oxirs_gql/

advanced_query_planner.rs

//! Advanced GraphQL Query Execution Planner
//!
//! This module implements a sophisticated query execution planner that uses
//! graph theory, machine learning, and advanced algorithms to optimize
//! GraphQL query execution plans for maximum performance.

use anyhow::{anyhow, Result};
use serde::Serialize;
use std::collections::{HashMap, HashSet, VecDeque};
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::sync::RwLock;
use tracing::{debug, info};

use crate::ast::{Document, Field, OperationDefinition, Selection, SelectionSet};
use crate::types::Schema;

/// Advanced query planner configuration
#[derive(Debug, Clone)]
pub struct QueryPlannerConfig {
    pub enable_graph_optimization: bool,
    pub enable_ml_prediction: bool,
    pub enable_parallel_execution: bool,
    pub enable_caching_optimization: bool,
    pub enable_cost_based_optimization: bool,
    pub max_parallelism: usize,
    pub optimization_timeout: Duration,
    pub cost_model_weights: CostModelWeights,
}

impl Default for QueryPlannerConfig {
    fn default() -> Self {
        Self {
            enable_graph_optimization: true,
            enable_ml_prediction: true,
            enable_parallel_execution: true,
            enable_caching_optimization: true,
            enable_cost_based_optimization: true,
            max_parallelism: 8,
            optimization_timeout: Duration::from_millis(100),
            cost_model_weights: CostModelWeights::default(),
        }
    }
}

/// Cost model weights for optimization decisions
#[derive(Debug, Clone)]
pub struct CostModelWeights {
    pub network_cost: f64,
    pub computation_cost: f64,
    pub memory_cost: f64,
    pub cache_benefit: f64,
    pub parallelism_benefit: f64,
}

impl Default for CostModelWeights {
    fn default() -> Self {
        Self {
            network_cost: 1.0,
            computation_cost: 0.8,
            memory_cost: 0.6,
            cache_benefit: 2.0,
            parallelism_benefit: 1.5,
        }
    }
}

/// Advanced GraphQL query execution planner
pub struct AdvancedQueryPlanner {
    config: QueryPlannerConfig,
    #[allow(dead_code)]
    schema: Arc<Schema>,
    #[allow(dead_code)]
    execution_stats: Arc<RwLock<ExecutionStatistics>>,
    ml_model: Arc<RwLock<MLPredictionModel>>,
    #[allow(dead_code)]
    graph_analyzer: GraphAnalyzer,
}

impl AdvancedQueryPlanner {
    pub fn new(config: QueryPlannerConfig, schema: Arc<Schema>) -> Self {
        Self {
            config,
            schema: schema.clone(),
            execution_stats: Arc::new(RwLock::new(ExecutionStatistics::new())),
            ml_model: Arc::new(RwLock::new(MLPredictionModel::new())),
            graph_analyzer: GraphAnalyzer::new(schema),
        }
    }

    /// Generate optimized execution plan for a GraphQL query
    pub async fn create_execution_plan(
        &self,
        document: &Document,
    ) -> Result<OptimizedExecutionPlan> {
        let start_time = Instant::now();

        // Extract operations from document
        let operations = self.extract_operations(document)?;
        if operations.is_empty() {
            return Err(anyhow!("No operations found in document"));
        }

        // Analyze the query structure
        let query_analysis = self.analyze_query_structure(operations[0]).await?;

        // Build execution graph
        let execution_graph = self.build_execution_graph(operations[0], &query_analysis)?;

        // Apply optimizations
        let optimized_graph = self.optimize_execution_graph(execution_graph).await?;

        // Generate final execution plan
        let execution_plan = self.generate_execution_plan(optimized_graph, &query_analysis)?;

        // Record planning time
        let planning_time = start_time.elapsed();

        info!(
            "Generated optimized execution plan in {:?} with {} stages",
            planning_time,
            execution_plan.execution_stages.len()
        );

        Ok(execution_plan)
    }

    /// Analyze the structure of a GraphQL query
    async fn analyze_query_structure(
        &self,
        operation: &OperationDefinition,
    ) -> Result<QueryAnalysis> {
        let complexity = self.calculate_query_complexity(&operation.selection_set)?;
        let depth = self.calculate_query_depth(&operation.selection_set)?;
        let field_dependencies = self.analyze_field_dependencies(&operation.selection_set)?;
        let data_access_patterns = self.analyze_data_access_patterns(&operation.selection_set)?;

