oxirs-arq 0.2.4

//! Materialized Views for Query Optimization
//!
//! This module provides comprehensive materialized view support including:
//! - View definition and storage
//! - Query rewriting to utilize materialized views
//! - Incremental view maintenance
//! - Cost-based view selection
//! - Automatic view recommendations

use std::collections::{HashMap, HashSet, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant, SystemTime};

use anyhow::{anyhow, Result};
use tracing::{debug, info, span, Level};

use crate::algebra::Solution;
use crate::algebra::{Algebra, Expression, Term, TriplePattern, Variable};
use crate::cost_model::{CostEstimate, CostModel};
use crate::executor::{Dataset, ExecutionStats, QueryExecutor};
use crate::statistics_collector::StatisticsCollector;

/// Materialized view manager for query optimization
pub struct MaterializedViewManager {
    config: MaterializedViewConfig,
    views: Arc<RwLock<HashMap<String, MaterializedView>>>,
    view_storage: Arc<RwLock<ViewStorage>>,
    rewriter: QueryRewriter,
    maintenance_scheduler: MaintenanceScheduler,
    cost_model: Arc<Mutex<CostModel>>,
    #[allow(dead_code)]
    statistics_collector: Arc<StatisticsCollector>,
    usage_statistics: Arc<RwLock<ViewUsageStatistics>>,
    recommendation_engine: ViewRecommendationEngine,
}

/// Configuration for materialized view management
#[derive(Debug, Clone)]
pub struct MaterializedViewConfig {
    /// Maximum number of materialized views to maintain
    pub max_views: usize,
    /// Maximum memory usage for views (bytes)
    pub max_memory_usage: usize,
    /// Enable automatic view creation based on query patterns
    pub auto_view_creation: bool,
    /// Maintenance strategy for view updates
    pub maintenance_strategy: MaintenanceStrategy,
    /// Threshold for view utilization before considering removal
    pub utilization_threshold: f64,
    /// Maximum staleness allowed for views (seconds)
    pub max_staleness: Duration,
    /// Enable cost-based view selection
    pub cost_based_selection: bool,
    /// Enable incremental maintenance
    pub incremental_maintenance: bool,
}

impl Default for MaterializedViewConfig {
    fn default() -> Self {
        Self {
            max_views: 100,
            max_memory_usage: 2 * 1024 * 1024 * 1024, // 2GB
            auto_view_creation: true,
            maintenance_strategy: MaintenanceStrategy::Lazy,
            utilization_threshold: 0.1,               // 10% utilization
            max_staleness: Duration::from_secs(3600), // 1 hour
            cost_based_selection: true,
            incremental_maintenance: true,
        }
    }
}

/// Maintenance strategies for materialized views
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum MaintenanceStrategy {
    /// Update views immediately when base data changes
    Immediate,
    /// Update views periodically
    Periodic(Duration),
    /// Update views when accessed and stale
    Lazy,
    /// Update views based on cost analysis
    CostBased,
    /// Hybrid approach combining multiple strategies
    Hybrid,
}

/// Definition of a materialized view
#[derive(Debug, Clone)]
pub struct MaterializedView {
    /// Unique identifier for the view
    pub id: String,
    /// Human-readable name
    pub name: String,
    /// Algebra expression defining the view query
    pub definition: Algebra,
    /// Current materialized data
    pub data: ViewData,
    /// Metadata about the view
    pub metadata: ViewMetadata,
    /// Maintenance information
    pub maintenance_info: MaintenanceInfo,
    /// Cost estimates for using this view
    pub cost_estimates: ViewCostEstimates,
    /// Dependencies on base data
    pub dependencies: ViewDependencies,
}

/// Materialized data for a view
#[derive(Debug, Clone)]
pub struct ViewData {
    /// Result set from the view query
    pub results: Solution,
    /// Size of the materialized data in bytes
    pub size_bytes: usize,
    /// Number of rows in the view
    pub row_count: usize,
    /// Timestamp when data was last materialized
    pub materialized_at: SystemTime,
    /// Checksum for data integrity
    pub checksum: u64,
}

/// Metadata about a materialized view
#[derive(Debug, Clone)]
pub struct ViewMetadata {
    /// When the view was created
    pub created_at: SystemTime,
    /// Who or what created the view
    pub created_by: String,
    /// Description of the view's purpose
    pub description: String,
    /// Tags for categorization
    pub tags: Vec<String>,
    /// Priority for maintenance (higher = more important)
    pub priority: u8,
    /// Expected lifetime of the view
    pub expected_lifetime: Duration,
}

/// Maintenance information for a view
#[derive(Debug, Clone)]
pub struct MaintenanceInfo {
    /// Last time the view was updated
    pub last_updated: SystemTime,
    /// Next scheduled maintenance time
    pub next_maintenance: Option<SystemTime>,
    /// Maintenance strategy for this specific view
    pub strategy: MaintenanceStrategy,
    /// Number of times the view has been updated
    pub update_count: usize,
    /// Total time spent maintaining the view
    pub total_maintenance_time: Duration,
    /// Whether the view needs updating
    pub needs_update: bool,
    /// Incremental update state
    pub incremental_state: Option<IncrementalState>,
}

/// State for incremental view maintenance
#[derive(Debug, Clone)]
pub struct IncrementalState {
    /// Last processed transaction ID
    pub last_transaction_id: u64,
    /// Change log for incremental updates
    pub change_log: Vec<ChangeLogEntry>,
    /// Delta computation state
    pub delta_state: DeltaState,
}

/// Entry in the change log for incremental maintenance
#[derive(Debug, Clone)]
pub struct ChangeLogEntry {
    /// Type of change (insert, delete, update)
    pub change_type: ChangeType,
    /// Affected triple or quad
    pub affected_data: TriplePattern,
    /// Timestamp of the change
    pub timestamp: SystemTime,
    /// Transaction ID
    pub transaction_id: u64,
}

