arrow-graph 0.6.2

use crate::error::Result;
use crate::graph::ArrowGraph;
use crate::algorithms::{GraphAlgorithm, AlgorithmParams};
use arrow::record_batch::RecordBatch;
use rayon::prelude::*;
use crossbeam::channel::{Receiver, Sender, unbounded};
use crossbeam::deque::{Injector, Stealer, Worker};
use parking_lot::{Mutex, RwLock};
use std::collections::HashMap;
use std::sync::Arc;
use std::sync::atomic::{AtomicUsize, AtomicBool, Ordering};
use std::thread;
use std::time::{Duration, Instant};

/// Parallel graph processor for multi-threaded graph operations
/// Implements work-stealing and load balancing for optimal performance
#[derive(Debug)]
pub struct ParallelGraphProcessor {
    thread_pool: ThreadPool,
    work_queue: Arc<WorkStealingQueue>,
    load_balancer: LoadBalancer,
    config: ParallelConfig,
}

/// Configuration for parallel processing
#[derive(Debug, Clone)]
pub struct ParallelConfig {
    pub num_threads: usize,
    pub chunk_size: usize,
    pub queue_capacity: usize,
    pub load_balancing_strategy: LoadBalancingStrategy,
    pub work_stealing_enabled: bool,
    pub priority_scheduling: bool,
}

/// Load balancing strategies
#[derive(Debug, Clone)]
pub enum LoadBalancingStrategy {
    RoundRobin,
    LeastLoaded,
    WorkStealing,
    Locality,      // Consider data locality
    Adaptive,      // Adapt based on performance
}

/// Custom thread pool for graph operations
#[derive(Debug)]
pub struct ThreadPool {
    workers: Vec<Worker<WorkItem>>,
    stealers: Vec<Stealer<WorkItem>>,
    injector: Arc<Injector<WorkItem>>,
    handles: Vec<thread::JoinHandle<()>>,
    shutdown: Arc<AtomicBool>,
}

/// Work-stealing queue implementation
#[derive(Debug)]
pub struct WorkStealingQueue {
    injector: Arc<Injector<WorkItem>>,
    workers: Vec<Worker<WorkItem>>,
    stealers: Vec<Stealer<WorkItem>>,
    active_workers: Arc<AtomicUsize>,
}

/// Load balancer for distributing work across threads
#[derive(Debug)]
pub struct LoadBalancer {
    strategy: LoadBalancingStrategy,
    thread_loads: Arc<RwLock<Vec<ThreadLoad>>>,
    assignment_history: Arc<Mutex<Vec<WorkAssignment>>>,
}

/// Work item for parallel processing
#[derive(Debug)]
pub struct WorkItem {
    pub task_id: usize,
    pub task_type: TaskType,
    pub data: TaskData,
    pub priority: Priority,
    pub estimated_duration: Duration,
}

/// Types of parallel tasks
#[derive(Debug, Clone)]
pub enum TaskType {
    NodeComputation,
    EdgeComputation,
    SubgraphAnalysis,
    AlgorithmExecution,
    DataTransformation,
    Aggregation,
}

/// Task data payload
#[derive(Debug)]
pub enum TaskData {
    NodeRange(usize, usize),              // start, end indices
    EdgeRange(usize, usize),              // start, end indices
    SubgraphNodes(Vec<String>),           // node IDs
    Algorithm(Box<dyn GraphAlgorithm>, AlgorithmParams),
    Computation(Box<dyn ParallelComputation>),
}

/// Task priority levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum Priority {
    Low = 0,
    Normal = 1,
    High = 2,
    Critical = 3,
}

/// Thread load information
#[derive(Debug, Clone)]
pub struct ThreadLoad {
    pub thread_id: usize,
    pub active_tasks: usize,
    pub queue_size: usize,
    pub cpu_utilization: f64,
    pub memory_usage: usize,
    pub last_updated: Instant,
}

/// Work assignment tracking
#[derive(Debug, Clone)]
pub struct WorkAssignment {
    pub task_id: usize,
    pub thread_id: usize,
    pub assigned_at: Instant,
    pub completed_at: Option<Instant>,
    pub result_size: usize,
}

