use std::sync::{Arc, Mutex};
use std::thread;
use std::sync::mpsc::{channel, Receiver, Sender};
use std::collections::VecDeque;
use std::time::{Duration, Instant};
use crate::dataframe::DataFrame;
use crate::series::Series;
use crate::performance::memory_compression::UltraFastMemoryPool;

/// Ultra-fast parallel processing framework for data operations
pub struct UltraFastParallelExecutor {
    thread_pool: Vec<thread::JoinHandle<()>>,
    task_sender: Sender<ParallelTask>,
    result_receiver: Arc<Mutex<Receiver<TaskResult>>>,
    num_threads: usize,
    memory_pool: Arc<UltraFastMemoryPool>,
}

/// Work-stealing thread pool for maximum CPU utilization
pub struct WorkStealingPool {
    workers: Vec<Worker>,
    task_queues: Vec<Arc<Mutex<VecDeque<ParallelTask>>>>,
    global_queue: Arc<Mutex<VecDeque<ParallelTask>>>,
    num_workers: usize,
}

/// NUMA-aware parallel operations
pub struct NumaParallelOps {
    numa_executors: Vec<UltraFastParallelExecutor>,
    numa_affinity: Vec<usize>,
    load_balancer: LoadBalancer,
}

/// Advanced load balancing for optimal resource utilization
pub struct LoadBalancer {
    thread_loads: Vec<Arc<Mutex<f64>>>,
    task_history: VecDeque<TaskMetrics>,
    prediction_model: SimplePredictor,
}

/// Task definition for parallel execution
#[derive(Debug, Clone)]
pub enum ParallelTask {
    MapOperation {
        data: Vec<u8>,
        operation: MapOperationType,
        chunk_id: usize,
    },
    ReduceOperation {
        intermediate_results: Vec<Vec<u8>>,
        operation: ReduceOperationType,
    },
    FilterOperation {
        data: Vec<u8>,
        predicate: FilterPredicate,
        chunk_id: usize,
    },
    AggregateOperation {
        data: Vec<u8>,
        operation: AggregateOperationType,
        chunk_id: usize,
    },
    SortOperation {
        data: Vec<u8>,
        chunk_id: usize,
        merge_phase: bool,
    },
    JoinOperation {
        left_data: Vec<u8>,
        right_data: Vec<u8>,
        join_type: JoinType,
    },
}

#[derive(Debug, Clone)]
pub enum MapOperationType {
    Transform(String),
    Compute(String),
    Extract(String),
}

#[derive(Debug, Clone)]
pub enum ReduceOperationType {
    Sum,
    Count,
    Max,
    Min,
    Concat,
}

#[derive(Debug, Clone)]
pub enum FilterPredicate {
    GreaterThan(f64),
    LessThan(f64),
    Equal(String),
    Contains(String),
    Complex(String),
}

#[derive(Debug, Clone)]
pub enum AggregateOperationType {
    Sum,
    Average,
    Count,
    StandardDeviation,
    Percentile(f64),
}

#[derive(Debug, Clone)]
pub enum JoinType {
    Inner,
    Left,
    Right,
    Full,
}

/// Task execution result
#[derive(Debug, Clone)]
pub struct TaskResult {
    pub task_id: usize,
    pub chunk_id: usize,
    pub result_data: Vec<u8>,
    pub execution_time: Duration,
    pub success: bool,
    pub error_message: Option<String>,
}

/// Worker thread in the thread pool
struct Worker {
    id: usize,
    thread: Option<thread::JoinHandle<()>>,
    local_queue: Arc<Mutex<VecDeque<ParallelTask>>>,
}

/// Task performance metrics
#[derive(Debug, Clone)]
pub struct TaskMetrics {
    pub task_type: String,
    pub execution_time: Duration,
    pub data_size: usize,
    pub throughput: f64,
    pub cpu_usage: f64,
    pub memory_usage: usize,
}

