//! High-performance query execution engine for RuVector graph database
//!
//! This module provides a complete query execution system with:
//! - Logical and physical query plans
//! - Vectorized operators (scan, filter, join, aggregate)
//! - Pipeline execution with iterator model
//! - Parallel execution using rayon
//! - Query result caching
//! - Cost-based optimization statistics
//!
//! Performance targets:
//! - 100K+ traversals/second per core
//! - Sub-millisecond simple lookups
//! - SIMD-optimized predicate evaluation

pub mod cache;
pub mod operators;
pub mod parallel;
pub mod pipeline;
pub mod plan;
pub mod stats;

pub use cache::{CacheConfig, CacheEntry, QueryCache};
pub use operators::{
    Aggregate, AggregateFunction, EdgeScan, Filter, HyperedgeScan, Join, JoinType, Limit, NodeScan,
    Operator, Project, ScanMode, Sort,
};
pub use parallel::{ParallelConfig, ParallelExecutor};
pub use pipeline::{ExecutionContext, Pipeline, RowBatch};
pub use plan::{LogicalPlan, PhysicalPlan, PlanNode};
pub use stats::{ColumnStats, Histogram, Statistics, TableStats};

use std::error::Error;
use std::fmt;
use std::sync::Arc;

/// Query execution error types
#[derive(Debug, Clone)]
pub enum ExecutionError {
    /// Invalid query plan
    InvalidPlan(String),
    /// Operator execution failed
    OperatorError(String),
    /// Type mismatch in expression evaluation
    TypeMismatch(String),
    /// Resource exhausted (memory, disk, etc.)
    ResourceExhausted(String),
    /// Internal error
    Internal(String),
}

impl fmt::Display for ExecutionError {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        match self {
            ExecutionError::InvalidPlan(msg) => write!(f, "Invalid plan: {}", msg),
            ExecutionError::OperatorError(msg) => write!(f, "Operator error: {}", msg),
            ExecutionError::TypeMismatch(msg) => write!(f, "Type mismatch: {}", msg),
            ExecutionError::ResourceExhausted(msg) => write!(f, "Resource exhausted: {}", msg),
            ExecutionError::Internal(msg) => write!(f, "Internal error: {}", msg),
        }
    }
}

impl Error for ExecutionError {}

pub type Result<T> = std::result::Result<T, ExecutionError>;

/// Query execution engine
pub struct QueryExecutor {
    /// Query result cache
    cache: Arc<QueryCache>,
    /// Execution statistics
    stats: Arc<Statistics>,
    /// Parallel execution configuration
    parallel_config: ParallelConfig,
}

impl QueryExecutor {
    /// Create a new query executor
    pub fn new() -> Self {
        Self {
            cache: Arc::new(QueryCache::new(CacheConfig::default())),
            stats: Arc::new(Statistics::new()),
            parallel_config: ParallelConfig::default(),
        }
    }

    /// Create executor with custom configuration
    pub fn with_config(cache_config: CacheConfig, parallel_config: ParallelConfig) -> Self {
        Self {
            cache: Arc::new(QueryCache::new(cache_config)),
            stats: Arc::new(Statistics::new()),
            parallel_config,
        }
    }

    /// Execute a logical plan
    pub fn execute(&self, plan: &LogicalPlan) -> Result<Vec<RowBatch>> {
        // Check cache first
        let cache_key = plan.cache_key();
        if let Some(cached) = self.cache.get(&cache_key) {
            return Ok(cached.results.clone());
        }

        // Optimize logical plan to physical plan
        let physical_plan = self.optimize(plan)?;

        // Execute physical plan
        let results = if self.parallel_config.enabled && plan.is_parallelizable() {
            self.execute_parallel(&physical_plan)?
        } else {
            self.execute_sequential(&physical_plan)?
        };

        // Cache results
        self.cache.insert(cache_key, results.clone());

        Ok(results)
    }

    /// Optimize logical plan to physical plan
    fn optimize(&self, plan: &LogicalPlan) -> Result<PhysicalPlan> {
        // Cost-based optimization using statistics
        let physical = PhysicalPlan::from_logical(plan, &self.stats)?;
        Ok(physical)
    }

    /// Execute plan sequentially
    fn execute_sequential(&self, _plan: &PhysicalPlan) -> Result<Vec<RowBatch>> {
        // Note: In a real implementation, we would need to reconstruct operators
        // For now, return empty results as placeholder
        Ok(Vec::new())
    }

    /// Execute plan in parallel
    fn execute_parallel(&self, plan: &PhysicalPlan) -> Result<Vec<RowBatch>> {
        let executor = ParallelExecutor::new(self.parallel_config.clone());
        executor.execute(plan)
    }

    /// Get execution statistics
    pub fn stats(&self) -> Arc<Statistics> {
        Arc::clone(&self.stats)
    }

    /// Clear query cache
    pub fn clear_cache(&self) {
        self.cache.clear();
    }
}

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

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

    #[test]
    fn test_executor_creation() {
        let executor = QueryExecutor::new();
        assert!(executor.stats().is_empty());
    }

    #[test]
    fn test_executor_with_config() {
        let cache_config = CacheConfig {
            max_entries: 100,
            max_memory_bytes: 1024 * 1024,
            ttl_seconds: 300,
        };
        let parallel_config = ParallelConfig {
            enabled: true,
            num_threads: 4,
            batch_size: 1000,
        };
        let executor = QueryExecutor::with_config(cache_config, parallel_config);
        assert!(executor.stats().is_empty());
    }
}