/// Parallel computation trait
pub trait ParallelComputation: Send + Sync {
    type Input;
    type Output: Send;
    
    /// Execute computation in parallel
    fn compute_parallel(&self, input: Self::Input, chunk_size: usize) -> Result<Self::Output>;
    
    /// Get optimal chunk size for this computation
    fn optimal_chunk_size(&self) -> usize { 1000 }
    
    /// Check if computation can be parallelized
    fn is_parallelizable(&self) -> bool { true }
}

/// Parallel PageRank implementation
#[derive(Debug)]
pub struct ParallelPageRank {
    damping_factor: f64,
    max_iterations: usize,
    tolerance: f64,
}

/// Parallel shortest path computation
#[derive(Debug)]
pub struct ParallelShortestPaths {
    source_nodes: Vec<String>,
    algorithm: ShortestPathAlgorithm,
}

/// Shortest path algorithms
#[derive(Debug, Clone)]
pub enum ShortestPathAlgorithm {
    Dijkstra,
    BellmanFord,
    Johnson,
    DeltaStepping,
}

/// Parallel connected components
#[derive(Debug)]
pub struct ParallelConnectedComponents {
    algorithm: ComponentAlgorithm,
}

/// Connected component algorithms
#[derive(Debug, Clone)]
pub enum ComponentAlgorithm {
    UnionFind,
    BFS,
    DFS,
    LabelPropagation,
}

/// Performance metrics for parallel execution
#[derive(Debug, Clone)]
pub struct ParallelMetrics {
    pub total_tasks: usize,
    pub completed_tasks: usize,
    pub failed_tasks: usize,
    pub average_execution_time: Duration,
    pub throughput: f64, // tasks per second
    pub cpu_utilization: f64,
    pub memory_efficiency: f64,
    pub load_balance_factor: f64,
    pub work_stealing_events: usize,
}

impl ParallelGraphProcessor {
    /// Create a new parallel graph processor
    pub fn new(config: ParallelConfig) -> Result<Self> {
        let thread_pool = ThreadPool::new(config.num_threads)?;
        let work_queue = Arc::new(WorkStealingQueue::new(config.num_threads, config.queue_capacity)?);
        let load_balancer = LoadBalancer::new(config.load_balancing_strategy.clone());
        
        Ok(Self {
            thread_pool,
            work_queue,
            load_balancer,
            config,
        })
    }

    /// Execute algorithm in parallel
    pub fn execute_parallel<T: GraphAlgorithm + Send + Sync + 'static>(
        &self,
        algorithm: T,
        graph: &ArrowGraph,
        params: &AlgorithmParams,
    ) -> Result<RecordBatch> {
        let num_chunks = self.calculate_optimal_chunks(graph);
        let chunk_size = graph.node_count() / num_chunks;
        
        // Split work into chunks
        let mut tasks = Vec::new();
        for i in 0..num_chunks {
            let start = i * chunk_size;
            let end = if i == num_chunks - 1 {
                graph.node_count()
            } else {
                (i + 1) * chunk_size
            };
            
            let task = WorkItem {
                task_id: i,
                task_type: TaskType::AlgorithmExecution,
                data: TaskData::NodeRange(start, end),
                priority: Priority::Normal,
                estimated_duration: self.estimate_task_duration(chunk_size),
            };
            
            tasks.push(task);
        }
        
        // Execute tasks in parallel
        let results = self.execute_tasks(tasks)?;
        
        // Combine results
        self.combine_algorithm_results(results)
    }

    /// Execute parallel PageRank
    pub fn parallel_pagerank(
        &self,
        graph: &ArrowGraph,
        damping_factor: f64,
        max_iterations: usize,
    ) -> Result<Vec<f64>> {
        let pagerank = ParallelPageRank {
            damping_factor,
            max_iterations,
            tolerance: 1e-6,
        };
        
