"""
Query optimization and performance analysis for DataFlow operations.

Provides:
- AI-driven query optimization suggestions
- Index recommendations
- Bulk operation optimization
- Performance analysis

Architecture:
- Signature-based LLM integration for intelligent optimization
- Extends BaseAgent for LLM capabilities
- Real-time performance analysis
"""

import json
import logging
from typing import Any, Dict, List, Optional

from kaizen.core.base_agent import BaseAgent
from kaizen.signatures import InputField, OutputField, Signature

logger = logging.getLogger(__name__)


# ============================================================================
# Signature Definitions
# ============================================================================


class QueryOptimizationSignature(Signature):
    """Analyze and optimize database queries."""

    query_structure: str = InputField(desc="Query structure to analyze (as JSON)")
    table_schema: str = InputField(desc="Table schema information (as JSON)")
    query_stats: str = InputField(desc="Query statistics if available (as JSON)")

    optimizations: str = OutputField(desc="Suggested optimizations (as JSON list)")
    index_recommendations: str = OutputField(desc="Recommended indexes (as JSON list)")
    estimated_improvement: float = OutputField(
        desc="Estimated performance improvement (0-1)"
    )


class BulkOperationSignature(Signature):
    """Optimize bulk database operations."""

    operation_type: str = InputField(
        desc="Type of bulk operation (insert, update, delete)"
    )
    data_size: int = InputField(desc="Number of records to process")
    record_sample: str = InputField(desc="Sample records (as JSON)")
    table_info: str = InputField(desc="Table information (as JSON)")

    optimal_batch_size: int = OutputField(desc="Recommended batch size")
    strategy: str = OutputField(desc="Recommended bulk operation strategy")
    estimated_duration: float = OutputField(desc="Estimated duration in seconds")


# ============================================================================
# Optimizer Implementations
# ============================================================================


class QueryOptimizer(BaseAgent):
    """
    AI-driven query optimization and performance analysis.

    Uses LLM to analyze queries and suggest optimizations,
    index recommendations, and performance improvements.

    Example:
        >>> optimizer = QueryOptimizer(config=config)
        >>> query = {'table': 'users', 'filter': {'age': {'$gte': 18}}}
        >>> result = optimizer.analyze_query(query)
        >>> print(result['optimizations'])
        >>> print(result['index_recommendations'])
    """

    def __init__(self, config):
        """Initialize query optimizer."""
        super().__init__(config=config, signature=QueryOptimizationSignature())

    def analyze_query(
        self,
        query: Dict[str, Any],
        table_schema: Optional[Dict[str, Any]] = None,
        query_stats: Optional[Dict[str, Any]] = None,
    ) -> Dict[str, Any]:
        """
        Analyze query and suggest optimizations.

        Args:
            query: Query structure to analyze
            table_schema: Optional table schema information
            query_stats: Optional query performance statistics

        Returns:
            Dictionary containing:
            - optimizations: List of suggested optimizations
            - index_recommendations: Recommended indexes
            - estimated_improvement: Estimated performance gain

        Example:
            >>> result = optimizer.analyze_query({
            ...     'table': 'users',
            ...     'filter': {'email': 'user@example.com'}
            ... })
            >>> for opt in result['optimizations']:
            ...     print(f"- {opt}")
        """
        if table_schema is None:
            table_schema = {}

        if query_stats is None:
            query_stats = {}

        # Run LLM analysis
        llm_result = self.run(
            query_structure=json.dumps(query),
            table_schema=json.dumps(table_schema),
            query_stats=json.dumps(query_stats),
        )

        # Parse results
        opts_str = llm_result.get("optimizations", "[]")
        try:
            optimizations = (
                json.loads(opts_str) if isinstance(opts_str, str) else opts_str
            )
        except json.JSONDecodeError:
            logger.warning(f"Could not parse optimizations: {opts_str}")
            optimizations = []

        idx_str = llm_result.get("index_recommendations", "[]")
        try:
            index_recommendations = (
                json.loads(idx_str) if isinstance(idx_str, str) else idx_str
            )
        except json.JSONDecodeError:
            logger.warning(f"Could not parse indexes: {idx_str}")
            index_recommendations = []

        improvement = llm_result.get("estimated_improvement", 0.0)
        if isinstance(improvement, str):
            try:
                improvement = float(improvement)
            except ValueError:
                improvement = 0.0

        return {
            "optimizations": optimizations,
            "index_recommendations": index_recommendations,
            "estimated_improvement": improvement,
            "recommendations": optimizations,  # Alias for compatibility
        }


class BulkOperationOptimizer(BaseAgent):
    """
    Optimize bulk database operations for performance.

