"""
Database operation monitoring wrapper

Per ADR-053, v1.1.2 Phase 1, and developer Q&A CQ1, IQ1, IQ3:
- Wraps SQLite connections at the pool level
- Times all database operations
- Extracts query type and table name (best effort)
- Detects slow queries based on configurable threshold
- Records metrics to the metrics collector

Example usage:
    >>> from starpunk.monitoring.database import MonitoredConnection
    >>> conn = sqlite3.connect(':memory:')
    >>> monitored = MonitoredConnection(conn, metrics_collector)
    >>> cursor = monitored.execute('SELECT * FROM notes')
"""

import re
import sqlite3
import time
from typing import Optional, Any, Tuple

from starpunk.monitoring.metrics import record_metric


class MonitoredConnection:
    """
    Wrapper for SQLite connections that monitors performance

    Per CQ1: Wraps connections at the pool level
    Per IQ1: Uses simple regex for table name extraction
    Per IQ3: Single configurable slow query threshold
    """

    def __init__(self, connection: sqlite3.Connection, slow_query_threshold: float = 1.0):
        """
        Initialize monitored connection wrapper

        Args:
            connection: SQLite connection to wrap
            slow_query_threshold: Threshold in seconds for slow query detection
        """
        self._connection = connection
        self._slow_query_threshold = slow_query_threshold

    def execute(self, query: str, parameters: Optional[Tuple] = None) -> sqlite3.Cursor:
        """
        Execute a query with performance monitoring

        Args:
            query: SQL query to execute
            parameters: Optional query parameters

        Returns:
            sqlite3.Cursor: Query cursor
        """
        start_time = time.perf_counter()
        query_type = self._get_query_type(query)
        table_name = self._extract_table_name(query)

        try:
            if parameters:
                cursor = self._connection.execute(query, parameters)
            else:
                cursor = self._connection.execute(query)

            duration_sec = time.perf_counter() - start_time
            duration_ms = duration_sec * 1000

            # Record metric (forced if slow query)
            is_slow = duration_sec >= self._slow_query_threshold
            metadata = {
                'query_type': query_type,
                'table': table_name,
                'is_slow': is_slow,
            }

            # Add query text for slow queries (for debugging)
            if is_slow:
                # Truncate query to avoid storing huge queries
                metadata['query'] = query[:200] if len(query) > 200 else query

            record_metric(
                'database',
                f'{query_type} {table_name}',
                duration_ms,
                metadata,
                force=is_slow  # Always record slow queries
            )

            return cursor

        except Exception as e:
            duration_sec = time.perf_counter() - start_time
            duration_ms = duration_sec * 1000

            # Record error metric
            metadata = {
                'query_type': query_type,
                'table': table_name,
                'error': str(e),
                'query': query[:200] if len(query) > 200 else query
            }

            record_metric(
                'database',
                f'{query_type} {table_name} ERROR',
                duration_ms,
                metadata,
                force=True  # Always record errors
            )

            raise

    def executemany(self, query: str, parameters) -> sqlite3.Cursor:
        """
        Execute a query with multiple parameter sets

        Args:
            query: SQL query to execute
            parameters: Sequence of parameter tuples

        Returns:
            sqlite3.Cursor: Query cursor
        """
        start_time = time.perf_counter()
        query_type = self._get_query_type(query)
        table_name = self._extract_table_name(query)

        try:
            cursor = self._connection.executemany(query, parameters)
            duration_ms = (time.perf_counter() - start_time) * 1000

            # Record metric
            metadata = {
                'query_type': query_type,
                'table': table_name,
                'batch': True,
            }

            record_metric(
                'database',
                f'{query_type} {table_name} BATCH',
                duration_ms,
                metadata
            )

            return cursor

        except Exception as e:
            duration_ms = (time.perf_counter() - start_time) * 1000

            metadata = {
                'query_type': query_type,
                'table': table_name,
                'error': str(e),
                'batch': True
            }

            record_metric(
                'database',
                f'{query_type} {table_name} BATCH ERROR',
                duration_ms,
                metadata,
                force=True
            )

            raise

    def _get_query_type(self, query: str) -> str:
        """
        Extract query type from SQL statement

        Args:
            query: SQL query

        Returns:
            Query type (SELECT, INSERT, UPDATE, DELETE, etc.)
        """
        query_upper = query.strip().upper()

        for query_type in ['SELECT', 'INSERT', 'UPDATE', 'DELETE', 'CREATE', 'DROP', 'ALTER', 'PRAGMA']:
            if query_upper.startswith(query_type):
                return query_type

        return 'OTHER'

    def _extract_table_name(self, query: str) -> str:
        """
        Extract table name from query (best effort)

        Per IQ1: Keep it simple with basic regex patterns.
        Returns "unknown" for complex queries.

        Note: Complex queries (JOINs, subqueries, CTEs) return "unknown".
        This covers 90% of queries accurately.

        Args:
            query: SQL query

        Returns:
            Table name or "unknown"
        """
        query_lower = query.lower().strip()

        # Simple patterns that cover 90% of cases
        patterns = [
            r'from\s+(\w+)',
            r'update\s+(\w+)',
            r'insert\s+into\s+(\w+)',
            r'delete\s+from\s+(\w+)',
            r'create\s+table\s+(?:if\s+not\s+exists\s+)?(\w+)',
            r'drop\s+table\s+(?:if\s+exists\s+)?(\w+)',
            r'alter\s+table\s+(\w+)',
        ]

        for pattern in patterns:
            match = re.search(pattern, query_lower)
            if match:
                return match.group(1)

        # Complex queries (JOINs, subqueries, CTEs)
        return "unknown"

    # Delegate all other connection methods to the wrapped connection
    def __getattr__(self, name: str) -> Any:
        """Delegate all other methods to the wrapped connection"""
        return getattr(self._connection, name)

    def __enter__(self):
        """Support context manager protocol"""
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        """Support context manager protocol"""
        return self._connection.__exit__(exc_type, exc_val, exc_tb)