablename__
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# Get basic counts
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total_records = await self.count(filters={"tenant_id": tenant_id})
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# Get recent activity (records in last 7 days)
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seven_days_ago = datetime.utcnow() - timedelta(days=7)
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recent_records = len(await self.get_by_date_range(
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tenant_id, seven_days_ago, datetime.utcnow(), limit=1000
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))
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# Get records by product if applicable
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product_stats = {}
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if hasattr(self.model, 'inventory_product_id'):
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product_query = text(f"""
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SELECT inventory_product_id, COUNT(*) as count
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FROM {table_name}
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WHERE tenant_id = :tenant_id
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GROUP BY inventory_product_id
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ORDER BY count DESC
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""")
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result = await self.session.execute(product_query, {"tenant_id": tenant_id})
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product_stats = {row.inventory_product_id: row.count for row in result.fetchall()}
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return {
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"total_records": total_records,
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"recent_records_7d": recent_records,
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"records_by_product": product_stats
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}
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except Exception as e:
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logger.error("Failed to get tenant statistics",
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model=self.model.__name__,
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tenant_id=tenant_id,
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error=str(e))
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return {
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"total_records": 0,
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"recent_records_7d": 0,
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"records_by_product": {}
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}
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def _validate_forecast_data(self, data: Dict[str, Any], required_fields: List[str]) -> Dict[str, Any]:
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"""Validate forecasting-related data"""
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errors = []
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for field in required_fields:
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if field not in data or data[field] is None:
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errors.append(f"Missing required field: {field}")
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# Validate tenant_id format if present
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if "tenant_id" in data and data["tenant_id"]:
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tenant_id = data["tenant_id"]
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if not isinstance(tenant_id, str) or len(tenant_id) < 1:
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errors.append("Invalid tenant_id format")
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# Validate inventory_product_id if present
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if "inventory_product_id" in data and data["inventory_product_id"]:
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inventory_product_id = data["inventory_product_id"]
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if not isinstance(inventory_product_id, str) or len(inventory_product_id) < 1:
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errors.append("Invalid inventory_product_id format")
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# Validate dates if present - accept datetime objects, date objects, and date strings
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date_fields = ["forecast_date", "created_at", "evaluation_date", "expires_at"]
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for field in date_fields:
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if field in data and data[field]:
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field_value = data[field]
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field_type = type(field_value).__name__
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if isinstance(field_value, (datetime, date)):
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logger.debug(f"Date field {field} is valid {field_type}", field_value=str(field_value))
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continue # Already a datetime or date, valid
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elif isinstance(field_value, str):
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# Try to parse the string date
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try:
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from dateutil.parser import parse
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parse(field_value) # Just validate, don't convert yet
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logger.debug(f"Date field {field} is valid string", field_value=field_value)
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except (ValueError, TypeError) as e:
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logger.error(f"Date parsing failed for {field}", field_value=field_value, error=str(e))
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errors.append(f"Invalid {field} format - must be datetime or valid date string")
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else:
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logger.error(f"Date field {field} has invalid type {field_type}", field_value=str(field_value))
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errors.append(f"Invalid {field} format - must be datetime or valid date string")
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# Validate numeric fields
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numeric_fields = [
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"predicted_demand", "confidence_lower", "confidence_upper",
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"mae", "mape", "rmse", "accuracy_score"
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]
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for field in numeric_fields:
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if field in data and data[field] is not None:
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try:
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float(data[field])
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except (ValueError, TypeError):
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errors.append(f"Invalid {field} format - must be numeric")
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return {
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"is_valid": len(errors) == 0,
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"errors": errors
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} |