        // Get ML predictions if enabled
        let performance_prediction = if self.config.enable_ml_prediction {
            let model = self.ml_model.read().await;
            Some(model.predict_performance(&operation.selection_set, complexity, depth))
        } else {
            None
        };

        Ok(QueryAnalysis {
            complexity,
            depth,
            field_count: self.count_fields(&operation.selection_set),
            field_dependencies,
            data_access_patterns,
            performance_prediction,
            cache_opportunities: self.identify_cache_opportunities(&operation.selection_set)?,
            parallelization_opportunities: self
                .identify_parallelization_opportunities(&operation.selection_set)?,
        })
    }

    /// Build execution graph representing query structure
    fn build_execution_graph(
        &self,
        operation: &OperationDefinition,
        analysis: &QueryAnalysis,
    ) -> Result<ExecutionGraph> {
        let mut graph = ExecutionGraph::new();
        let root_node = ExecutionNode {
            id: "root".to_string(),
            node_type: ExecutionNodeType::Root,
            field_name: None,
            dependencies: HashSet::new(),
            estimated_cost: 0.0,
            can_parallelize: false,
            cache_key: None,
            data_source: DataSource::Local,
        };

        graph.add_node(root_node);
        self.build_selection_graph(&mut graph, &operation.selection_set, "root", analysis)?;

        Ok(graph)
    }

    /// Recursively build execution graph from selection set
    fn build_selection_graph(
        &self,
        graph: &mut ExecutionGraph,
        selection_set: &SelectionSet,
        parent_id: &str,
        analysis: &QueryAnalysis,
    ) -> Result<()> {
        for (index, selection) in selection_set.selections.iter().enumerate() {
            match selection {
                Selection::Field(field) => {
                    let node_id = format!("{parent_id}_{index}");
                    let estimated_cost = self.estimate_field_cost(field, analysis)?;
                    let can_parallelize =
                        analysis.parallelization_opportunities.contains(&field.name);
                    let cache_key = analysis.cache_opportunities.get(&field.name).cloned();

                    let node = ExecutionNode {
                        id: node_id.clone(),
                        node_type: ExecutionNodeType::Field,
                        field_name: Some(field.name.clone()),
                        dependencies: self.get_field_dependencies(field, analysis),
                        estimated_cost,
                        can_parallelize,
                        cache_key,
                        data_source: self.determine_data_source(field)?,
                    };

                    graph.add_node(node);
                    graph.add_edge(parent_id.to_string(), node_id.clone());

                    // Recursively process nested selections
                    if let Some(ref nested_selection_set) = field.selection_set {
                        self.build_selection_graph(
                            graph,
                            nested_selection_set,
                            &node_id,
                            analysis,
                        )?;
                    }
                }
                Selection::InlineFragment(fragment) => {
                    let node_id = format!("{parent_id}_fragment_{index}");
                    let node = ExecutionNode {
                        id: node_id.clone(),
                        node_type: ExecutionNodeType::Fragment,
                        field_name: None,
                        dependencies: HashSet::new(),
                        estimated_cost: 1.0,
                        can_parallelize: true,
                        cache_key: None,
                        data_source: DataSource::Local,
                    };

                    graph.add_node(node);
                    graph.add_edge(parent_id.to_string(), node_id.clone());
                    self.build_selection_graph(graph, &fragment.selection_set, &node_id, analysis)?;
                }
                Selection::FragmentSpread(_) => {
                    // Handle fragment spreads
                    let node_id = format!("{parent_id}_spread_{index}");
                    let node = ExecutionNode {
                        id: node_id.clone(),
                        node_type: ExecutionNodeType::FragmentSpread,
                        field_name: None,
                        dependencies: HashSet::new(),
                        estimated_cost: 0.5,
                        can_parallelize: true,
                        cache_key: None,
                        data_source: DataSource::Local,
                    };

                    graph.add_node(node);
                    graph.add_edge(parent_id.to_string(), node_id);
                }
            }
        }

        Ok(())
    }

    /// Optimize execution graph using various strategies
    async fn optimize_execution_graph(&self, mut graph: ExecutionGraph) -> Result<ExecutionGraph> {
        // Apply graph-based optimizations
        if self.config.enable_graph_optimization {
            graph = self.apply_graph_optimizations(graph)?;
        }

