lens-core 1.0.0

//! Parallel Pipeline Executor with Async Overlap Processing
//!
//! Implements true stage parallelism and async overlap for maximum performance.
//! Replaces sequential stage execution with overlapping parallel processing.
//! 
//! Target: >40% latency reduction through parallelism per TODO.md

use super::{
    PipelineContext, PipelineData, PipelineConfig, PipelineStage, PipelineStageProcessor,
    PipelineError, memory::ZeroCopyBuffer, stopping::CrossShardStopper, learning::LearningStopModel,
    prefetch::PrefetchManager,
};
use crate::lsp::LspState;
use crate::search::SearchEngine;
use anyhow::Result;
use std::collections::{HashMap, HashSet};
use std::sync::Arc;
use std::time::{Duration, Instant};
use tokio::sync::{RwLock, Semaphore, mpsc, watch, Barrier};
use tokio::time::timeout;
use tokio::{select, spawn};
use tokio_util::sync::CancellationToken;
use tracing::{debug, error, info, warn};
use serde::{Deserialize, Serialize};

/// Enhanced parallel pipeline executor
pub struct ParallelPipelineExecutor {
    config: PipelineConfig,
    
    // Core engines with enhanced coordination
    search_engine: Arc<SearchEngine>,
    lsp_manager: Arc<LspState>,
    
    // Advanced optimization engines
    cross_shard_stopper: Arc<CrossShardStopper>,
    learning_model: Arc<LearningStopModel>,
    prefetch_manager: Arc<PrefetchManager>,
    
    // Stage coordination
    stage_coordinators: HashMap<PipelineStage, Arc<StageCoordinator>>,
    
    // Parallel execution control
    execution_barrier: Arc<Barrier>,
    stage_semaphores: HashMap<PipelineStage, Arc<Semaphore>>,
    
    // Fusion management
    fusion_controller: Arc<FusionController>,
    
    // Performance metrics
    metrics: Arc<RwLock<ParallelExecutionMetrics>>,
}

/// Stage coordinator for parallel execution
pub struct StageCoordinator {
    stage: PipelineStage,
    processor: Arc<dyn PipelineStageProcessor>,
    dependencies: HashSet<PipelineStage>,
    can_overlap: bool,
    fusion_group: Option<FusionGroupId>,
    concurrency_limit: Arc<Semaphore>,
    metrics: Arc<RwLock<StageMetrics>>,
}

/// Fusion controller for stage combining
pub struct FusionController {
    fusion_groups: HashMap<FusionGroupId, FusionGroup>,
    fusion_opportunities: HashMap<PipelineStage, Vec<PipelineStage>>,
    fusion_benefits: HashMap<FusionGroupId, FusionBenefit>,
    active_fusions: RwLock<HashMap<ExecutionId, Vec<FusionGroupId>>>,
}

/// Fusion group for combined stage execution
#[derive(Debug, Clone)]
pub struct FusionGroup {
    id: FusionGroupId,
    stages: Vec<PipelineStage>,
    fusion_strategy: FusionStrategy,
    expected_speedup: f64,
    memory_sharing: bool,
}

/// Fusion strategy for different stage combinations
#[derive(Debug, Clone, PartialEq)]
pub enum FusionStrategy {
    Pipeline,      // Stages execute in sequence within fusion
    DataParallel,  // Stages operate on different data slices
    TaskParallel,  // Stages execute completely parallel tasks
    Hybrid,        // Mix of pipeline and parallel
}

/// Fusion benefit analysis
#[derive(Debug, Clone)]
pub struct FusionBenefit {
    latency_reduction: f64,
    memory_savings: f64,
    cpu_efficiency: f64,
    cache_locality: f64,
    total_score: f64,
}

/// Parallel execution context with coordination
#[derive(Debug, Clone)]
pub struct ParallelExecutionContext {
    execution_id: ExecutionId,
    base_context: PipelineContext,
    
    // Coordination channels
    stage_completion_tx: mpsc::UnboundedSender<StageCompletion>,
    data_flow_tx: mpsc::UnboundedSender<DataFlow>,
    
    // Cancellation and timeout
    cancellation_token: CancellationToken,
    stage_timeouts: HashMap<PipelineStage, Instant>,
    
    // Resource management
    memory_budget: Arc<RwLock<MemoryBudget>>,
    cpu_budget: Arc<RwLock<CpuBudget>>,
}

/// Stage completion notification
#[derive(Debug, Clone)]
pub struct StageCompletion {
    stage: PipelineStage,
    execution_id: ExecutionId,
    success: bool,
    duration: Duration,
    output_data: Option<PipelineData>,
    error: Option<PipelineError>,
}

/// Data flow between stages
#[derive(Debug, Clone)]
pub struct DataFlow {
    from_stage: PipelineStage,
    to_stage: PipelineStage,
    data: PipelineData,
    flow_type: DataFlowType,
}

/// Type of data flow between stages
#[derive(Debug, Clone, PartialEq)]
pub enum DataFlowType {
    Direct,        // Direct stage-to-stage
    Broadcast,     // One-to-many
    Scatter,       // Split data across stages
    Gather,        // Collect data from stages
}

/// Memory budget tracking
#[derive(Debug, Clone)]
pub struct MemoryBudget {
    total_mb: f64,
    allocated_mb: f64,
    stage_allocations: HashMap<PipelineStage, f64>,
    fusion_savings: f64,
}

/// CPU budget tracking
#[derive(Debug, Clone)]
pub struct CpuBudget {
    total_time_ms: f64,
    allocated_time_ms: f64,
    stage_allocations: HashMap<PipelineStage, f64>,
    overlap_savings: f64,
}

/// Parallel execution metrics
#[derive(Debug, Default, Clone, Serialize, Deserialize)]
pub struct ParallelExecutionMetrics {
    pub total_executions: u64,
    pub parallel_executions: u64,
    pub fusion_executions: u64,
    pub overlap_executions: u64,
    
    pub avg_latency_ms: f64,
    pub avg_parallel_latency_ms: f64,
    pub latency_reduction_percent: f64,
    
    pub stage_parallelism: f64,
    pub fusion_effectiveness: f64,
    pub memory_efficiency: f64,
    pub cpu_utilization: f64,
    
    pub early_stopping_rate: f64,
    pub cache_hit_rate: f64,
    pub prefetch_accuracy: f64,
}

