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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
Comprehensive API testing including unit tests, integration tests, performance testing, and automated test pipelines. Use when testing REST endpoints, validating API responses, load testing, or setting up test automation.
Manage code quality for Football Prediction project using Make commands. Use when running quality checks, formatting code, running tests, or preparing for commits. Supports black, flake8, mypy, bandit, pytest, and coverage tools.
Collect football data from external APIs including FotMob L2 data, odds information, and real-time match statistics. Use when gathering match data, collecting historical statistics, or updating prediction datasets.
Design and optimize data pipelines for football prediction system. Use when building ETL processes, data transformations, or optimizing data flow between PostgreSQL, Redis, and external APIs.
Manage PostgreSQL database operations including migrations, query optimization, connection pooling, and data backup/restore. Use when handling database tasks, optimizing queries, or managing database connections.
Manage application deployment including Docker containers, production environments, and service orchestration. Use when deploying to production, managing Docker services, or handling deployment rollbacks.
SOC 직업 분류 기준
| 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"
deploy/monitoring/prometheus.yml - Prometheus配置deploy/monitoring/alerts.yml - 告警规则deploy/monitoring/telegraf.conf - 系统指标收集docker-compose.yml - 监控服务定义deployment-management: Deployment managementdocker-devops: Docker and DevOps best practicesdatabase-operations: Database operationsdata-engineering: Data pipeline engineering