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ALwrity/backend/models/platform_insights_monitoring_models.py

101 lines
3.6 KiB
Python

"""
Platform Insights Monitoring Models
Database models for tracking platform insights (GSC/Bing) fetch tasks.
"""
from sqlalchemy import Column, Integer, String, Text, DateTime, JSON, Index, ForeignKey
from sqlalchemy.orm import relationship
from datetime import datetime
# Import the same Base from enhanced_strategy_models
from models.enhanced_strategy_models import Base
class PlatformInsightsTask(Base):
"""
Model for storing platform insights fetch tasks.
Tracks per-user, per-platform insights fetching with weekly updates.
"""
__tablename__ = "platform_insights_tasks"
id = Column(Integer, primary_key=True, index=True)
# User and Platform Identification
user_id = Column(String(255), nullable=False, index=True) # Clerk user ID (string)
platform = Column(String(50), nullable=False) # 'gsc' or 'bing'
site_url = Column(String(500), nullable=True) # Optional: specific site URL
# Task Status
status = Column(String(50), default='active') # 'active', 'failed', 'paused'
# Execution Tracking
last_check = Column(DateTime, nullable=True)
last_success = Column(DateTime, nullable=True)
last_failure = Column(DateTime, nullable=True)
failure_reason = Column(Text, nullable=True)
# Scheduling
next_check = Column(DateTime, nullable=True, index=True) # Next scheduled check time
# Metadata
created_at = Column(DateTime, default=datetime.utcnow)
updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
# Execution Logs Relationship
execution_logs = relationship(
"PlatformInsightsExecutionLog",
back_populates="task",
cascade="all, delete-orphan"
)
# Indexes for efficient queries
__table_args__ = (
Index('idx_platform_insights_user_platform', 'user_id', 'platform'),
Index('idx_platform_insights_next_check', 'next_check'),
Index('idx_platform_insights_status', 'status'),
)
def __repr__(self):
return f"<PlatformInsightsTask(id={self.id}, user_id={self.user_id}, platform={self.platform}, status={self.status})>"
class PlatformInsightsExecutionLog(Base):
"""
Model for storing platform insights fetch execution logs.
Tracks individual execution attempts with results and error details.
"""
__tablename__ = "platform_insights_execution_logs"
id = Column(Integer, primary_key=True, index=True)
# Task Reference
task_id = Column(Integer, ForeignKey("platform_insights_tasks.id"), nullable=False, index=True)
# Execution Details
execution_date = Column(DateTime, default=datetime.utcnow, nullable=False)
status = Column(String(50), nullable=False) # 'success', 'failed', 'skipped'
# Results
result_data = Column(JSON, nullable=True) # Insights data, metrics, etc.
error_message = Column(Text, nullable=True)
execution_time_ms = Column(Integer, nullable=True)
data_source = Column(String(50), nullable=True) # 'cached', 'api', 'onboarding'
# Metadata
created_at = Column(DateTime, default=datetime.utcnow)
# Relationship to task
task = relationship("PlatformInsightsTask", back_populates="execution_logs")
# Indexes for efficient queries
__table_args__ = (
Index('idx_platform_insights_log_task_execution_date', 'task_id', 'execution_date'),
Index('idx_platform_insights_log_status', 'status'),
)
def __repr__(self):
return f"<PlatformInsightsExecutionLog(id={self.id}, task_id={self.task_id}, status={self.status}, execution_date={self.execution_date})>"