/// Stage-specific metrics
#[derive(Debug, Default, Clone)]
pub struct StageMetrics {
    pub executions: u64,
    pub parallel_executions: u64,
    pub avg_duration_ms: f64,
    pub overlap_time_saved_ms: f64,
    pub fusion_time_saved_ms: f64,
    pub memory_peak_mb: f64,
    pub throughput_ops_per_sec: f64,
}

// Type aliases
pub type FusionGroupId = u32;
pub type ExecutionId = u64;

impl ParallelPipelineExecutor {
    /// Create a new parallel pipeline executor
    pub async fn new(config: PipelineConfig) -> Result<Self> {
        // Initialize core engines
        let search_engine = Arc::new(SearchEngine::new(&config.max_concurrent.to_string()).await?);
        
        let lsp_state = Arc::new(LspState::new(crate::lsp::LspConfig {
            enabled: true,
            server_timeout_ms: config.max_latency_ms / 3,
            cache_ttl_hours: 24,
            max_concurrent_requests: config.max_concurrent,
            routing_percentage: 0.6, // 60% routing for better coverage
            ..Default::default()
        }));
        lsp_state.initialize().await?;
        
        // Initialize advanced optimization engines
        let stopper_config = crate::pipeline::stopping::StoppingConfig {
            confidence_threshold: 0.85,
            quality_threshold: 0.8,
            max_computation_budget: 0.7,
            num_shards: 4,
            ..Default::default()
        };
        let cross_shard_stopper = Arc::new(CrossShardStopper::new(stopper_config).await?);
        
        let learning_config = crate::pipeline::learning::LearningConfig {
            confidence_threshold: 0.85,
            min_training_samples: 20,
            ..Default::default()
        };
        let learning_model = Arc::new(LearningStopModel::new(learning_config).await?);
        
        let prefetch_config = crate::pipeline::prefetch::PrefetchConfig {
            similarity_threshold: 0.7,
            max_cached_visited_sets: 10000,
            ..Default::default()
        };
        let prefetch_manager = Arc::new(PrefetchManager::new(prefetch_config).await?);
        
        // Initialize stage coordinators
        let mut stage_coordinators = HashMap::new();
        let mut stage_semaphores = HashMap::new();
        
        // Query Analysis - can be parallelized and fused
        let query_coordinator = Arc::new(StageCoordinator::new(
            PipelineStage::QueryAnalysis,
            Arc::new(EnhancedQueryAnalysisStage::new(learning_model.clone())),
            HashSet::new(), // No dependencies
            true, // Can overlap
            Some(1), // Fusion group 1
            4, // Concurrency limit
        ).await);
        stage_coordinators.insert(PipelineStage::QueryAnalysis, query_coordinator);
        stage_semaphores.insert(PipelineStage::QueryAnalysis, Arc::new(Semaphore::new(4)));
        
        // LSP Routing - can overlap with analysis, fusion with search
        let mut lsp_deps = HashSet::new();
        lsp_deps.insert(PipelineStage::QueryAnalysis);
        let lsp_coordinator = Arc::new(StageCoordinator::new(
            PipelineStage::LspRouting,
            Arc::new(EnhancedLspRoutingStage::new(lsp_state.clone(), prefetch_manager.clone())),
            lsp_deps,
            true, // Can overlap
            Some(2), // Fusion group 2
            8, // Higher concurrency for LSP
        ).await);
        stage_coordinators.insert(PipelineStage::LspRouting, lsp_coordinator);
        stage_semaphores.insert(PipelineStage::LspRouting, Arc::new(Semaphore::new(8)));
        
        // Parallel Search - main computational stage
        let mut search_deps = HashSet::new();
        search_deps.insert(PipelineStage::LspRouting);
        let search_coordinator = Arc::new(StageCoordinator::new(
            PipelineStage::ParallelSearch,
            Arc::new(EnhancedParallelSearchStage::new(
                search_engine.clone(),
                lsp_state.clone(),
                cross_shard_stopper.clone(),
            )),
            search_deps,
            true, // Can overlap
            Some(2), // Fusion group 2 with LSP
            16, // High concurrency for search
        ).await);
        stage_coordinators.insert(PipelineStage::ParallelSearch, search_coordinator);
        stage_semaphores.insert(PipelineStage::ParallelSearch, Arc::new(Semaphore::new(16)));
        
        // Result Fusion - combines results with zero-copy
        let mut fusion_deps = HashSet::new();
        fusion_deps.insert(PipelineStage::ParallelSearch);
        let fusion_coordinator = Arc::new(StageCoordinator::new(
            PipelineStage::ResultFusion,
            Arc::new(EnhancedResultFusionStage::new()),
            fusion_deps,
            true, // Can overlap
            Some(3), // Fusion group 3
            4, // Medium concurrency
        ).await);
        stage_coordinators.insert(PipelineStage::ResultFusion, fusion_coordinator);
        stage_semaphores.insert(PipelineStage::ResultFusion, Arc::new(Semaphore::new(4)));
        
        // Post Processing - final cleanup and optimization
        let mut post_deps = HashSet::new();
        post_deps.insert(PipelineStage::ResultFusion);
        let post_coordinator = Arc::new(StageCoordinator::new(
            PipelineStage::PostProcess,
            Arc::new(EnhancedPostProcessStage::new(learning_model.clone())),
            post_deps,
            false, // Sequential for final processing
            Some(3), // Fusion group 3 with result fusion
            2, // Low concurrency
        ).await);
        stage_coordinators.insert(PipelineStage::PostProcess, post_coordinator);
        stage_semaphores.insert(PipelineStage::PostProcess, Arc::new(Semaphore::new(2)));
        
        // Create execution barrier (6 stages total)
        let execution_barrier = Arc::new(Barrier::new(6));
        
        // Initialize fusion controller
        let fusion_controller = Arc::new(FusionController::new());
        fusion_controller.initialize_fusion_groups().await?;
        
        let metrics = Arc::new(RwLock::new(ParallelExecutionMetrics::default()));
        
        info!("Initialized parallel pipeline executor with {} fusion groups", 
              fusion_controller.fusion_groups.len());
        
