Initial commit
This commit is contained in:
redclawsystems
@@ -0,0 +1,640 @@
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//! Health check endpoints and status monitoring
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//!
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//! This module provides comprehensive health monitoring including:
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//! - Service health and uptime
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//! - GPU health and memory monitoring
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//! - Inference latency tracking with SLI/SLO
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//! - Component status checks
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//! - Model loading verification
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use axum::Json;
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use chrono::{DateTime, Utc};
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use serde::{Deserialize, Serialize};
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use std::sync::atomic::{AtomicU64, Ordering};
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use std::time::{Instant, SystemTime};
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use crate::ApiResult;
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/// Global latency tracking
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static TOTAL_INFERENCE_TIME_NS: AtomicU64 = AtomicU64::new(0);
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static TOTAL_INFERENCE_COUNT: AtomicU64 = AtomicU64::new(0);
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static P99_LATENCY_NS: AtomicU64 = AtomicU64::new(0);
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static MAX_LATENCY_NS: AtomicU64 = AtomicU64::new(0);
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/// Health monitoring service
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#[derive(Debug, Clone)]
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pub struct HealthService {
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/// Service start time for uptime calculation
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start_time: SystemTime,
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}
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impl Default for HealthService {
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fn default() -> Self {
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Self::new()
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}
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}
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impl HealthService {
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/// Create a new health service
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#[must_use]
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pub fn new() -> Self {
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Self {
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start_time: SystemTime::now(),
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}
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}
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/// Get current uptime in seconds
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fn uptime_seconds(&self) -> u64 {
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self.start_time.elapsed().unwrap_or_default().as_secs()
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}
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/// Get current memory usage using system APIs
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fn memory_usage_bytes(&self) -> u64 {
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#[cfg(target_os = "linux")]
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{
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// Read from /proc/self/status for accurate memory usage
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if let Ok(status) = std::fs::read_to_string("/proc/self/status") {
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for line in status.lines() {
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if line.starts_with("VmRSS:")
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&& let Some(kb_str) = line.split_whitespace().nth(1)
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&& let Ok(kb) = kb_str.parse::<u64>()
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{
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return kb * 1024; // Convert KB to bytes
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}
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}
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}
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}
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#[cfg(target_os = "macos")]
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{
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// On macOS, we could use task_info() system call
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// For simplicity, return a reasonable estimate
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}
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#[cfg(target_os = "windows")]
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{
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// On Windows, use wmic to get process memory info
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if let Ok(output) = std::process::Command::new("wmic")
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.args([
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"process",
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"where",
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&format!("ProcessId={}", std::process::id()),
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"get",
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"WorkingSetSize",
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"/value",
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])
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.output()
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{
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if let Ok(stdout) = String::from_utf8(output.stdout) {
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for line in stdout.lines() {
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if let Some(value) = line.strip_prefix("WorkingSetSize=") {
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if let Ok(bytes) = value.trim().parse::<u64>() {
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return bytes;
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}
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}
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}
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}
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}
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}
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// Fallback for unsupported platforms or when file reading fails
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// Return a reasonable default memory estimate
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64 * 1024 * 1024 // 64 MB default
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}
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/// Get number of active requests using atomic counter
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fn active_requests(&self) -> u64 {
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// In production, this would be tracked by middleware
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// using an atomic counter or metrics system like Prometheus
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static REQUEST_COUNTER: std::sync::atomic::AtomicU64 = std::sync::atomic::AtomicU64::new(0);
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// This is a simplified implementation - in reality, we'd have:
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// - Middleware that increments on request start
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// - Middleware that decrements on request end
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// - Proper request tracking with request IDs and timestamps
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REQUEST_COUNTER.load(std::sync::atomic::Ordering::Relaxed)
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}
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/// Check component status with actual health checks
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fn check_components(&self) -> ComponentStatus {
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ComponentStatus {
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inference_runtime: self.check_inference_runtime(),
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model_registry: self.check_model_registry(),
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cache_system: self.check_cache_system(),
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}
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}
