scirs2_graph/memory/

mod.rs

//! Memory profiling and optimization utilities for graph data structures
//!
//! This module provides tools to analyze and optimize memory usage in graph operations.

#![allow(missing_docs)]

pub mod compact;

pub use compact::{BitPackedGraph, CSRGraph, CompressedAdjacencyList, HybridGraph, MemmapGraph};

// Re-export from compact module only

use crate::{DiGraph, Graph};
use std::collections::HashMap;
use std::mem;
use std::sync::{Arc, Mutex};
use std::thread;
use std::time::{Duration, Instant};
use sysinfo::System;

/// Memory usage statistics for a graph
#[derive(Debug, Clone)]
pub struct MemoryStats {
    /// Total memory used by the graph structure (bytes)
    pub total_bytes: usize,
    /// Memory used by node storage
    pub node_bytes: usize,
    /// Memory used by edge storage
    pub edge_bytes: usize,
    /// Memory used by adjacency lists
    pub adjacency_bytes: usize,
    /// Overhead from allocator metadata
    pub overhead_bytes: usize,
    /// Memory efficiency (useful data / total memory)
    pub efficiency: f64,
}

/// Memory profiler for graph structures
pub struct MemoryProfiler;

impl MemoryProfiler {
    /// Calculate memory statistics for an undirected graph
    pub fn profile_graph<N, E, Ix>(graph: &Graph<N, E, Ix>) -> MemoryStats
    where
        N: crate::base::Node + std::fmt::Debug,
        E: crate::base::EdgeWeight,
        Ix: petgraph::graph::IndexType,
    {
        let node_count = graph.node_count();
        let edge_count = graph.edge_count();

        // Calculate node storage - nodes are stored with their data
        let node_bytes = node_count
            * (mem::size_of::<N>() + mem::size_of::<petgraph::graph::NodeIndex<Ix>>())
            + mem::size_of::<std::collections::HashMap<N, petgraph::graph::NodeIndex<Ix>>>();

        // Calculate adjacency list storage based on actual _graph structure
        let mut adjacency_bytes = 0;
        for node in graph.nodes() {
            if let Ok(neighbors) = graph.neighbors(node) {
                let neighbor_count = neighbors.len();
                adjacency_bytes += neighbor_count * mem::size_of::<E>() // edge weights
                    + mem::size_of::<Vec<E>>(); // Vec overhead per adjacency list
            }
        }

        // Calculate edge storage
        let edge_bytes = edge_count * (mem::size_of::<N>() * 2 + mem::size_of::<E>());

        // Estimate allocator overhead (typically 8-16 bytes per allocation)
        let allocation_count = node_count + 1; // nodes + main structure
        let overhead_bytes = allocation_count * 16;

        let total_bytes = node_bytes + adjacency_bytes + edge_bytes + overhead_bytes;
        let useful_bytes = node_bytes + adjacency_bytes;
        let efficiency = if total_bytes > 0 {
            useful_bytes as f64 / total_bytes as f64
        } else {
            1.0
        };

        MemoryStats {
            total_bytes,
            node_bytes,
            edge_bytes,
            adjacency_bytes,
            overhead_bytes,
            efficiency,
        }
    }

    /// Calculate memory statistics for a directed graph
    pub fn profile_digraph<N, E, Ix>(graph: &DiGraph<N, E, Ix>) -> MemoryStats
    where
        N: crate::base::Node + std::fmt::Debug,
        E: crate::base::EdgeWeight,
        Ix: petgraph::graph::IndexType,
    {
        let node_count = graph.node_count();
        let edge_count = graph.edge_count();

        // Similar to undirected but with separate in/out adjacency lists
        let node_bytes = node_count
            * (mem::size_of::<N>() + mem::size_of::<petgraph::graph::NodeIndex<Ix>>())
            + mem::size_of::<std::collections::HashMap<N, petgraph::graph::NodeIndex<Ix>>>();