        Ok(Self {
            config,
            search_engine,
            lsp_manager: lsp_state,
            cross_shard_stopper,
            learning_model,
            prefetch_manager,
            stage_coordinators,
            execution_barrier,
            stage_semaphores,
            fusion_controller,
            metrics,
        })
    }
    
    /// Execute pipeline with parallel stages and fusion
    pub async fn execute_parallel(&self, context: PipelineContext) -> Result<PipelineData, PipelineError> {
        let start_time = Instant::now();
        let execution_id = self.generate_execution_id();
        
        debug!("Starting parallel execution {}", execution_id);
        
        // Check if we can use prefetch results
        let prefetch_result = self.prefetch_manager
            .process_query(&context.query, context.file_path.clone())
            .await
            .map_err(|e| PipelineError::FusionError {
                stage: PipelineStage::Input,
                message: format!("Prefetch failed: {}", e),
            })?;
        
        if prefetch_result.cache_hit {
            info!("Using prefetch cache for execution {}", execution_id);
            return self.create_cached_result(prefetch_result, context).await;
        }
        
        // Set up parallel execution context
        let (completion_tx, mut completion_rx) = mpsc::unbounded_channel::<StageCompletion>();
        let (data_flow_tx, mut data_flow_rx) = mpsc::unbounded_channel::<DataFlow>();
        let cancellation_token = CancellationToken::new();
        
        let parallel_context = ParallelExecutionContext {
            execution_id,
            base_context: context.clone(),
            stage_completion_tx: completion_tx,
            data_flow_tx,
            cancellation_token: cancellation_token.clone(),
            stage_timeouts: self.calculate_stage_timeouts(&context),
            memory_budget: Arc::new(RwLock::new(MemoryBudget::new(256.0))), // 256MB budget
            cpu_budget: Arc::new(RwLock::new(CpuBudget::new(context.timeout.as_millis() as f64))),
        };
        
        // Analyze fusion opportunities
        let fusion_plan = self.fusion_controller.create_fusion_plan(execution_id).await?;
        
        // Start parallel stage execution
        let stage_handles = self.spawn_parallel_stages(parallel_context.clone(), fusion_plan).await?;
        
        // Monitor execution progress
        let result = self.monitor_parallel_execution(
            parallel_context,
            stage_handles,
            &mut completion_rx,
            &mut data_flow_rx,
            cancellation_token,
        ).await?;
        
        // Update metrics
        let execution_time = start_time.elapsed();
        self.update_parallel_metrics(execution_time, result.is_ok()).await;
        
        result
    }
    
    /// Spawn parallel stage execution tasks
    async fn spawn_parallel_stages(
        &self,
        context: ParallelExecutionContext,
        fusion_plan: FusionPlan,
    ) -> Result<Vec<tokio::task::JoinHandle<Result<(), PipelineError>>>, PipelineError> {
        let mut handles = Vec::new();
        
        // Execute fused stages first
        for fusion_group in fusion_plan.fusion_groups {
            let handle = self.spawn_fused_stages(
                context.clone(),
                fusion_group,
            ).await?;
            handles.push(handle);
        }
        
        // Execute independent stages
        for stage in fusion_plan.independent_stages {
            let handle = self.spawn_single_stage(
                context.clone(),
                stage,
            ).await?;
            handles.push(handle);
        }
        
        Ok(handles)
    }
    
    /// Spawn fused stage execution
    async fn spawn_fused_stages(
        &self,
        context: ParallelExecutionContext,
        fusion_group: FusionGroup,
    ) -> Result<tokio::task::JoinHandle<Result<(), PipelineError>>, PipelineError> {
        let stages = fusion_group.stages.clone();
        let fusion_strategy = fusion_group.fusion_strategy.clone();
        let stage_coordinators = self.stage_coordinators.clone();
        let barrier = self.execution_barrier.clone();
        
        let handle = spawn(async move {
            debug!("Executing fused stages: {:?} with strategy {:?}", stages, fusion_strategy);
            
            match fusion_strategy {
                FusionStrategy::Pipeline => {
                    Self::execute_pipeline_fusion(context, stages, stage_coordinators, barrier).await
                }
                FusionStrategy::DataParallel => {
                    Self::execute_data_parallel_fusion(context, stages, stage_coordinators, barrier).await
                }
                FusionStrategy::TaskParallel => {
                    Self::execute_task_parallel_fusion(context, stages, stage_coordinators, barrier).await
                }
                FusionStrategy::Hybrid => {
                    Self::execute_hybrid_fusion(context, stages, stage_coordinators, barrier).await
                }
            }
        });
        
        Ok(handle)
    }
    
    /// Execute pipeline fusion (sequential within group)
    async fn execute_pipeline_fusion(
        mut context: ParallelExecutionContext,
        stages: Vec<PipelineStage>,
        coordinators: HashMap<PipelineStage, Arc<StageCoordinator>>,
        _barrier: Arc<Barrier>,
    ) -> Result<(), PipelineError> {
        let mut current_data = PipelineData::new(8192); // 8KB initial buffer
        
        for stage in stages {
            if let Some(coordinator) = coordinators.get(&stage) {
                // Check timeout
                if let Some(&deadline) = context.stage_timeouts.get(&stage) {
                    if Instant::now() > deadline {
                        return Err(PipelineError::StageTimeout {
                            stage,
                            elapsed_ms: context.base_context.elapsed().as_millis() as u64,
                        });
                    }
                }
                
                // Execute stage
                let stage_start = Instant::now();
                match coordinator.execute_stage(&context.base_context, current_data).await {
                    Ok(output_data) => {
                        current_data = output_data;
                        let duration = stage_start.elapsed();
                        
                        // Send completion notification
                        let completion = StageCompletion {
                            stage,
                            execution_id: context.execution_id,
                            success: true,
                            duration,
                            output_data: Some(current_data.clone()),
                            error: None,
                        };
                        
                        if context.stage_completion_tx.send(completion).is_err() {
                            warn!("Failed to send stage completion notification");
                        }
                        
                        debug!("Pipeline fusion stage {:?} completed in {}ms", stage, duration.as_millis());
                    }
                    Err(e) => {
                        let completion = StageCompletion {
                            stage,
                            execution_id: context.execution_id,
                            success: false,
                            duration: stage_start.elapsed(),
                            output_data: None,
                            error: Some(e.clone()),
                        };
                        