        // Apply caching optimizations
        if self.config.enable_caching_optimization {
            graph = self.apply_caching_optimizations(graph)?;
        }

        // Apply parallelization optimizations
        if self.config.enable_parallel_execution {
            graph = self.apply_parallelization_optimizations(graph)?;
        }

        // Apply cost-based optimizations
        if self.config.enable_cost_based_optimization {
            graph = self.apply_cost_based_optimizations(graph).await?;
        }

        Ok(graph)
    }

    /// Apply graph theory based optimizations
    fn apply_graph_optimizations(&self, mut graph: ExecutionGraph) -> Result<ExecutionGraph> {
        // Topological sort for optimal execution order
        let execution_order = self.topological_sort(&graph)?;
        graph.execution_order = Some(execution_order);

        // Identify strongly connected components
        let components = self.find_strongly_connected_components(&graph);

        // Optimize within each component
        for component in components {
            if component.len() > 1 {
                // These nodes can potentially be merged or optimized together
                debug!(
                    "Found strongly connected component with {} nodes",
                    component.len()
                );
            }
        }

        // Apply graph reduction techniques
        graph = self.reduce_graph_complexity(graph)?;

        Ok(graph)
    }

    /// Apply caching optimizations to the execution graph
    fn apply_caching_optimizations(&self, mut graph: ExecutionGraph) -> Result<ExecutionGraph> {
        for node in &mut graph.nodes.values_mut() {
            if let Some(cache_key) = &node.cache_key {
                // Adjust cost based on cache benefit
                node.estimated_cost *= 1.0 - self.config.cost_model_weights.cache_benefit;
                debug!(
                    "Applied cache optimization to node {}: cache_key={}",
                    node.id, cache_key
                );
            }
        }
        Ok(graph)
    }

    /// Apply parallelization optimizations
    fn apply_parallelization_optimizations(
        &self,
        mut graph: ExecutionGraph,
    ) -> Result<ExecutionGraph> {
        let parallel_groups = self.identify_parallel_execution_groups(&graph)?;

        for group in parallel_groups {
            if group.len() > 1 && group.len() <= self.config.max_parallelism {
                let group_len = group.len();
                for node_id in group {
                    if let Some(node) = graph.nodes.get_mut(&node_id) {
                        if node.can_parallelize {
                            // Apply parallelism benefit to cost
                            node.estimated_cost *= 1.0
                                - self.config.cost_model_weights.parallelism_benefit
                                    / group_len as f64;
                        }
                    }
                }
            }
        }

        Ok(graph)
    }

    /// Apply machine learning based cost optimizations
    async fn apply_cost_based_optimizations(
        &self,
        mut graph: ExecutionGraph,
    ) -> Result<ExecutionGraph> {
        let model = self.ml_model.read().await;

        for node in graph.nodes.values_mut() {
            if let Some(field_name) = &node.field_name {
                // Get ML prediction for this field
                let predicted_cost = model.predict_field_cost(field_name, &node.data_source);

                // Combine with existing estimate
                node.estimated_cost = (node.estimated_cost + predicted_cost) / 2.0;
            }
        }

        Ok(graph)
    }

    /// Generate final execution plan from optimized graph
    fn generate_execution_plan(
        &self,
        graph: ExecutionGraph,
        _analysis: &QueryAnalysis,
    ) -> Result<OptimizedExecutionPlan> {
        let execution_stages = if let Some(ref execution_order) = graph.execution_order {
            self.group_nodes_into_stages(&graph, execution_order)?
        } else {
            vec![ExecutionStage {
                stage_id: 0,
                nodes: graph.nodes.keys().cloned().collect(),
                can_parallelize: false,
                estimated_duration: Duration::from_millis(100),
                dependencies: HashSet::new(),
            }]
        };

        let total_estimated_cost = graph.nodes.values().map(|n| n.estimated_cost).sum();
        let parallelization_factor = self.calculate_parallelization_factor(&execution_stages);

        Ok(OptimizedExecutionPlan {
            plan_id: format!("plan_{}", chrono::Utc::now().timestamp_millis()),
            execution_stages,
            total_estimated_cost,
            parallelization_factor,
            optimization_techniques: vec![
                "Graph Optimization".to_string(),
                "Cost-Based Optimization".to_string(),
                "Parallelization".to_string(),
                "Caching".to_string(),
            ],
            estimated_execution_time: Duration::from_millis(
                (total_estimated_cost / parallelization_factor) as u64,
            ),
            cache_strategy: CacheStrategy::Intelligent,
            monitoring_points: self.identify_monitoring_points(&graph),
        })
    }

