# تثبيت: uv add prometheus-fastapi-instrumentator
from fastapi import FastAPI
from prometheus_fastapi_instrumentator import Instrumentator

app = FastAPI()

# إضافة مقاييس افتراضية: عدد الطلبات، المدة، الحجم، الاستثناءات
Instrumentator().instrument(app).expose(app)

# الآن نقطة النهاية /metrics تكشف مقاييس Prometheus
# بما في ذلك: http_requests_total, http_request_duration_seconds, إلخ

# التنفيذ ناجح

from prometheus_client import Counter, Histogram, Gauge
from fastapi import FastAPI

app = FastAPI()

# مقاييس أعمال مخصصة
PRICE_UPDATES = Counter(
    "pricetracker_price_updates_total",
    "العدد الإجمالي لتحديثات الأسعار",
    ["category", "currency"],
)

REQUEST_LATENCY = Histogram(
    "pricetracker_request_latency_seconds",
    "زمن استجابة الطلب بالثواني",
    buckets=[0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0],
)

ACTIVE_USERS = Gauge(
    "pricetracker_active_users",
    "عدد المستخدمين النشطين في آخر 5 دقائق",
)

CACHE_HIT_RATE = Gauge(
    "pricetracker_cache_hit_rate",
    "نسبة إصابة ذاكرة التخزين المؤقت Redis بالنسبة المئوية",
)

@app.post("/api/v1/prices")
async def create_price(price: PriceCreate):
    result = await service.create_price(price)
    # تسجيل المقياس المخصص
    PRICE_UPDATES.labels(category="electronics", currency="USD").inc()
    return result

# تم تعريف الدالة بنجاح

{
  "dashboard": {
    "title": "PriceTracker Monitoring",
    "panels": [
      {
        "title": "Request Rate (QPS)",
        "type": "timeseries",
        "targets": [
          {
            "expr": "sum(rate(http_requests_total[5m]))",
            "legendFormat": "Total QPS"
          }
        ]
      },
      {
        "title": "P99 Latency",
        "type": "stat",
        "targets": [
          {
            "expr": "histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))",
            "legendFormat": "P99"
          }
        ],
        "thresholds": {
          "steps": [
            { "value": 0, "color": "green" },
            { "value": 0.1, "color": "yellow" },
            { "value": 0.5, "color": "red" }
          ]
        }
      },
      {
        "title": "Cache Hit Rate",
        "type": "gauge",
        "targets": [
          {
            "expr": "pricetracker_cache_hit_rate",
            "legendFormat": "Hit Rate %"
          }
        ]
      }
    ]
  }
}

{
  "dashboard": {
    "title": "PriceTracker Monitoring",
    "panels": [
      {
        "title": "Request Rate (QPS)",
        "type": "timeseries",
        "targets": [
          {
            "expr": "sum(rate(http_requests_total[5m]))",
            "legendFormat": "Total QPS"
          }
        ]
      },
      {
        "title": "P99 Latency",
        "type": "stat",
        "targets": [
          {
            "expr": "histogram_quantile(0.99, sum(rate(http_request_duration_seconds_buck

# prometheus/alert_rules.yml
groups:
  - name: pricetracker_alerts
    rules:
      - alert: HighP99Latency
        expr: histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le)) > 0.5
        for: 2m
        labels:
          severity: warning
        annotations:
          summary: "زمن الاستجابة P99 يتجاوز 500 مللي ثانية"
          description: "زمن الاستجابة P99 هو {{ $value }}s، الحد هو 0.5s"

      - alert: HighErrorRate
        expr: sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m])) > 0.05
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "معدل الخطأ يتجاوز 5%"
          description: "معدل الخطأ هو {{ $value | humanizePercentage }}"

      - alert: LowCacheHitRate
        expr: pricetracker_cache_hit_rate < 60
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "معدل إصابة التخزين المؤقت أقل من 60%"
          description: "معدل الإصابة الحالي هو {{ $value }}%"

      - alert: DatabaseConnectionPoolExhausted
        expr: pricetracker_db_connections_in_use / pricetracker_db_connections_max > 0.9
        for: 3m
        labels:
          severity: critical
        annotations:
          summary: "تجمع اتصالات قاعدة البيانات مستخدم بنسبة تتجاوز 90%"

تم تحميل إعداد المراقبة
أهداف Prometheus: 3 نشطة
لوحة Grafana: جاهزة

# أضف إلى docker-compose.yml
  prometheus:
    image: prom/prometheus:latest
    ports:
      - "9090:9090"
    volumes:
      - ./docker/prometheus.yml:/etc/prometheus/prometheus.yml
      - ./docker/alert_rules.yml:/etc/prometheus/alert_rules.yml
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--alert.rule-files=/etc/prometheus/alert_rules.yml'

  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin
    volumes:
      - grafana_data:/var/lib/grafana

  alertmanager:
    image: prom/alertmanager:latest
    ports:
      - "9093:9093"
    volumes:
      - ./docker/alertmanager.yml:/etc/alertmanager/alertmanager.yml

CONTAINER ID   IMAGE          STATUS         PORTS
abc123         nginx:latest   Up 2 hours     0.0.0.0:80->80/tcp

global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: "pricetracker-api"
    metrics_path: "/metrics"
    static_configs:
      - targets: ["api:8000"]

rule_files:
  - "alert_rules.yml"

alerting:
  alertmanagers:
    - static_configs:
        - targets: ["alertmanager:9093"]

تم تفعيل الإعداد.