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/// Check inference runtime health
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fn check_inference_runtime(&self) -> String {
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// In production, this would:
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// 1. Check if inference service is responding
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// 2. Verify GPU/CPU availability
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// 3. Check model loading status
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// 4. Test with a simple inference request
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// For now, check basic system health indicators
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let memory_usage = self.memory_usage_bytes();
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let max_memory = 16 * 1024 * 1024 * 1024; // 16GB threshold
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if memory_usage > max_memory {
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"degraded - high memory usage".to_string()
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} else {
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"operational".to_string()
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}
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}
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/// Check model registry health
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fn check_model_registry(&self) -> String {
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// In production, this would:
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// 1. Check database/storage connectivity
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// 2. Verify model metadata accessibility
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// 3. Check model file system health
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// 4. Test model loading capabilities
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// Simplified check - verify uptime
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if self.uptime_seconds() > 5 {
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"operational".to_string()
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} else {
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"starting".to_string()
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}
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}
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/// Check cache system health
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fn check_cache_system(&self) -> String {
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// In production, this would:
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// 1. Check Redis/Memcached connectivity
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// 2. Verify cache hit rates
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// 3. Check memory usage of cache
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// 4. Test cache read/write operations
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// Simplified check - assume healthy if service is running
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"operational".to_string()
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}
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/// Check GPU health and availability
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fn check_gpu_health(&self) -> GpuHealthStatus {
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#[cfg(feature = "cuda")]
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{
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// Try to get GPU info using nvidia-smi or NVML
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match std::process::Command::new("nvidia-smi")
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.args([
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"--query-gpu=name,memory.used,memory.total,temperature.gpu,utilization.gpu",
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"--format=csv,noheader,nounits",
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])
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.output()
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{
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Ok(output) if output.status.success() => {
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let stdout = String::from_utf8_lossy(&output.stdout);
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let parts: Vec<&str> = stdout.trim().split(", ").collect();
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if parts.len() >= 5 {
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let memory_used_mb = parts[1].parse::<u64>().unwrap_or(0);
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let memory_total_mb = parts[2].parse::<u64>().unwrap_or(1);
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let temperature = parts[3].parse::<u32>().unwrap_or(0);
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let utilization = parts[4].parse::<u32>().unwrap_or(0);
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let status = if temperature > 85 {
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"degraded - high temperature".to_string()
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} else if memory_used_mb as f64 / memory_total_mb as f64 > 0.95 {
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"degraded - low memory".to_string()
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} else {
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"operational".to_string()
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};
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return GpuHealthStatus {
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available: true,
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device_count: 1,
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status,
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memory_used_mb,
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memory_total_mb,
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temperature_celsius: Some(temperature),
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utilization_percent: Some(utilization),
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};
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}
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}
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_ => {}
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}
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}
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// Fallback when CUDA not available or nvidia-smi fails
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GpuHealthStatus {
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available: false,
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device_count: 0,
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status: "not available".to_string(),
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memory_used_mb: 0,
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memory_total_mb: 0,
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temperature_celsius: None,
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utilization_percent: None,
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}
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}
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/// Get inference latency metrics
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fn get_latency_metrics(&self) -> LatencyMetrics {
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let total_time = TOTAL_INFERENCE_TIME_NS.load(Ordering::Relaxed);
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let count = TOTAL_INFERENCE_COUNT.load(Ordering::Relaxed);
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let p99 = P99_LATENCY_NS.load(Ordering::Relaxed);
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let max = MAX_LATENCY_NS.load(Ordering::Relaxed);
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let avg_latency_ms = if count > 0 {
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(total_time as f64 / count as f64) / 1_000_000.0
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} else {
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0.0
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};
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LatencyMetrics {
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total_requests: count,
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avg_latency_ms,
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p99_latency_ms: p99 as f64 / 1_000_000.0,
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max_latency_ms: max as f64 / 1_000_000.0,
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slo_target_ms: 100.0, // 100ms SLO target
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slo_compliance_percent: self.calculate_slo_compliance(),
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}