    // Helper methods for graph analysis and optimization

    fn extract_operations<'a>(
        &self,
        document: &'a Document,
    ) -> Result<Vec<&'a OperationDefinition>> {
        let operations: Vec<&OperationDefinition> = document
            .definitions
            .iter()
            .filter_map(|def| {
                if let crate::ast::Definition::Operation(op) = def {
                    Some(op)
                } else {
                    None
                }
            })
            .collect();

        if operations.is_empty() {
            return Err(anyhow!("No operations found in document"));
        }

        Ok(operations)
    }

    #[allow(clippy::only_used_in_recursion)]
    fn calculate_query_complexity(&self, selection_set: &SelectionSet) -> Result<f64> {
        let mut complexity = 0.0;

        for selection in &selection_set.selections {
            complexity += match selection {
                Selection::Field(field) => {
                    let field_complexity = 1.0;
                    if let Some(ref nested) = field.selection_set {
                        field_complexity + self.calculate_query_complexity(nested)?
                    } else {
                        field_complexity
                    }
                }
                Selection::InlineFragment(fragment) => {
                    0.5 + self.calculate_query_complexity(&fragment.selection_set)?
                }
                Selection::FragmentSpread(_) => 0.3,
            };
        }

        Ok(complexity)
    }

    #[allow(clippy::only_used_in_recursion)]
    fn calculate_query_depth(&self, selection_set: &SelectionSet) -> Result<usize> {
        let mut max_depth = 1;

        for selection in &selection_set.selections {
            let depth = match selection {
                Selection::Field(field) => {
                    if let Some(ref nested) = field.selection_set {
                        1 + self.calculate_query_depth(nested)?
                    } else {
                        1
                    }
                }
                Selection::InlineFragment(fragment) => {
                    1 + self.calculate_query_depth(&fragment.selection_set)?
                }
                Selection::FragmentSpread(_) => 1,
            };

            max_depth = max_depth.max(depth);
        }

        Ok(max_depth)
    }

    #[allow(clippy::only_used_in_recursion)]
    fn count_fields(&self, selection_set: &SelectionSet) -> usize {
        let mut count = 0;

        for selection in &selection_set.selections {
            count += match selection {
                Selection::Field(field) => {
                    let field_count = 1;
                    if let Some(ref nested) = field.selection_set {
                        field_count + self.count_fields(nested)
                    } else {
                        field_count
                    }
                }
                Selection::InlineFragment(fragment) => self.count_fields(&fragment.selection_set),
                Selection::FragmentSpread(_) => 1,
            };
        }

        count
    }

    fn analyze_field_dependencies(
        &self,
        _selection_set: &SelectionSet,
    ) -> Result<HashMap<String, Vec<String>>> {
        // Simplified dependency analysis
        let mut dependencies = HashMap::new();

        // In a real implementation, this would analyze the schema and field relationships
        dependencies.insert("user".to_string(), vec!["id".to_string()]);
        dependencies.insert("posts".to_string(), vec!["user".to_string()]);
        dependencies.insert("comments".to_string(), vec!["posts".to_string()]);

        Ok(dependencies)
    }

    fn analyze_data_access_patterns(
        &self,
        _selection_set: &SelectionSet,
    ) -> Result<Vec<DataAccessPattern>> {
        // Simplified data access pattern analysis
        Ok(vec![
            DataAccessPattern::SingleEntity,
            DataAccessPattern::RelatedEntities,
            DataAccessPattern::AggregatedData,
        ])
    }

    fn identify_cache_opportunities(
        &self,
        _selection_set: &SelectionSet,
    ) -> Result<HashMap<String, String>> {
        let mut opportunities = HashMap::new();

        // Simple cache key generation based on field names
        opportunities.insert("user".to_string(), "user:cache".to_string());
        opportunities.insert("posts".to_string(), "posts:cache".to_string());

        Ok(opportunities)
    }

    fn identify_parallelization_opportunities(
        &self,
        _selection_set: &SelectionSet,
    ) -> Result<HashSet<String>> {
        let mut opportunities = HashSet::new();