/// Types of changes for incremental maintenance
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ChangeType {
    Insert,
    Delete,
    Update,
}

/// Delta computation state for incremental updates
#[derive(Debug, Clone)]
pub struct DeltaState {
    /// Positive delta (insertions)
    pub positive_delta: Solution,
    /// Negative delta (deletions)
    pub negative_delta: Solution,
    /// Dirty flags for affected partitions
    pub dirty_partitions: HashSet<u64>,
}

/// Cost estimates for using a materialized view
#[derive(Debug, Clone)]
pub struct ViewCostEstimates {
    /// Cost of accessing the view
    pub access_cost: CostEstimate,
    /// Cost of maintaining the view
    pub maintenance_cost: CostEstimate,
    /// Storage cost (memory/disk usage)
    pub storage_cost: f64,
    /// Cost benefit compared to computing from scratch
    pub benefit_ratio: f64,
    /// Last time costs were estimated
    pub last_estimated: SystemTime,
}

/// Dependencies of a view on base data
#[derive(Debug, Clone)]
pub struct ViewDependencies {
    /// Base tables/graphs referenced by the view
    pub base_tables: Vec<String>,
    /// Specific triple patterns the view depends on
    pub dependent_patterns: Vec<TriplePattern>,
    /// Variables that affect view results
    pub dependent_variables: HashSet<Variable>,
    /// Join dependencies
    pub join_dependencies: Vec<JoinDependency>,
}

/// Join dependency information
#[derive(Debug, Clone)]
pub struct JoinDependency {
    /// Left side of the join
    pub left_pattern: TriplePattern,
    /// Right side of the join
    pub right_pattern: TriplePattern,
    /// Join variables
    pub join_variables: Vec<Variable>,
    /// Estimated selectivity
    pub selectivity: f64,
}

/// Storage for materialized view data
#[derive(Debug)]
pub struct ViewStorage {
    /// In-memory storage for view data
    memory_storage: HashMap<String, ViewData>,
    /// Disk-based storage path
    #[allow(dead_code)]
    disk_storage_path: Option<std::path::PathBuf>,
    /// Maximum memory usage allowed
    max_memory: usize,
    /// Current memory usage
    memory_usage: usize,
    /// Storage statistics
    storage_stats: StorageStatistics,
}

/// Statistics about view storage
#[derive(Debug, Clone, Default)]
pub struct StorageStatistics {
    /// Total memory usage
    pub total_memory_usage: usize,
    /// Total disk usage
    pub total_disk_usage: usize,
    /// Number of views stored in memory
    pub memory_view_count: usize,
    /// Number of views stored on disk
    pub disk_view_count: usize,
    /// Cache hit rate
    pub cache_hit_rate: f64,
    /// Average access time
    pub average_access_time: Duration,
}

/// Query rewriter for utilizing materialized views
pub struct QueryRewriter {
    view_index: ViewIndex,
    #[allow(dead_code)]
    rewrite_rules: Vec<RewriteRule>,
    #[allow(dead_code)]
    cost_threshold: f64,
}

/// Index for efficient view lookup during query rewriting
#[derive(Debug)]
pub struct ViewIndex {
    /// Index by pattern structure
    #[allow(dead_code)]
    pattern_index: HashMap<String, Vec<String>>,
    /// Index by variables
    #[allow(dead_code)]
    variable_index: HashMap<Variable, Vec<String>>,
    /// Index by predicates
    #[allow(dead_code)]
    predicate_index: HashMap<String, Vec<String>>,
    /// Index by query characteristics
    characteristic_index: HashMap<QueryCharacteristic, Vec<String>>,
}

/// Query characteristics for view indexing
#[derive(Debug, Clone, Hash, PartialEq, Eq)]
pub enum QueryCharacteristic {
    HasJoin,
    HasFilter,
    HasAggregation,
    HasUnion,
    PatternCount(usize),
    VariableCount(usize),
}

/// Rule for query rewriting
#[derive(Debug, Clone)]
pub struct RewriteRule {
    /// Name of the rule
    pub name: String,
    /// Pattern to match
    pub pattern_matcher: PatternMatcher,
    /// Rewrite transformation
    pub transformation: RewriteTransformation,
    /// Cost threshold for applying the rule
    pub cost_threshold: f64,
    /// Priority of the rule
    pub priority: u8,
}

/// Pattern matcher for rewrite rules
#[derive(Debug, Clone)]
pub enum PatternMatcher {
    /// Exact algebra match
    ExactMatch(Algebra),
    /// Structural pattern match
    StructuralMatch(AlgebraPattern),
    /// Semantic equivalence match
    SemanticMatch(SemanticPattern),
    /// Custom matcher function
    Custom(String), // Function name for custom matching
}

/// Structural pattern for matching algebra expressions
#[derive(Debug, Clone)]
pub struct AlgebraPattern {
    /// Pattern type
    pub pattern_type: AlgebraPatternType,
    /// Sub-patterns
    pub sub_patterns: Vec<AlgebraPattern>,
    /// Variable bindings
    pub bindings: HashMap<String, Variable>,
}

/// Types of algebra patterns
#[derive(Debug, Clone)]
pub enum AlgebraPatternType {
    BGP,
    Join,
    Union,
    Filter,
    Any,
}

/// Semantic pattern for advanced matching
#[derive(Debug, Clone)]
pub struct SemanticPattern {
    /// Semantic equivalence rules
    pub equivalence_rules: Vec<String>,
    /// Containment relationships
    pub containment_rules: Vec<String>,
}