        // Both in-edges and out-edges storage - directed graphs have separate lists
        let mut adjacency_bytes = 0;
        for node in graph.nodes() {
            // Count successors (outgoing edges)
            if let Ok(successors) = graph.successors(node) {
                adjacency_bytes +=
                    successors.len() * mem::size_of::<E>() + mem::size_of::<Vec<E>>();
            }
            // Count predecessors (incoming edges)
            if let Ok(predecessors) = graph.predecessors(node) {
                adjacency_bytes +=
                    predecessors.len() * mem::size_of::<E>() + mem::size_of::<Vec<E>>();
            }
        }

        let edge_bytes = edge_count * (mem::size_of::<N>() * 2 + mem::size_of::<E>());

        let allocation_count = node_count * 2 + 1; // in/out vecs + main structure
        let overhead_bytes = allocation_count * 16;

        let total_bytes = node_bytes + adjacency_bytes + edge_bytes + overhead_bytes;
        let useful_bytes = node_bytes + adjacency_bytes;
        let efficiency = if total_bytes > 0 {
            useful_bytes as f64 / total_bytes as f64
        } else {
            1.0
        };

        MemoryStats {
            total_bytes,
            node_bytes,
            edge_bytes,
            adjacency_bytes,
            overhead_bytes,
            efficiency,
        }
    }

    /// Estimate memory usage for a graph of given size
    pub fn estimate_memory(nodes: usize, edges: usize, directed: bool) -> usize {
        let _avg_degree = if nodes > 0 {
            edges as f64 / nodes as f64
        } else {
            0.0
        };

        // Base node storage
        let node_bytes = nodes * mem::size_of::<usize>();

        // Adjacency list storage
        let edge_entry_size = mem::size_of::<(usize, f64)>();
        let adjacency_multiplier = if directed { 2.0 } else { 1.0 };
        let adjacency_bytes =
            (edges as f64 * adjacency_multiplier * edge_entry_size as f64) as usize;

        // Vec overhead (capacity often > size)
        let vec_overhead =
            nodes * mem::size_of::<Vec<(usize, f64)>>() * if directed { 2 } else { 1 };

        // Allocator overhead
        let overhead = (nodes + edges / 100) * 16;

        node_bytes + adjacency_bytes + vec_overhead + overhead
    }

    /// Analyze memory fragmentation in the graph
    pub fn analyze_fragmentation<N, E, Ix>(graph: &Graph<N, E, Ix>) -> FragmentationReport
    where
        N: crate::base::Node + std::fmt::Debug,
        E: crate::base::EdgeWeight,
        Ix: petgraph::graph::IndexType,
    {
        let mut degree_distribution = HashMap::new();
        let mut total_capacity = 0;
        let mut total_used = 0;

        for node in graph.nodes() {
            let degree = graph.degree(node);
            *degree_distribution.entry(degree).or_insert(0) += 1;

            // Estimate Vec capacity vs actual usage
            // Vecs typically grow by 2x when resizing
            let capacity = degree.next_power_of_two().max(4);
            total_capacity += capacity;
            total_used += degree;
        }

        let fragmentation = if total_capacity > 0 {
            1.0 - (total_used as f64 / total_capacity as f64)
        } else {
            0.0
        };

        FragmentationReport {
            degree_distribution,
            total_capacity,
            total_used,
            fragmentation_ratio: fragmentation,
            wasted_bytes: (total_capacity - total_used) * mem::size_of::<(N, E)>(),
        }
    }
}

/// Report on memory fragmentation in graph structures
#[derive(Debug)]
pub struct FragmentationReport {
    /// Distribution of node degrees
    pub degree_distribution: HashMap<usize, usize>,
    /// Total capacity allocated for adjacency lists
    pub total_capacity: usize,
    /// Total capacity actually used
    pub total_used: usize,
    /// Fragmentation ratio (0.0 = no fragmentation, 1.0 = all wasted)
    pub fragmentation_ratio: f64,
    /// Estimated wasted bytes due to over-allocation
    pub wasted_bytes: usize,
}