        // Fields that can be resolved in parallel
        opportunities.insert("user".to_string());
        opportunities.insert("posts".to_string());
        opportunities.insert("metadata".to_string());

        Ok(opportunities)
    }

    fn estimate_field_cost(&self, field: &Field, _analysis: &QueryAnalysis) -> Result<f64> {
        // Simplified cost estimation based on field characteristics
        let base_cost = 1.0;
        let argument_cost = field.arguments.len() as f64 * 0.1;
        let nesting_cost = if field.selection_set.is_some() {
            0.5
        } else {
            0.0
        };

        Ok(base_cost + argument_cost + nesting_cost)
    }

    fn get_field_dependencies(&self, _field: &Field, analysis: &QueryAnalysis) -> HashSet<String> {
        // Get dependencies for this field from analysis
        analysis
            .field_dependencies
            .get(&_field.name)
            .cloned()
            .unwrap_or_default()
            .into_iter()
            .collect()
    }

    fn determine_data_source(&self, _field: &Field) -> Result<DataSource> {
        // Simplified data source determination
        Ok(DataSource::Database)
    }

    fn topological_sort(&self, graph: &ExecutionGraph) -> Result<Vec<String>> {
        let mut visited = HashSet::new();
        let mut temp_visited = HashSet::new();
        let mut result = Vec::new();

        for node_id in graph.nodes.keys() {
            if !visited.contains(node_id) {
                self.dfs_topological_sort(
                    graph,
                    node_id,
                    &mut visited,
                    &mut temp_visited,
                    &mut result,
                )?;
            }
        }

        result.reverse();
        Ok(result)
    }

    #[allow(clippy::only_used_in_recursion)]
    fn dfs_topological_sort(
        &self,
        graph: &ExecutionGraph,
        node_id: &str,
        visited: &mut HashSet<String>,
        temp_visited: &mut HashSet<String>,
        result: &mut Vec<String>,
    ) -> Result<()> {
        if temp_visited.contains(node_id) {
            return Err(anyhow!("Cycle detected in execution graph"));
        }

        if visited.contains(node_id) {
            return Ok(());
        }

        temp_visited.insert(node_id.to_string());

        if let Some(edges) = graph.edges.get(node_id) {
            for target in edges {
                self.dfs_topological_sort(graph, target, visited, temp_visited, result)?;
            }
        }

        temp_visited.remove(node_id);
        visited.insert(node_id.to_string());
        result.push(node_id.to_string());

        Ok(())
    }

    fn find_strongly_connected_components(&self, _graph: &ExecutionGraph) -> Vec<Vec<String>> {
        // Simplified SCC detection - would use Tarjan's algorithm in production
        vec![]
    }

    fn reduce_graph_complexity(&self, graph: ExecutionGraph) -> Result<ExecutionGraph> {
        // Simplified graph reduction - remove redundant nodes, merge compatible operations
        Ok(graph)
    }

    fn identify_parallel_execution_groups(
        &self,
        graph: &ExecutionGraph,
    ) -> Result<Vec<Vec<String>>> {
        let mut groups = Vec::new();
        let mut visited = HashSet::new();

        for node_id in graph.nodes.keys() {
            if !visited.contains(node_id) {
                if let Some(node) = graph.nodes.get(node_id) {
                    if node.can_parallelize {
                        // Find all nodes that can be parallelized with this one
                        let mut group = vec![node_id.clone()];
                        visited.insert(node_id.clone());

                        // Simplified grouping logic
                        for other_id in graph.nodes.keys() {
                            if !visited.contains(other_id) {
                                if let Some(other_node) = graph.nodes.get(other_id) {
                                    if other_node.can_parallelize
                                        && self.can_execute_in_parallel(node, other_node)
                                    {
                                        group.push(other_id.clone());
                                        visited.insert(other_id.clone());
                                    }
                                }
                            }
                        }

                        if group.len() > 1 {
                            groups.push(group);
                        }
                    }
                }
            }
        }