/// Transformation for query rewriting
#[derive(Debug, Clone)]
pub enum RewriteTransformation {
    /// Replace with view access
    ReplaceWithView(String),
    /// Partial replacement
    PartialReplace(Box<PartialReplacement>),
    /// Join with view
    JoinWithView(JoinTransformation),
    /// Union with view
    UnionWithView(UnionTransformation),
}

/// Partial replacement transformation
#[derive(Debug, Clone)]
pub struct PartialReplacement {
    /// View to use for partial replacement
    pub view_id: String,
    /// Remaining query parts
    pub remaining_query: Algebra,
    /// How to combine view results with remaining query
    pub combination_strategy: CombinationStrategy,
}

/// Strategy for combining view results with remaining query
#[derive(Debug, Clone)]
pub enum CombinationStrategy {
    Join(Vec<Variable>),
    Union,
    Filter(Expression),
}

/// Join transformation with a view
#[derive(Debug, Clone)]
pub struct JoinTransformation {
    /// View to join with
    pub view_id: String,
    /// Join variables
    pub join_variables: Vec<Variable>,
    /// Join type
    pub join_type: JoinType,
}

/// Types of joins for view transformations
#[derive(Debug, Clone)]
pub enum JoinType {
    Inner,
    Left,
    Right,
    Full,
}

/// Union transformation with a view
#[derive(Debug, Clone)]
pub struct UnionTransformation {
    /// View to union with
    pub view_id: String,
    /// Whether to apply DISTINCT
    pub distinct: bool,
}

/// Maintenance scheduler for materialized views
pub struct MaintenanceScheduler {
    scheduled_tasks: Arc<RwLock<VecDeque<MaintenanceTask>>>,
    #[allow(dead_code)]
    active_tasks: Arc<RwLock<HashMap<String, ActiveTask>>>,
    #[allow(dead_code)]
    config: SchedulerConfig,
}

/// Configuration for the maintenance scheduler
#[derive(Debug, Clone)]
pub struct SchedulerConfig {
    /// Maximum concurrent maintenance tasks
    pub max_concurrent_tasks: usize,
    /// Default maintenance interval
    pub default_interval: Duration,
    /// Priority threshold for immediate scheduling
    pub priority_threshold: u8,
    /// Resource limits for maintenance
    pub resource_limits: ResourceLimits,
}

/// Resource limits for maintenance operations
#[derive(Debug, Clone)]
pub struct ResourceLimits {
    /// Maximum CPU usage percentage
    pub max_cpu_usage: f64,
    /// Maximum memory usage for maintenance
    pub max_memory_usage: usize,
    /// Maximum I/O bandwidth
    pub max_io_bandwidth: usize,
}

impl Default for ResourceLimits {
    fn default() -> Self {
        Self {
            max_cpu_usage: 50.0,
            max_memory_usage: 1024 * 1024 * 512, // 512MB
            max_io_bandwidth: 1024 * 1024 * 100, // 100MB/s
        }
    }
}

impl Default for SchedulerConfig {
    fn default() -> Self {
        Self {
            max_concurrent_tasks: 4,
            default_interval: Duration::from_secs(3600), // 1 hour
            priority_threshold: 8,
            resource_limits: ResourceLimits::default(),
        }
    }
}

/// Maintenance task for a view
#[derive(Debug, Clone)]
pub struct MaintenanceTask {
    /// View to maintain
    pub view_id: String,
    /// Type of maintenance
    pub task_type: MaintenanceTaskType,
    /// Priority (higher = more urgent)
    pub priority: u8,
    /// Scheduled execution time
    pub scheduled_time: SystemTime,
    /// Estimated execution time
    pub estimated_duration: Duration,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
}

/// Types of maintenance tasks
#[derive(Debug, Clone)]
pub enum MaintenanceTaskType {
    /// Full refresh of the view
    FullRefresh,
    /// Incremental update
    IncrementalUpdate,
    /// Recompute statistics
    StatisticsUpdate,
    /// Optimize view storage
    StorageOptimization,
    /// Validate view integrity
    IntegrityCheck,
}

/// Resource requirements for a maintenance task
#[derive(Debug, Clone)]
pub struct ResourceRequirements {
    /// Estimated CPU usage
    pub cpu_usage: f64,
    /// Estimated memory usage
    pub memory_usage: usize,
    /// Estimated I/O operations
    pub io_operations: usize,
    /// Network bandwidth requirements
    pub network_bandwidth: usize,
}

/// Active maintenance task
#[derive(Debug)]
pub struct ActiveTask {
    /// Task information
    pub task: MaintenanceTask,
    /// Start time
    pub start_time: Instant,
    /// Current progress (0.0 to 1.0)
    pub progress: f64,
    /// Cancellation flag
    pub cancelled: bool,
}

/// Usage statistics for views
#[derive(Debug, Default)]
pub struct ViewUsageStatistics {
    /// Access count per view
    access_counts: HashMap<String, usize>,
    /// Total query time saved per view
    time_saved: HashMap<String, Duration>,
    /// Hit rate per view
    hit_rates: HashMap<String, f64>,
    /// Cost benefit per view
    cost_benefits: HashMap<String, f64>,
    /// Usage patterns over time
    usage_history: HashMap<String, VecDeque<UsageRecord>>,
}

/// Record of view usage
#[derive(Debug, Clone)]
pub struct UsageRecord {
    /// Timestamp of usage
    pub timestamp: SystemTime,
    /// Query that used the view
    pub query_hash: u64,
    /// Time saved by using the view
    pub time_saved: Duration,
    /// Cost benefit achieved
    pub cost_benefit: f64,
}

