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Monitoring

Monitor your Hive-Pal installation with comprehensive logging and metrics.

Health Monitoring​

Application Health​

# Health check endpoint
curl http://localhost:3000/health

# Response format
{
  "status": "ok",
  "timestamp": "2024-01-01T00:00:00Z",
  "uptime": 3600,
  "database": "connected",
  "version": "1.0.0"
}

Database Health​

-- Connection status
SELECT count(*) FROM pg_stat_activity;

-- Database size
SELECT pg_size_pretty(pg_database_size('hivepal'));

-- Table statistics
SELECT schemaname,tablename,n_tup_ins,n_tup_upd,n_tup_del 
FROM pg_stat_user_tables;

Logging​

Application Logs​

{
  "timestamp": "2024-01-01T00:00:00Z",
  "level": "info",
  "message": "User login successful",
  "userId": "123",
  "ip": "192.168.1.100",
  "userAgent": "Mozilla/5.0..."
}

Log Aggregation​

# Using journalctl
journalctl -u hive-pal -f

# Using Docker
docker-compose logs -f backend

# Custom log parser
tail -f /var/log/hive-pal/app.log | jq '.'

Metrics Collection​

Prometheus Integration​

# prometheus.yml
scrape_configs:
  - job_name: 'hive-pal'
    static_configs:
      - targets: ['localhost:3000']
    metrics_path: /metrics
    scrape_interval: 15s

Key Metrics​

  • Request count and duration
  • Database query performance
  • Memory and CPU usage
  • Error rates
  • User session duration

Grafana Dashboards​

System Metrics​

  • CPU usage
  • Memory consumption
  • Disk I/O
  • Network traffic

Application Metrics​

  • API response times
  • Database connections
  • User activity
  • Error rates

Business Metrics​

  • User registrations
  • Inspection records
  • Data growth

Alerting​

Alert Rules​

# alerts.yml
groups:
  - name: hive-pal
    rules:
      - alert: HighErrorRate
        expr: rate(http_requests_total{status=~"5.."}[5m]) > 0.1
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: High error rate detected

      - alert: DatabaseConnections
        expr: pg_stat_activity_count > 80
        for: 2m
        labels:
          severity: warning

Notification Channels​

  • Email alerts
  • Slack integration
  • Webhook notifications
  • SMS alerts (via services)

Log Management​

Log Rotation​

# logrotate configuration
/var/log/hive-pal/*.log {
    daily
    missingok
    rotate 30
    compress
    delaycompress
    notifempty
    create 0644 hive-pal hive-pal
    postrotate
        systemctl reload hive-pal
    endscript
}

Centralized Logging​

# docker-compose with Loki
services:
  loki:
    image: grafana/loki:latest
    ports:
      - "3100:3100"
    
  promtail:
    image: grafana/promtail:latest
    volumes:
      - /var/log:/var/log:ro
      - ./promtail-config.yml:/etc/promtail/config.yml

Performance Monitoring​

Database Performance​

-- Slow queries
SELECT query, mean_time, calls
FROM pg_stat_statements
ORDER BY mean_time DESC
LIMIT 10;

-- Index usage
SELECT schemaname,tablename,attname,n_distinct,correlation
FROM pg_stats
WHERE tablename = 'inspections';

Application Performance​

# Memory usage
ps aux | grep hive-pal

# File descriptor usage
lsof -p $(pgrep -f hive-pal) | wc -l

# Network connections
netstat -an | grep :3000

Security Monitoring​

Access Logs​

  • Failed login attempts
  • Suspicious IP addresses
  • Unusual access patterns
  • API abuse detection

Security Alerts​

# Monitor failed logins
tail -f /var/log/hive-pal/app.log | grep "login failed"

# Check for brute force attempts
grep "authentication failed" /var/log/hive-pal/app.log | \
  awk '{print $4}' | sort | uniq -c | sort -nr

Troubleshooting​

Common Issues​

  • High memory usage
  • Database connection limits
  • Disk space exhaustion
  • SSL certificate expiration

Debug Tools​

# Process monitoring
htop
iotop

# Network debugging
tcpdump -i eth0 port 3000

# Database debugging
pg_stat_activity
pg_locks

Monitoring Best Practices​

Metrics Strategy​

  • Monitor what matters
  • Set meaningful thresholds
  • Avoid alert fatigue
  • Regular review and tuning

Log Management​

  • Structured logging
  • Appropriate log levels
  • Log retention policies
  • Security considerations

Performance Tuning​

  • Regular performance reviews
  • Capacity planning
  • Bottleneck identification
  • Optimization implementation