服务性能指标收集策略

服务性能指标收集策略

核心监控指标配置

1. 模型推理延迟监控

# prometheus配置文件
scrape_configs:
  - job_name: 'model-inference'
    metrics_path: '/metrics'
    static_configs:
      - targets: ['localhost:8080']
    metric_relabel_configs:
      - source_labels: [__name__]
        regex: 'model_latency_seconds'
        target_label: metric_type
        replacement: latency

2. 资源使用率监控

# 监控脚本
import psutil
import time
from prometheus_client import Gauge, start_http_server

# 创建指标
memory_usage = Gauge('model_memory_percent', 'Memory usage percentage')
cpu_usage = Gauge('model_cpu_percent', 'CPU usage percentage')

# 指标收集循环
while True:
    memory_usage.set(psutil.virtual_memory().percent)
    cpu_usage.set(psutil.cpu_percent())
    time.sleep(30)

告警配置方案

3. 关键告警阈值设置

• 延迟超过500ms触发警告
• CPU使用率超过85%触发告警
• 内存使用率超过90%触发紧急告警

# alertmanager配置
route:
  receiver: 'slack-notifications'
  routes:
    - match:
        severity: critical
      receiver: 'pagerduty'

receivers:
  - name: 'slack-notifications'
    slack_configs:
      - send_resolved: true

4. 实施步骤

1. 部署Prometheus服务器
2. 配置指标采集器
3. 设置告警规则
4. 集成通知渠道