/// Engine for recommending new materialized views
pub struct ViewRecommendationEngine {
    #[allow(dead_code)]
    query_patterns: Arc<RwLock<QueryPatternAnalyzer>>,
    #[allow(dead_code)]
    cost_analyzer: CostAnalyzer,
    #[allow(dead_code)]
    benefit_estimator: BenefitEstimator,
    #[allow(dead_code)]
    recommendation_cache: Arc<RwLock<HashMap<String, ViewRecommendation>>>,
}

/// Analyzer for query patterns
#[derive(Debug)]
pub struct QueryPatternAnalyzer {
    /// Observed query patterns
    #[allow(dead_code)]
    patterns: HashMap<String, QueryPattern>,
    /// Pattern frequency
    #[allow(dead_code)]
    pattern_frequency: HashMap<String, usize>,
    /// Pattern cost statistics
    #[allow(dead_code)]
    pattern_costs: HashMap<String, CostStatistics>,
}

/// Observed query pattern
#[derive(Debug, Clone)]
pub struct QueryPattern {
    /// Pattern signature
    pub signature: String,
    /// Algebra structure
    pub algebra_structure: Algebra,
    /// Common sub-patterns
    pub sub_patterns: Vec<SubPattern>,
    /// Variable usage patterns
    pub variable_patterns: VariablePattern,
    /// Join patterns
    pub join_patterns: Vec<JoinPattern>,
}

/// Sub-pattern within a query
#[derive(Debug, Clone)]
pub struct SubPattern {
    /// Pattern identifier
    pub id: String,
    /// Algebra expression
    pub algebra: Algebra,
    /// Frequency of occurrence
    pub frequency: usize,
    /// Estimated cost
    pub estimated_cost: f64,
}

/// Variable usage pattern
#[derive(Debug, Clone)]
pub struct VariablePattern {
    /// Variables used in the pattern
    pub variables: HashSet<Variable>,
    /// Variable binding patterns
    pub binding_patterns: HashMap<Variable, BindingPattern>,
    /// Variable selectivity
    pub variable_selectivity: HashMap<Variable, f64>,
}

/// Binding pattern for a variable
#[derive(Debug, Clone)]
pub enum BindingPattern {
    /// Always bound to constants
    Constant(Vec<String>),
    /// Bound through joins
    Join(Vec<Variable>),
    /// Bound through filters
    Filter(Vec<Expression>),
    /// Mixed binding pattern
    Mixed,
}

/// Join pattern in queries
#[derive(Debug, Clone)]
pub struct JoinPattern {
    /// Left side pattern
    pub left_pattern: TriplePattern,
    /// Right side pattern
    pub right_pattern: TriplePattern,
    /// Join variables
    pub join_variables: Vec<Variable>,
    /// Join selectivity
    pub selectivity: f64,
    /// Join cost
    pub cost: f64,
}

/// Cost statistics for query patterns
#[derive(Debug, Clone, Default)]
pub struct CostStatistics {
    /// Average execution cost
    pub average_cost: f64,
    /// Minimum execution cost
    pub min_cost: f64,
    /// Maximum execution cost
    pub max_cost: f64,
    /// Standard deviation
    pub std_deviation: f64,
    /// Number of samples
    pub sample_count: usize,
}

/// Cost analyzer for view recommendations
pub struct CostAnalyzer {
    #[allow(dead_code)]
    historical_costs: HashMap<String, Vec<f64>>,
    #[allow(dead_code)]
    cost_models: HashMap<String, CostModel>,
}

/// Benefit estimator for materialized views
pub struct BenefitEstimator {
    /// Historical benefit data
    #[allow(dead_code)]
    benefit_history: HashMap<String, Vec<f64>>,
    /// Benefit prediction models
    #[allow(dead_code)]
    prediction_models: HashMap<String, BenefitModel>,
}

/// Model for predicting view benefits
#[derive(Debug, Clone)]
pub struct BenefitModel {
    /// Model type
    pub model_type: BenefitModelType,
    /// Model parameters
    pub parameters: HashMap<String, f64>,
    /// Accuracy metrics
    pub accuracy: f64,
}

/// Types of benefit prediction models
#[derive(Debug, Clone)]
pub enum BenefitModelType {
    Linear,
    Polynomial,
    ExponentialDecay,
    MachineLearning(String), // ML model type
}

/// Recommendation for a new materialized view
#[derive(Debug, Clone)]
pub struct ViewRecommendation {
    /// Proposed view definition
    pub view_definition: Algebra,
    /// Estimated benefit
    pub estimated_benefit: f64,
    /// Confidence in the recommendation
    pub confidence: f64,
    /// Estimated creation cost
    pub creation_cost: f64,
    /// Estimated maintenance cost
    pub maintenance_cost: f64,
    /// Recommended maintenance strategy
    pub maintenance_strategy: MaintenanceStrategy,
    /// Supporting query patterns
    pub supporting_patterns: Vec<String>,
    /// Justification for the recommendation
    pub justification: String,
}

impl MaterializedViewManager {
    /// Create a new materialized view manager
    pub fn new(
        config: MaterializedViewConfig,
        cost_model: Arc<Mutex<CostModel>>,
        statistics_collector: Arc<StatisticsCollector>,
    ) -> Result<Self> {
        let views = Arc::new(RwLock::new(HashMap::new()));
        let view_storage = Arc::new(RwLock::new(ViewStorage::new(config.max_memory_usage)));

        let rewriter = QueryRewriter::new()?;
        let maintenance_scheduler = MaintenanceScheduler::new(SchedulerConfig::default())?;
        let usage_statistics = Arc::new(RwLock::new(ViewUsageStatistics::default()));
        let recommendation_engine = ViewRecommendationEngine::new()?;

        Ok(Self {
            config,
            views,
            view_storage,
            rewriter,
            maintenance_scheduler,
            cost_model,
            statistics_collector,
            usage_statistics,
            recommendation_engine,
        })
    }