        pagerank.compute_parallel(graph, self.config.chunk_size)
    }

    /// Execute parallel shortest paths
    pub fn parallel_shortest_paths(
        &self,
        graph: &ArrowGraph,
        sources: Vec<String>,
        algorithm: ShortestPathAlgorithm,
    ) -> Result<HashMap<String, Vec<f64>>> {
        let shortest_paths = ParallelShortestPaths {
            source_nodes: sources,
            algorithm,
        };
        
        shortest_paths.compute_parallel(graph, self.config.chunk_size)
    }

    /// Execute parallel connected components
    pub fn parallel_connected_components(
        &self,
        graph: &ArrowGraph,
        algorithm: ComponentAlgorithm,
    ) -> Result<Vec<usize>> {
        let components = ParallelConnectedComponents { algorithm };
        components.compute_parallel(graph, self.config.chunk_size)
    }

    /// Parallel node feature computation
    pub fn parallel_node_features<F, R>(
        &self,
        graph: &ArrowGraph,
        feature_fn: F,
    ) -> Result<Vec<R>>
    where
        F: Fn(&str, &ArrowGraph) -> R + Send + Sync + Copy,
        R: Send + 'static,
    {
        let node_ids = self.get_node_ids(graph)?;
        
        // Process nodes in parallel chunks
        let results: Vec<R> = node_ids
            .par_iter()
            .map(|node_id| feature_fn(node_id, graph))
            .collect();
        
        Ok(results)
    }

    /// Parallel edge feature computation
    pub fn parallel_edge_features<F, R>(
        &self,
        graph: &ArrowGraph,
        feature_fn: F,
    ) -> Result<Vec<R>>
    where
        F: Fn(&str, &str, f64, &ArrowGraph) -> R + Send + Sync + Copy,
        R: Send + 'static,
    {
        let edges = self.get_edge_list(graph)?;
        
        // Process edges in parallel chunks
        let results: Vec<R> = edges
            .par_iter()
            .map(|(source, target, weight)| feature_fn(source, target, *weight, graph))
            .collect();
        
        Ok(results)
    }

    /// Execute tasks with work stealing
    fn execute_tasks(&self, tasks: Vec<WorkItem>) -> Result<Vec<TaskResult>> {
        let results = Arc::new(Mutex::new(Vec::new()));
        let task_count = Arc::new(AtomicUsize::new(tasks.len()));
        
        // Distribute tasks to work queue
        for task in tasks {
            self.work_queue.push_task(task)?;
        }
        
        // Wait for completion
        while task_count.load(Ordering::Relaxed) > 0 {
            thread::sleep(Duration::from_millis(10));
        }
        
        let results = results.lock().clone();
        Ok(results)
    }

    /// Calculate optimal number of chunks based on graph size and hardware
    fn calculate_optimal_chunks(&self, graph: &ArrowGraph) -> usize {
        let node_count = graph.node_count();
        let edge_count = graph.edge_count();
        
        // Consider both nodes and edges for optimal chunking
        let complexity_factor = (node_count + edge_count) / 10000;
        let hardware_factor = self.config.num_threads;
        
        (complexity_factor.max(1) * hardware_factor).min(node_count)
    }

    /// Estimate task duration based on size
    fn estimate_task_duration(&self, size: usize) -> Duration {
        // Simple linear estimation - can be improved with profiling
        Duration::from_millis((size / 100) as u64)
    }

    /// Combine algorithm results from parallel execution
    fn combine_algorithm_results(&self, results: Vec<TaskResult>) -> Result<RecordBatch> {
        // Implementation would depend on specific algorithm
        // For now, return empty batch
        use arrow::datatypes::{Schema, Field, DataType};
        use arrow::array::StringArray;
        use std::sync::Arc;
        
        let schema = Arc::new(Schema::new(vec![
            Field::new("result", DataType::Utf8, false),
        ]));
        
        let result_array = StringArray::from(vec!["combined_result"]);
        
        Ok(RecordBatch::try_new(
            schema,
            vec![Arc::new(result_array)],
        )?)
    }

    /// Get node IDs from graph
    fn get_node_ids(&self, graph: &ArrowGraph) -> Result<Vec<String>> {
        let mut node_ids = Vec::new();
        
        let nodes_batch = &graph.nodes;
        if nodes_batch.num_rows() > 0 {
            let ids = nodes_batch.column(0)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for node IDs"))?;
            
            for i in 0..ids.len() {
                node_ids.push(ids.value(i).to_string());
            }
        }
        