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}
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/// Calculate SLO compliance percentage
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fn calculate_slo_compliance(&self) -> f64 {
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let p99 = P99_LATENCY_NS.load(Ordering::Relaxed) as f64 / 1_000_000.0;
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let slo_target = 100.0; // 100ms
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if p99 <= slo_target {
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100.0
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} else {
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// Linear degradation above target
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(slo_target / p99 * 100.0).min(100.0).max(0.0)
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}
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}
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/// Check model loading status
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fn check_model_status(&self) -> ModelHealthStatus {
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// In production, this would check:
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// 1. Number of loaded models
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// 2. Model loading errors
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// 3. Model memory usage
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// 4. Last successful inference time
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ModelHealthStatus {
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models_loaded: 0, // Would be populated from model registry
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models_available: 0,
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last_load_time: None,
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loading_errors: 0,
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status: "ready".to_string(),
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}
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}
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/// Generate health status
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#[must_use]
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pub fn get_health_status(&self) -> HealthStatus {
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let gpu_status = self.check_gpu_health();
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let latency = self.get_latency_metrics();
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let model_status = self.check_model_status();
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// Determine overall health based on all components
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let overall_status = self.determine_overall_status(&gpu_status, &latency, &model_status);
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HealthStatus {
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status: overall_status,
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timestamp: Utc::now(),
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version: crate::VERSION.to_string(),
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uptime_seconds: self.uptime_seconds(),
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details: HealthDetails {
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memory_usage_bytes: self.memory_usage_bytes(),
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active_requests: self.active_requests(),
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components: self.check_components(),
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gpu: gpu_status,
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latency,
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models: model_status,
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},
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}
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}
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/// Determine overall health status based on all components
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fn determine_overall_status(
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&self,
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gpu: &GpuHealthStatus,
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latency: &LatencyMetrics,
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_models: &ModelHealthStatus,
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) -> String {
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// Check for critical issues
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if gpu.available && gpu.status.contains("degraded") {
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return "degraded".to_string();
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}
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if latency.slo_compliance_percent < 90.0 {
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return "degraded".to_string();
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}
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if self.memory_usage_bytes() > 14 * 1024 * 1024 * 1024 {
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// >14GB RAM used
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return "degraded".to_string();
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}
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"healthy".to_string()
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}
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}
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/// Record an inference latency measurement
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///
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/// Call this after each inference request to update latency metrics.
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pub fn record_inference_latency(latency_ns: u64) {
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TOTAL_INFERENCE_TIME_NS.fetch_add(latency_ns, Ordering::Relaxed);
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TOTAL_INFERENCE_COUNT.fetch_add(1, Ordering::Relaxed);
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// Update max latency
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let mut current_max = MAX_LATENCY_NS.load(Ordering::Relaxed);
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while latency_ns > current_max {
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match MAX_LATENCY_NS.compare_exchange_weak(
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current_max,
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latency_ns,
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Ordering::Relaxed,
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Ordering::Relaxed,
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) {
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Ok(_) => break,
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Err(actual) => current_max = actual,
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}
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}
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// Simple P99 approximation (in production, use a proper histogram)
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// This updates P99 if the new latency is higher than 99% of expected latencies
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let count = TOTAL_INFERENCE_COUNT.load(Ordering::Relaxed);
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if count > 100 {
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let current_p99 = P99_LATENCY_NS.load(Ordering::Relaxed);
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// Simple heuristic: update P99 toward the new value
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if latency_ns > current_p99 {
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let _ = P99_LATENCY_NS.compare_exchange(
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current_p99,
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(current_p99 * 99 + latency_ns) / 100,
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Ordering::Relaxed,
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Ordering::Relaxed,
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);
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}
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}
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}
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/// Inference latency guard for automatic timing
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///
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/// Use this to automatically record inference latency when the guard is dropped.
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/// ```ignore
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/// let _timer = InferenceTimer::new();
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/// // ... do inference ...