/// Memory-optimized graph builder
pub struct OptimizedGraphBuilder {
    nodes: Vec<usize>,
    edges: Vec<(usize, usize, f64)>,
    estimated_edges_per_node: Option<usize>,
}

impl Default for OptimizedGraphBuilder {
    fn default() -> Self {
        Self::new()
    }
}

impl OptimizedGraphBuilder {
    /// Create a new optimized graph builder
    pub fn new() -> Self {
        Self {
            nodes: Vec::new(),
            edges: Vec::new(),
            estimated_edges_per_node: None,
        }
    }

    /// Set expected number of edges per node for better memory allocation
    pub fn with_estimated_edges_per_node(mut self, edges_pernode: usize) -> Self {
        self.estimated_edges_per_node = Some(edges_pernode);
        self
    }

    /// Reserve capacity for nodes
    pub fn reserve_nodes(mut self, capacity: usize) -> Self {
        self.nodes.reserve(capacity);
        self
    }

    /// Reserve capacity for edges
    pub fn reserve_edges(mut self, capacity: usize) -> Self {
        self.edges.reserve(capacity);
        self
    }

    /// Add a node
    pub fn add_node(&mut self, node: usize) {
        self.nodes.push(node);
    }

    /// Add an edge
    pub fn add_edge(&mut self, from: usize, to: usize, weight: f64) {
        self.edges.push((from, to, weight));
    }

    /// Build the optimized graph
    pub fn build(self) -> Result<Graph<usize, f64>, String> {
        let mut graph = Graph::new();

        // Pre-allocate with estimated sizes
        if let Some(_epn) = self.estimated_edges_per_node {
            // Reserve capacity in adjacency lists
            for &node in &self.nodes {
                let _ = graph.add_node(node);
                // Internal method to reserve adjacency list capacity
                // This would need to be added to Graph API
            }
        } else {
            for &node in &self.nodes {
                let _ = graph.add_node(node);
            }
        }

        // Add edges
        for (from, to, weight) in self.edges {
            graph
                .add_edge(from, to, weight)
                .map_err(|e| format!("Failed to add edge: {e:?}"))?;
        }

        Ok(graph)
    }
}

/// Memory optimization suggestions
#[derive(Debug)]
pub struct OptimizationSuggestions {
    pub suggestions: Vec<String>,
    pub potential_savings: usize,
}

/// Analyze a graph and provide memory optimization suggestions
#[allow(dead_code)]
pub fn suggest_optimizations(
    stats: &MemoryStats,
    fragmentation: &FragmentationReport,
) -> OptimizationSuggestions {
    let mut suggestions = Vec::new();
    let mut potential_savings = 0;

    // Check efficiency
    if stats.efficiency < 0.7 {
        suggestions.push(format!(
            "Low memory efficiency ({:.1}%). Consider using a more compact representation.",
            stats.efficiency * 100.0
        ));
    }

    // Check fragmentation
    if fragmentation.fragmentation_ratio > 0.3 {
        suggestions.push(format!(
            "High fragmentation ({:.1}%). Pre-allocate adjacency lists based on expected degree.",
            fragmentation.fragmentation_ratio * 100.0
        ));
        potential_savings += fragmentation.wasted_bytes;
    }

    // Check degree distribution
    let max_degree = fragmentation
        .degree_distribution
        .keys()
        .max()
        .copied()
        .unwrap_or(0);
    let avg_degree =
        if fragmentation.total_used > 0 && !fragmentation.degree_distribution.is_empty() {
            fragmentation.total_used as f64 / fragmentation.degree_distribution.len() as f64
        } else {
            0.0
        };

    if max_degree > avg_degree as usize * 10 {
        suggestions.push(
            "Highly skewed degree distribution. Consider using a hybrid representation \
             with different storage for high-degree nodes."
                .to_string(),
        );
    }