                        let _ = context.stage_completion_tx.send(completion);
                        return Err(e);
                    }
                }
            }
        }
        
        Ok(())
    }
    
    /// Execute data parallel fusion (split data across stages)
    async fn execute_data_parallel_fusion(
        context: ParallelExecutionContext,
        stages: Vec<PipelineStage>,
        coordinators: HashMap<PipelineStage, Arc<StageCoordinator>>,
        barrier: Arc<Barrier>,
    ) -> Result<(), PipelineError> {
        let initial_data = PipelineData::new(8192);
        let data_slices = Self::split_data_for_parallel_processing(&initial_data, stages.len());
        
        let mut handles = Vec::new();
        
        for (stage, data_slice) in stages.into_iter().zip(data_slices.into_iter()) {
            if let Some(coordinator) = coordinators.get(&stage).cloned() {
                let ctx = context.clone();
                let barrier_ref = barrier.clone();
                
                let handle = spawn(async move {
                    barrier_ref.wait().await;
                    
                    let stage_start = Instant::now();
                    let result = coordinator.execute_stage(&ctx.base_context, data_slice).await;
                    let duration = stage_start.elapsed();
                    
                    let completion = match result {
                        Ok(output_data) => StageCompletion {
                            stage,
                            execution_id: ctx.execution_id,
                            success: true,
                            duration,
                            output_data: Some(output_data),
                            error: None,
                        },
                        Err(e) => StageCompletion {
                            stage,
                            execution_id: ctx.execution_id,
                            success: false,
                            duration,
                            output_data: None,
                            error: Some(e.clone()),
                        },
                    };
                    
                    let _ = ctx.stage_completion_tx.send(completion);
                    result.map(|_| ())
                });
                
                handles.push(handle);
            }
        }
        
        // Wait for all parallel stages to complete
        for handle in handles {
            handle.await.map_err(|e| PipelineError::FusionError {
                stage: PipelineStage::Input,
                message: format!("Parallel stage execution failed: {}", e),
            })??;
        }
        
        Ok(())
    }
    
    /// Execute task parallel fusion (completely parallel)
    async fn execute_task_parallel_fusion(
        context: ParallelExecutionContext,
        stages: Vec<PipelineStage>,
        coordinators: HashMap<PipelineStage, Arc<StageCoordinator>>,
        barrier: Arc<Barrier>,
    ) -> Result<(), PipelineError> {
        let mut handles = Vec::new();
        let initial_data = PipelineData::new(8192);
        
        for stage in stages {
            if let Some(coordinator) = coordinators.get(&stage).cloned() {
                let ctx = context.clone();
                let data = initial_data.clone();
                let barrier_ref = barrier.clone();
                
                let handle = spawn(async move {
                    barrier_ref.wait().await;
                    
                    let stage_start = Instant::now();
                    let result = coordinator.execute_stage(&ctx.base_context, data).await;
                    let duration = stage_start.elapsed();
                    
                    let completion = match result {
                        Ok(output_data) => StageCompletion {
                            stage,
                            execution_id: ctx.execution_id,
                            success: true,
                            duration,
                            output_data: Some(output_data),
                            error: None,
                        },
                        Err(e) => StageCompletion {
                            stage,
                            execution_id: ctx.execution_id,
                            success: false,
                            duration,
                            output_data: None,
                            error: Some(e.clone()),
                        },
                    };
                    
                    let _ = ctx.stage_completion_tx.send(completion);
                    result.map(|_| ())
                });
                
                handles.push(handle);
            }
        }
        
        // Wait for all tasks to complete
        for handle in handles {
            handle.await.map_err(|e| PipelineError::FusionError {
                stage: PipelineStage::Input,
                message: format!("Task parallel execution failed: {}", e),
            })??;
        }
        
        Ok(())
    }
    
    /// Execute hybrid fusion (mix of strategies)
    async fn execute_hybrid_fusion(
        context: ParallelExecutionContext,
        stages: Vec<PipelineStage>,
        coordinators: HashMap<PipelineStage, Arc<StageCoordinator>>,
        barrier: Arc<Barrier>,
    ) -> Result<(), PipelineError> {
        // For hybrid fusion, we can analyze stages and apply the best strategy for each subset
        // This is a simplified implementation - real logic would be more complex
        
        let (pipeline_stages, parallel_stages): (Vec<_>, Vec<_>) = stages.into_iter()
            .partition(|stage| {
                coordinators.get(stage)
                    .map(|c| !c.can_overlap)
                    .unwrap_or(false)
            });
        
        // Execute pipeline stages first
        if !pipeline_stages.is_empty() {
            Self::execute_pipeline_fusion(
                context.clone(),
                pipeline_stages,
                coordinators.clone(),
                barrier.clone(),
            ).await?;
        }
        
        // Then execute parallel stages
        if !parallel_stages.is_empty() {
            Self::execute_task_parallel_fusion(
                context,
                parallel_stages,
                coordinators,
                barrier,
            ).await?;
        }
        
        Ok(())
    }
    
    /// Spawn single stage execution
    async fn spawn_single_stage(
        &self,
        context: ParallelExecutionContext,
        stage: PipelineStage,
    ) -> Result<tokio::task::JoinHandle<Result<(), PipelineError>>, PipelineError> {
        let coordinator = self.stage_coordinators.get(&stage)
            .ok_or_else(|| PipelineError::FusionError {
                stage,
                message: "Stage coordinator not found".to_string(),
            })?
            .clone();
        
        let handle = spawn(async move {
            let stage_start = Instant::now();
            let initial_data = PipelineData::new(8192);
            
            let result = coordinator.execute_stage(&context.base_context, initial_data).await;
            let duration = stage_start.elapsed();
            
            let completion = match result {
                Ok(output_data) => StageCompletion {
                    stage,
                    execution_id: context.execution_id,
                    success: true,
                    duration,
                    output_data: Some(output_data),
                    error: None,
                },
                Err(e) => StageCompletion {
                    stage,
                    execution_id: context.execution_id,
                    success: false,
                    duration,
                    output_data: None,
                    error: Some(e.clone()),
                },
            };
            
            let _ = context.stage_completion_tx.send(completion);
            result.map(|_| ())
        });
        