/// Simple performance prediction model
pub struct SimplePredictor {
    historical_data: VecDeque<TaskMetrics>,
    max_history: usize,
}

/// Parallel operation results
#[derive(Debug)]
pub struct ParallelResults {
    pub total_time: Duration,
    pub tasks_completed: usize,
    pub throughput: f64,
    pub cpu_utilization: f64,
    pub memory_efficiency: f64,
    pub load_balance_score: f64,
}

impl UltraFastParallelExecutor {
    pub fn new(num_threads: usize) -> Result<Self, String> {
        let memory_pool = Arc::new(UltraFastMemoryPool::new(4096));
        let (task_sender, task_receiver) = channel();
        let (result_sender, result_receiver) = channel();
        let result_receiver = Arc::new(Mutex::new(result_receiver));
        let task_receiver = Arc::new(Mutex::new(task_receiver));
        
        let mut thread_pool = Vec::with_capacity(num_threads);
        
        // Spawn worker threads
        for thread_id in 0..num_threads {
            let task_receiver_clone = Arc::clone(&task_receiver);
            let result_sender = result_sender.clone();
            let memory_pool_clone = Arc::clone(&memory_pool);
            
            let handle = thread::spawn(move || {
                Self::worker_thread(thread_id, task_receiver_clone, result_sender, memory_pool_clone);
            });
            
            thread_pool.push(handle);
        }
        
        Ok(Self {
            thread_pool,
            task_sender,
            result_receiver,
            num_threads,
            memory_pool,
        })
    }

    /// Parallel map operation across data chunks
    pub fn parallel_map(&self, data: Vec<Vec<u8>>, operation: MapOperationType) -> Result<ParallelResults, String> {
        let start_time = Instant::now();
        let total_chunks = data.len();
        
        // Send map tasks to worker threads
        for (chunk_id, chunk_data) in data.into_iter().enumerate() {
            let task = ParallelTask::MapOperation {
                data: chunk_data,
                operation: operation.clone(),
                chunk_id,
            };
            
            self.task_sender.send(task)
                .map_err(|e| format!("Failed to send task: {}", e))?;
        }
        
        // Collect results
        let mut results = Vec::with_capacity(total_chunks);
        let result_receiver = self.result_receiver.lock().unwrap();
        
        for _ in 0..total_chunks {
            match result_receiver.recv_timeout(Duration::from_secs(30)) {
                Ok(result) => {
                    if result.success {
                        results.push(result);
                    } else {
                        return Err(format!("Task failed: {:?}", result.error_message));
                    }
                }
                Err(_) => {
                    return Err("Task timeout".to_string());
                }
            }
        }
        
        let total_time = start_time.elapsed();
        let throughput = total_chunks as f64 / total_time.as_secs_f64();
        
        Ok(ParallelResults {
            total_time,
            tasks_completed: results.len(),
            throughput,
            cpu_utilization: self.calculate_cpu_utilization(),
            memory_efficiency: self.calculate_memory_efficiency(),
            load_balance_score: self.calculate_load_balance_score(),
        })
    }

    /// Parallel reduce operation
    pub fn parallel_reduce(&self, intermediate_data: Vec<Vec<u8>>, operation: ReduceOperationType) -> Result<ParallelResults, String> {
        let start_time = Instant::now();
        let total_items = intermediate_data.len();
        
        // Tree-reduction approach for optimal parallelism
        let mut current_level = intermediate_data;
        
        while current_level.len() > 1 {
            let mut next_level = Vec::new();
            let pairs = current_level.chunks(2);
            
            for (_pair_id, pair) in pairs.enumerate() {
                let task = ParallelTask::ReduceOperation {
                    intermediate_results: pair.to_vec(),
                    operation: operation.clone(),
                };
                
                self.task_sender.send(task)
                    .map_err(|e| format!("Failed to send reduce task: {}", e))?;
            }
            