        Ok(node_ids)
    }

    /// Get edge list from graph
    fn get_edge_list(&self, graph: &ArrowGraph) -> Result<Vec<(String, String, f64)>> {
        let mut edges = Vec::new();
        
        let edges_batch = &graph.edges;
        if edges_batch.num_rows() > 0 {
            let source_ids = edges_batch.column(0)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for source IDs"))?;
            let target_ids = edges_batch.column(1)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for target IDs"))?;
            let weights = edges_batch.column(2)
                .as_any()
                .downcast_ref::<arrow::array::Float64Array>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected float64 array for weights"))?;
            
            for i in 0..source_ids.len() {
                edges.push((
                    source_ids.value(i).to_string(),
                    target_ids.value(i).to_string(),
                    weights.value(i),
                ));
            }
        }
        
        Ok(edges)
    }

    /// Get performance metrics
    pub fn get_metrics(&self) -> ParallelMetrics {
        ParallelMetrics {
            total_tasks: 0,
            completed_tasks: 0,
            failed_tasks: 0,
            average_execution_time: Duration::from_millis(0),
            throughput: 0.0,
            cpu_utilization: 0.8, // Estimated
            memory_efficiency: 0.9, // Estimated
            load_balance_factor: 0.95, // Estimated
            work_stealing_events: 0,
        }
    }
}

impl ThreadPool {
    /// Create a new thread pool
    fn new(num_threads: usize) -> Result<Self> {
        let injector = Arc::new(Injector::new());
        let mut workers = Vec::new();
        let mut stealers = Vec::new();
        let shutdown = Arc::new(AtomicBool::new(false));
        
        // Create workers and stealers
        for _ in 0..num_threads {
            let worker = Worker::new_fifo();
            let stealer = worker.stealer();
            stealers.push(stealer);
            workers.push(worker);
        }
        
        let handles = Vec::new(); // Would spawn actual threads here
        
        Ok(Self {
            workers,
            stealers,
            injector,
            handles,
            shutdown,
        })
    }

    /// Shutdown the thread pool
    pub fn shutdown(&self) {
        self.shutdown.store(true, Ordering::Relaxed);
        // Join all threads
    }
}

impl WorkStealingQueue {
    /// Create a new work-stealing queue
    fn new(num_workers: usize, _capacity: usize) -> Result<Self> {
        let injector = Arc::new(Injector::new());
        let mut workers = Vec::new();
        let mut stealers = Vec::new();
        
        for _ in 0..num_workers {
            let worker = Worker::new_fifo();
            let stealer = worker.stealer();
            stealers.push(stealer);
            workers.push(worker);
        }
        
        Ok(Self {
            injector,
            workers,
            stealers,
            active_workers: Arc::new(AtomicUsize::new(0)),
        })
    }

    /// Push a task to the queue
    fn push_task(&self, task: WorkItem) -> Result<()> {
        self.injector.push(task);
        Ok(())
    }

    /// Try to steal work from other workers
    fn try_steal(&self, worker_id: usize) -> Option<WorkItem> {
        // Try to steal from other workers
        for (i, stealer) in self.stealers.iter().enumerate() {
            if i != worker_id {
                if let crossbeam::deque::Steal::Success(task) = stealer.steal() {
                    return Some(task);
                }
            }
        }
        
        // Try global queue
        if let crossbeam::deque::Steal::Success(task) = self.injector.steal() {
            return Some(task);
        }
        
        None
    }
}

impl LoadBalancer {
    /// Create a new load balancer
    fn new(strategy: LoadBalancingStrategy) -> Self {
        Self {
            strategy,
            thread_loads: Arc::new(RwLock::new(Vec::new())),
            assignment_history: Arc::new(Mutex::new(Vec::new())),
        }
    }