    // Check for sparse graphs
    if avg_degree < 5.0 {
        suggestions.push(
            "Very sparse graph. Consider using a sparse matrix representation \
             or compressed adjacency lists."
                .to_string(),
        );
    }

    OptimizationSuggestions {
        suggestions,
        potential_savings,
    }
}

/// Real-time memory profiler for monitoring memory usage during algorithm execution
#[derive(Debug)]
pub struct RealTimeMemoryProfiler {
    /// Process ID for monitoring
    pid: u32,
    /// System information handle
    system: Arc<Mutex<System>>,
    /// Is monitoring active
    is_monitoring: Arc<Mutex<bool>>,
    /// Memory usage samples
    samples: Arc<Mutex<Vec<MemorySample>>>,
    /// Monitoring thread handle
    monitor_thread: Option<thread::JoinHandle<()>>,
}

/// Memory usage sample at a specific time
#[derive(Debug, Clone)]
pub struct MemorySample {
    /// Timestamp when sample was taken
    pub timestamp: Instant,
    /// Physical memory used (RSS) in bytes
    pub physical_memory: u64,
    /// Virtual memory used in bytes
    pub virtual_memory: u64,
    /// Memory growth since last sample in bytes
    pub growth_rate: i64,
}

/// Memory monitoring metrics collected over time
#[derive(Debug, Clone)]
pub struct MemoryMetrics {
    /// Peak memory usage in bytes
    pub peak_memory: u64,
    /// Average memory usage in bytes
    pub average_memory: u64,
    /// Memory growth rate in bytes per second
    pub growth_rate: f64,
    /// Total monitoring duration
    pub duration: Duration,
    /// Number of samples collected
    pub sample_count: usize,
    /// Memory variance (indicates stability)
    pub memory_variance: f64,
}

impl Default for RealTimeMemoryProfiler {
    fn default() -> Self {
        Self::new()
    }
}

impl RealTimeMemoryProfiler {
    /// Create a new real-time memory profiler
    pub fn new() -> Self {
        let mut system = System::new_all();
        system.refresh_all();

        let pid = std::process::id();

        Self {
            pid,
            system: Arc::new(Mutex::new(system)),
            is_monitoring: Arc::new(Mutex::new(false)),
            samples: Arc::new(Mutex::new(Vec::new())),
            monitor_thread: None,
        }
    }

    /// Start monitoring memory usage
    pub fn start_monitoring(&mut self, sampleinterval: Duration) {
        let mut is_monitoring = self.is_monitoring.lock().unwrap();
        if *is_monitoring {
            return; // Already monitoring
        }
        *is_monitoring = true;
        drop(is_monitoring);

        // Clear previous samples
        self.samples.lock().unwrap().clear();

        let pid = self.pid;
        let system = Arc::clone(&self.system);
        let is_monitoring = Arc::clone(&self.is_monitoring);
        let samples = Arc::clone(&self.samples);

        let handle = thread::spawn(move || {
            let mut last_memory = 0u64;
            let _start_time = Instant::now();

            while *is_monitoring.lock().unwrap() {
                {
                    let mut sys = system.lock().unwrap();
                    sys.refresh_processes(
                        sysinfo::ProcessesToUpdate::Some(&[(pid as usize).into()]),
                        false,
                    );

                    if let Some(process) = sys.process((pid as usize).into()) {
                        let physical_memory = process.memory() * 1024; // Convert KB to bytes
                        let virtual_memory = process.virtual_memory() * 1024;
                        let growth_rate = physical_memory as i64 - last_memory as i64;

                        let sample = MemorySample {
                            timestamp: Instant::now(),
                            physical_memory,
                            virtual_memory,
                            growth_rate,
                        };