        Ok(handle)
    }
    
    /// Monitor parallel execution progress
    async fn monitor_parallel_execution(
        &self,
        context: ParallelExecutionContext,
        stage_handles: Vec<tokio::task::JoinHandle<Result<(), PipelineError>>>,
        completion_rx: &mut mpsc::UnboundedReceiver<StageCompletion>,
        data_flow_rx: &mut mpsc::UnboundedReceiver<DataFlow>,
        cancellation_token: CancellationToken,
    ) -> Result<PipelineData, PipelineError> {
        let mut completed_stages = HashSet::new();
        let mut stage_outputs = HashMap::new();
        let total_stages = self.stage_coordinators.len();
        
        // Set up timeout
        let timeout_future = timeout(context.base_context.timeout, async {
            loop {
                select! {
                    completion = completion_rx.recv() => {
                        if let Some(completion) = completion {
                            debug!("Stage {:?} completed: success={}", completion.stage, completion.success);
                            
                            if !completion.success {
                                if let Some(error) = completion.error {
                                    return Err(error);
                                }
                                return Err(PipelineError::FusionError {
                                    stage: completion.stage,
                                    message: "Stage execution failed".to_string(),
                                });
                            }
                            
                            completed_stages.insert(completion.stage);
                            if let Some(output) = completion.output_data {
                                stage_outputs.insert(completion.stage, output);
                            }
                            
                            // Check if all stages completed
                            if completed_stages.len() == total_stages {
                                return Ok(self.merge_stage_outputs(stage_outputs).await?);
                            }
                        }
                    }
                    
                    data_flow = data_flow_rx.recv() => {
                        if let Some(flow) = data_flow {
                            debug!("Data flow: {:?} -> {:?}", flow.from_stage, flow.to_stage);
                            // Handle data flow between stages
                        }
                    }
                    
                    _ = cancellation_token.cancelled() => {
                        warn!("Parallel execution cancelled");
                        return Err(PipelineError::DeadlineExceeded {
                            elapsed_ms: context.base_context.elapsed().as_millis() as u64,
                            deadline_ms: context.base_context.timeout.as_millis() as u64,
                        });
                    }
                }
            }
        });
        
        let result = timeout_future.await.map_err(|_| {
            cancellation_token.cancel();
            PipelineError::DeadlineExceeded {
                elapsed_ms: context.base_context.elapsed().as_millis() as u64,
                deadline_ms: context.base_context.timeout.as_millis() as u64,
            }
        })??;
        
        // Wait for all stage handles to complete
        for handle in stage_handles {
            if let Err(e) = handle.await {
                warn!("Stage handle completion error: {:?}", e);
            }
        }
        
        Ok(result)
    }
    
    /// Merge outputs from parallel stages
    async fn merge_stage_outputs(&self, outputs: HashMap<PipelineStage, PipelineData>) -> Result<PipelineData, PipelineError> {
        // Use the result fusion stage output if available
        if let Some(fusion_output) = outputs.get(&PipelineStage::ResultFusion) {
            return Ok(fusion_output.clone());
        }
        
        // Otherwise, merge available outputs (simplified)
        let mut merged_data = PipelineData::new(16384); // 16KB buffer
        
        for (stage, data) in outputs {
            debug!("Merging output from stage {:?} with {} segments", stage, data.segments.len());
            for segment in data.segments {
                merged_data.add_segment(segment);
            }
            
            // Merge metadata
            merged_data.metadata.stage_transitions += data.metadata.stage_transitions;
            merged_data.metadata.bytes_processed += data.metadata.bytes_processed;
            merged_data.metadata.cache_hits += data.metadata.cache_hits;
            merged_data.metadata.lsp_queries += data.metadata.lsp_queries;
            merged_data.metadata.search_results_count += data.metadata.search_results_count;
        }
        
        Ok(merged_data)
    }
    
    /// Helper methods
    fn generate_execution_id(&self) -> ExecutionId {
        std::time::SystemTime::now()
            .duration_since(std::time::UNIX_EPOCH)
            .unwrap()
            .as_nanos() as u64
    }
    
    fn calculate_stage_timeouts(&self, context: &PipelineContext) -> HashMap<PipelineStage, Instant> {
        let mut timeouts = HashMap::new();
        let base_deadline = context.sla_deadline;
        
        for stage in [
            PipelineStage::QueryAnalysis,
            PipelineStage::LspRouting,
            PipelineStage::ParallelSearch,
            PipelineStage::ResultFusion,
            PipelineStage::PostProcess,
        ] {
            let timeout_duration = self.config.stage_timeouts.calculate_timeout(
                self.config.max_latency_ms,
                stage,
            );
            timeouts.insert(stage, base_deadline - timeout_duration);
        }
        
        timeouts
    }
    
    fn split_data_for_parallel_processing(data: &PipelineData, num_splits: usize) -> Vec<PipelineData> {
        // Simplified data splitting - real implementation would be more sophisticated
        let mut splits = Vec::new();
        
        for i in 0..num_splits {
            let mut split_data = PipelineData::new(data.buffer.len() / num_splits);
            split_data.metadata.stage_transitions = data.metadata.stage_transitions;
            
            // Create a view of the original data
            if let Ok(segment) = data.create_view(
                i * (data.total_size() / num_splits),
                data.total_size() / num_splits,
            ) {
                split_data.add_segment(segment);
            }
            
            splits.push(split_data);
        }
        
        splits
    }
    
    async fn create_cached_result(
        &self,
        prefetch_result: crate::pipeline::prefetch::PrefetchResult,
        context: PipelineContext,
    ) -> Result<PipelineData, PipelineError> {
        let mut cached_data = PipelineData::new(4096);
        
        // Use prefetch hints to populate result
        cached_data.metadata.search_results_count = prefetch_result.result_hints.len();
        cached_data.metadata.cache_hits = 1;
        cached_data.stage = PipelineStage::Output;
        
        debug!("Created cached result with {} hints", prefetch_result.result_hints.len());
        Ok(cached_data)
    }
    
    async fn update_parallel_metrics(&self, execution_time: Duration, success: bool) {
        let mut metrics = self.metrics.write().await;
        metrics.total_executions += 1;
        
        if success {
            metrics.parallel_executions += 1;
        }
        
        let execution_time_ms = execution_time.as_millis() as f64;
        let total = metrics.total_executions as f64;
        
        metrics.avg_latency_ms = (metrics.avg_latency_ms * (total - 1.0) + execution_time_ms) / total;
        
        if success {
            let parallel_total = metrics.parallel_executions as f64;
            metrics.avg_parallel_latency_ms = 
                (metrics.avg_parallel_latency_ms * (parallel_total - 1.0) + execution_time_ms) / parallel_total;
        }
        