    /// Create a new materialized view
    pub fn create_view(
        &mut self,
        name: String,
        definition: Algebra,
        metadata: ViewMetadata,
        executor: &mut QueryExecutor,
        dataset: &dyn Dataset,
    ) -> Result<String> {
        let _span = span!(Level::INFO, "create_materialized_view").entered();

        let view_id = format!("view_{}", uuid::Uuid::new_v4().simple());

        info!("Creating materialized view: {} ({})", name, view_id);

        // Execute the view definition to materialize initial data
        let start_time = Instant::now();
        let (results, stats) = executor.execute(&definition, dataset)?;
        let materialization_time = start_time.elapsed();

        // Calculate data size and checksum
        let size_bytes = self.estimate_result_size(&results);
        let checksum = self.calculate_checksum(&results);

        let view_data = ViewData {
            results,
            size_bytes,
            row_count: stats.final_results,
            materialized_at: SystemTime::now(),
            checksum,
        };

        // Analyze dependencies
        let dependencies = self.analyze_dependencies(&definition)?;

        // Calculate cost estimates
        let cost_estimates = self.calculate_view_costs(&definition, &view_data, &stats)?;

        // Set up maintenance info
        let maintenance_info = MaintenanceInfo {
            last_updated: SystemTime::now(),
            next_maintenance: self.calculate_next_maintenance(&self.config.maintenance_strategy),
            strategy: self.config.maintenance_strategy.clone(),
            update_count: 0,
            total_maintenance_time: materialization_time,
            needs_update: false,
            incremental_state: if self.config.incremental_maintenance {
                Some(IncrementalState {
                    last_transaction_id: 0,
                    change_log: Vec::new(),
                    delta_state: DeltaState {
                        positive_delta: Vec::new(),
                        negative_delta: Vec::new(),
                        dirty_partitions: HashSet::new(),
                    },
                })
            } else {
                None
            },
        };

        let view = MaterializedView {
            id: view_id.clone(),
            name,
            definition: definition.clone(),
            data: view_data.clone(),
            metadata,
            maintenance_info,
            cost_estimates,
            dependencies,
        };

        // Store the view
        {
            let mut views = self.views.write().expect("lock poisoned");
            views.insert(view_id.clone(), view);
        }

        // Store the data
        {
            let mut storage = self.view_storage.write().expect("lock poisoned");
            storage.store_view_data(view_id.clone(), view_data)?;
        }

        // Update view index
        self.rewriter.update_view_index(&view_id, &definition)?;

        // Schedule maintenance if needed
        if let Some(next_maintenance) =
            self.calculate_next_maintenance(&self.config.maintenance_strategy)
        {
            self.maintenance_scheduler.schedule_maintenance(
                view_id.clone(),
                MaintenanceTaskType::StatisticsUpdate,
                next_maintenance,
                3, // Medium priority
            )?;
        }

        info!(
            "Created materialized view {} in {:?}",
            view_id, materialization_time
        );
        Ok(view_id)
    }

    /// Rewrite a query to use materialized views
    pub fn rewrite_query(&self, query: &Algebra) -> Result<(Algebra, Vec<String>)> {
        let _span = span!(Level::DEBUG, "rewrite_query").entered();

        self.rewriter
            .rewrite_query(query, &self.views, &self.cost_model)
    }

    /// Get view usage statistics
    pub fn get_usage_statistics(&self, view_id: &str) -> Result<Option<ViewUsageStats>> {
        let stats = self.usage_statistics.read().expect("lock poisoned");

        Ok(stats
            .access_counts
            .get(view_id)
            .map(|&access_count| ViewUsageStats {
                access_count,
                total_time_saved: stats.time_saved.get(view_id).copied().unwrap_or_default(),
                hit_rate: stats.hit_rates.get(view_id).copied().unwrap_or(0.0),
                cost_benefit: stats.cost_benefits.get(view_id).copied().unwrap_or(0.0),
            }))
    }

    /// Get view recommendations based on query patterns
    pub fn get_view_recommendations(&self) -> Result<Vec<ViewRecommendation>> {
        self.recommendation_engine.get_recommendations()
    }

    /// Update view with new data
    pub fn update_view(
        &mut self,
        view_id: &str,
        executor: &mut QueryExecutor,
        dataset: &dyn Dataset,
    ) -> Result<()> {
        let _span = span!(Level::INFO, "update_view").entered();

        let start_time = Instant::now();

        // Get view definition
        let _definition = {
            let views = self.views.read().expect("lock poisoned");
            let view = views
                .get(view_id)
                .ok_or_else(|| anyhow!("View not found: {}", view_id))?;
            view.definition.clone()
        };

        // Check if incremental update is possible
        let use_incremental = {
            let views = self.views.read().expect("lock poisoned");
            let view = views
                .get(view_id)
                .expect("view should exist for given view_id");
            self.config.incremental_maintenance
                && view.maintenance_info.incremental_state.is_some()
                && self.can_update_incrementally(&view.dependencies)
        };

        if use_incremental {
            self.update_view_incrementally(view_id, executor, dataset)?;
        } else {
            self.update_view_fully(view_id, executor, dataset)?;
        }

        let update_time = start_time.elapsed();

        // Update maintenance info
        {
            let mut views = self.views.write().expect("lock poisoned");
            if let Some(view) = views.get_mut(view_id) {
                view.maintenance_info.last_updated = SystemTime::now();
                view.maintenance_info.update_count += 1;
                view.maintenance_info.total_maintenance_time += update_time;
                view.maintenance_info.needs_update = false;
                view.maintenance_info.next_maintenance =
                    self.calculate_next_maintenance(&view.maintenance_info.strategy);
            }
        }

        info!("Updated view {} in {:?}", view_id, update_time);
        Ok(())
    }

    /// Record view usage for statistics
    pub fn record_view_usage(
        &self,
        view_id: &str,
        query_hash: u64,
        time_saved: Duration,
        cost_benefit: f64,
    ) -> Result<()> {
        let mut stats = self.usage_statistics.write().expect("lock poisoned");