                        samples.lock().unwrap().push(sample);
                        last_memory = physical_memory;
                    }
                }

                thread::sleep(sampleinterval);
            }
        });

        self.monitor_thread = Some(handle);
    }

    /// Stop monitoring and return collected metrics
    pub fn stop_monitoring(&mut self) -> MemoryMetrics {
        {
            let mut is_monitoring = self.is_monitoring.lock().unwrap();
            *is_monitoring = false;
        }

        if let Some(handle) = self.monitor_thread.take() {
            let _ = handle.join();
        }

        let samples = self.samples.lock().unwrap();
        self.calculate_metrics(&samples)
    }

    /// Get current memory metrics without stopping monitoring
    pub fn get_current_metrics(&self) -> MemoryMetrics {
        let samples = self.samples.lock().unwrap();
        self.calculate_metrics(&samples)
    }

    /// Calculate metrics from collected samples
    fn calculate_metrics(&self, samples: &[MemorySample]) -> MemoryMetrics {
        if samples.is_empty() {
            return MemoryMetrics {
                peak_memory: 0,
                average_memory: 0,
                growth_rate: 0.0,
                duration: Duration::new(0, 0),
                sample_count: 0,
                memory_variance: 0.0,
            };
        }

        let peak_memory = samples.iter().map(|s| s.physical_memory).max().unwrap_or(0);
        let total_memory: u64 = samples.iter().map(|s| s.physical_memory).sum();
        let average_memory = total_memory / samples.len() as u64;

        let duration = if samples.len() > 1 {
            samples
                .last()
                .unwrap()
                .timestamp
                .duration_since(samples[0].timestamp)
        } else {
            Duration::new(0, 0)
        };

        let growth_rate = if duration.as_secs_f64() > 0.0 && samples.len() > 1 {
            let total_growth =
                samples.last().unwrap().physical_memory as i64 - samples[0].physical_memory as i64;
            total_growth as f64 / duration.as_secs_f64()
        } else {
            0.0
        };

        // Calculate memory variance
        let variance = if samples.len() > 1 {
            let mean = average_memory as f64;
            let sum_sq_diff: f64 = samples
                .iter()
                .map(|s| {
                    let diff = s.physical_memory as f64 - mean;
                    diff * diff
                })
                .sum();
            sum_sq_diff / samples.len() as f64
        } else {
            0.0
        };

        MemoryMetrics {
            peak_memory,
            average_memory,
            growth_rate,
            duration,
            sample_count: samples.len(),
            memory_variance: variance,
        }
    }

    /// Check for memory leaks based on growth rate
    pub fn detect_memory_leaks(&self, threshold_bytes_persec: f64) -> bool {
        let metrics = self.get_current_metrics();
        metrics.growth_rate > threshold_bytes_persec && metrics.sample_count > 10
    }

    /// Generate memory usage report
    pub fn generate_report(&self) -> String {
        let metrics = self.get_current_metrics();
        let _samples = self.samples.lock().unwrap();

        let mut report = String::new();
        report.push_str("=== Memory Usage Report ===\n");
        report.push_str(&format!(
            "Peak Memory: {:.2} MB\n",
            metrics.peak_memory as f64 / 1_048_576.0
        ));
        report.push_str(&format!(
            "Average Memory: {:.2} MB\n",
            metrics.average_memory as f64 / 1_048_576.0
        ));
        report.push_str(&format!(
            "Growth Rate: {:.2} KB/s\n",
            metrics.growth_rate / 1024.0
        ));
        report.push_str(&format!(
            "Duration: {:.2} seconds\n",
            metrics.duration.as_secs_f64()
        ));
        report.push_str(&format!("Samples: {}\n", metrics.sample_count));
        report.push_str(&format!(
            "Memory Variance: {:.2} MB²\n",
            metrics.memory_variance / 1_048_576.0_f64.powi(2)
        ));

        if metrics.growth_rate > 1024.0 * 1024.0 {
            // > 1 MB/s
            report.push_str("\n⚠️  WARNING: High memory growth rate detected!\n");
        }