            // Collect reduced results
            let result_receiver = self.result_receiver.lock().unwrap();
            let num_pairs = (current_level.len() + 1) / 2;
            
            for _ in 0..num_pairs {
                match result_receiver.recv_timeout(Duration::from_secs(10)) {
                    Ok(result) => {
                        if result.success {
                            next_level.push(result.result_data);
                        } else {
                            return Err(format!("Reduce task failed: {:?}", result.error_message));
                        }
                    }
                    Err(_) => {
                        return Err("Reduce task timeout".to_string());
                    }
                }
            }
            
            current_level = next_level;
        }
        
        let total_time = start_time.elapsed();
        
        Ok(ParallelResults {
            total_time,
            tasks_completed: total_items,
            throughput: total_items as f64 / total_time.as_secs_f64(),
            cpu_utilization: self.calculate_cpu_utilization(),
            memory_efficiency: self.calculate_memory_efficiency(),
            load_balance_score: self.calculate_load_balance_score(),
        })
    }

    /// Parallel filter operation with predicate pushdown
    pub fn parallel_filter(&self, data: Vec<Vec<u8>>, predicate: FilterPredicate) -> Result<ParallelResults, String> {
        let start_time = Instant::now();
        let total_chunks = data.len();
        
        // Send filter tasks
        for (chunk_id, chunk_data) in data.into_iter().enumerate() {
            let task = ParallelTask::FilterOperation {
                data: chunk_data,
                predicate: predicate.clone(),
                chunk_id,
            };
            
            self.task_sender.send(task)
                .map_err(|e| format!("Failed to send filter task: {}", e))?;
        }
        
        // Collect filtered results
        let mut filtered_results = Vec::with_capacity(total_chunks);
        let result_receiver = self.result_receiver.lock().unwrap();
        
        for _ in 0..total_chunks {
            match result_receiver.recv_timeout(Duration::from_secs(30)) {
                Ok(result) => {
                    if result.success {
                        filtered_results.push(result);
                    } else {
                        return Err(format!("Filter task failed: {:?}", result.error_message));
                    }
                }
                Err(_) => {
                    return Err("Filter task timeout".to_string());
                }
            }
        }
        
        let total_time = start_time.elapsed();
        let throughput = total_chunks as f64 / total_time.as_secs_f64();
        
        Ok(ParallelResults {
            total_time,
            tasks_completed: filtered_results.len(),
            throughput,
            cpu_utilization: self.calculate_cpu_utilization(),
            memory_efficiency: self.calculate_memory_efficiency(),
            load_balance_score: self.calculate_load_balance_score(),
        })
    }

    /// Parallel sorting with merge phases
    pub fn parallel_sort(&self, data: Vec<Vec<u8>>) -> Result<ParallelResults, String> {
        let start_time = Instant::now();
        let total_chunks = data.len();
        
        // Phase 1: Sort individual chunks
        for (chunk_id, chunk_data) in data.into_iter().enumerate() {
            let task = ParallelTask::SortOperation {
                data: chunk_data,
                chunk_id,
                merge_phase: false,
            };
            
            self.task_sender.send(task)
                .map_err(|e| format!("Failed to send sort task: {}", e))?;
        }
        
        // Collect sorted chunks
        let mut sorted_chunks = Vec::with_capacity(total_chunks);
        let result_receiver = self.result_receiver.lock().unwrap();
        
        for _ in 0..total_chunks {
            match result_receiver.recv_timeout(Duration::from_secs(30)) {
                Ok(result) => {
                    if result.success {
                        sorted_chunks.push(result.result_data);
                    } else {
                        return Err(format!("Sort task failed: {:?}", result.error_message));
                    }
                }
                Err(_) => {
                    return Err("Sort task timeout".to_string());
                }
            }
        }
        
        // Phase 2: Merge sorted chunks (simplified for demo)
        // In production, this would be a proper parallel merge
        
        let total_time = start_time.elapsed();
        let throughput = total_chunks as f64 / total_time.as_secs_f64();
        
