Prometheus Monitoring

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

Overview

Implement comprehensive Prometheus monitoring infrastructure for collecting, storing, and querying time-series metrics from applications and infrastructure.

When to Use

• Setting up metrics collection
• Creating custom application metrics
• Configuring scraping targets
• Implementing service discovery
• Building monitoring infrastructure

Quick Start

Minimal working example:

# /etc/prometheus/prometheus.yml
global:
  scrape_interval: 15s
  evaluation_interval: 15s
  external_labels:
    cluster: production

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

rule_files:
  - "/etc/prometheus/alert_rules.yml"

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

  - job_name: "node"
    static_configs:
      - targets: ["localhost:9100"]

  - job_name: "api-service"
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

| Guide | Contents | |---|---| | Prometheus Configuration | Prometheus Configuration | | Node.js Metrics Implementation | Node.js Metrics Implementation | | Python Prometheus Integration | Python Prometheus Integration | | Alert Rules | Alert Rules | | Docker Compose Setup | Docker Compose Setup |

Best Practices

✅ DO

• Use consistent metric naming conventions
• Add comprehensive labels for filtering
• Set appropriate scrape intervals (10-60s)
• Implement retention policies
• Monitor Prometheus itself
• Test alert rules before deployment
• Document metric meanings

❌ DON'T

• Add unbounded cardinality labels
• Scrape too frequently (< 10s)
• Ignore metric naming conventions
• Create alerts without runbooks
• Store raw event data in Prometheus
• Use counters for gauge-like values