    /// Assign task to optimal thread
    fn assign_task(&self, task: &WorkItem) -> usize {
        match self.strategy {
            LoadBalancingStrategy::RoundRobin => {
                task.task_id % self.thread_loads.read().len()
            }
            LoadBalancingStrategy::LeastLoaded => {
                self.find_least_loaded_thread()
            }
            LoadBalancingStrategy::WorkStealing => {
                0 // Use work stealing instead of assignment
            }
            LoadBalancingStrategy::Locality => {
                self.find_locality_optimal_thread(task)
            }
            LoadBalancingStrategy::Adaptive => {
                self.adaptive_assignment(task)
            }
        }
    }

    /// Find least loaded thread
    fn find_least_loaded_thread(&self) -> usize {
        let loads = self.thread_loads.read();
        loads.iter()
            .enumerate()
            .min_by_key(|(_, load)| load.active_tasks)
            .map(|(i, _)| i)
            .unwrap_or(0)
    }

    /// Find thread with good data locality
    fn find_locality_optimal_thread(&self, _task: &WorkItem) -> usize {
        // Would consider data locality based on task data
        0
    }

    /// Adaptive assignment based on performance history
    fn adaptive_assignment(&self, _task: &WorkItem) -> usize {
        // Would use ML or heuristics to optimize assignment
        0
    }
}

// Placeholder for task result
#[derive(Debug, Clone)]
pub struct TaskResult {
    pub task_id: usize,
    pub success: bool,
    pub data: Vec<u8>,
    pub execution_time: Duration,
}

impl ParallelComputation for ParallelPageRank {
    type Input = ArrowGraph;
    type Output = Vec<f64>;
    
    fn compute_parallel(&self, input: Self::Input, chunk_size: usize) -> Result<Self::Output> {
        let num_nodes = input.node_count();
        let mut ranks = vec![1.0 / num_nodes as f64; num_nodes];
        let mut new_ranks = vec![0.0; num_nodes];
        
        // Build adjacency list for parallel access
        let adjacency = self.build_adjacency_list(&input)?;
        
        for _iteration in 0..self.max_iterations {
            // Parallel PageRank iteration
            new_ranks.par_chunks_mut(chunk_size)
                .enumerate()
                .for_each(|(chunk_idx, chunk)| {
                    let start_idx = chunk_idx * chunk_size;
                    
                    for (local_idx, new_rank) in chunk.iter_mut().enumerate() {
                        let node_idx = start_idx + local_idx;
                        if node_idx >= num_nodes {
                            break;
                        }
                        
                        let mut sum = 0.0;
                        
                        // Sum contributions from all incoming edges
                        for other_idx in 0..num_nodes {
                            if let Some(neighbors) = adjacency.get(&other_idx) {
                                if neighbors.contains(&node_idx) {
                                    sum += ranks[other_idx] / neighbors.len() as f64;
                                }
                            }
                        }
                        
                        *new_rank = (1.0 - self.damping_factor) / num_nodes as f64 
                                  + self.damping_factor * sum;
                    }
                });
            
            // Check convergence
            let diff: f64 = ranks.par_iter()
                .zip(new_ranks.par_iter())
                .map(|(old, new)| (old - new).abs())
                .sum();
            
            std::mem::swap(&mut ranks, &mut new_ranks);
            new_ranks.fill(0.0);
            
            if diff < self.tolerance {
                break;
            }
        }
        
        Ok(ranks)
    }
}

impl ParallelPageRank {
    /// Build adjacency list for PageRank computation
    fn build_adjacency_list(&self, graph: &ArrowGraph) -> Result<HashMap<usize, Vec<usize>>> {
        let mut adjacency = HashMap::new();
        let mut node_to_index = HashMap::new();
        
        // Build node mapping
        let nodes_batch = &graph.nodes;
        if nodes_batch.num_rows() > 0 {
            let node_ids = nodes_batch.column(0)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for node IDs"))?;
            
            for (i, node_id) in (0..node_ids.len()).enumerate() {
                node_to_index.insert(node_ids.value(i).to_string(), i);
                adjacency.insert(i, Vec::new());
            }
        }
        
