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application/use_cases/adaptive_rate_limit_use_case.py
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application/use_cases/adaptive_rate_limit_use_case.py
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"""
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Adaptive Rate Limit Use Case - Passt Geschwindigkeit dynamisch an
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"""
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import logging
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from typing import Dict, Any, Optional
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from datetime import datetime, timedelta
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from domain.services.rate_limit_service import IRateLimitService
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from domain.value_objects.action_timing import ActionTiming, ActionType
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from domain.entities.rate_limit_policy import RateLimitPolicy
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logger = logging.getLogger("adaptive_rate_limit_use_case")
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class AdaptiveRateLimitUseCase:
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"""
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Use Case für adaptive Geschwindigkeitsanpassung basierend auf Systemverhalten.
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Analysiert Response-Zeiten, passt Delays dynamisch an und erkennt Anomalien.
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"""
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def __init__(self, rate_limit_service: IRateLimitService):
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self.rate_limit_service = rate_limit_service
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self.anomaly_threshold = 2.0 # Standardabweichungen für Anomalie
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self.adaptation_interval = timedelta(minutes=5)
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self.last_adaptation = {}
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def execute(self, action_type: ActionType, context: Optional[Dict[str, Any]] = None) -> float:
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"""
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Führt adaptive Rate Limiting Logik aus.
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Args:
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action_type: Typ der auszuführenden Aktion
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context: Zusätzlicher Kontext (z.B. Session-ID, Platform)
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Returns:
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Optimale Verzögerung in Sekunden
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"""
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# Prüfe ob Adaptation notwendig ist
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if self._should_adapt(action_type):
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self._adapt_policy(action_type)
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# Berechne Delay mit aktuellem Policy
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delay = self.rate_limit_service.calculate_delay(action_type, context)
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# Warte wenn nötig
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actual_wait = self.rate_limit_service.wait_if_needed(action_type)
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logger.debug(f"Adaptive delay for {action_type.value}: {delay:.2f}s (waited: {actual_wait:.2f}s)")
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return delay
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def record_timing(self, timing: ActionTiming) -> None:
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"""
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Zeichnet Timing auf und triggert ggf. Anpassungen.
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Args:
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timing: Timing-Informationen der ausgeführten Aktion
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"""
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# Zeichne Timing auf
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self.rate_limit_service.record_action(timing)
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# Analysiere auf Anomalien
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if self._is_anomaly(timing):
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logger.warning(f"Anomaly detected for {timing.action_type.value}: "
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f"duration={timing.duration}s, success={timing.success}")
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self._handle_anomaly(timing)
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def _should_adapt(self, action_type: ActionType) -> bool:
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"""Prüft ob Policy angepasst werden sollte"""
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last = self.last_adaptation.get(action_type, datetime.min)
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return datetime.now() - last > self.adaptation_interval
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def _adapt_policy(self, action_type: ActionType) -> None:
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"""Passt Policy basierend auf gesammelten Daten an"""
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# Hole Statistiken
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stats = self.rate_limit_service.get_statistics(
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action_type,
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timeframe=timedelta(hours=1)
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)
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if not stats or 'success_rate' not in stats:
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return
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current_policy = self.rate_limit_service.get_policy(action_type)
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success_rate = stats['success_rate']
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avg_duration = stats.get('avg_duration_ms', 0) / 1000.0
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# Neue Policy-Parameter berechnen
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new_policy = self._calculate_new_policy(
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current_policy,
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success_rate,
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avg_duration
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)
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if new_policy != current_policy:
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self.rate_limit_service.update_policy(action_type, new_policy)
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logger.info(f"Adapted policy for {action_type.value}: "
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f"min_delay={new_policy.min_delay:.2f}, "
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f"max_delay={new_policy.max_delay:.2f}")
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self.last_adaptation[action_type] = datetime.now()
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def _calculate_new_policy(self, current: RateLimitPolicy,
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success_rate: float,
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avg_duration: float) -> RateLimitPolicy:
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"""Berechnet neue Policy-Parameter"""
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# Kopiere aktuelle Policy
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new_min = current.min_delay
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new_max = current.max_delay
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new_backoff = current.backoff_multiplier
