菜单
Monitor system performance, prediction accuracy, and real-time metrics using Prometheus and Grafana. Use when checking system health, analyzing performance data, or tracking prediction accuracy.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | performance-monitoring |
| description | Monitor system performance, prediction accuracy, and real-time metrics using Prometheus and Grafana. Use when checking system health, analyzing performance data, or tracking prediction accuracy. |
全面的系统性能监控技能，集成Prometheus指标收集和Grafana可视化，提供实时监控和告警功能。
应用 → Prometheus → Grafana → 用户
↓ ↓ ↓
指标 存储 可视化
# 自定义指标
from prometheus_client import Counter, Histogram, Gauge
# 请求计数器
REQUEST_COUNT = Counter(
'http_requests_total',
'Total HTTP requests',
['method', 'endpoint', 'status']
)
# 响应时间
REQUEST_DURATION = Histogram(
'http_request_duration_seconds',
'HTTP request duration',
['method', 'endpoint']
)
# 预测准确率
PREDICTION_ACCURACY = Gauge(
'prediction_accuracy_rate',
'Current prediction accuracy rate'
)
# 启动完整监控栈
docker-compose up -d
# 查看服务状态
docker-compose ps
# Prometheus指标
http://localhost:9090
# Grafana仪表板
http://localhost:3000
# 用户名/密码: admin/admin123
# 通过API查询
curl "http://localhost:9090/api/v1/query?query=http_requests_total"
# 通过PromQL查询
rate(http_requests_total[5m])
histogram_quantile(0.95, http_request_duration_seconds_bucket)
# prometheus/alerts.yml
groups:
- name: api-alerts
rules:
- alert: HighErrorRate
expr: rate(http_requests_total{status=~"5.."}[5m]) > 0.05
for: 2m
labels:
severity: warning
annotations:
summary: "High error rate detected"
- alert: HighLatency
expr: histogram_quantile(0.95, http_request_duration_seconds_bucket) > 1
for: 5m
labels:
severity: warning
annotations:
summary: "High P95 latency detected"
- alert: LowPredictionAccuracy
expr: prediction_accuracy_rate < 0.60
for: 10m
labels:
severity: critical
annotations:
summary: "Prediction accuracy below threshold"
- alert: ModelInferenceTimeHigh
expr: model_inference_duration_seconds > 0.5
for: 5m
labels:
severity: warning
annotations:
summary: "Model inference time too high"
- alert: HighCPUUsage
expr: cpu_usage_percent > 80
for: 10m
labels:
severity: warning
annotations:
summary: "CPU usage above 80%"
- alert: LowCacheHitRate
expr: redis_cache_hit_rate < 0.50
for: 15m
labels:
severity: warning
annotations:
summary: "Redis cache hit rate below 50%"
# 使用直方图减少基数
REQUEST_DURATION = Histogram(
'http_request_duration_seconds',
'HTTP request duration',
buckets=[0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0]
)
# 批量提交指标
from prometheus_client import CollectorRegistry
registry = CollectorRegistry()
from prometheus_client import start_http_server, Counter
# 启动指标服务器
start_http_server(8001)
# 在API中记录指标
REQUEST_COUNT.labels(
method='POST',
endpoint='/predict',
status='200'
).inc()
from prometheus_client import make_asgi_app
from fastapi import FastAPI
app = FastAPI()
metrics_app = make_asgi_app()
app.mount("/metrics", metrics_app)
@app.middleware("http")
async def metrics_middleware(request, call_next):
start_time = time.time()
response = await call_next(request)
REQUEST_DURATION.observe(
time.time() - start_time,
labels={
'method': request.method,
'endpoint': request.url.path
}
)
return response
Prometheus无法采集数据
Grafana无法连接Prometheus
告警不触发
# 查看Prometheus目标
curl http://localhost:9090/api/v1/targets
# 测试告警规则
curl -X POST http://localhost:9090/api/v1/alerts
# 查看指标标签
curl "http://localhost:9090/api/v1/label/__name__/values"
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