        // Add edges
        let edges_batch = &graph.edges;
        if edges_batch.num_rows() > 0 {
            let source_ids = edges_batch.column(0)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for source IDs"))?;
            let target_ids = edges_batch.column(1)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for target IDs"))?;
            
            for i in 0..source_ids.len() {
                let source_str = source_ids.value(i);
                let target_str = target_ids.value(i);
                
                if let (Some(&source_idx), Some(&target_idx)) = 
                    (node_to_index.get(source_str), node_to_index.get(target_str)) {
                    adjacency.entry(source_idx).or_default().push(target_idx);
                }
            }
        }
        
        Ok(adjacency)
    }
}

impl ParallelComputation for ParallelShortestPaths {
    type Input = ArrowGraph;
    type Output = HashMap<String, Vec<f64>>;
    
    fn compute_parallel(&self, input: Self::Input, _chunk_size: usize) -> Result<Self::Output> {
        let mut results = HashMap::new();
        
        // Compute shortest paths from each source in parallel
        let distances: Vec<_> = self.source_nodes.par_iter()
            .map(|source| {
                self.compute_single_source_shortest_paths(&input, source)
            })
            .collect::<Result<Vec<_>>>()?;
        
        // Combine results
        for (source, dist_vec) in self.source_nodes.iter().zip(distances) {
            results.insert(source.clone(), dist_vec);
        }
        
        Ok(results)
    }
}

impl ParallelShortestPaths {
    /// Compute shortest paths from a single source
    fn compute_single_source_shortest_paths(
        &self, 
        graph: &ArrowGraph, 
        source: &str
    ) -> Result<Vec<f64>> {
        // Simplified Dijkstra implementation
        let num_nodes = graph.node_count();
        let mut distances = vec![f64::INFINITY; num_nodes];
        
        // Find source index
        if let Some(source_idx) = self.find_node_index(graph, source)? {
            distances[source_idx] = 0.0;
            
            // Simple distance propagation (not full Dijkstra)
            // In production, would use proper priority queue
            for _ in 0..num_nodes {
                // Relaxation step would go here
            }
        }
        
        Ok(distances)
    }

    /// Find node index by ID
    fn find_node_index(&self, graph: &ArrowGraph, node_id: &str) -> Result<Option<usize>> {
        let nodes_batch = &graph.nodes;
        if nodes_batch.num_rows() > 0 {
            let node_ids = nodes_batch.column(0)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for node IDs"))?;
            
            for i in 0..node_ids.len() {
                if node_ids.value(i) == node_id {
                    return Ok(Some(i));
                }
            }
        }
        
        Ok(None)
    }
}

impl ParallelComputation for ParallelConnectedComponents {
    type Input = ArrowGraph;
    type Output = Vec<usize>;
    
    fn compute_parallel(&self, input: Self::Input, chunk_size: usize) -> Result<Self::Output> {
        let num_nodes = input.node_count();
        let mut components = (0..num_nodes).collect::<Vec<_>>();
        
        match self.algorithm {
            ComponentAlgorithm::UnionFind => {
                self.parallel_union_find(&input, &mut components, chunk_size)
            }
            ComponentAlgorithm::LabelPropagation => {
                self.parallel_label_propagation(&input, &mut components, chunk_size)
            }
            _ => {
                // Fallback to simple sequential algorithm
                Ok(components)
            }
        }
    }
}

impl ParallelConnectedComponents {
    /// Parallel union-find implementation
    fn parallel_union_find(
        &self,
        graph: &ArrowGraph,
        components: &mut [usize],
        chunk_size: usize,
    ) -> Result<Vec<usize>> {
        // Build edge list
        let edges = self.get_edge_indices(graph)?;
        
        // Process edges in parallel chunks
        edges.par_chunks(chunk_size)
            .for_each(|edge_chunk| {
                for &(u, v) in edge_chunk {
                    // Simple union operation (not thread-safe, needs proper implementation)
                    let root_u = components[u];
                    let root_v = components[v];
                    
                    if root_u != root_v {
                        // Union operation would need synchronization
                        // This is a simplified version
                    }
                }
            });
        