        if metrics.memory_variance > (100.0 * 1_048_576.0_f64).powi(2) {
            // > 100 MB variance
            report.push_str("⚠️  WARNING: High memory usage variance!\n");
        }

        report
    }
}

/// Advanced memory analysis for graph algorithms
pub struct AdvancedMemoryAnalyzer;

impl AdvancedMemoryAnalyzer {
    /// Analyze memory allocation patterns for different graph operations
    pub fn analyze_operation_memory<F, R>(
        operation_name: &str,
        operation: F,
        sample_interval: Duration,
    ) -> (R, MemoryMetrics)
    where
        F: FnOnce() -> R,
    {
        let mut profiler = RealTimeMemoryProfiler::new();
        profiler.start_monitoring(sample_interval);

        let result = operation();

        let metrics = profiler.stop_monitoring();

        println!(
            "Memory analysis for '{}': Peak={:.2}MB, Growth={:.2}KB/s",
            operation_name,
            metrics.peak_memory as f64 / 1_048_576.0,
            metrics.growth_rate / 1024.0
        );

        (result, metrics)
    }

    /// Compare memory usage between different algorithm implementations
    pub fn compare_implementations<F1, F2, R>(
        impl1: F1,
        impl2: F2,
        impl1_name: &str,
        impl2_name: &str,
    ) -> (MemoryMetrics, MemoryMetrics)
    where
        F1: FnOnce() -> R,
        F2: FnOnce() -> R,
    {
        let sample_interval = Duration::from_millis(10);

        let (_, metrics1) = Self::analyze_operation_memory(impl1_name, impl1, sample_interval);
        let (_, metrics2) = Self::analyze_operation_memory(impl2_name, impl2, sample_interval);

        println!("\n=== Implementation Comparison ===");
        println!(
            "{} - Peak: {:.2}MB, Growth: {:.2}KB/s",
            impl1_name,
            metrics1.peak_memory as f64 / 1_048_576.0,
            metrics1.growth_rate / 1024.0
        );
        println!(
            "{} - Peak: {:.2}MB, Growth: {:.2}KB/s",
            impl2_name,
            metrics2.peak_memory as f64 / 1_048_576.0,
            metrics2.growth_rate / 1024.0
        );

        let memory_improvement = if metrics2.peak_memory > 0 {
            ((metrics1.peak_memory as f64 - metrics2.peak_memory as f64)
                / metrics2.peak_memory as f64)
                * 100.0
        } else {
            0.0
        };

        println!("Memory improvement: {memory_improvement:.1}%");

        (metrics1, metrics2)
    }

    /// Benchmark memory usage for graph algorithms at different scales
    pub fn scale_analysis<F>(
        algorithm_factory: F,
        scales: Vec<usize>,
        algorithm_name: &str,
    ) -> Vec<(usize, MemoryMetrics)>
    where
        F: Fn(usize) -> Box<dyn FnOnce()>,
    {
        let mut results = Vec::new();

        println!("\n=== Scaling Analysis for {algorithm_name} ===");
        for scale in scales {
            let operation = algorithm_factory(scale);
            let (_, metrics) = Self::analyze_operation_memory(
                &format!("{algorithm_name} (n={scale})"),
                operation,
                Duration::from_millis(5),
            );
            results.push((scale, metrics));
        }

        // Print scaling summary
        println!("\nScaling Summary:");
        for (scale, metrics) in &results {
            println!(
                "  n={}: {:.2}MB peak",
                scale,
                metrics.peak_memory as f64 / 1_048_576.0
            );
        }

        results
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::generators;

    #[test]
    fn test_memory_profiling() {
        let graph = generators::complete_graph(100).unwrap();
        let stats = MemoryProfiler::profile_graph(&graph);

        assert!(stats.total_bytes > 0);
        assert!(stats.efficiency > 0.0 && stats.efficiency <= 1.0);
        assert_eq!(graph.node_count(), 100);
        assert_eq!(graph.edge_count(), 100 * 99 / 2); // Complete graph
    }

