claude-home/monitoring/CONTEXT.md

System Monitoring and Alerting - Technology Context

Overview

Comprehensive monitoring and alerting system for home lab infrastructure with focus on automated health checks, Discord notifications, and proactive system maintenance.

Architecture Patterns

Distributed Monitoring Strategy

Pattern: Service-specific monitoring with centralized alerting

• Tdarr Monitoring: API-based transcoding health checks
• Windows Desktop Monitoring: Reboot detection and system events
• Network Monitoring: Connectivity and service availability
• Container Monitoring: Docker/Podman health and resource usage

Alert Management

Pattern: Structured notifications with actionable information

# Discord webhook integration
curl -X POST "$DISCORD_WEBHOOK" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "**System Alert**\n```\nService: Tdarr\nIssue: Staging timeout\nAction: Automatic cleanup performed\n```\n<@user_id>"
  }'

Core Monitoring Components

Tdarr System Monitoring

Purpose: Monitor transcoding pipeline health and performance Location: scripts/tdarr_monitor.py

Key Features:

• API-based status monitoring with dataclass structures
• Staging section timeout detection and cleanup
• Discord notifications with professional formatting
• Log rotation and retention management

Windows Desktop Monitoring

Purpose: Track Windows system reboots and power events Location: scripts/windows-desktop/

Components:

• PowerShell monitoring script
• Scheduled task automation
• Discord notification integration
• System event correlation

Network and Service Monitoring

Purpose: Monitor critical infrastructure availability Implementation:

# Service health check pattern
SERVICES="https://homelab.local http://nas.homelab.local"
for service in $SERVICES; do
    if curl -sSf --max-time 10 "$service" >/dev/null 2>&1; then
        echo "✅ $service: Available"
    else
        echo "❌ $service: Failed" | send_alert
    fi
done

Automation Patterns

Cron-Based Scheduling

Pattern: Regular health checks with intelligent alerting

# Monitoring schedule examples
*/20 * * * * /path/to/tdarr-timeout-monitor.sh    # Every 20 minutes
0 */6 * * * /path/to/cleanup-temp-dirs.sh         # Every 6 hours
0 2 * * * /path/to/backup-monitor.sh              # Daily at 2 AM

Event-Driven Monitoring

Pattern: Reactive monitoring for critical events

• System Startup: Windows boot detection
• Service Failures: Container restart alerts
• Resource Exhaustion: Disk space warnings
• Security Events: Failed login attempts

Data Collection and Analysis

Log Management

Pattern: Centralized logging with rotation

# Log rotation configuration
LOG_FILE="/var/log/homelab-monitor.log"
MAX_SIZE="10M"
RETENTION_DAYS=30

# Rotate logs when size exceeded
if [ $(stat -c%s "$LOG_FILE") -gt $((10*1024*1024)) ]; then
    mv "$LOG_FILE" "$LOG_FILE.$(date +%Y%m%d)"
    touch "$LOG_FILE"
fi

Metrics Collection

Pattern: Time-series data for trend analysis

• System Metrics: CPU, memory, disk usage
• Service Metrics: Response times, error rates
• Application Metrics: Transcoding progress, queue sizes
• Network Metrics: Bandwidth usage, latency

Alert Integration

Discord Notification System

Pattern: Rich, actionable notifications

# Professional alert format
**🔧 System Maintenance**
Service: Tdarr Transcoding
Issue: 3 files timed out in staging
Resolution: Automatic cleanup completed
Status: System operational

Manual review recommended <@user_id>

Alert Escalation

Pattern: Tiered alerting based on severity

1. Info: Routine maintenance completed
2. Warning: Service degradation detected
3. Critical: Service failure requiring immediate attention
4. Emergency: System-wide failure requiring manual intervention

Best Practices Implementation

Monitoring Strategy

1. Proactive: Monitor trends to predict issues
2. Reactive: Alert on current failures
3. Preventive: Automated cleanup and maintenance
4. Comprehensive: Cover all critical services
5. Actionable: Provide clear resolution paths

Performance Optimization

1. Efficient Polling: Balance monitoring frequency with resource usage
2. Smart Alerting: Avoid alert fatigue with intelligent filtering
3. Resource Management: Monitor the monitoring system itself
4. Scalable Architecture: Design for growth and additional services

This technology context provides the foundation for implementing comprehensive monitoring and alerting in home lab environments.