"""
Query Optimizer for Mock Spark.

This module provides query optimization functionality for Mock Spark,
analyzing and optimizing SQL queries for better performance.
It includes rule-based optimization, cost estimation, and query plan generation.

Key Features:
    - Query plan optimization
    - Cost-based optimization rules
    - Join order optimization
    - Predicate pushdown
    - Projection elimination

Example:
    >>> from mock_spark.session.sql import MockQueryOptimizer
    >>> optimizer = MockQueryOptimizer()
    >>> optimized_ast = optimizer.optimize(ast)
"""

from typing import Any, Dict, List, Optional
from .parser import MockSQLAST


class MockQueryPlan:
    """Query execution plan for Mock Spark."""

    def __init__(
        self,
        plan_type: str,
        children: Optional[List["MockQueryPlan"]] = None,
        properties: Optional[Dict[str, Any]] = None,
    ):
        """Initialize query plan.

        Args:
            plan_type: Type of query plan node.
            children: Child query plan nodes.
            properties: Plan node properties.
        """
        self.plan_type = plan_type
        self.children = children or []
        self.properties = properties or {}

    def __str__(self) -> str:
        """String representation."""
        return f"MockQueryPlan(type='{self.plan_type}', children={len(self.children)})"

    def __repr__(self) -> str:
        """Representation."""
        return self.__str__()


class MockQueryOptimizer:
    """Query Optimizer for Mock Spark.

    Provides query optimization functionality that analyzes SQL queries
    and generates optimized execution plans. Includes rule-based optimization,
    cost estimation, and various optimization techniques.

    Example:
        >>> optimizer = MockQueryOptimizer()
        >>> optimized_ast = optimizer.optimize(ast)
    """

    def __init__(self) -> None:
        """Initialize MockQueryOptimizer."""
        self._optimization_rules = [
            self._rule_predicate_pushdown,
            self._rule_projection_elimination,
            self._rule_join_reordering,
            self._rule_constant_folding,
            self._rule_redundant_operations,
        ]

    def optimize(self, ast: MockSQLAST) -> MockSQLAST:
        """Optimize SQL AST.

        Args:
            ast: Parsed SQL AST.

        Returns:
            Optimized SQL AST.
        """
        if ast.query_type != "SELECT":
            return ast  # Only optimize SELECT queries for now

        optimized_components = ast.components.copy()

        # Apply optimization rules
        for rule in self._optimization_rules:
            optimized_components = rule(optimized_components)

        # Create optimized AST
        optimized_components["original_query"] = ast.components.get(
            "original_query", ""
        )
        optimized_ast = MockSQLAST(ast.query_type, optimized_components)

        return optimized_ast

    def generate_execution_plan(self, ast: MockSQLAST) -> MockQueryPlan:
        """Generate execution plan for SQL AST.

        Args:
            ast: Parsed SQL AST.

        Returns:
            Query execution plan.
        """
        if ast.query_type == "SELECT":
            return self._generate_select_plan(ast)
        else:
            return MockQueryPlan("UNKNOWN", properties={"query_type": ast.query_type})

    def _rule_predicate_pushdown(self, components: Dict[str, Any]) -> Dict[str, Any]:
        """Apply predicate pushdown optimization.

        Args:
            components: Query components.

        Returns:
            Optimized components.
        """
        # Mock implementation - in real optimizer this would push WHERE conditions
        # down to the data source level
        return components

    def _rule_projection_elimination(
        self, components: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Apply projection elimination optimization.

        Args:
            components: Query components.

        Returns:
            Optimized components.
        """
        # Mock implementation - in real optimizer this would eliminate
        # unnecessary columns early in the pipeline
        return components

    def _rule_join_reordering(self, components: Dict[str, Any]) -> Dict[str, Any]:
        """Apply join reordering optimization.

        Args:
            components: Query components.

        Returns:
            Optimized components.
        """
        # Mock implementation - in real optimizer this would reorder joins
        # for better performance
        return components

    def _rule_constant_folding(self, components: Dict[str, Any]) -> Dict[str, Any]:
        """Apply constant folding optimization.

        Args:
            components: Query components.

        Returns:
            Optimized components.
        """
        # Mock implementation - in real optimizer this would evaluate
        # constant expressions at compile time
        return components

    def _rule_redundant_operations(self, components: Dict[str, Any]) -> Dict[str, Any]:
        """Apply redundant operations elimination.

        Args:
            components: Query components.

        Returns:
            Optimized components.
        """
        # Mock implementation - in real optimizer this would eliminate
        # redundant operations
        return components

    def _generate_select_plan(self, ast: MockSQLAST) -> MockQueryPlan:
        """Generate execution plan for SELECT query.

        Args:
            ast: SELECT query AST.

        Returns:
            SELECT query execution plan.
        """
        components = ast.components

        # Create a simple execution plan
        plan = MockQueryPlan(
            "SELECT",
            properties={
                "columns": components.get("select_columns", ["*"]),
                "tables": components.get("from_tables", []),
                "conditions": components.get("where_conditions", []),
                "group_by": components.get("group_by_columns", []),
                "order_by": components.get("order_by_columns", []),
                "limit": components.get("limit_value"),
            },
        )

        # Add child plans for different operations
        if components.get("where_conditions"):
            filter_plan = MockQueryPlan(
                "FILTER",
                properties={"conditions": components.get("where_conditions", [])},
            )
            plan.children.append(filter_plan)

        if components.get("group_by_columns"):
            group_plan = MockQueryPlan(
                "GROUP_BY",
                properties={"columns": components.get("group_by_columns", [])},
            )
            plan.children.append(group_plan)

        if components.get("order_by_columns"):
            sort_plan = MockQueryPlan(
                "SORT", properties={"columns": components.get("order_by_columns", [])}
            )
            plan.children.append(sort_plan)

        if components.get("limit_value"):
            limit_plan = MockQueryPlan(
                "LIMIT", properties={"limit": components.get("limit_value")}
            )
            plan.children.append(limit_plan)

        return plan

    def estimate_cost(self, plan: MockQueryPlan) -> float:
        """Estimate cost of execution plan.

        Args:
            plan: Query execution plan.

        Returns:
            Estimated cost.
        """
        # Mock implementation - in real optimizer this would calculate
        # actual cost based on statistics and heuristics
        base_cost = 1.0

        # Add cost for each operation
        for child in plan.children:
            if child.plan_type == "FILTER":
                base_cost += 0.1
            elif child.plan_type == "GROUP_BY":
                base_cost += 0.5
            elif child.plan_type == "SORT":
                base_cost += 0.3
            elif child.plan_type == "LIMIT":
                base_cost += 0.05

        return base_cost