        Ok(ParallelResults {
            total_time,
            tasks_completed: sorted_chunks.len(),
            throughput,
            cpu_utilization: self.calculate_cpu_utilization(),
            memory_efficiency: self.calculate_memory_efficiency(),
            load_balance_score: self.calculate_load_balance_score(),
        })
    }

    /// Worker thread implementation
    fn worker_thread(
        thread_id: usize,
        task_receiver: Arc<Mutex<std::sync::mpsc::Receiver<ParallelTask>>>,
        result_sender: Sender<TaskResult>,
        memory_pool: Arc<UltraFastMemoryPool>,
    ) {
        loop {
            let task = {
                let receiver = task_receiver.lock().unwrap();
                receiver.recv()
            };
            
            match task {
                Ok(task) => {
                    let start_time = Instant::now();
                    let task_result = Self::execute_task(task, &memory_pool);
                    let execution_time = start_time.elapsed();
                    
                    let result = TaskResult {
                        task_id: thread_id,
                        chunk_id: task_result.0,
                        result_data: task_result.1,
                        execution_time,
                        success: task_result.2,
                        error_message: task_result.3,
                    };
                    
                    if result_sender.send(result).is_err() {
                        break; // Main thread has disconnected
                    }
                }
                Err(_) => break, // Channel closed
            }
        }
    }

    /// Execute individual task
    fn execute_task(task: ParallelTask, _memory_pool: &UltraFastMemoryPool) -> (usize, Vec<u8>, bool, Option<String>) {
        match task {
            ParallelTask::MapOperation { data, operation, chunk_id } => {
                // Simulate map operation
                match operation {
                    MapOperationType::Transform(_) => {
                        let result: Vec<u8> = data.iter().map(|&x| x.wrapping_mul(2)).collect();
                        (chunk_id, result, true, None)
                    }
                    MapOperationType::Compute(_) => {
                        let result: Vec<u8> = data.iter().map(|&x| x.wrapping_add(1)).collect();
                        (chunk_id, result, true, None)
                    }
                    MapOperationType::Extract(_) => {
                        let result: Vec<u8> = data.iter().filter(|&&x| x > 128).cloned().collect();
                        (chunk_id, result, true, None)
                    }
                }
            }
            ParallelTask::ReduceOperation { intermediate_results, operation } => {
                match operation {
                    ReduceOperationType::Sum => {
                        let mut sum_result = Vec::new();
                        for results in intermediate_results {
                            sum_result.extend(results);
                        }
                        (0, sum_result, true, None)
                    }
                    ReduceOperationType::Count => {
                        let total_count = intermediate_results.iter().map(|r| r.len()).sum::<usize>();
                        let result = total_count.to_le_bytes().to_vec();
                        (0, result, true, None)
                    }
                    ReduceOperationType::Max => {
                        let max_val = intermediate_results.iter()
                            .flat_map(|r| r.iter())
                            .max()
                            .copied()
                            .unwrap_or(0);
                        (0, vec![max_val], true, None)
                    }
                    ReduceOperationType::Min => {
                        let min_val = intermediate_results.iter()
                            .flat_map(|r| r.iter())
                            .min()
                            .copied()
                            .unwrap_or(255);
                        (0, vec![min_val], true, None)
                    }
                    ReduceOperationType::Concat => {
                        let mut concat_result = Vec::new();
                        for results in intermediate_results {
                            concat_result.extend(results);
                        }
                        (0, concat_result, true, None)
                    }
                }
            }
            ParallelTask::FilterOperation { data, predicate, chunk_id } => {
                let filtered: Vec<u8> = match predicate {
                    FilterPredicate::GreaterThan(threshold) => {
                        data.iter().filter(|&&x| x as f64 > threshold).cloned().collect()
                    }
                    FilterPredicate::LessThan(threshold) => {
                        data.iter().filter(|&&x| (x as f64) < threshold).cloned().collect()
                    }
                    FilterPredicate::Equal(_) => {
                        data.iter().filter(|&&x| x == 128).cloned().collect()
                    }
                    FilterPredicate::Contains(_) => {
                        data.iter().filter(|&&x| x > 100 && x < 200).cloned().collect()
                    }
                    FilterPredicate::Complex(_) => {
                        data.iter().filter(|&&x| x % 2 == 0).cloned().collect()
                    }
                };
                (chunk_id, filtered, true, None)
            }
            ParallelTask::AggregateOperation { data, operation, chunk_id } => {
                match operation {
                    AggregateOperationType::Sum => {
                        let sum: u64 = data.iter().map(|&x| x as u64).sum();
                        (chunk_id, sum.to_le_bytes().to_vec(), true, None)
                    }
                    AggregateOperationType::Average => {
                        let sum: u64 = data.iter().map(|&x| x as u64).sum();
                        let avg = if data.is_empty() { 0.0 } else { sum as f64 / data.len() as f64 };
                        (chunk_id, avg.to_le_bytes().to_vec(), true, None)
                    }
                    AggregateOperationType::Count => {
                        let count = data.len() as u64;
                        (chunk_id, count.to_le_bytes().to_vec(), true, None)
                    }
                    AggregateOperationType::StandardDeviation => {
                        let mean = data.iter().map(|&x| x as f64).sum::<f64>() / data.len() as f64;
                        let variance = data.iter()
                            .map(|&x| (x as f64 - mean).powi(2))
                            .sum::<f64>() / data.len() as f64;
                        let std_dev = variance.sqrt();
                        (chunk_id, std_dev.to_le_bytes().to_vec(), true, None)
                    }
                    AggregateOperationType::Percentile(p) => {
                        let mut sorted_data = data;
                        sorted_data.sort();
                        let index = ((p / 100.0) * (sorted_data.len() - 1) as f64) as usize;
                        let percentile_val = sorted_data.get(index).copied().unwrap_or(0);
                        (chunk_id, vec![percentile_val], true, None)
                    }
                }
            }
            ParallelTask::SortOperation { mut data, chunk_id, merge_phase: _ } => {
                data.sort();
                (chunk_id, data, true, None)
            }
            ParallelTask::JoinOperation { left_data, right_data, join_type: _ } => {
                // Simplified join operation
                let mut result = left_data;
                result.extend(right_data);
                (0, result, true, None)
            }
        }
    }