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# Anpassung basierend auf Erfolgsrate
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if success_rate < 0.7: # Niedrige Erfolgsrate
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# Erhöhe Delays signifikant
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new_min = min(new_min * 1.3, 10.0)
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new_max = min(new_max * 1.3, 30.0)
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new_backoff = min(new_backoff * 1.1, 3.0)
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elif success_rate < 0.85: # Mittlere Erfolgsrate
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# Moderate Erhöhung
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new_min = min(new_min * 1.1, 10.0)
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new_max = min(new_max * 1.1, 30.0)
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elif success_rate > 0.95: # Hohe Erfolgsrate
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# Vorsichtige Verringerung
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if avg_duration < current.min_delay * 0.8:
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new_min = max(new_min * 0.9, 0.1)
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new_max = max(new_max * 0.9, new_min * 3)
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# Stelle sicher dass max > min
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new_max = max(new_max, new_min * 2)
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return RateLimitPolicy(
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min_delay=round(new_min, 2),
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max_delay=round(new_max, 2),
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adaptive=current.adaptive,
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backoff_multiplier=round(new_backoff, 2),
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max_retries=current.max_retries
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)
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def _is_anomaly(self, timing: ActionTiming) -> bool:
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"""Erkennt ob ein Timing eine Anomalie darstellt"""
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# Hole Statistiken für Vergleich
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stats = self.rate_limit_service.get_statistics(
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timing.action_type,
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timeframe=timedelta(hours=1)
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)
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if not stats or 'avg_duration_ms' not in stats:
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return False
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avg_duration = stats['avg_duration_ms'] / 1000.0
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# Sehr langsame Requests sind Anomalien
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if timing.duration > avg_duration * self.anomaly_threshold:
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return True
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# Fehler nach mehreren Erfolgen sind Anomalien
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if not timing.success and stats.get('success_rate', 0) > 0.9:
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return True
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return False
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def _handle_anomaly(self, timing: ActionTiming) -> None:
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"""Behandelt erkannte Anomalien"""
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# Sofortige Policy-Anpassung bei kritischen Anomalien
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if not timing.success and timing.error_message:
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if any(indicator in timing.error_message.lower()
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for indicator in ['rate limit', 'too many', 'blocked']):
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# Rate Limit erkannt - sofort anpassen
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current_policy = self.rate_limit_service.get_policy(timing.action_type)
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emergency_policy = RateLimitPolicy(
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min_delay=min(current_policy.min_delay * 2, 10.0),
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max_delay=min(current_policy.max_delay * 2, 30.0),
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adaptive=current_policy.adaptive,
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backoff_multiplier=min(current_policy.backoff_multiplier * 1.5, 3.0),
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max_retries=current_policy.max_retries
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)
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self.rate_limit_service.update_policy(timing.action_type, emergency_policy)
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logger.warning(f"Emergency policy update for {timing.action_type.value} due to rate limit")
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def get_recommendations(self) -> Dict[str, Any]:
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"""Gibt Empfehlungen basierend auf aktuellen Metriken"""
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recommendations = {
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'actions': [],
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'warnings': [],
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'optimizations': []
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}
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# Analysiere alle Action Types
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for action_type in ActionType:
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stats = self.rate_limit_service.get_statistics(
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action_type,
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timeframe=timedelta(hours=24)
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)
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if not stats or stats.get('total_actions', 0) < 10:
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continue
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success_rate = stats.get('success_rate', 0)
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avg_retries = stats.get('avg_retry_count', 0)
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# Empfehlungen basierend auf Metriken
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if success_rate < 0.5:
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recommendations['warnings'].append(
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f"{action_type.value}: Sehr niedrige Erfolgsrate ({success_rate:.1%})"
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)
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recommendations['actions'].append(
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f"Erhöhe Delays für {action_type.value} oder prüfe auf Blocking"
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)
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if avg_retries > 2:
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recommendations['warnings'].append(
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f"{action_type.value}: Hohe Retry-Rate ({avg_retries:.1f})"
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)
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if success_rate > 0.98 and stats.get('avg_duration_ms', 0) < 500:
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recommendations['optimizations'].append(
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f"{action_type.value}: Könnte schneller ausgeführt werden"
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)
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return recommendations
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