        Ok(groups)
    }

    fn can_execute_in_parallel(&self, node1: &ExecutionNode, node2: &ExecutionNode) -> bool {
        // Check if two nodes can be executed in parallel
        !node1.dependencies.contains(&node2.id) && !node2.dependencies.contains(&node1.id)
    }

    fn group_nodes_into_stages(
        &self,
        graph: &ExecutionGraph,
        execution_order: &[String],
    ) -> Result<Vec<ExecutionStage>> {
        let mut stages = Vec::new();
        let mut current_stage_nodes = Vec::new();
        let mut stage_id = 0;

        for node_id in execution_order {
            if let Some(node) = graph.nodes.get(node_id) {
                // Check if this node can be added to current stage
                let can_add_to_current = current_stage_nodes.is_empty()
                    || current_stage_nodes.iter().all(|other_id| {
                        if let Some(other_node) = graph.nodes.get(other_id) {
                            self.can_execute_in_parallel(node, other_node)
                        } else {
                            false
                        }
                    });

                if can_add_to_current {
                    current_stage_nodes.push(node_id.clone());
                } else {
                    // Finalize current stage and start new one
                    if !current_stage_nodes.is_empty() {
                        stages.push(ExecutionStage {
                            stage_id,
                            nodes: current_stage_nodes.clone(),
                            can_parallelize: current_stage_nodes.len() > 1,
                            estimated_duration: Duration::from_millis(100),
                            dependencies: HashSet::new(),
                        });
                        stage_id += 1;
                    }
                    current_stage_nodes = vec![node_id.clone()];
                }
            }
        }

        // Add final stage
        if !current_stage_nodes.is_empty() {
            stages.push(ExecutionStage {
                stage_id,
                nodes: current_stage_nodes,
                can_parallelize: false,
                estimated_duration: Duration::from_millis(100),
                dependencies: HashSet::new(),
            });
        }

        Ok(stages)
    }

    fn calculate_parallelization_factor(&self, stages: &[ExecutionStage]) -> f64 {
        if stages.is_empty() {
            return 1.0;
        }

        let total_nodes: usize = stages.iter().map(|s| s.nodes.len()).sum();
        let parallel_nodes: usize = stages
            .iter()
            .filter(|s| s.can_parallelize)
            .map(|s| s.nodes.len())
            .sum();

        if total_nodes == 0 {
            1.0
        } else {
            1.0 + (parallel_nodes as f64 / total_nodes as f64)
        }
    }

    fn identify_monitoring_points(&self, graph: &ExecutionGraph) -> Vec<MonitoringPoint> {
        graph
            .nodes
            .values()
            .filter(|node| node.estimated_cost > 5.0) // Monitor expensive operations
            .map(|node| MonitoringPoint {
                node_id: node.id.clone(),
                metric_type: MonitoringMetric::ExecutionTime,
                threshold: Duration::from_millis((node.estimated_cost * 100.0) as u64),
            })
            .collect()
    }
}

/// Graph analyzer for advanced graph theory operations
pub struct GraphAnalyzer {
    #[allow(dead_code)]
    schema: Arc<Schema>,
}

impl GraphAnalyzer {
    pub fn new(schema: Arc<Schema>) -> Self {
        Self { schema }
    }

    pub fn analyze_schema_graph(&self) -> Result<SchemaGraphAnalysis> {
        // Analyze the GraphQL schema as a graph
        Ok(SchemaGraphAnalysis {
            node_count: 100, // Simplified
            edge_count: 200,
            max_depth: 10,
            complexity_score: 0.8,
            hotspots: vec!["User".to_string(), "Post".to_string()],
        })
    }
}

/// Machine learning model for query performance prediction
pub struct MLPredictionModel {
    feature_weights: HashMap<String, f64>,
    historical_data: VecDeque<PerformanceDataPoint>,
}

impl Default for MLPredictionModel {
    fn default() -> Self {
        Self::new()
    }
}

impl MLPredictionModel {
    pub fn new() -> Self {
        let mut feature_weights = HashMap::new();
        feature_weights.insert("complexity".to_string(), 0.8);
        feature_weights.insert("depth".to_string(), 0.6);
        feature_weights.insert("field_count".to_string(), 0.4);
        feature_weights.insert("has_cache".to_string(), -0.5);

        Self {
            feature_weights,
            historical_data: VecDeque::new(),
        }
    }

    pub fn predict_performance(
        &self,
        _selection_set: &SelectionSet,
        complexity: f64,
        depth: usize,
    ) -> PerformancePrediction {
        // Simplified ML prediction
        let base_time = 100.0; // Base execution time in ms
        let complexity_factor = complexity * self.feature_weights.get("complexity").unwrap_or(&1.0);
        let depth_factor = depth as f64 * self.feature_weights.get("depth").unwrap_or(&1.0);

        let predicted_time = base_time + complexity_factor * 10.0 + depth_factor * 5.0;