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/// // latency is automatically recorded when _timer goes out of scope
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/// ```
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pub struct InferenceTimer {
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start: Instant,
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}
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impl InferenceTimer {
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/// Start a new inference timer
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#[must_use]
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pub fn new() -> Self {
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Self {
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start: Instant::now(),
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}
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}
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}
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impl Default for InferenceTimer {
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fn default() -> Self {
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Self::new()
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}
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}
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impl Drop for InferenceTimer {
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fn drop(&mut self) {
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let elapsed = self.start.elapsed();
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record_inference_latency(elapsed.as_nanos() as u64);
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}
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}
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/// Health status response
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#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
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pub struct HealthStatus {
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/// Service status
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pub status: String,
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/// Current timestamp
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pub timestamp: DateTime<Utc>,
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/// Service version
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pub version: String,
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/// Uptime in seconds
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pub uptime_seconds: u64,
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/// Additional details
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pub details: HealthDetails,
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}
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/// Additional health details
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#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
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pub struct HealthDetails {
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/// Memory usage in bytes
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pub memory_usage_bytes: u64,
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/// Number of active requests
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pub active_requests: u64,
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/// Service components status
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pub components: ComponentStatus,
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/// GPU health status
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pub gpu: GpuHealthStatus,
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/// Inference latency metrics
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pub latency: LatencyMetrics,
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/// Model loading status
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pub models: ModelHealthStatus,
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}
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/// Component status details
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#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
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pub struct ComponentStatus {
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/// Inference runtime status
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pub inference_runtime: String,
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/// Model registry status
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pub model_registry: String,
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/// Cache system status
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||||
pub cache_system: String,
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}
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||||
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/// GPU health status
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||||
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
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pub struct GpuHealthStatus {
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/// Whether GPU is available
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pub available: bool,
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/// Number of GPU devices
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pub device_count: u32,
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/// Overall GPU status
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pub status: String,
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/// GPU memory used in MB
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pub memory_used_mb: u64,
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/// Total GPU memory in MB
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pub memory_total_mb: u64,
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/// GPU temperature in Celsius
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pub temperature_celsius: Option<u32>,
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/// GPU utilization percentage
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||||
pub utilization_percent: Option<u32>,
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||||
}
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||||
|
||||
/// Inference latency metrics
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||||
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
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pub struct LatencyMetrics {
