"""
Database Analytics Module
Provides insights and statistics about database usage
"""

from typing import Dict, List
from datetime import datetime, timedelta
from app.query_engine import create_query_engine

class DatabaseAnalytics:
    """Analyze database usage and provide insights"""
    
    def __init__(self):
        pass
    
    def get_table_stats(self, user_store, database: str, table: str) -> Dict:
        """Get detailed statistics for a table"""
        engine = create_query_engine(user_store)
        
        try:
            # Get row count
            count_result = engine.execute_sql(f'SELECT COUNT(*) as count FROM "{database}"."{table}"')
            row_count = count_result['data'][0]['count'] if count_result['ok'] else 0
            
            # Get column info
            schema_result = engine.execute_sql(f'DESCRIBE "{database}"."{table}"')
            columns = schema_result['data'] if schema_result['ok'] else []
            
            # Get sample data
            sample_result = engine.execute_sql(f'SELECT * FROM "{database}"."{table}" LIMIT 5')
            sample_data = sample_result['data'] if sample_result['ok'] else []
            
            return {
                'ok': True,
                'database': database,
                'table': table,
                'row_count': row_count,
                'column_count': len(columns),
                'columns': columns,
                'sample_data': sample_data,
                'size_estimate': self._estimate_size(row_count, len(columns))
            }
        except Exception as e:
            return {'ok': False, 'error': str(e)}
        finally:
            engine.close()
    
    def get_database_summary(self, user_store, database: str) -> Dict:
        """Get summary statistics for entire database"""
        from app.table_manager import table_manager
        
        tables = table_manager.list(user_store)
        db_tables = [t for t in tables if t['database'] == database]
        
        total_rows = 0
        total_columns = 0
        
        engine = create_query_engine(user_store)
        
        try:
            for table_info in db_tables:
                try:
                    result = engine.execute_sql(f'SELECT COUNT(*) as count FROM "{database}"."{table_info["table"]}"')
                    if result['ok']:
                        total_rows += result['data'][0]['count']
                    
                    schema = engine.execute_sql(f'DESCRIBE "{database}"."{table_info["table"]}"')
                    if schema['ok']:
                        total_columns += len(schema['data'])
                except:
                    pass
            
            return {
                'ok': True,
                'database': database,
                'table_count': len(db_tables),
                'total_rows': total_rows,
                'total_columns': total_columns,
                'tables': [t['table'] for t in db_tables],
                'size_estimate': self._estimate_size(total_rows, total_columns)
            }
        finally:
            engine.close()
    
    def get_query_suggestions(self, user_store, database: str, table: str) -> List[str]:
        """Generate helpful query suggestions"""
        suggestions = [
            f'SELECT * FROM "{database}"."{table}" LIMIT 10',
            f'SELECT COUNT(*) FROM "{database}"."{table}"',
            f'SELECT * FROM "{database}"."{table}" ORDER BY id DESC LIMIT 10',
        ]
        
        # Get column names for more suggestions
        engine = create_query_engine(user_store)
        try:
            schema = engine.execute_sql(f'DESCRIBE "{database}"."{table}"')
            if schema['ok'] and schema['data']:
                first_col = schema['data'][0].get('column_name', 'id')
                suggestions.append(f'SELECT {first_col}, COUNT(*) FROM "{database}"."{table}" GROUP BY {first_col}')
        except:
            pass
        finally:
            engine.close()
        
        return suggestions
    
    def _estimate_size(self, rows: int, columns: int) -> str:
        """Estimate data size"""
        # Rough estimate: 100 bytes per cell
        bytes_estimate = rows * columns * 100
        
        if bytes_estimate < 1024:
            return f"{bytes_estimate} B"
        elif bytes_estimate < 1024 * 1024:
            return f"{bytes_estimate / 1024:.2f} KB"
        elif bytes_estimate < 1024 * 1024 * 1024:
            return f"{bytes_estimate / (1024 * 1024):.2f} MB"
        else:
            return f"{bytes_estimate / (1024 * 1024 * 1024):.2f} GB"

database_analytics = DatabaseAnalytics()