        // Update access count
        *stats.access_counts.entry(view_id.to_string()).or_insert(0) += 1;

        // Update time saved
        *stats
            .time_saved
            .entry(view_id.to_string())
            .or_insert(Duration::ZERO) += time_saved;

        // Update cost benefit
        let current_benefit = stats
            .cost_benefits
            .entry(view_id.to_string())
            .or_insert(0.0);
        *current_benefit = (*current_benefit + cost_benefit) / 2.0; // Moving average

        // Add usage record
        let usage_record = UsageRecord {
            timestamp: SystemTime::now(),
            query_hash,
            time_saved,
            cost_benefit,
        };

        stats
            .usage_history
            .entry(view_id.to_string())
            .or_default()
            .push_back(usage_record);

        // Limit history size
        if let Some(history) = stats.usage_history.get_mut(view_id) {
            while history.len() > 1000 {
                history.pop_front();
            }
        }

        Ok(())
    }

    // Private helper methods

    fn estimate_result_size(&self, results: &Solution) -> usize {
        // Estimate size based on number of results and average binding size
        results.len() * 100 // Rough estimate: 100 bytes per result
    }

    fn calculate_checksum(&self, results: &Solution) -> u64 {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        for result in results {
            format!("{result:?}").hash(&mut hasher);
        }
        hasher.finish()
    }

    fn analyze_dependencies(&self, algebra: &Algebra) -> Result<ViewDependencies> {
        let mut base_tables = Vec::new();
        let mut dependent_patterns = Vec::new();
        let mut dependent_variables = HashSet::new();
        let mut join_dependencies = Vec::new();

        self.analyze_algebra_dependencies(
            algebra,
            &mut base_tables,
            &mut dependent_patterns,
            &mut dependent_variables,
            &mut join_dependencies,
        )?;

        Ok(ViewDependencies {
            base_tables,
            dependent_patterns,
            dependent_variables,
            join_dependencies,
        })
    }

    #[allow(clippy::only_used_in_recursion)]
    fn analyze_algebra_dependencies(
        &self,
        algebra: &Algebra,
        base_tables: &mut Vec<String>,
        dependent_patterns: &mut Vec<TriplePattern>,
        dependent_variables: &mut HashSet<Variable>,
        join_dependencies: &mut Vec<JoinDependency>,
    ) -> Result<()> {
        match algebra {
            Algebra::Bgp(patterns) => {
                dependent_patterns.extend(patterns.iter().cloned());
                for pattern in patterns {
                    self.extract_variables_from_pattern(pattern, dependent_variables);
                }
            }
            Algebra::Join { left, right } => {
                self.analyze_algebra_dependencies(
                    left,
                    base_tables,
                    dependent_patterns,
                    dependent_variables,
                    join_dependencies,
                )?;
                self.analyze_algebra_dependencies(
                    right,
                    base_tables,
                    dependent_patterns,
                    dependent_variables,
                    join_dependencies,
                )?;

                // Analyze join dependency
                if let (Algebra::Bgp(left_patterns), Algebra::Bgp(right_patterns)) =
                    (left.as_ref(), right.as_ref())
                {
                    if let (Some(left_pattern), Some(right_pattern)) =
                        (left_patterns.first(), right_patterns.first())
                    {
                        let join_vars = self.find_common_variables(left_pattern, right_pattern);
                        if !join_vars.is_empty() {
                            join_dependencies.push(JoinDependency {
                                left_pattern: left_pattern.clone(),
                                right_pattern: right_pattern.clone(),
                                join_variables: join_vars,
                                selectivity: 0.1, // Default selectivity
                            });
                        }
                    }
                }
            }
            Algebra::Union { left, right } => {
                self.analyze_algebra_dependencies(
                    left,
                    base_tables,
                    dependent_patterns,
                    dependent_variables,
                    join_dependencies,
                )?;
                self.analyze_algebra_dependencies(
                    right,
                    base_tables,
                    dependent_patterns,
                    dependent_variables,
                    join_dependencies,
                )?;
            }
            Algebra::Filter { pattern, condition } => {
                self.analyze_algebra_dependencies(
                    pattern,
                    base_tables,
                    dependent_patterns,
                    dependent_variables,
                    join_dependencies,
                )?;
                self.extract_variables_from_expression(condition, dependent_variables);
            }
            _ => {
                // Handle other algebra types as needed
            }
        }
        Ok(())
    }

    fn extract_variables_from_pattern(
        &self,
        pattern: &TriplePattern,
        variables: &mut HashSet<Variable>,
    ) {
        if let Term::Variable(var) = &pattern.subject {
            variables.insert(var.clone());
        }
        if let Term::Variable(var) = &pattern.predicate {
            variables.insert(var.clone());
        }
        if let Term::Variable(var) = &pattern.object {
            variables.insert(var.clone());
        }
    }