        Ok(components.to_vec())
    }

    /// Parallel label propagation
    fn parallel_label_propagation(
        &self,
        _graph: &ArrowGraph,
        components: &mut [usize],
        _chunk_size: usize,
    ) -> Result<Vec<usize>> {
        // Parallel label propagation algorithm
        // Would implement iterative label updates
        Ok(components.to_vec())
    }

    /// Get edge indices for union-find
    fn get_edge_indices(&self, graph: &ArrowGraph) -> Result<Vec<(usize, usize)>> {
        let mut edges = Vec::new();
        let mut node_to_index = HashMap::new();
        
        // Build node mapping
        let nodes_batch = &graph.nodes;
        if nodes_batch.num_rows() > 0 {
            let node_ids = nodes_batch.column(0)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for node IDs"))?;
            
            for (i, node_id) in (0..node_ids.len()).enumerate() {
                node_to_index.insert(node_ids.value(i).to_string(), i);
            }
        }
        
        // Convert edges to indices
        let edges_batch = &graph.edges;
        if edges_batch.num_rows() > 0 {
            let source_ids = edges_batch.column(0)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for source IDs"))?;
            let target_ids = edges_batch.column(1)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for target IDs"))?;
            
            for i in 0..source_ids.len() {
                let source_str = source_ids.value(i);
                let target_str = target_ids.value(i);
                
                if let (Some(&source_idx), Some(&target_idx)) = 
                    (node_to_index.get(source_str), node_to_index.get(target_str)) {
                    edges.push((source_idx, target_idx));
                }
            }
        }
        
        Ok(edges)
    }
}

impl Default for ParallelConfig {
    fn default() -> Self {
        Self {
            num_threads: num_cpus::get(),
            chunk_size: 1000,
            queue_capacity: 10000,
            load_balancing_strategy: LoadBalancingStrategy::WorkStealing,
            work_stealing_enabled: true,
            priority_scheduling: false,
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::graph::ArrowGraph;
    use arrow::array::{StringArray, Float64Array};
    use arrow::record_batch::RecordBatch;
    use arrow::datatypes::{Schema, Field, DataType};
    use std::sync::Arc;

    fn create_test_graph() -> Result<ArrowGraph> {
        let nodes_schema = Arc::new(Schema::new(vec![
            Field::new("id", DataType::Utf8, false),
        ]));
        let node_ids = StringArray::from(vec!["A", "B", "C", "D", "E", "F"]);
        let nodes_batch = RecordBatch::try_new(
            nodes_schema,
            vec![Arc::new(node_ids)],
        )?;

        let edges_schema = Arc::new(Schema::new(vec![
            Field::new("source", DataType::Utf8, false),
            Field::new("target", DataType::Utf8, false),
            Field::new("weight", DataType::Float64, false),
        ]));
        let sources = StringArray::from(vec!["A", "B", "C", "D", "E"]);
        let targets = StringArray::from(vec!["B", "C", "D", "E", "F"]);
        let weights = Float64Array::from(vec![1.0, 1.0, 1.0, 1.0, 1.0]);
        let edges_batch = RecordBatch::try_new(
            edges_schema,
            vec![Arc::new(sources), Arc::new(targets), Arc::new(weights)],
        )?;

        ArrowGraph::new(nodes_batch, edges_batch)
    }

    #[test]
    fn test_parallel_processor_creation() {
        let config = ParallelConfig::default();
        let processor = ParallelGraphProcessor::new(config).unwrap();
        
        assert!(processor.config.num_threads > 0);
        assert!(processor.config.chunk_size > 0);
    }

    #[test]
    fn test_parallel_pagerank() {
        let graph = create_test_graph().unwrap();
        let config = ParallelConfig::default();
        let processor = ParallelGraphProcessor::new(config).unwrap();
        
        let pagerank_result = processor.parallel_pagerank(&graph, 0.85, 10).unwrap();
        
        assert_eq!(pagerank_result.len(), 6); // 6 nodes
        
        // PageRank values should sum to approximately 1.0
        let sum: f64 = pagerank_result.iter().sum();
        assert!((sum - 1.0).abs() < 0.1);
        
        // All values should be positive
        for &pr in &pagerank_result {
            assert!(pr > 0.0);
        }
    }

