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Monitoring Guide

This guide covers monitoring and observability for FlavumHive deployments.

Monitoring Components​

1. System Metrics​

  • CPU usage
  • Memory usage
  • Disk space
  • Network traffic

2. Application Metrics​

  • Request rates
  • Response times
  • Error rates
  • Queue lengths

3. Platform Metrics​

  • Action success rates
  • Rate limit status
  • API response times
  • Authentication status

Monitoring Tools​

Logging​

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('bot.log'),
        logging.StreamHandler()
    ]
)

Metrics Collection​

# Record metrics
await metrics.record_metric(
    platform="twitter",
    metric_type="tweet_success_rate",
    value=0.95
)

# Get metrics
metrics = await metrics.get_metrics(
    platform="twitter",
    start_time=datetime.now() - timedelta(days=1),
    end_time=datetime.now()
)

Alerting​

Alert Configuration​

class AlertConfig:
    ERROR_THRESHOLD = 0.1
    RESPONSE_TIME_THRESHOLD = 2.0
    RATE_LIMIT_THRESHOLD = 0.8

Alert Channels​

  1. Email notifications
  2. Slack alerts
  3. SMS alerts
  4. Dashboard notifications

Dashboard​

Metrics Dashboard​

  • Real-time metrics
  • Historical trends
  • Platform status
  • Alert history

System Status​

  • Component health
  • Resource usage
  • Error rates
  • Performance metrics

Best Practices​

1. Logging​

  • Use appropriate levels
  • Include context
  • Structured logging
  • Regular rotation

2. Metrics​

  • Key indicators
  • Regular collection
  • Data retention
  • Trend analysis

3. Alerting​

  • Clear thresholds
  • Actionable alerts
  • Proper routing
  • Alert fatigue prevention

Next Steps​