        // Update latency reduction percentage
        if metrics.avg_latency_ms > 0.0 {
            metrics.latency_reduction_percent = 
                (1.0 - metrics.avg_parallel_latency_ms / metrics.avg_latency_ms) * 100.0;
        }
    }
    
    /// Get parallel execution metrics
    pub async fn get_metrics(&self) -> ParallelExecutionMetrics {
        self.metrics.read().await.clone()
    }
}

/// Fusion plan for execution
#[derive(Debug, Clone)]
pub struct FusionPlan {
    pub fusion_groups: Vec<FusionGroup>,
    pub independent_stages: Vec<PipelineStage>,
    pub expected_speedup: f64,
}

// Enhanced stage implementations would be in separate files
// These are simplified placeholders showing the interface

pub struct EnhancedQueryAnalysisStage {
    learning_model: Arc<LearningStopModel>,
}

impl EnhancedQueryAnalysisStage {
    pub fn new(learning_model: Arc<LearningStopModel>) -> Self {
        Self { learning_model }
    }
}

pub struct EnhancedLspRoutingStage {
    lsp_manager: Arc<LspState>,
    prefetch_manager: Arc<PrefetchManager>,
}

impl EnhancedLspRoutingStage {
    pub fn new(lsp_manager: Arc<LspState>, prefetch_manager: Arc<PrefetchManager>) -> Self {
        Self { lsp_manager, prefetch_manager }
    }
}

pub struct EnhancedParallelSearchStage {
    search_engine: Arc<SearchEngine>,
    lsp_manager: Arc<LspState>,
    cross_shard_stopper: Arc<CrossShardStopper>,
}

impl EnhancedParallelSearchStage {
    pub fn new(
        search_engine: Arc<SearchEngine>,
        lsp_manager: Arc<LspState>,
        cross_shard_stopper: Arc<CrossShardStopper>,
    ) -> Self {
        Self {
            search_engine,
            lsp_manager,
            cross_shard_stopper,
        }
    }
}

pub struct EnhancedResultFusionStage;

impl EnhancedResultFusionStage {
    pub fn new() -> Self {
        Self
    }
}

pub struct EnhancedPostProcessStage {
    learning_model: Arc<LearningStopModel>,
}

impl EnhancedPostProcessStage {
    pub fn new(learning_model: Arc<LearningStopModel>) -> Self {
        Self { learning_model }
    }
}

// Implementations of stage coordination
impl StageCoordinator {
    pub async fn new(
        stage: PipelineStage,
        processor: Arc<dyn PipelineStageProcessor>,
        dependencies: HashSet<PipelineStage>,
        can_overlap: bool,
        fusion_group: Option<FusionGroupId>,
        concurrency_limit: usize,
    ) -> Self {
        Self {
            stage,
            processor,
            dependencies,
            can_overlap,
            fusion_group,
            concurrency_limit: Arc::new(Semaphore::new(concurrency_limit)),
            metrics: Arc::new(RwLock::new(StageMetrics::default())),
        }
    }
    
    pub async fn execute_stage(
        &self,
        context: &PipelineContext,
        data: PipelineData,
    ) -> Result<PipelineData, PipelineError> {
        let _permit = self.concurrency_limit.acquire().await.map_err(|_| {
            PipelineError::FusionError {
                stage: self.stage,
                message: "Failed to acquire concurrency permit".to_string(),
            }
        })?;
        
        let start_time = Instant::now();
        let result = self.processor.process(context, data).await;
        let duration = start_time.elapsed();
        
        // Update metrics
        {
            let mut metrics = self.metrics.write().await;
            metrics.executions += 1;
            let total = metrics.executions as f64;
            metrics.avg_duration_ms = 
                (metrics.avg_duration_ms * (total - 1.0) + duration.as_millis() as f64) / total;
        }
        
        result
    }
}

impl FusionController {
    pub fn new() -> Self {
        Self {
            fusion_groups: HashMap::new(),
            fusion_opportunities: HashMap::new(),
            fusion_benefits: HashMap::new(),
            active_fusions: RwLock::new(HashMap::new()),
        }
    }
    
    pub async fn initialize_fusion_groups(&self) -> Result<()> {
        // This would analyze stage dependencies and create fusion opportunities
        // Simplified implementation
        info!("Initialized fusion controller with stage analysis");
        Ok(())
    }
    
    pub async fn create_fusion_plan(&self, execution_id: ExecutionId) -> Result<FusionPlan> {
        // Simplified fusion planning
        Ok(FusionPlan {
            fusion_groups: vec![
                FusionGroup {
                    id: 1,
                    stages: vec![PipelineStage::QueryAnalysis],
                    fusion_strategy: FusionStrategy::Pipeline,
                    expected_speedup: 1.2,
                    memory_sharing: true,
                },
                FusionGroup {
                    id: 2,
                    stages: vec![PipelineStage::LspRouting, PipelineStage::ParallelSearch],
                    fusion_strategy: FusionStrategy::TaskParallel,
                    expected_speedup: 1.8,
                    memory_sharing: false,
                },
                FusionGroup {
                    id: 3,
                    stages: vec![PipelineStage::ResultFusion, PipelineStage::PostProcess],
                    fusion_strategy: FusionStrategy::Pipeline,
                    expected_speedup: 1.1,
                    memory_sharing: true,
                },
            ],
            independent_stages: vec![],
            expected_speedup: 1.5,
        })
    }
}

impl MemoryBudget {
    pub fn new(total_mb: f64) -> Self {
        Self {
            total_mb,
            allocated_mb: 0.0,
            stage_allocations: HashMap::new(),
            fusion_savings: 0.0,
        }
    }
}

impl CpuBudget {
    pub fn new(total_time_ms: f64) -> Self {
        Self {
            total_time_ms,
            allocated_time_ms: 0.0,
            stage_allocations: HashMap::new(),
            overlap_savings: 0.0,
        }
    }
}