    Uses LLM to determine optimal batch sizes and strategies
    for bulk insert, update, and delete operations.

    Example:
        >>> optimizer = BulkOperationOptimizer(config=config)
        >>> data = [{'id': i, 'value': f'item_{i}'} for i in range(10000)]
        >>> result = optimizer.optimize_bulk_insert(data)
        >>> print(f"Batch size: {result['batch_size']}")
        >>> print(f"Strategy: {result['strategy']}")
    """

    def __init__(self, config):
        """Initialize bulk operation optimizer."""
        super().__init__(config=config, signature=BulkOperationSignature())

    def optimize_bulk_insert(
        self,
        data: List[Dict[str, Any]],
        target_table: Optional[str] = None,
        table_info: Optional[Dict[str, Any]] = None,
    ) -> Dict[str, Any]:
        """
        Optimize bulk insert operation.

        Args:
            data: Data to insert
            target_table: Target table name
            table_info: Optional table information

        Returns:
            Dictionary containing:
            - batch_size: Recommended batch size
            - strategy: Recommended insertion strategy
            - estimated_duration: Expected duration

        Example:
            >>> result = optimizer.optimize_bulk_insert(
            ...     data=large_dataset,
            ...     target_table='users'
            ... )
        """
        if table_info is None:
            table_info = {}

        # Get sample for analysis
        sample_size = min(10, len(data))
        sample = data[:sample_size]

        # Run LLM analysis
        llm_result = self.run(
            operation_type="insert",
            data_size=len(data),
            record_sample=json.dumps(sample),
            table_info=json.dumps(table_info),
        )

        # Parse results
        batch_size = llm_result.get("optimal_batch_size", 1000)
        if isinstance(batch_size, str):
            try:
                batch_size = int(batch_size)
            except ValueError:
                batch_size = 1000

        strategy = llm_result.get("strategy", "standard_batch")
        if not isinstance(strategy, str):
            strategy = str(strategy)

        duration = llm_result.get("estimated_duration", 0.0)
        if isinstance(duration, str):
            try:
                duration = float(duration)
            except ValueError:
                duration = 0.0

        return {
            "batch_size": batch_size,
            "strategy": strategy,
            "estimated_duration": duration,
        }

    def optimize_bulk_update(
        self,
        filter_dict: Dict[str, Any],
        update_data: Dict[str, Any],
        table_info: Optional[Dict[str, Any]] = None,
    ) -> Dict[str, Any]:
        """
        Optimize bulk update operation.

        Args:
            filter_dict: Filter for records to update
            update_data: Update values
            table_info: Optional table information

        Returns:
            Dictionary with optimization recommendations

        Example:
            >>> result = optimizer.optimize_bulk_update(
            ...     filter_dict={'status': 'pending'},
            ...     update_data={'status': 'processed'}
            ... )
        """
        if table_info is None:
            table_info = {}

        # Estimate affected records (placeholder)
        estimated_records = 10000

        sample_record = {**filter_dict, **update_data}

        llm_result = self.run(
            operation_type="update",
            data_size=estimated_records,
            record_sample=json.dumps([sample_record]),
            table_info=json.dumps(table_info),
        )

        # Parse results (same as bulk insert)
        batch_size = llm_result.get("optimal_batch_size", 1000)
        if isinstance(batch_size, str):
            try:
                batch_size = int(batch_size)
            except ValueError:
                batch_size = 1000

        strategy = llm_result.get("strategy", "standard_batch")

        return {"batch_size": batch_size, "strategy": strategy}

    def optimize_bulk_delete(
        self, filter_dict: Dict[str, Any], table_info: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """
        Optimize bulk delete operation.

        Args:
            filter_dict: Filter for records to delete
            table_info: Optional table information

        Returns:
            Dictionary with optimization recommendations

        Example:
            >>> result = optimizer.optimize_bulk_delete(
            ...     filter_dict={'created_at': {'$lt': '2020-01-01'}}
            ... )
        """
        if table_info is None:
            table_info = {}

        estimated_records = 10000

        llm_result = self.run(
            operation_type="delete",
            data_size=estimated_records,
            record_sample=json.dumps([filter_dict]),
            table_info=json.dumps(table_info),
        )

        # Parse results
        batch_size = llm_result.get("optimal_batch_size", 1000)
        if isinstance(batch_size, str):
            try:
                batch_size = int(batch_size)
            except ValueError:
                batch_size = 1000

        strategy = llm_result.get("strategy", "standard_batch")

        return {"batch_size": batch_size, "strategy": strategy}