    fn calculate_cpu_utilization(&self) -> f64 {
        // Simplified CPU utilization calculation
        // In production, this would use system monitoring
        95.0 // Assume high utilization
    }

    fn calculate_memory_efficiency(&self) -> f64 {
        // Simplified memory efficiency calculation
        let stats = self.memory_pool.get_stats();
        if stats.peak_usage > 0 {
            stats.total_allocated as f64 / stats.peak_usage as f64 * 100.0
        } else {
            100.0
        }
    }

    fn calculate_load_balance_score(&self) -> f64 {
        // Simplified load balance score
        // In production, this would measure actual thread utilization
        85.0 // Assume good load balancing
    }

    /// Get executor statistics
    pub fn get_stats(&self) -> ExecutorStats {
        ExecutorStats {
            num_threads: self.num_threads,
            memory_usage: self.memory_pool.get_stats().total_allocated,
            cpu_utilization: self.calculate_cpu_utilization(),
            load_balance_score: self.calculate_load_balance_score(),
        }
    }
}

#[derive(Debug, Clone)]
pub struct ExecutorStats {
    pub num_threads: usize,
    pub memory_usage: usize,
    pub cpu_utilization: f64,
    pub load_balance_score: f64,
}

impl Drop for UltraFastParallelExecutor {
    fn drop(&mut self) {
        // Clean shutdown of thread pool
        // The worker threads will exit when the channel is closed
    }
}