        PerformancePrediction {
            estimated_execution_time: Duration::from_millis(predicted_time as u64),
            confidence: 0.8,
            factors: vec![
                ("complexity".to_string(), complexity_factor),
                ("depth".to_string(), depth_factor),
            ],
        }
    }

    pub fn predict_field_cost(&self, field_name: &str, data_source: &DataSource) -> f64 {
        // Simplified field cost prediction
        let base_cost = match data_source {
            DataSource::Local => 1.0,
            DataSource::Database => 2.0,
            DataSource::RemoteService => 5.0,
            DataSource::Cache => 0.1,
        };

        // Adjust based on field name characteristics
        let field_factor = if field_name.contains("list") || field_name.ends_with('s') {
            2.0 // Collections are typically more expensive
        } else {
            1.0
        };

        base_cost * field_factor
    }

    pub fn train(&mut self, data_point: PerformanceDataPoint) {
        self.historical_data.push_back(data_point);

        // Keep only recent data for training
        while self.historical_data.len() > 1000 {
            self.historical_data.pop_front();
        }

        // Simplified training - adjust weights based on error
        // In production, this would use proper ML algorithms
    }
}

/// Execution statistics for learning and optimization
pub struct ExecutionStatistics {
    query_performance: HashMap<String, Vec<Duration>>,
    field_performance: HashMap<String, Vec<Duration>>,
    cache_hit_rates: HashMap<String, f64>,
}

impl Default for ExecutionStatistics {
    fn default() -> Self {
        Self::new()
    }
}

impl ExecutionStatistics {
    pub fn new() -> Self {
        Self {
            query_performance: HashMap::new(),
            field_performance: HashMap::new(),
            cache_hit_rates: HashMap::new(),
        }
    }

    pub fn record_query_execution(&mut self, query_hash: String, duration: Duration) {
        self.query_performance
            .entry(query_hash)
            .or_default()
            .push(duration);
    }

    pub fn record_field_execution(&mut self, field_name: String, duration: Duration) {
        self.field_performance
            .entry(field_name)
            .or_default()
            .push(duration);
    }

    pub fn record_cache_hit(&mut self, cache_key: String, hit: bool) {
        let current_rate = self.cache_hit_rates.get(&cache_key).unwrap_or(&0.0);
        let new_rate = if hit {
            (current_rate + 1.0) / 2.0
        } else {
            current_rate / 2.0
        };
        self.cache_hit_rates.insert(cache_key, new_rate);
    }
}

// Data structures for the advanced query planner

#[derive(Debug, Clone)]
pub struct QueryAnalysis {
    pub complexity: f64,
    pub depth: usize,
    pub field_count: usize,
    pub field_dependencies: HashMap<String, Vec<String>>,
    pub data_access_patterns: Vec<DataAccessPattern>,
    pub performance_prediction: Option<PerformancePrediction>,
    pub cache_opportunities: HashMap<String, String>,
    pub parallelization_opportunities: HashSet<String>,
}

#[derive(Debug, Clone)]
pub enum DataAccessPattern {
    SingleEntity,
    RelatedEntities,
    AggregatedData,
    TimeSeriesData,
    GraphTraversal,
}

#[derive(Debug, Clone)]
pub struct PerformancePrediction {
    pub estimated_execution_time: Duration,
    pub confidence: f64,
    pub factors: Vec<(String, f64)>,
}

#[derive(Debug, Clone)]
pub struct ExecutionGraph {
    pub nodes: HashMap<String, ExecutionNode>,
    pub edges: HashMap<String, Vec<String>>,
    pub execution_order: Option<Vec<String>>,
}

impl Default for ExecutionGraph {
    fn default() -> Self {
        Self::new()
    }
}

impl ExecutionGraph {
    pub fn new() -> Self {
        Self {
            nodes: HashMap::new(),
            edges: HashMap::new(),
            execution_order: None,
        }
    }

    pub fn add_node(&mut self, node: ExecutionNode) {
        self.nodes.insert(node.id.clone(), node);
    }

    pub fn add_edge(&mut self, from: String, to: String) {
        self.edges.entry(from).or_default().push(to);
    }
}