// Placeholder implementations for enhanced stages
#[async_trait::async_trait]
impl PipelineStageProcessor for EnhancedQueryAnalysisStage {
    async fn process(&self, context: &PipelineContext, data: PipelineData) -> Result<PipelineData, PipelineError> {
        // Enhanced query analysis with learning model integration
        debug!("Enhanced query analysis for: {}", context.query);
        Ok(data)
    }
    
    fn stage_id(&self) -> PipelineStage {
        PipelineStage::QueryAnalysis
    }
    
    fn supports_fusion(&self) -> bool {
        true
    }
}

#[async_trait::async_trait]
impl PipelineStageProcessor for EnhancedLspRoutingStage {
    async fn process(&self, context: &PipelineContext, data: PipelineData) -> Result<PipelineData, PipelineError> {
        // Enhanced LSP routing with prefetch integration
        debug!("Enhanced LSP routing for: {}", context.query);
        Ok(data)
    }
    
    fn stage_id(&self) -> PipelineStage {
        PipelineStage::LspRouting
    }
    
    fn supports_fusion(&self) -> bool {
        true
    }
}

#[async_trait::async_trait]
impl PipelineStageProcessor for EnhancedParallelSearchStage {
    async fn process(&self, context: &PipelineContext, data: PipelineData) -> Result<PipelineData, PipelineError> {
        // Enhanced parallel search with cross-shard stopping
        debug!("Enhanced parallel search for: {}", context.query);
        Ok(data)
    }
    
    fn stage_id(&self) -> PipelineStage {
        PipelineStage::ParallelSearch
    }
    
    fn supports_fusion(&self) -> bool {
        false // Complex search should not be fused
    }
}

#[async_trait::async_trait]
impl PipelineStageProcessor for EnhancedResultFusionStage {
    async fn process(&self, _context: &PipelineContext, data: PipelineData) -> Result<PipelineData, PipelineError> {
        // Enhanced result fusion with zero-copy operations
        debug!("Enhanced result fusion");
        Ok(data)
    }
    
    fn stage_id(&self) -> PipelineStage {
        PipelineStage::ResultFusion
    }
    
    fn supports_fusion(&self) -> bool {
        true
    }
}

#[async_trait::async_trait]
impl PipelineStageProcessor for EnhancedPostProcessStage {
    async fn process(&self, _context: &PipelineContext, data: PipelineData) -> Result<PipelineData, PipelineError> {
        // Enhanced post-processing with learning feedback
        debug!("Enhanced post-processing");
        Ok(data)
    }
    
    fn stage_id(&self) -> PipelineStage {
        PipelineStage::PostProcess
    }
    
    fn supports_fusion(&self) -> bool {
        true
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::pipeline::{PipelineContext, PipelineData, PipelineStage, PipelineConfig};
    use std::sync::atomic::{AtomicU64, Ordering};
    use tokio::time::{sleep, Duration};

    // Helper to create test pipeline config
    fn create_test_config() -> PipelineConfig {
        PipelineConfig {
            max_concurrent: 4,
            max_latency_ms: 5000,
            enable_parallelism: true,
            enable_fusion: true,
            enable_prefetch: true,
            enable_learning: true,
            fusion_threshold: 0.1,
            memory_limit_mb: 1024,
            cpu_budget_ms: 1000,
        }
    }

    // Helper to create test pipeline context
    fn create_test_context() -> PipelineContext {
        PipelineContext {
            query: "test query".to_string(),
            execution_id: 12345,
            timeout: Duration::from_millis(5000),
            enable_caching: true,
            debug_mode: false,
            max_results: 100,
            metadata: std::collections::HashMap::new(),
        }
    }

    // Helper to create test pipeline data
    fn create_test_data() -> PipelineData {
        PipelineData {
            query_analysis: None,
            lsp_results: Vec::new(),
            search_results: Vec::new(),
            fused_results: Vec::new(),
            final_results: Vec::new(),
            metadata: std::collections::HashMap::new(),
            stage_timings: std::collections::HashMap::new(),
            memory_usage: 0,
        }
    }

    #[tokio::test]
    async fn test_fusion_controller_initialization() {
        let controller = FusionController::new();
        assert!(controller.fusion_groups.is_empty());
        assert!(controller.fusion_opportunities.is_empty());
        assert!(controller.fusion_benefits.is_empty());
    }

    #[tokio::test]
    async fn test_fusion_group_creation() {
        let group = FusionGroup {
            id: 1,
            stages: vec![PipelineStage::QueryAnalysis, PipelineStage::LspRouting],
            fusion_strategy: FusionStrategy::Pipeline,
            expected_speedup: 1.2,
            memory_sharing: true,
        };

        assert_eq!(group.id, 1);
        assert_eq!(group.stages.len(), 2);
        assert_eq!(group.fusion_strategy, FusionStrategy::Pipeline);
        assert!(group.memory_sharing);
    }

    #[tokio::test]
    async fn test_fusion_strategy_types() {
        let strategies = vec![
            FusionStrategy::Pipeline,
            FusionStrategy::DataParallel,
            FusionStrategy::TaskParallel,
            FusionStrategy::Hybrid,
        ];

        for strategy in strategies {
            match strategy {
                FusionStrategy::Pipeline => assert!(true),
                FusionStrategy::DataParallel => assert!(true),
                FusionStrategy::TaskParallel => assert!(true),
                FusionStrategy::Hybrid => assert!(true),
            }
        }
    }

    #[tokio::test]
    async fn test_fusion_benefit_calculation() {
        let benefit = FusionBenefit {
            latency_reduction: 0.3,
            memory_savings: 0.2,
            cpu_efficiency: 0.4,
            cache_locality: 0.1,
            total_score: 1.0,
        };

        assert_eq!(benefit.latency_reduction, 0.3);
        assert_eq!(benefit.memory_savings, 0.2);
        assert_eq!(benefit.cpu_efficiency, 0.4);
        assert_eq!(benefit.cache_locality, 0.1);
        assert_eq!(benefit.total_score, 1.0);
    }

    #[tokio::test]
    async fn test_resource_budget_memory() {
        let budget = MemoryBudget::new(1024.0);
        
        assert_eq!(budget.total_mb, 1024.0);
        assert_eq!(budget.allocated_mb, 0.0);
        assert_eq!(budget.fusion_savings, 0.0);
        assert!(budget.stage_allocations.is_empty());

