prometheus

Prometheus monitoring and alerting for cloud-native observability. Use when implementing metrics collection, PromQL queries, alerting rules, or service discovery. Triggers: prometheus, promql, metrics, alertmanager, service discovery, recording rules, alerting, scrape config.

الانتقال إلى التثبيت

المستودع: ForceInjection/domain-driven-design-skills
آخر نشاط في المصدر: ٨ مايو ٢٠٢٦ في ٠٣:٠٧
لغة SKILL.md المكتشفة: الإنجليزية
النجوم: ٢٥
التفرعات: ٧

خيارات التثبيت

يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.

npx skills add https://github.com/ForceInjection/domain-driven-design-skills --skill prometheus

يبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.

تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.

مراجعة ملفات المصدر

اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.

مستكشف الملفات

2 ملفات

SKILL.md

تعليمات المصدر · معاينة للقراءة فقط

العودة إلى البحث العودة إلى التثبيت

Prometheus Monitoring and Alerting

Overview

Prometheus is a powerful open-source monitoring and alerting system designed for reliability and scalability in cloud-native environments.

Architecture Components

Prometheus Server: Core component that scrapes and stores time-series data

Alertmanager: Handles alerts, deduplication, grouping, routing, and notifications

Pushgateway: Allows ephemeral jobs to push metrics (use sparingly)

Exporters: Convert metrics from third-party systems to Prometheus format

Client Libraries: Instrument application code (Go, Java, Python, etc.)

Prometheus Operator: Kubernetes-native deployment and management

Data Model

Metrics: Time-series data identified by metric name and key-value labels

Metric Types:

Counter: Monotonically increasing value (requests, errors)
Gauge: Value that can go up/down (temperature, memory usage)
Histogram: Observations in configurable buckets (latency, request size)
Summary: Similar to histogram but calculates quantiles client-side

Setup and Configuration

Basic Prometheus Server Configuration

# prometheus.yml global: scrape_interval: 15s [] [] [] [, ] [] [] []

name	prometheus
description	Prometheus monitoring and alerting for cloud-native observability. Use when implementing metrics collection, PromQL queries, alerting rules, or service discovery. Triggers: prometheus, promql, metrics, alertmanager, service discovery, recording rules, alerting, scrape config.

Prometheus Monitoring and Alerting

Overview

Prometheus is a powerful open-source monitoring and alerting system designed for reliability and scalability in cloud-native environments.

Architecture Components

Prometheus Server: Core component that scrapes and stores time-series data
Alertmanager: Handles alerts, deduplication, grouping, routing, and notifications
Pushgateway: Allows ephemeral jobs to push metrics (use sparingly)
Exporters: Convert metrics from third-party systems to Prometheus format
Client Libraries: Instrument application code (Go, Java, Python, etc.)
Prometheus Operator: Kubernetes-native deployment and management

Data Model

Metrics: Time-series data identified by metric name and key-value labels
Metric Types:
- Counter: Monotonically increasing value (requests, errors)
- Gauge: Value that can go up/down (temperature, memory usage)
- Histogram: Observations in configurable buckets (latency, request size)
- Summary: Similar to histogram but calculates quantiles client-side

Setup and Configuration

Basic Prometheus Server Configuration

# prometheus.yml
global:
  scrape_interval: 15s
   
   
  
     
     



  
     
         
             



   
   



  
    
    
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// main.go package main import ( "net/http" "time" "github.com/prometheus/client_golang/prometheus" "github.com/prometheus/client_golang/prometheus/promauto" "github.com/prometheus/client_golang/prometheus/promhttp" ) var ( // Counter for total requests httpRequestsTotal = promauto.NewCounterVec( prometheus.CounterOpts{ Name: "http_requests_total", Help: "Total number of HTTP requests", }, []string{"method", "endpoint", "status"}, ) // Histogram for request duration httpRequestDuration = promauto.NewHistogramVec( prometheus.HistogramOpts{ Name: "http_request_duration_seconds", Help: "HTTP request duration in seconds", Buckets: []float64{.001, .005, .01, .025, .05, .1, .25, .5, 1, 2.5, 5, 10}, }, []string{"method", "endpoint"}, ) // Gauge for active connections activeConnections = promauto.NewGauge( prometheus.GaugeOpts{ Name: "active_connections", Help: "Number of active connections", }, ) // Summary for response sizes responseSizeBytes = promauto.NewSummaryVec( prometheus.SummaryOpts{ Name: "http_response_size_bytes", Help: "HTTP response size in bytes", Objectives: map[float64]float64{0.5: 0.05, 0.9: 0.01, 0.99: 0.001}, }, []string{"endpoint"}, ) ) // Middleware to instrument HTTP handlers func instrumentHandler(endpoint string, handler http.HandlerFunc) http.HandlerFunc { return func(w http.ResponseWriter, r *http.Request) { start := time.Now() activeConnections.Inc() defer activeConnections.Dec() // Wrap response writer to capture status code wrapped := &responseWriter{ResponseWriter: w, statusCode: 200} handler(wrapped, r) duration := time.Since(start).Seconds() httpRequestDuration.WithLabelValues(r.Method, endpoint).Observe(duration) httpRequestsTotal.WithLabelValues(r.Method, endpoint, http.StatusText(wrapped.statusCode)).Inc() } } type responseWriter struct { http.ResponseWriter statusCode int } func (rw *responseWriter) WriteHeader(code int) { rw.statusCode = code rw.ResponseWriter.WriteHeader(code) } func handleUsers(w http.ResponseWriter, r *http.Request) { w.Header().Set("Content-Type", "application/json") w.Write([]byte(`{"users": []}`)) } func main() { // Register handlers http.HandleFunc("/api/users", instrumentHandler("/api/users", handleUsers)) http.Handle("/metrics", promhttp.Handler()) // Start server http.ListenAndServe(":8080", nil) }

prometheus

معلومات المصدر

خيارات التثبيت

مراجعة ملفات المصدر

المزيد من هذا المستودع

المزيد من هذا المستودع

Prometheus Monitoring and Alerting

Overview

Architecture Components

Data Model

Setup and Configuration

Basic Prometheus Server Configuration

Prometheus Monitoring and Alerting

Overview

Architecture Components

Data Model

Setup and Configuration

Basic Prometheus Server Configuration

Alertmanager Configuration

Best Practices

Metric Naming Conventions

Label Cardinality Management

DO

DON'T

Guidelines

Recording Rules for Performance

Alert Design (Symptoms vs Causes)

Alert on symptoms (user-facing impact), not causes

Cause-based alerts (use for debugging, not paging)

Alert Best Practices

PromQL Examples

Rate Calculations

Error Ratios

Histogram Queries

Aggregation Operations

Advanced Queries

Complex Aggregations

Kubernetes Integration

ServiceMonitor for Prometheus Operator

PodMonitor for Direct Pod Scraping

PrometheusRule for Alerts and Recording Rules

Prometheus Custom Resource

Application Instrumentation Examples

Go Application

Python Application (Flask)

Production Deployment Checklist

Troubleshooting Commands

Additional Resources