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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.
///
/// This lightweight monitor reports only the quantities it can genuinely
/// observe from the host:
///
/// * `timestamp` — the real wall-clock instant of the sample.
/// * `step_time` — the elapsed time since the previous sample (the
/// sampling cadence). On the very first sample this is zero.
/// * `memory_usage` — the process resident set size (RSS) read from the
/// operating system. Returns `0` on platforms where it cannot be read.
///
/// The remaining fields (CPU/GPU utilisation, cache-hit rate, network
/// bandwidth, solver error/convergence) require hardware performance
/// counters or solver-level instrumentation that this monitor does not
/// have. Rather than fabricating plausible-looking values, they are
/// reported as `0.0` to signal "not measured". Callers that need those
/// quantities must feed them in through a dedicated instrumentation path.
pub fn collect_metrics(&mut self) -> IntegrateResult<PerformanceMetrics> {
let timestamp = Instant::now();
// Real sampling interval: time elapsed since the previous sample.
let step_time = self
.performance_history
.metrics_history
.back()
.map(|prev| timestamp.saturating_duration_since(prev.timestamp))
.unwrap_or_else(|| Duration::from_secs(0));
// Real process resident-set-size in bytes (0 if unavailable).
let memory_usage = Self::process_resident_memory_bytes();
// Throughput is samples-per-second of the monitor itself when we have
// a positive interval; otherwise it is unknown (0.0).
let throughput = if step_time > Duration::from_secs(0) {
1.0 / step_time.as_secs_f64()
} else {
0.0
};
let metrics = PerformanceMetrics::new(
timestamp,
step_time,
throughput,
memory_usage,
0.0, // cpu_utilization: requires per-core counters (not measured)
0.0, // gpu_utilization: requires a GPU runtime (not measured)
0.0, // cache_hit_rate: requires hardware counters (not measured)
0.0, // network_bandwidth: requires NIC counters (not measured)
0.0, // error_accuracy: requires solver instrumentation (not measured)
0.0, // convergence_rate: requires solver instrumentation (not measured)
);
self.performance_history.add_metrics(metrics.clone());
Ok(metrics)
}
/// Read the current process resident set size (RSS) in bytes.
///
/// Returns `0` when the value cannot be determined on the current
/// platform (e.g. `/proc` is unavailable).
fn process_resident_memory_bytes() -> usize {
#[cfg(target_os = "linux")]
{
// `/proc/self/status` exposes `VmRSS` directly in kibibytes, which
// avoids any dependency on the system page size.
if let Ok(contents) = std::fs::read_to_string("/proc/self/status") {
for line in contents.lines() {
if let Some(rest) = line.strip_prefix("VmRSS:") {
if let Some(kb_str) = rest.split_whitespace().next() {
if let Ok(kb) = kb_str.parse::<usize>() {
return kb.saturating_mul(1024);
}
}
}
}
}
0
}
#[cfg(not(target_os = "linux"))]
{
0
}
}
/// 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();
}
}