oxirs-cluster 0.2.4

//! Revolutionary Cluster Optimizer
//!
//! Main orchestrator for AI-powered cluster optimization.

use anyhow::Result;
use scirs2_core::error::CoreError;
use scirs2_core::memory::{BufferPool, GlobalBufferPool};
use scirs2_core::metrics::{Counter, Timer, Histogram, MetricRegistry};
use scirs2_core::profiling::Profiler;
use std::sync::{Arc, RwLock};

use crate::error::{ClusterError, Result as ClusterResult};

use super::config::*;
use super::consensus_optimizer::AIConsensusOptimizer;
use super::data_distribution::IntelligentDataDistributionEngine;
use super::replication_manager::AdaptiveReplicationManager;
use super::network_optimizer::QuantumNetworkOptimizer;
use super::analytics::AdvancedClusterAnalytics;
use super::scaling::PredictiveScalingEngine;
use super::coordinator::ClusterUnifiedCoordinator;
use super::types::*;

/// Revolutionary cluster optimizer with AI-powered distributed coordination
pub struct RevolutionaryClusterOptimizer {
    config: RevolutionaryClusterConfig,
    consensus_optimizer: Arc<RwLock<AIConsensusOptimizer>>,
    data_distribution_engine: Arc<RwLock<IntelligentDataDistributionEngine>>,
    replication_manager: Arc<RwLock<AdaptiveReplicationManager>>,
    network_optimizer: Arc<RwLock<QuantumNetworkOptimizer>>,
    cluster_analytics: Arc<RwLock<AdvancedClusterAnalytics>>,
    scaling_predictor: Arc<RwLock<PredictiveScalingEngine>>,
    unified_coordinator: Arc<RwLock<ClusterUnifiedCoordinator>>,
    metrics: MetricRegistry,
    profiler: Profiler,
    optimization_stats: Arc<RwLock<ClusterOptimizationStatistics>>,
}

impl RevolutionaryClusterOptimizer {
    /// Create a new revolutionary cluster optimizer
    pub async fn new(config: RevolutionaryClusterConfig) -> Result<Self> {
        // Initialize consensus optimizer
        let consensus_optimizer = Arc::new(RwLock::new(
            AIConsensusOptimizer::new(config.consensus_config.clone()).await?,
        ));

        // Initialize data distribution engine
        let data_distribution_engine = Arc::new(RwLock::new(
            IntelligentDataDistributionEngine::new(config.data_distribution_config.clone()).await?,
        ));

        // Initialize replication manager
        let replication_manager = Arc::new(RwLock::new(
            AdaptiveReplicationManager::new(config.replication_config.clone()).await?,
        ));

        // Initialize network optimizer
        let network_optimizer = Arc::new(RwLock::new(
            QuantumNetworkOptimizer::new(config.network_config.clone()).await?,
        ));

        // Initialize cluster analytics
        let cluster_analytics = Arc::new(RwLock::new(
            AdvancedClusterAnalytics::new().await?,
        ));

        // Initialize scaling predictor
        let scaling_predictor = Arc::new(RwLock::new(
            PredictiveScalingEngine::new().await?,
        ));

        // Initialize unified coordinator
        let unified_coordinator = Arc::new(RwLock::new(
            ClusterUnifiedCoordinator::new(config.clone()).await?,
        ));

        // Initialize metrics and profiler
        let metrics = MetricRegistry::new();
        let profiler = Profiler::new();

        // Initialize optimization statistics
        let optimization_stats = Arc::new(RwLock::new(ClusterOptimizationStatistics::new()));

        Ok(Self {
            config,
            consensus_optimizer,
            data_distribution_engine,
            replication_manager,
            network_optimizer,
            cluster_analytics,
            scaling_predictor,
            unified_coordinator,
            metrics,
            profiler,
            optimization_stats,
        })
    }

    /// Optimize cluster operations with revolutionary techniques
    pub async fn optimize_cluster_operations(
        &self,
        cluster_state: &ClusterState,
        optimization_context: &ClusterOptimizationContext,
    ) -> Result<ClusterOptimizationResult> {
        let start_time = Instant::now();
        let timer = self.metrics.timer("cluster_optimization");

        // Stage 1: Unified coordination analysis
        let coordination_analysis = {
            let coordinator = self.unified_coordinator.read().expect("rwlock should not be poisoned");
            coordinator
                .analyze_cluster_coordination_requirements(cluster_state, optimization_context)
                .await?
        };

        // Stage 2: AI-powered consensus optimization
        let consensus_optimization = if self.config.enable_ai_consensus_optimization {
            let optimizer = self.consensus_optimizer.read().expect("rwlock should not be poisoned");
            Some(optimizer.optimize_consensus_protocol(cluster_state).await?)
        } else {
            None
        };

        // Stage 3: Intelligent data distribution
        let data_distribution_optimization = if self.config.enable_intelligent_data_distribution {
            let engine = self.data_distribution_engine.read().expect("rwlock should not be poisoned");
            Some(engine.optimize_data_distribution(cluster_state).await?)
        } else {
            None
        };

        // Stage 4: Adaptive replication optimization
        let replication_optimization = if self.config.enable_adaptive_replication {
            let manager = self.replication_manager.read().expect("rwlock should not be poisoned");
            Some(manager.optimize_replication_strategy(cluster_state).await?)
        } else {
            None
        };