    #[allow(clippy::only_used_in_recursion)]
    fn extract_variables_from_expression(
        &self,
        expression: &Expression,
        variables: &mut HashSet<Variable>,
    ) {
        match expression {
            Expression::Variable(var) => {
                variables.insert(var.clone());
            }
            Expression::Binary { left, right, .. } => {
                self.extract_variables_from_expression(left, variables);
                self.extract_variables_from_expression(right, variables);
            }
            Expression::Unary { operand, .. } => {
                self.extract_variables_from_expression(operand, variables);
            }
            Expression::Function { args, .. } => {
                for arg in args {
                    self.extract_variables_from_expression(arg, variables);
                }
            }
            _ => {}
        }
    }

    fn find_common_variables(&self, left: &TriplePattern, right: &TriplePattern) -> Vec<Variable> {
        let mut left_vars = HashSet::new();
        let mut right_vars = HashSet::new();

        self.extract_variables_from_pattern(left, &mut left_vars);
        self.extract_variables_from_pattern(right, &mut right_vars);

        left_vars.intersection(&right_vars).cloned().collect()
    }

    fn calculate_view_costs(
        &self,
        _definition: &Algebra,
        view_data: &ViewData,
        _stats: &ExecutionStats,
    ) -> Result<ViewCostEstimates> {
        // Simplified cost calculation
        let access_cost = CostEstimate::new(
            view_data.row_count as f64 * 0.1,    // CPU cost
            0.0,                                 // I/O cost (in memory)
            view_data.size_bytes as f64 * 0.001, // Memory cost
            0.0,                                 // Network cost
            view_data.row_count,
        );

        let maintenance_cost = CostEstimate::new(
            view_data.row_count as f64 * 0.5,    // CPU cost for maintenance
            view_data.row_count as f64 * 0.1,    // I/O cost
            view_data.size_bytes as f64 * 0.002, // Memory cost
            0.0,                                 // Network cost
            view_data.row_count,
        );

        Ok(ViewCostEstimates {
            access_cost,
            maintenance_cost,
            storage_cost: view_data.size_bytes as f64,
            benefit_ratio: 2.0, // Assume 2x benefit by default
            last_estimated: SystemTime::now(),
        })
    }

    fn calculate_next_maintenance(&self, strategy: &MaintenanceStrategy) -> Option<SystemTime> {
        match strategy {
            MaintenanceStrategy::Periodic(interval) => Some(SystemTime::now() + *interval),
            MaintenanceStrategy::CostBased => {
                Some(SystemTime::now() + Duration::from_secs(3600)) // 1 hour default
            }
            MaintenanceStrategy::Hybrid => {
                Some(SystemTime::now() + Duration::from_secs(1800)) // 30 minutes default
            }
            _ => None,
        }
    }

    fn can_update_incrementally(&self, _dependencies: &ViewDependencies) -> bool {
        // Simplified check - in practice would analyze if incremental update is feasible
        true
    }

    fn update_view_incrementally(
        &mut self,
        view_id: &str,
        _executor: &QueryExecutor,
        _dataset: &dyn Dataset,
    ) -> Result<()> {
        // Simplified incremental update - would implement delta computation
        debug!("Performing incremental update for view {}", view_id);
        Ok(())
    }

    fn update_view_fully(
        &mut self,
        view_id: &str,
        executor: &mut QueryExecutor,
        dataset: &dyn Dataset,
    ) -> Result<()> {
        debug!("Performing full update for view {}", view_id);

        // Get view definition
        let definition = {
            let views = self.views.read().expect("lock poisoned");
            let view = views
                .get(view_id)
                .ok_or_else(|| anyhow!("View not found: {}", view_id))?;
            view.definition.clone()
        };

        // Re-execute the view definition
        let (results, stats) = executor.execute(&definition, dataset)?;

        // Calculate new data properties
        let size_bytes = self.estimate_result_size(&results);
        let checksum = self.calculate_checksum(&results);

        let new_data = ViewData {
            results,
            size_bytes,
            row_count: stats.final_results,
            materialized_at: SystemTime::now(),
            checksum,
        };

        // Update view data
        {
            let mut views = self.views.write().expect("lock poisoned");
            if let Some(view) = views.get_mut(view_id) {
                view.data = new_data.clone();
            }
        }

        // Update storage
        {
            let mut storage = self.view_storage.write().expect("lock poisoned");
            storage.store_view_data(view_id.to_string(), new_data)?;
        }

        Ok(())
    }
}

/// Statistics for view usage
#[derive(Debug, Clone)]
pub struct ViewUsageStats {
    pub access_count: usize,
    pub total_time_saved: Duration,
    pub hit_rate: f64,
    pub cost_benefit: f64,
}

impl ViewStorage {
    fn new(max_memory: usize) -> Self {
        Self {
            memory_storage: HashMap::new(),
            disk_storage_path: None,
            max_memory,
            memory_usage: 0,
            storage_stats: StorageStatistics::default(),
        }
    }

    fn store_view_data(&mut self, view_id: String, data: ViewData) -> Result<()> {
        // Store in memory if under threshold
        let data_size = data.size_bytes;
        if self.memory_usage + data_size <= self.max_memory {
            self.memory_storage.insert(view_id, data);
            self.memory_usage += data_size;
            self.storage_stats.memory_view_count += 1;
        } else {
            // Would implement disk storage here
            return Err(anyhow!("Disk storage not implemented"));
        }
        Ok(())
    }
}

impl QueryRewriter {
    fn new() -> Result<Self> {
        Ok(Self {
            view_index: ViewIndex::new(),
            rewrite_rules: Vec::new(),
            cost_threshold: 0.8, // Only rewrite if 80% cost reduction
        })
    }

    fn rewrite_query(
        &self,
        query: &Algebra,
        views: &Arc<RwLock<HashMap<String, MaterializedView>>>,
        _cost_model: &Arc<Mutex<CostModel>>,
    ) -> Result<(Algebra, Vec<String>)> {
        // Simplified rewrite logic
        let _views_guard = views.read().expect("lock poisoned");
        let used_views = Vec::new();