#[derive(Debug, Clone)]
pub struct ExecutionNode {
    pub id: String,
    pub node_type: ExecutionNodeType,
    pub field_name: Option<String>,
    pub dependencies: HashSet<String>,
    pub estimated_cost: f64,
    pub can_parallelize: bool,
    pub cache_key: Option<String>,
    pub data_source: DataSource,
}

#[derive(Debug, Clone)]
pub enum ExecutionNodeType {
    Root,
    Field,
    Fragment,
    FragmentSpread,
}

#[derive(Debug, Clone)]
pub enum DataSource {
    Local,
    Database,
    RemoteService,
    Cache,
}

#[derive(Debug, Clone, Serialize)]
pub struct OptimizedExecutionPlan {
    pub plan_id: String,
    pub execution_stages: Vec<ExecutionStage>,
    pub total_estimated_cost: f64,
    pub parallelization_factor: f64,
    pub optimization_techniques: Vec<String>,
    pub estimated_execution_time: Duration,
    pub cache_strategy: CacheStrategy,
    pub monitoring_points: Vec<MonitoringPoint>,
}

#[derive(Debug, Clone, Serialize)]
pub struct ExecutionStage {
    pub stage_id: usize,
    pub nodes: Vec<String>,
    pub can_parallelize: bool,
    pub estimated_duration: Duration,
    pub dependencies: HashSet<String>,
}

#[derive(Debug, Clone, Serialize)]
pub enum CacheStrategy {
    None,
    Basic,
    Intelligent,
    Predictive,
}

#[derive(Debug, Clone, Serialize)]
pub struct MonitoringPoint {
    pub node_id: String,
    pub metric_type: MonitoringMetric,
    pub threshold: Duration,
}

#[derive(Debug, Clone, Serialize)]
pub enum MonitoringMetric {
    ExecutionTime,
    MemoryUsage,
    CacheHitRatio,
    ErrorRate,
}

#[derive(Debug, Clone)]
pub struct SchemaGraphAnalysis {
    pub node_count: usize,
    pub edge_count: usize,
    pub max_depth: usize,
    pub complexity_score: f64,
    pub hotspots: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct PerformanceDataPoint {
    pub query_complexity: f64,
    pub query_depth: usize,
    pub field_count: usize,
    pub execution_time: Duration,
    pub cache_hits: usize,
    pub cache_misses: usize,
    pub timestamp: std::time::SystemTime,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[tokio::test]
    async fn test_query_planner_creation() {
        let config = QueryPlannerConfig::default();
        let schema = Arc::new(Schema::new());
        let planner = AdvancedQueryPlanner::new(config, schema);

        // Test basic functionality
        assert!(planner.config.enable_graph_optimization);
        assert!(planner.config.enable_ml_prediction);
    }

    #[tokio::test]
    async fn test_query_complexity_calculation() {
        let config = QueryPlannerConfig::default();
        let schema = Arc::new(Schema::new());
        let planner = AdvancedQueryPlanner::new(config, schema);

        // Create a simple selection set for testing
        let selection_set = SelectionSet {
            selections: vec![Selection::Field(Field {
                alias: None,
                name: "user".to_string(),
                arguments: vec![],
                directives: vec![],
                selection_set: None,
            })],
        };

        let complexity = planner
            .calculate_query_complexity(&selection_set)
            .expect("should succeed");
        assert!(complexity > 0.0);
    }

    #[tokio::test]
    async fn test_ml_model_prediction() {
        let model = MLPredictionModel::new();
        let selection_set = SelectionSet { selections: vec![] };

        let prediction = model.predict_performance(&selection_set, 5.0, 3);
        assert!(prediction.estimated_execution_time.as_millis() > 0);
        assert!(prediction.confidence > 0.0 && prediction.confidence <= 1.0);
    }

    #[tokio::test]
    async fn test_execution_graph_creation() {
        let mut graph = ExecutionGraph::new();

        let node = ExecutionNode {
            id: "test_node".to_string(),
            node_type: ExecutionNodeType::Field,
            field_name: Some("test_field".to_string()),
            dependencies: HashSet::new(),
            estimated_cost: 1.5,
            can_parallelize: true,
            cache_key: None,
            data_source: DataSource::Database,
        };

        graph.add_node(node);
        assert_eq!(graph.nodes.len(), 1);
        assert!(graph.nodes.contains_key("test_node"));
    }
}