210 lines
7.6 KiB
Python
210 lines
7.6 KiB
Python
"""
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Bing Analytics Database Models
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Models for storing and analyzing Bing Webmaster Tools analytics data
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including raw query data, aggregated metrics, and trend analysis.
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"""
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from sqlalchemy import Column, Integer, String, Float, DateTime, Text, Boolean, Index
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from sqlalchemy.ext.declarative import declarative_base
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from sqlalchemy.sql import func
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from datetime import datetime
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from typing import Dict, Any, List, Optional
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Base = declarative_base()
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class BingQueryStats(Base):
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"""Raw query statistics from Bing Webmaster Tools API"""
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__tablename__ = 'bing_query_stats'
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id = Column(Integer, primary_key=True, autoincrement=True)
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user_id = Column(String(255), nullable=False, index=True)
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site_url = Column(String(500), nullable=False, index=True)
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# Query data
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query = Column(Text, nullable=False, index=True)
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clicks = Column(Integer, default=0)
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impressions = Column(Integer, default=0)
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avg_click_position = Column(Float, default=-1)
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avg_impression_position = Column(Float, default=-1)
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ctr = Column(Float, default=0) # Calculated: clicks/impressions * 100
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# Date information
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query_date = Column(DateTime, nullable=False, index=True)
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collected_at = Column(DateTime, default=func.now(), index=True)
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# Additional metadata
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query_length = Column(Integer, default=0) # For analysis
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is_brand_query = Column(Boolean, default=False) # Contains brand name
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category = Column(String(100), default='general') # ai_writing, business, etc.
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# Indexes for performance
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__table_args__ = (
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Index('idx_user_site_date', 'user_id', 'site_url', 'query_date'),
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Index('idx_query_performance', 'query', 'clicks', 'impressions'),
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Index('idx_collected_at', 'collected_at'),
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)
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class BingDailyMetrics(Base):
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"""Daily aggregated metrics for Bing analytics"""
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__tablename__ = 'bing_daily_metrics'
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id = Column(Integer, primary_key=True, autoincrement=True)
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user_id = Column(String(255), nullable=False, index=True)
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site_url = Column(String(500), nullable=False, index=True)
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# Date
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metric_date = Column(DateTime, nullable=False, index=True)
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collected_at = Column(DateTime, default=func.now())
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# Aggregated metrics
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total_clicks = Column(Integer, default=0)
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total_impressions = Column(Integer, default=0)
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total_queries = Column(Integer, default=0)
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avg_ctr = Column(Float, default=0)
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avg_position = Column(Float, default=0)
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# Top performing queries (JSON)
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top_queries = Column(Text) # JSON string of top 10 queries
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top_clicks = Column(Text) # JSON string of queries with most clicks
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top_impressions = Column(Text) # JSON string of queries with most impressions
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# Trend indicators (compared to previous day)
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clicks_change = Column(Float, default=0) # Percentage change
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impressions_change = Column(Float, default=0)
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ctr_change = Column(Float, default=0)
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# Indexes
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__table_args__ = (
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Index('idx_user_site_metric_date', 'user_id', 'site_url', 'metric_date'),
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)
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class BingTrendAnalysis(Base):
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"""Weekly/Monthly trend analysis data"""
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__tablename__ = 'bing_trend_analysis'
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id = Column(Integer, primary_key=True, autoincrement=True)
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user_id = Column(String(255), nullable=False, index=True)
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site_url = Column(String(500), nullable=False, index=True)
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# Period information
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period_start = Column(DateTime, nullable=False, index=True)
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period_end = Column(DateTime, nullable=False, index=True)
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period_type = Column(String(20), nullable=False) # 'weekly', 'monthly'
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# Trend metrics
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total_clicks = Column(Integer, default=0)
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total_impressions = Column(Integer, default=0)
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total_queries = Column(Integer, default=0)
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avg_ctr = Column(Float, default=0)
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avg_position = Column(Float, default=0)
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# Growth indicators
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clicks_growth = Column(Float, default=0) # vs previous period
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impressions_growth = Column(Float, default=0)
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ctr_growth = Column(Float, default=0)
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# Top categories and queries
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top_categories = Column(Text) # JSON of category performance
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trending_queries = Column(Text) # JSON of trending queries
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declining_queries = Column(Text) # JSON of declining queries
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created_at = Column(DateTime, default=func.now(), index=True)
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# Indexes
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__table_args__ = (
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Index('idx_user_site_period', 'user_id', 'site_url', 'period_type', 'period_start'),
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)
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class BingAlertRules(Base):
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"""Alert rules for Bing analytics monitoring"""
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__tablename__ = 'bing_alert_rules'
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id = Column(Integer, primary_key=True, autoincrement=True)
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user_id = Column(String(255), nullable=False, index=True)
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site_url = Column(String(500), nullable=False, index=True)
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# Alert configuration
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rule_name = Column(String(255), nullable=False)
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alert_type = Column(String(50), nullable=False) # 'ctr_drop', 'query_spike', 'position_drop'
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# Thresholds
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threshold_value = Column(Float, nullable=False)
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comparison_operator = Column(String(10), nullable=False) # '>', '<', '>=', '<=', '=='
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# Alert settings
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is_active = Column(Boolean, default=True)
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last_triggered = Column(DateTime)
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trigger_count = Column(Integer, default=0)
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created_at = Column(DateTime, default=func.now())
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updated_at = Column(DateTime, default=func.now(), onupdate=func.now())
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class BingAlertHistory(Base):
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"""History of triggered alerts"""
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__tablename__ = 'bing_alert_history'
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id = Column(Integer, primary_key=True, autoincrement=True)
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user_id = Column(String(255), nullable=False, index=True)
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site_url = Column(String(500), nullable=False, index=True)
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alert_rule_id = Column(Integer, nullable=False, index=True)
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# Alert details
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alert_type = Column(String(50), nullable=False)
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trigger_value = Column(Float, nullable=False)
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threshold_value = Column(Float, nullable=False)
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message = Column(Text, nullable=False)
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# Context data
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context_data = Column(Text) # JSON with additional context
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triggered_at = Column(DateTime, default=func.now(), index=True)
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is_resolved = Column(Boolean, default=False)
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resolved_at = Column(DateTime)
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# Indexes
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__table_args__ = (
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Index('idx_user_alert_triggered', 'user_id', 'triggered_at'),
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Index('idx_alert_rule_triggered', 'alert_rule_id', 'triggered_at'),
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)
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class BingSitePerformance(Base):
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"""Overall site performance summary"""
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__tablename__ = 'bing_site_performance'
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id = Column(Integer, primary_key=True, autoincrement=True)
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user_id = Column(String(255), nullable=False, index=True)
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site_url = Column(String(500), nullable=False, index=True)
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# Performance summary
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total_clicks_all_time = Column(Integer, default=0)
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total_impressions_all_time = Column(Integer, default=0)
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total_queries_all_time = Column(Integer, default=0)
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best_avg_ctr = Column(Float, default=0)
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best_avg_position = Column(Float, default=0)
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# Top performers
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best_performing_query = Column(Text)
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best_performing_date = Column(DateTime)
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most_impressions_query = Column(Text)
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most_clicks_query = Column(Text)
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# Rankings and insights
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query_diversity_score = Column(Float, default=0) # Unique queries / total queries
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brand_query_percentage = Column(Float, default=0)
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# Last updated
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last_updated = Column(DateTime, default=func.now(), onupdate=func.now())
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data_collection_start = Column(DateTime)
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# Indexes
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__table_args__ = (
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Index('idx_user_site_performance', 'user_id', 'site_url'),
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)
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