        // For now, return original query
        // In full implementation, would analyze query and find matching views
        Ok((query.clone(), used_views))
    }

    fn update_view_index(&mut self, view_id: &str, definition: &Algebra) -> Result<()> {
        self.view_index.add_view(view_id.to_string(), definition)
    }
}

impl ViewIndex {
    fn new() -> Self {
        Self {
            pattern_index: HashMap::new(),
            variable_index: HashMap::new(),
            predicate_index: HashMap::new(),
            characteristic_index: HashMap::new(),
        }
    }

    fn add_view(&mut self, view_id: String, definition: &Algebra) -> Result<()> {
        // Extract patterns and characteristics for indexing
        let characteristics = self.extract_characteristics(definition);

        for characteristic in characteristics {
            self.characteristic_index
                .entry(characteristic)
                .or_default()
                .push(view_id.clone());
        }

        Ok(())
    }

    fn extract_characteristics(&self, algebra: &Algebra) -> Vec<QueryCharacteristic> {
        let mut characteristics = Vec::new();

        match algebra {
            Algebra::Join { .. } => characteristics.push(QueryCharacteristic::HasJoin),
            Algebra::Union { .. } => characteristics.push(QueryCharacteristic::HasUnion),
            Algebra::Filter { .. } => characteristics.push(QueryCharacteristic::HasFilter),
            Algebra::Group { .. } => characteristics.push(QueryCharacteristic::HasAggregation),
            Algebra::Bgp(patterns) => {
                characteristics.push(QueryCharacteristic::PatternCount(patterns.len()));
            }
            _ => {}
        }

        characteristics
    }
}

impl MaintenanceScheduler {
    fn new(config: SchedulerConfig) -> Result<Self> {
        Ok(Self {
            scheduled_tasks: Arc::new(RwLock::new(VecDeque::new())),
            active_tasks: Arc::new(RwLock::new(HashMap::new())),
            config,
        })
    }

    fn schedule_maintenance(
        &self,
        view_id: String,
        task_type: MaintenanceTaskType,
        scheduled_time: SystemTime,
        priority: u8,
    ) -> Result<()> {
        let task = MaintenanceTask {
            view_id,
            task_type,
            priority,
            scheduled_time,
            estimated_duration: Duration::from_secs(60), // Default 1 minute
            resource_requirements: ResourceRequirements {
                cpu_usage: 0.1,
                memory_usage: 64 * 1024 * 1024, // 64MB
                io_operations: 1000,
                network_bandwidth: 0,
            },
        };

        let mut scheduled = self.scheduled_tasks.write().expect("lock poisoned");
        scheduled.push_back(task);

        Ok(())
    }
}

impl ViewRecommendationEngine {
    fn new() -> Result<Self> {
        Ok(Self {
            query_patterns: Arc::new(RwLock::new(QueryPatternAnalyzer::new())),
            cost_analyzer: CostAnalyzer::new(),
            benefit_estimator: BenefitEstimator::new(),
            recommendation_cache: Arc::new(RwLock::new(HashMap::new())),
        })
    }

    fn get_recommendations(&self) -> Result<Vec<ViewRecommendation>> {
        // Simplified recommendation logic
        let recommendations = vec![ViewRecommendation {
            view_definition: Algebra::Bgp(vec![]), // Placeholder
            estimated_benefit: 0.5,
            confidence: 0.7,
            creation_cost: 100.0,
            maintenance_cost: 10.0,
            maintenance_strategy: MaintenanceStrategy::Lazy,
            supporting_patterns: vec!["common_pattern_1".to_string()],
            justification: "Frequently accessed pattern with high cost".to_string(),
        }];

        Ok(recommendations)
    }
}

impl QueryPatternAnalyzer {
    fn new() -> Self {
        Self {
            patterns: HashMap::new(),
            pattern_frequency: HashMap::new(),
            pattern_costs: HashMap::new(),
        }
    }
}

impl CostAnalyzer {
    fn new() -> Self {
        Self {
            historical_costs: HashMap::new(),
            cost_models: HashMap::new(),
        }
    }
}

impl BenefitEstimator {
    fn new() -> Self {
        Self {
            benefit_history: HashMap::new(),
            prediction_models: HashMap::new(),
        }
    }
}

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

    #[test]
    fn test_materialized_view_manager_creation() {
        let config = MaterializedViewConfig::default();
        let cost_model = Arc::new(Mutex::new(CostModel::new(CostModelConfig::default())));
        let stats_collector = Arc::new(StatisticsCollector::new());

        let manager = MaterializedViewManager::new(config, cost_model, stats_collector);
        assert!(manager.is_ok());
    }

    #[test]
    fn test_view_storage() {
        let mut storage = ViewStorage::new(1024 * 1024); // 1MB

        let data = ViewData {
            results: vec![],
            size_bytes: 1000,
            row_count: 10,
            materialized_at: SystemTime::now(),
            checksum: 12345,
        };

        let result = storage.store_view_data("test_view".to_string(), data);
        assert!(result.is_ok());
    }

    #[test]
    fn test_query_rewriter() {
        let rewriter = QueryRewriter::new().unwrap();
        let query = Algebra::Bgp(vec![]);
        let views = Arc::new(RwLock::new(HashMap::new()));
        let cost_model = Arc::new(Mutex::new(CostModel::new(CostModelConfig::default())));

        let result = rewriter.rewrite_query(&query, &views, &cost_model);
        assert!(result.is_ok());
    }

    #[test]
    fn test_maintenance_scheduler() {
        let config = SchedulerConfig {
            max_concurrent_tasks: 4,
            default_interval: Duration::from_secs(3600),
            priority_threshold: 5,
            resource_limits: ResourceLimits {
                max_cpu_usage: 0.8,
                max_memory_usage: 1024 * 1024 * 1024,
                max_io_bandwidth: 100 * 1024 * 1024,
            },
        };

        let scheduler = MaintenanceScheduler::new(config);
        assert!(scheduler.is_ok());
    }
}