        // Test that we can create multiple budgets
        let budget2 = MemoryBudget::new(512.0);
        assert_eq!(budget2.total_mb, 512.0);
    }

    #[tokio::test]
    async fn test_resource_budget_cpu() {
        let budget = CpuBudget::new(1000.0);
        
        assert_eq!(budget.total_time_ms, 1000.0);
        assert_eq!(budget.allocated_time_ms, 0.0);
        assert_eq!(budget.overlap_savings, 0.0);
        assert!(budget.stage_allocations.is_empty());

        // Test that we can create multiple budgets
        let budget2 = CpuBudget::new(500.0);
        assert_eq!(budget2.total_time_ms, 500.0);
    }

    #[tokio::test]
    async fn test_enhanced_stages_creation() {
        // Test EnhancedResultFusionStage
        let fusion_stage = EnhancedResultFusionStage::new();
        assert_eq!(fusion_stage.stage_id(), PipelineStage::ResultFusion);
        assert!(fusion_stage.supports_fusion());

        // Test EnhancedPostProcessStage
        let post_stage = EnhancedPostProcessStage::new();
        assert_eq!(post_stage.stage_id(), PipelineStage::PostProcess);
        assert!(post_stage.supports_fusion());
    }

    #[tokio::test]
    async fn test_stage_processing() {
        let context = create_test_context();
        let data = create_test_data();

        // Test result fusion stage processing
        let fusion_stage = EnhancedResultFusionStage::new();
        let result = fusion_stage.process(&context, data.clone()).await;
        assert!(result.is_ok());

        // Test post process stage processing
        let post_stage = EnhancedPostProcessStage::new();
        let result = post_stage.process(&context, data.clone()).await;
        assert!(result.is_ok());
    }

    #[tokio::test]
    async fn test_stage_dependencies() {
        // Test that stages have correct dependencies
        let mut dependencies = HashSet::new();
        dependencies.insert(PipelineStage::QueryAnalysis);

        // LSP should depend on QueryAnalysis
        assert!(dependencies.contains(&PipelineStage::QueryAnalysis));

        // Search should depend on LSP
        let mut search_deps = HashSet::new();
        search_deps.insert(PipelineStage::LspRouting);
        assert!(search_deps.contains(&PipelineStage::LspRouting));
    }

    #[tokio::test]
    async fn test_concurrent_stage_execution() {
        // Test concurrent execution patterns
        let counter = Arc::new(AtomicU64::new(0));
        let tasks: Vec<_> = (0..4).map(|i| {
            let counter_clone = counter.clone();
            tokio::spawn(async move {
                // Simulate stage processing
                sleep(Duration::from_millis(10)).await;
                counter_clone.fetch_add(1, Ordering::SeqCst);
                i
            })
        }).collect();

        let results: Vec<_> = futures::future::join_all(tasks).await;
        
        assert_eq!(results.len(), 4);
        assert_eq!(counter.load(Ordering::SeqCst), 4);
        
        for (i, result) in results.into_iter().enumerate() {
            assert_eq!(result.unwrap(), i);
        }
    }

    #[tokio::test]
    async fn test_pipeline_error_handling() {
        // Test error propagation in pipeline stages
        let context = create_test_context();
        let data = create_test_data();

        // Create a stage that might fail (test the error handling path)
        let fusion_stage = EnhancedResultFusionStage::new();
        
        // Normal processing should work
        let result = fusion_stage.process(&context, data).await;
        assert!(result.is_ok());

        // Test the stage identification
        assert_eq!(fusion_stage.stage_id(), PipelineStage::ResultFusion);
    }

    #[tokio::test]
    async fn test_execution_id_generation() {
        // Test that execution IDs are properly typed
        let id1: ExecutionId = 123456;
        let id2: ExecutionId = 789012;
        
        assert_ne!(id1, id2);
        assert!(id1 > 0);
        assert!(id2 > 0);
    }

    #[tokio::test]
    async fn test_fusion_group_id_generation() {
        // Test that fusion group IDs are properly typed
        let group1: FusionGroupId = 1;
        let group2: FusionGroupId = 2;
        
        assert_ne!(group1, group2);
        assert!(group1 > 0);
        assert!(group2 > 0);
    }

    // Performance-focused tests
    #[tokio::test]
    async fn test_stage_performance_metrics() {
        let start = std::time::Instant::now();
        
        // Simulate stage execution
        let context = create_test_context();
        let data = create_test_data();
        let post_stage = EnhancedPostProcessStage::new();
        let _result = post_stage.process(&context, data).await;
        
        let duration = start.elapsed();
        
        // Ensure processing is reasonably fast (should be much less than 1 second for this simple test)
        assert!(duration < Duration::from_secs(1));
    }

    #[tokio::test]
    async fn test_parallel_execution_metrics() {
        let metrics = ParallelExecutionMetrics {
            total_executions: 10,
            successful_executions: 9,
            failed_executions: 1,
            average_latency_ms: 150.5,
            p95_latency_ms: 200.0,
            p99_latency_ms: 250.0,
            average_parallelism: 3.2,
            fusion_utilization: 0.8,
            memory_efficiency: 0.9,
            cpu_utilization: 0.75,
            stage_metrics: std::collections::HashMap::new(),
        };

        assert_eq!(metrics.total_executions, 10);
        assert_eq!(metrics.successful_executions, 9);
        assert_eq!(metrics.failed_executions, 1);
        assert_eq!(metrics.average_latency_ms, 150.5);
        assert_eq!(metrics.fusion_utilization, 0.8);
    }

    #[tokio::test]
    async fn test_stage_metrics() {
        let stage_metrics = StageMetrics {
            executions: 5,
            average_duration_ms: 100.0,
            p95_duration_ms: 150.0,
            success_rate: 0.9,
            parallelism_factor: 2.5,
            memory_peak_mb: 256.0,
            cpu_utilization: 0.8,
            fusion_participations: 3,
        };

        assert_eq!(stage_metrics.executions, 5);
        assert_eq!(stage_metrics.average_duration_ms, 100.0);
        assert_eq!(stage_metrics.success_rate, 0.9);
        assert_eq!(stage_metrics.fusion_participations, 3);
    }
}