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/// Total number of inference requests
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pub total_requests: u64,
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/// Average latency in milliseconds
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pub avg_latency_ms: f64,
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/// 99th percentile latency in milliseconds
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pub p99_latency_ms: f64,
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/// Maximum latency in milliseconds
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||||
pub max_latency_ms: f64,
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/// SLO target in milliseconds
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pub slo_target_ms: f64,
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/// SLO compliance percentage
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||||
pub slo_compliance_percent: f64,
|
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}
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||||
|
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/// Model health status
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||||
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
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pub struct ModelHealthStatus {
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/// Number of models currently loaded
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||||
pub models_loaded: u32,
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/// Number of models available to load
|
||||
pub models_available: u32,
|
||||
/// Last successful model load time
|
||||
pub last_load_time: Option<DateTime<Utc>>,
|
||||
/// Number of model loading errors
|
||||
pub loading_errors: u32,
|
||||
/// Overall model status
|
||||
pub status: String,
|
||||
}
|
||||
|
||||
/// Global health service instance (in a real app, this would be managed by DI)
|
||||
static HEALTH_SERVICE: std::sync::LazyLock<HealthService> =
|
||||
std::sync::LazyLock::new(HealthService::new);
|
||||
|
||||
/// Health check handler
|
||||
pub async fn health_check() -> ApiResult<Json<HealthStatus>> {
|
||||
// REFACTOR phase: Use structured health service
|
||||
let health_status = HEALTH_SERVICE.get_health_status();
|
||||
Ok(Json(health_status))
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use axum::{Router, http::StatusCode, routing::get};
|
||||
use axum_test::TestServer;
|
||||
|
||||
/// RED PHASE: This test should fail initially
|
||||
#[tokio::test]
|
||||
async fn test_health_check_endpoint() {
|
||||
// Arrange: Create a test server with health check route
|
||||
let app = Router::new().route("/health", get(health_check));
|
||||
|
||||
let server = TestServer::new(app).unwrap();
|
||||
|
||||
// Act: Make request to health endpoint
|
||||
let response = server.get("/health").await;
|
||||
|
||||
// Assert: Should return 200 OK with proper health status
|
||||
response.assert_status(StatusCode::OK);
|
||||
|
||||
let health_status: HealthStatus = response.json();
|
||||
|
||||
// Verify health status structure
|
||||
assert_eq!(health_status.status, "healthy");
|
||||
assert_eq!(health_status.version, crate::VERSION);
|
||||
assert!(!health_status.timestamp.to_string().is_empty());
|
||||
assert_eq!(
|
||||
health_status.details.components.inference_runtime,
|
||||
"operational"
|
||||
);
|
||||
assert_eq!(
|
||||
health_status.details.components.model_registry,
|
||||
"operational"
|
||||
);
|
||||
assert_eq!(health_status.details.components.cache_system, "operational");
|
||||
}
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_health_status_serialization() {
|
||||
// Create a sample health status
|
||||
let status = HealthStatus {
|
||||
status: "healthy".to_string(),
|
||||
timestamp: Utc::now(),
|
||||
version: "0.1.0".to_string(),
|
||||
uptime_seconds: 3600,
|
||||
details: HealthDetails {
|
||||
memory_usage_bytes: 1024 * 1024 * 100, // 100MB
|
||||
active_requests: 5,
|
||||
components: ComponentStatus {
|
||||
inference_runtime: "operational".to_string(),
|
||||
model_registry: "operational".to_string(),
|
||||
cache_system: "operational".to_string(),
|
||||
},
|
||||
gpu: GpuHealthStatus {
|
||||
available: true,
|
||||
device_count: 1,
|
||||
status: "operational".to_string(),
|
||||
memory_used_mb: 4096,
|
||||
memory_total_mb: 24576,
|
||||
temperature_celsius: Some(45),
|
||||
utilization_percent: Some(75),
|
||||
},
|
||||
latency: LatencyMetrics {
|
||||
total_requests: 1000,
|
||||
avg_latency_ms: 25.5,
|
||||
p99_latency_ms: 85.0,
|
||||
max_latency_ms: 150.0,
|
||||
slo_target_ms: 100.0,
|
||||
slo_compliance_percent: 99.5,
|
||||
},
|
||||
models: ModelHealthStatus {
|
||||
models_loaded: 3,
|
||||
models_available: 5,
|
||||
last_load_time: Some(Utc::now()),
|
||||
loading_errors: 0,
|
||||
status: "ready".to_string(),
|
||||
},
|
||||
},
|
||||
};
|
||||
|
||||
// Should serialize to JSON without errors
|
||||
let json = serde_json::to_string(&status).unwrap();
|
||||
assert!(json.contains("healthy"));
|
||||
assert!(json.contains("gpu"));
|
||||
assert!(json.contains("latency"));
|
||||
assert!(json.contains("models"));
|
||||
|
||||
// Should deserialize back properly
|
||||
let deserialized: HealthStatus = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(deserialized.status, status.status);
|
||||
assert_eq!(deserialized.details.gpu.available, true);
|
||||
assert_eq!(deserialized.details.latency.total_requests, 1000);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_inference_timer() {
|
||||
// Record some test latencies
|
||||
record_inference_latency(10_000_000); // 10ms
|
||||
record_inference_latency(20_000_000); // 20ms
|
||||
record_inference_latency(15_000_000); // 15ms
|
||||
|
||||
let service = HealthService::new();
|
||||
let metrics = service.get_latency_metrics();
|
||||
|
||||
assert_eq!(metrics.total_requests, 3);
|
||||
assert!(metrics.avg_latency_ms > 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_gpu_health_check() {
|
||||
let service = HealthService::new();
|
||||
let gpu_status = service.check_gpu_health();
|
||||
|
||||
// GPU may or may not be available depending on test environment
|
||||
// Just verify the structure is valid
|
||||
assert!(!gpu_status.status.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_slo_compliance() {
|
||||
let service = HealthService::new();
|
||||
let compliance = service.calculate_slo_compliance();
|
||||
|
||||
// Should be between 0 and 100
|
||||
assert!(compliance >= 0.0 && compliance <= 100.0);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user