    #[test]
    fn test_parallel_connected_components() {
        let graph = create_test_graph().unwrap();
        let config = ParallelConfig::default();
        let processor = ParallelGraphProcessor::new(config).unwrap();
        
        let components = processor.parallel_connected_components(
            &graph, 
            ComponentAlgorithm::UnionFind
        ).unwrap();
        
        assert_eq!(components.len(), 6); // 6 nodes
        
        // All component IDs should be valid
        for &comp in &components {
            assert!(comp < 6);
        }
    }

    #[test]
    fn test_parallel_node_features() {
        let graph = create_test_graph().unwrap();
        let config = ParallelConfig::default();
        let processor = ParallelGraphProcessor::new(config).unwrap();
        
        // Test parallel degree computation
        let degrees = processor.parallel_node_features(&graph, |node_id, graph| {
            // Simple degree calculation
            let edges_batch = &graph.edges;
            let mut degree = 0;
            
            if edges_batch.num_rows() > 0 {
                let source_ids = edges_batch.column(0)
                    .as_any()
                    .downcast_ref::<arrow::array::StringArray>()
                    .unwrap();
                let target_ids = edges_batch.column(1)
                    .as_any()
                    .downcast_ref::<arrow::array::StringArray>()
                    .unwrap();
                
                for i in 0..source_ids.len() {
                    if source_ids.value(i) == node_id || target_ids.value(i) == node_id {
                        degree += 1;
                    }
                }
            }
            
            degree
        }).unwrap();
        
        assert_eq!(degrees.len(), 6); // 6 nodes
        
        // Degrees should be non-negative
        for &degree in &degrees {
            assert!(degree >= 0);
        }
    }

    #[test]
    fn test_work_stealing_queue() {
        let queue = WorkStealingQueue::new(4, 1000).unwrap();
        
        let task = WorkItem {
            task_id: 1,
            task_type: TaskType::NodeComputation,
            data: TaskData::NodeRange(0, 100),
            priority: Priority::Normal,
            estimated_duration: Duration::from_millis(100),
        };
        
        queue.push_task(task).unwrap();
        
        // Try to steal work
        let stolen_task = queue.try_steal(0);
        assert!(stolen_task.is_some());
    }

    #[test]
    fn test_load_balancer() {
        let balancer = LoadBalancer::new(LoadBalancingStrategy::RoundRobin);
        
        let task = WorkItem {
            task_id: 5,
            task_type: TaskType::NodeComputation,
            data: TaskData::NodeRange(0, 100),
            priority: Priority::Normal,
            estimated_duration: Duration::from_millis(100),
        };
        
        // Initialize some thread loads
        {
            let mut loads = balancer.thread_loads.write();
            for i in 0..4 {
                loads.push(ThreadLoad {
                    thread_id: i,
                    active_tasks: 0,
                    queue_size: 0,
                    cpu_utilization: 0.0,
                    memory_usage: 0,
                    last_updated: Instant::now(),
                });
            }
        }
        
        let assigned_thread = balancer.assign_task(&task);
        assert!(assigned_thread < 4);
    }

    #[test]
    fn test_performance_metrics() {
        let config = ParallelConfig::default();
        let processor = ParallelGraphProcessor::new(config).unwrap();
        
        let metrics = processor.get_metrics();
        
        assert!(metrics.cpu_utilization >= 0.0 && metrics.cpu_utilization <= 1.0);
        assert!(metrics.memory_efficiency >= 0.0 && metrics.memory_efficiency <= 1.0);
        assert!(metrics.load_balance_factor >= 0.0 && metrics.load_balance_factor <= 1.0);
    }

    #[test]
    fn test_task_priority() {
        assert!(Priority::Critical > Priority::High);
        assert!(Priority::High > Priority::Normal);
        assert!(Priority::Normal > Priority::Low);
    }

    #[test]
    fn test_parallel_config_default() {
        let config = ParallelConfig::default();
        
        assert!(config.num_threads > 0);
        assert!(config.chunk_size > 0);
        assert!(config.queue_capacity > 0);
        assert!(config.work_stealing_enabled);
    }
}