        // Stage 5: Quantum network optimization
        let network_optimization = if self.config.enable_quantum_networking {
            let optimizer = self.network_optimizer.read().expect("rwlock should not be poisoned");
            Some(optimizer.optimize_network_topology(cluster_state).await?)
        } else {
            None
        };

        // Stage 6: Advanced cluster analytics
        if self.config.enable_advanced_cluster_analytics {
            let mut analytics = self.cluster_analytics.write().expect("rwlock should not be poisoned");
            analytics.collect_cluster_metrics(cluster_state).await?;
        }

        // Stage 7: Predictive scaling
        let scaling_prediction = if self.config.enable_predictive_scaling {
            let predictor = self.scaling_predictor.read().expect("rwlock should not be poisoned");
            Some(predictor.predict_scaling_requirements(cluster_state).await?)
        } else {
            None
        };

        // Stage 8: Apply optimizations
        let applied_optimizations = self
            .apply_cluster_optimizations(
                &coordination_analysis,
                consensus_optimization.as_ref(),
                data_distribution_optimization.as_ref(),
                replication_optimization.as_ref(),
                network_optimization.as_ref(),
                cluster_state,
            )
            .await?;

        // Stage 9: Update optimization statistics
        let optimization_time = start_time.elapsed();
        {
            let mut stats = self.optimization_stats.write().expect("rwlock should not be poisoned");
            stats.record_optimization(
                cluster_state.nodes.len(),
                optimization_time,
                applied_optimizations.clone(),
            );
        }

        timer.record("cluster_optimization", optimization_time);

        Ok(ClusterOptimizationResult {
            optimization_time,
            coordination_analysis,
            consensus_optimization,
            data_distribution_optimization,
            replication_optimization,
            network_optimization,
            scaling_prediction,
            applied_optimizations,
            performance_improvement: self.calculate_performance_improvement(cluster_state, optimization_time),
        })
    }

    /// Apply cluster optimizations
    async fn apply_cluster_optimizations(
        &self,
        coordination_analysis: &ClusterCoordinationAnalysis,
        consensus_optimization: Option<&ConsensusOptimizationResult>,
        data_distribution_optimization: Option<&DataDistributionOptimizationResult>,
        replication_optimization: Option<&ReplicationOptimizationResult>,
        network_optimization: Option<&NetworkOptimizationResult>,
        _cluster_state: &ClusterState,
    ) -> Result<AppliedClusterOptimizations> {
        let mut applied = AppliedClusterOptimizations::new();

        // Apply coordination optimizations
        if coordination_analysis.requires_coordination_optimization {
            applied.coordination_optimizations = vec![
                "unified_consensus_coordination".to_string(),
                "cross_component_synchronization".to_string(),
            ];
        }

        // Apply consensus optimizations
        if let Some(consensus_opt) = consensus_optimization {
            applied.consensus_optimizations = consensus_opt.applied_optimizations.clone();
        }

        // Apply data distribution optimizations
        if let Some(data_opt) = data_distribution_optimization {
            applied.data_distribution_optimizations = data_opt.optimization_actions.clone();
        }

        // Apply replication optimizations
        if let Some(repl_opt) = replication_optimization {
            applied.replication_optimizations = repl_opt.replication_adjustments.iter()
                .map(|adj| format!("Adjusted replication factor for node {} to {}", adj.node_id, adj.new_factor))
                .collect();
        }

        // Apply network optimizations
        if let Some(net_opt) = network_optimization {
            applied.network_optimizations = net_opt.routing_optimizations.clone();
        }

        Ok(applied)
    }

    /// Calculate performance improvement factor
    fn calculate_performance_improvement(&self, cluster_state: &ClusterState, _optimization_time: Duration) -> f64 {
        // Calculate performance improvement based on cluster metrics
        let baseline_throughput = 1000.0; // Baseline queries per second
        let current_throughput = cluster_state.performance_metrics.query_throughput_qps;

        if baseline_throughput > 0.0 {
            current_throughput / baseline_throughput
        } else {
            1.0
        }
    }

    /// Optimize consensus protocol with AI
    pub async fn optimize_consensus_protocol(
        &self,
        cluster_state: &ClusterState,
        consensus_context: &ConsensusContext,
    ) -> Result<ConsensusOptimizationResult> {
        let optimizer = self.consensus_optimizer.read().expect("rwlock should not be poisoned");
        optimizer.optimize_consensus_with_context(cluster_state, consensus_context).await
    }

    /// Optimize data distribution across cluster
    pub async fn optimize_data_distribution(&self, cluster_state: &ClusterState) -> Result<DataDistributionOptimizationResult> {
        let engine = self.data_distribution_engine.read().expect("rwlock should not be poisoned");
        engine.optimize_data_distribution(cluster_state).await
    }

    /// Get cluster analytics
    pub async fn get_cluster_analytics(&self) -> ClusterAnalytics {
        let analytics = self.cluster_analytics.read().expect("rwlock should not be poisoned");
        analytics.get_analytics().await
    }

    /// Get optimization metrics
    pub async fn get_optimization_metrics(&self) -> HashMap<String, f64> {
        self.metrics.get_all_metrics().await
    }

    /// Predict cluster scaling needs
    pub async fn predict_scaling_needs(&self, cluster_state: &ClusterState) -> Result<ScalingPrediction> {
        let predictor = self.scaling_predictor.read().expect("rwlock should not be poisoned");
        predictor.predict_scaling_requirements(cluster_state).await
    }
}