Prometheus & Grafana

Collect metrics and visualize system performance with the Prometheus-Grafana stack.

When to Use This Skill

Use this skill when:

• Setting up metrics collection infrastructure
• Creating monitoring dashboards
• Writing PromQL queries for analysis
• Configuring alerting rules
• Monitoring Kubernetes clusters

Prerequisites

• Docker or Kubernetes for deployment
• Network access to monitored targets
• Basic understanding of metrics concepts

Prometheus Setup

Docker Deployment

# docker-compose.yml
version: '3.8'

services:
  prometheus:
    image: prom/prometheus:v2.48.0
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      - ./rules:/etc/prometheus/rules
      - prometheus-data:/prometheus
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.path=/prometheus'
      - '--storage.tsdb.retention.time=15d'

  grafana:
    image: grafana/grafana:10.2.0
    ports:
      - "3000:3000"
    volumes:
      - grafana-data:/var/lib/grafana
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin

volumes:
  prometheus-data:
  grafana-data:

Configuration

# prometheus.yml
global:
  scrape_interval: 15s
  evaluation_interval: 15s

alerting:
  alertmanagers:
    - static_configs:
        - targets:
            - alertmanager:9093

rule_files:
  - /etc/prometheus/rules/*.yml

scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']

  - job_name: 'node'
    static_configs:
      - targets:
          - 'node-exporter:9100'

  - job_name: 'applications'
    static_configs:
      - targets:
          - 'app1:8080'
          - 'app2:8080'
    metrics_path: /metrics

Kubernetes Deployment

Using Helm

# Add Prometheus community Helm repo
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts

# Install kube-prometheus-stack
helm install prometheus prometheus-community/kube-prometheus-stack \
  --namespace monitoring \
  --create-namespace \
  --set grafana.adminPassword=admin

ServiceMonitor

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: myapp
  namespace: monitoring
spec:
  selector:
    matchLabels:
      app: myapp
  endpoints:
    - port: metrics
      interval: 30s
      path: /metrics
  namespaceSelector:
    matchNames:
      - default

PromQL Queries

Basic Queries

# Current CPU usage
node_cpu_seconds_total{mode="idle"}

# Rate of HTTP requests per second
rate(http_requests_total[5m])

# Average response time
avg(http_request_duration_seconds_sum / http_request_duration_seconds_count)

# Memory usage percentage
(1 - (node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)) * 100

Aggregations

# Sum requests by status code
sum by (status_code) (rate(http_requests_total[5m]))

# Average CPU by instance
avg by (instance) (rate(node_cpu_seconds_total{mode!="idle"}[5m]))

# Top 5 endpoints by request count
topk(5, sum by (endpoint) (rate(http_requests_total[5m])))

# 95th percentile latency
histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))

Time-Based Queries

# Compare to 1 hour ago
http_requests_total - http_requests_total offset 1h

# Predict disk space in 4 hours
predict_linear(node_filesystem_avail_bytes[1h], 4 * 3600)

# Changes in last 5 minutes
changes(up[5m])

# Average over 24 hours
avg_over_time(http_requests_total[24h])

Alerting Rules

# rules/alerts.yml
groups:
  - name: application
    rules:
      - alert: HighErrorRate
        expr: |
          sum(rate(http_requests_total{status=~"5.."}[5m]))
          / sum(rate(http_requests_total[5m])) > 0.05
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High error rate detected"
          description: "Error rate is {{ $value | humanizePercentage }}"

      - alert: ServiceDown
        expr: up == 0
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "Service {{ $labels.instance }} is down"

      - alert: HighMemoryUsage
        expr: |
          (1 - (node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes)) > 0.9
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High memory usage on {{ $labels.instance }}"

…