scirs2-integrate 0.4.2

Numerical integration module for SciRS2 (scirs2-integrate)
Documentation 
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//! Performance monitoring implementation
//!
//! This module contains the implementation for performance monitoring,
//! metrics collection, and system resource tracking.

use super::types::*;
use crate::common::IntegrateFloat;
use crate::error::{IntegrateError, IntegrateResult};
use std::time::{Duration, Instant};

impl PerformanceMonitoringEngine {
    /// Create a new performance monitoring engine
    pub fn new() -> Self {
        Self {
            metrics_collector: MetricsCollector::default(),
            performance_history: PerformanceHistory::default(),
            system_monitor: SystemResourceMonitor::default(),
            network_monitor: NetworkPerformanceMonitor::default(),
        }
    }

    /// Start continuous performance monitoring
    pub fn start_monitoring(&mut self) -> IntegrateResult<()> {
        // Implementation for starting monitoring would go here
        Ok(())
    }

    /// Stop performance monitoring
    pub fn stop_monitoring(&mut self) -> IntegrateResult<()> {
        // Implementation for stopping monitoring would go here
        Ok(())
    }

    /// Collect current performance metrics
    pub fn collect_metrics(&mut self) -> IntegrateResult<PerformanceMetrics> {
        let timestamp = Instant::now();

        // Mock implementation - in real code this would collect actual metrics
        let metrics = PerformanceMetrics::new(
            timestamp,
            Duration::from_millis(10),
            100.0,
            1024 * 1024,
            50.0,
            30.0,
            0.85,
            1000.0,
            0.99,
            0.95,
        );

        self.performance_history.add_metrics(metrics.clone());
        Ok(metrics)
    }

    /// Get performance analysis from collected metrics
    pub fn get_performance_analysis(&self) -> IntegrateResult<PerformanceAnalysis> {
        if self.performance_history.metrics_history.is_empty() {
            return Ok(PerformanceAnalysis {
                average_throughput: 0.0,
                average_cpu_utilization: 0.0,
                average_memory_usage: 0,
                performance_trend: PerformanceTrend::Stable,
                bottlenecks: Vec::new(),
            });
        }

        let metrics = &self.performance_history.metrics_history;
        let count = metrics.len() as f64;

        let avg_throughput = metrics.iter().map(|m| m.throughput).sum::<f64>() / count;
        let avg_cpu = metrics.iter().map(|m| m.cpu_utilization).sum::<f64>() / count;
        let avg_memory = metrics.iter().map(|m| m.memory_usage).sum::<usize>() / metrics.len();

        // Simple trend analysis
        let trend = if metrics.len() > 1 {
            let recent_avg = metrics
                .iter()
                .rev()
                .take(5)
                .map(|m| m.throughput)
                .sum::<f64>()
                / 5.0_f64.min(metrics.len() as f64);
            let older_avg = metrics.iter().take(5).map(|m| m.throughput).sum::<f64>()
                / 5.0_f64.min(metrics.len() as f64);

            if recent_avg > older_avg * 1.05 {
                PerformanceTrend::Improving
            } else if recent_avg < older_avg * 0.95 {
                PerformanceTrend::Degrading
            } else {
                PerformanceTrend::Stable
            }
        } else {
            PerformanceTrend::Stable
        };

        // Simple bottleneck detection
        let mut bottlenecks = Vec::new();
        if avg_cpu > 80.0 {
            bottlenecks.push(PerformanceBottleneck::CPU);
        }
        if avg_memory > 1024 * 1024 * 1024 {
            bottlenecks.push(PerformanceBottleneck::Memory);
        }

        Ok(PerformanceAnalysis {
            average_throughput: avg_throughput,
            average_cpu_utilization: avg_cpu,
            average_memory_usage: avg_memory,
            performance_trend: trend,
            bottlenecks,
        })
    }
}

impl PerformanceHistory {
    /// Add new metrics to history
    pub fn add_metrics(&mut self, metrics: PerformanceMetrics) {
        self.metrics_history.push_back(metrics);

        // Keep history size under limit
        if self.metrics_history.len() > self.max_history_size {
            self.metrics_history.pop_front();
        }
    }

    /// Clear all history
    pub fn clear(&mut self) {
        self.metrics_history.clear();
        self.aggregated_stats.clear();
    }
}