    #[test]
    fn test_memory_estimation() {
        let estimated = MemoryProfiler::estimate_memory(1000, 5000, false);
        assert!(estimated > 0);

        let estimated_directed = MemoryProfiler::estimate_memory(1000, 5000, true);
        assert!(estimated_directed > estimated); // Directed graphs use more memory
    }

    #[test]
    fn test_fragmentation_analysis() {
        let mut graph: Graph<i32, f64> = Graph::new();

        // Create a graph with varied degrees
        for i in 0..100 {
            graph.add_node(i);
        }

        // Add edges to create different degree nodes
        for i in 0..10 {
            for j in 10..20 {
                if i != j {
                    graph.add_edge(i, j, 1.0).unwrap();
                }
            }
        }

        let report = MemoryProfiler::analyze_fragmentation(&graph);
        assert!(report.fragmentation_ratio >= 0.0 && report.fragmentation_ratio <= 1.0);
    }

    #[test]
    fn test_optimized_builder() {
        let mut builder = OptimizedGraphBuilder::new()
            .reserve_nodes(100)
            .reserve_edges(200)
            .with_estimated_edges_per_node(4);

        for i in 0..100 {
            builder.add_node(i);
        }

        for i in 0..99 {
            builder.add_edge(i, i + 1, 1.0);
        }

        let graph = builder.build().unwrap();
        assert_eq!(graph.node_count(), 100);
        assert_eq!(graph.edge_count(), 99);
    }

    #[test]
    fn test_real_time_memory_profiler() {
        use std::time::Duration;

        let mut profiler = RealTimeMemoryProfiler::new();
        profiler.start_monitoring(Duration::from_millis(10));

        // Simulate some work
        std::thread::sleep(Duration::from_millis(50));

        let metrics = profiler.stop_monitoring();
        assert!(metrics.sample_count > 0);
        assert!(metrics.peak_memory > 0);
    }

    #[test]
    fn test_advanced_memory_analyzer() {
        let (result, metrics) = AdvancedMemoryAnalyzer::analyze_operation_memory(
            "test_operation",
            || {
                // Simulate some graph operation with larger memory allocation
                let mut v = Vec::new();
                for i in 0..100_000 {
                    v.push(i);
                }
                std::thread::sleep(Duration::from_millis(10)); // Allow time for monitoring
                v.len()
            },
            Duration::from_millis(5),
        );

        assert_eq!(result, 100_000);
        // Note: System-level memory monitoring may not always detect small allocations
        // The analyzer should at least run without crashing
        assert!(metrics.sample_count > 0);
    }

    #[test]
    fn test_memory_leak_detection() {
        let profiler = RealTimeMemoryProfiler::new();

        // Should not detect leaks for stable memory usage
        let has_leak = profiler.detect_memory_leaks(1024.0 * 1024.0); // 1MB/s threshold
        assert!(!has_leak); // Should be false for new profiler
    }

    #[test]
    fn test_optimization_suggestions() {
        // Create a mock fragmentation report with high fragmentation
        let fragmentation = FragmentationReport {
            degree_distribution: std::collections::HashMap::new(),
            total_capacity: 1000,
            total_used: 500,
            fragmentation_ratio: 0.5, // High fragmentation
            wasted_bytes: 500 * mem::size_of::<(usize, f64)>(),
        };

        let stats = MemoryStats {
            total_bytes: 10000,
            node_bytes: 2000,
            edge_bytes: 3000,
            adjacency_bytes: 4000,
            overhead_bytes: 1000,
            efficiency: 0.5, // Low efficiency
        };

        let suggestions = suggest_optimizations(&stats, &fragmentation);
        assert!(!suggestions.suggestions.is_empty());
        assert!(suggestions.potential_savings > 0);
    }
}