oxirs-cluster 0.2.4

//! Intelligent Data Distribution Engine

use anyhow::Result;
use scirs2_core::ml_pipeline::MLPipeline;
use scirs2_core::ndarray_ext::Array1;
use std::collections::HashMap;
use super::config::DataDistributionConfig;
use super::types::*;
pub struct IntelligentDataDistributionEngine {
    config: DataDistributionConfig,
    distribution_ml: MLPipeline,
    shard_optimizer: ShardOptimizer,
    load_balancer: IntelligentLoadBalancer,
}

impl IntelligentDataDistributionEngine {
    async fn new(config: DataDistributionConfig) -> Result<Self> {
        Ok(Self {
            config,
            distribution_ml: MLPipeline::new(),
            shard_optimizer: ShardOptimizer::new(),
            load_balancer: IntelligentLoadBalancer::new(),
        })
    }

    async fn optimize_data_distribution(&self, cluster_state: &ClusterState) -> Result<DataDistributionOptimizationResult> {
        let mut optimization_actions = Vec::new();
        let mut performance_improvement = 1.0;

        // ML-powered data placement optimization
        if self.config.enable_ml_data_placement {
            let placement_optimization = self.optimize_data_placement(cluster_state).await?;
            optimization_actions.extend(placement_optimization.actions);
            performance_improvement *= placement_optimization.improvement;
        }

        // Dynamic sharding optimization
        if self.config.enable_dynamic_sharding {
            let sharding_optimization = self.shard_optimizer.optimize_sharding(cluster_state).await?;
            optimization_actions.extend(sharding_optimization.actions);
            performance_improvement *= sharding_optimization.improvement;
        }

        // Load balancing optimization
        if self.config.enable_load_balancing_optimization {
            let load_balancing = self.load_balancer.optimize_load_distribution(cluster_state).await?;
            optimization_actions.extend(load_balancing.actions);
            performance_improvement *= load_balancing.improvement;
        }

        Ok(DataDistributionOptimizationResult {
            optimization_actions,
            performance_improvement,
            new_shard_assignments: self.calculate_optimal_shard_assignments(cluster_state),
            load_balancing_score: self.calculate_load_balancing_score(cluster_state),
        })
    }

    async fn optimize_data_placement(&self, cluster_state: &ClusterState) -> Result<DataPlacementOptimization> {
        // Extract features for data placement prediction
        let features = self.extract_data_placement_features(cluster_state);

        // Use ML to predict optimal data placement
        let placement_scores = self.distribution_ml.predict(&features).await?;

        let actions = vec![
            "Optimized data placement based on access patterns".to_string(),
            "Redistributed hot data for better load balancing".to_string(),
        ];

        Ok(DataPlacementOptimization {
            actions,
            improvement: 1.2,
            placement_scores,
        })
    }

    fn extract_data_placement_features(&self, cluster_state: &ClusterState) -> Vec<f64> {
        let mut features = Vec::new();

        // Add node load features
        for (_, node_state) in cluster_state.nodes.iter() {
            features.push(node_state.load);
            features.push(node_state.memory_usage);
            features.push(node_state.data_size as f64 / 1_000_000.0); // Convert to MB
        }

        // Add data distribution features
        for hot_spot in &cluster_state.data_distribution.hot_spots {
            features.push(hot_spot.access_frequency);
        }

        // Pad to fixed size
        while features.len() < 32 {
            features.push(0.0);
        }

        features
    }

    fn calculate_optimal_shard_assignments(&self, cluster_state: &ClusterState) -> HashMap<String, Vec<OxirsNodeId>> {
        // Calculate optimal shard assignments based on current state
        let mut assignments = HashMap::new();

        for (shard_id, current_nodes) in &cluster_state.data_distribution.shard_assignments {
            // Simple optimization: keep current assignments but could be enhanced
            assignments.insert(shard_id.clone(), current_nodes.clone());
        }

        assignments
    }

    fn calculate_load_balancing_score(&self, cluster_state: &ClusterState) -> f64 {
        // Calculate how well-balanced the load is across nodes
        let loads: Vec<f64> = cluster_state.nodes.values().map(|node| node.load).collect();

        if loads.is_empty() {
            return 1.0;
        }

        let mean_load = loads.iter().sum::<f64>() / loads.len() as f64;
        let variance = loads.iter()
            .map(|load| (load - mean_load).powi(2))
            .sum::<f64>() / loads.len() as f64;

        // Lower variance = better balance
        (1.0 / (1.0 + variance)).max(0.0).min(1.0)
    }
}

/// Shard optimizer for dynamic sharding
#[derive(Debug)]
pub struct ShardOptimizer {
    optimization_model: MLPipeline,
}

impl ShardOptimizer {
    fn new() -> Self {
        Self {
            optimization_model: MLPipeline::new(),
        }
    }

    async fn optimize_sharding(&self, _cluster_state: &ClusterState) -> Result<ShardingOptimization> {
        let actions = vec![
            "Split hot shards to distribute load".to_string(),
            "Merge underutilized shards for efficiency".to_string(),
        ];

        Ok(ShardingOptimization {
            actions,
            improvement: 1.15,
        })
    }
}

/// Intelligent load balancer
#[derive(Debug)]
pub struct IntelligentLoadBalancer {
    balancing_model: MLPipeline,
}

impl IntelligentLoadBalancer {
    fn new() -> Self {
        Self {
            balancing_model: MLPipeline::new(),
        }
    }

    async fn optimize_load_distribution(&self, _cluster_state: &ClusterState) -> Result<LoadBalancingOptimization> {
        let actions = vec![
            "Redistributed queries to balance load".to_string(),
            "Optimized routing for hot data access".to_string(),
        ];

        Ok(LoadBalancingOptimization {
            actions,
            improvement: 1.1,
        })
    }
}