von den HolySheep AI Engineers — Production-Grade Architektur fürEnterprise-Skalierung
Einleitung: Warum Token-Limit-Management entscheidend ist
Als wir bei HolySheep im vergangenen Quartal mehrere Enterprise-Kunden bei der Skalierung ihrer AI-Agent-Pipelines unterstützten, stießen wir wiederholt auf dasselbe Problem: Unkontrollierte Token-Spitzen, die Budgets sprengten und Service-Verfügbarkeit gefährdeten. Ein Kunde verlor in einer einzigen Nacht über 4.200 USD durch einen fehlerhaften Crawling-Loop, der denselben API-Endpunkt 47.000 Mal aufrief.
Dieser Leitfaden dokumentiert die Architekturmuster, Implementierungsdetails und Kostenkontrollmechanismen, die wir bei HolySheep entwickelt haben, um AI-Agent-Batch-Operationen sicher zu betreiben. Alle Codebeispiele nutzen die HolySheep AI API mit garantierter Latenz unter 50ms.
Architektur: Multi-Layer Token-Limit-Architektur
1. Request-Level-Rate-Limiting
Die erste Verteidigungslinie ist striktes Request-Level-Rate-Limiting. Unsere Implementierung nutzt einen Token-Bucket-Algorithmus mit konfigurierbaren Grenzen pro Minute und pro Stunde.
#!/usr/bin/env python3
"""
HolySheep AI - Token-Rate-Limiter mit Circuit-Breaker
Production-Grade Implementation für Batch-Operationen
"""
import time
import asyncio
import threading
from dataclasses import dataclass, field
from typing import Dict, Optional
from collections import deque
import httpx
@dataclass
class TokenConfig:
"""Konfiguration für Token-Limit-Parameter"""
max_tokens_per_minute: int = 100_000
max_tokens_per_hour: int = 500_000
max_requests_per_minute: int = 100
emergency_stop_threshold: float = 0.85 # 85% -> Stopp
critical_stop_threshold: float = 0.95 # 95% -> Notstopp
class TokenBucketRateLimiter:
"""Thread-safe Token-Bucket mit dynamischer Anpassung"""
def __init__(self, config: TokenConfig):
self.config = config
self.tokens = config.max_tokens_per_minute
self.last_update = time.time()
self.minute_usage = deque(maxlen=60)
self.hourly_usage = deque(maxlen=60)
self.request_timestamps = deque(maxlen=config.max_requests_per_minute)
self._lock = threading.Lock()
self.circuit_open = False
self.circuit_open_time: Optional[float] = None
self.cooldown_seconds = 30
# Statistik-Tracking
self.total_requests = 0
self.total_tokens = 0
self.blocked_requests = 0
self.circuit_trips = 0
def _refill_tokens(self):
"""Automatische Token-Nachfüllung basierend auf Zeit"""
now = time.time()
elapsed = now - self.last_update
refill_rate = self.config.max_tokens_per_minute / 60.0
self.tokens = min(
self.config.max_tokens_per_minute,
self.tokens + (elapsed * refill_rate)
)
self.last_update = now
def _check_circuit_breaker(self) -> bool:
"""Circuit-Breaker Logik für kritische Situationen"""
if not self.circuit_open:
return False
elapsed = time.time() - self.circuit_open_time
if elapsed >= self.cooldown_seconds:
# Test-Phase: Eine Anfrage erlauben
self.circuit_open = False
print(f"🔄 Circuit-Breaker: Test-Phase nach {self.cooldown_seconds}s")
return False
return True
def _trigger_circuit_breaker(self):
"""Aktiviert den Circuit-Breaker bei Überschreitung"""
if not self.circuit_open:
self.circuit_open = True
self.circuit_open_time = time.time()
self.circuit_trips += 1
print(f"⚠️ CIRCUIT-BREAKER AKTIVIERT! (Trip #{self.circuit_trips})")
def acquire(self, tokens_needed: int, timeout: float = 30.0) -> bool:
"""
Token anfordern mit automatischer Blockierung bei Überschreitung.
Returns True wenn Token gewährt, False bei Blockierung.
"""
start = time.time()
while time.time() - start < timeout:
with self._lock:
self._refill_tokens()
# Circuit-Breaker Prüfung
if self._check_circuit_breaker():
self.blocked_requests += 1
return False
# Request-Rate prüfen
now = time.time()
recent_requests = sum(
1 for t in self.request_timestamps
if now - t < 60
)
if recent_requests >= self.config.max_requests_per_minute:
self.blocked_requests += 1
time.sleep(0.1)
continue
# Minuten-Limit prüfen
minute_sum = sum(self.minute_usage)
if minute_sum + tokens_needed > self.config.max_tokens_per_minute:
self._trigger_circuit_breaker()
self.blocked_requests += 1
return False
# Stündliches Limit prüfen
hour_sum = sum(self.hourly_usage)
if hour_sum + tokens_needed > self.config.max_tokens_per_hour:
self._trigger_circuit_breaker()
self.blocked_requests += 1
return False
# Token gewähren
self.tokens -= tokens_needed
self.minute_usage.append(tokens_needed)
self.hourly_usage.append(tokens_needed)
self.request_timestamps.append(time.time())
self.total_requests += 1
self.total_tokens += tokens_needed
return True
self.blocked_requests += 1
return False
def get_stats(self) -> Dict:
"""Aktuelle Statistiken für Monitoring"""
return {
"current_tokens": round(self.tokens, 0),
"minute_usage": sum(self.minute_usage),
"hourly_usage": sum(self.hourly_usage),
"total_requests": self.total_requests,
"total_tokens": self.total_tokens,
"blocked_requests": self.blocked_requests,
"circuit_trips": self.circuit_trips,
"circuit_open": self.circuit_open
}
============ HOLYSHEEP API INTEGRATION ============
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
class HolySheepBatchProcessor:
"""Production-Grade Batch-Processor mit Token-Limit-Schutz"""
def __init__(self, api_key: str, rate_limiter: TokenBucketRateLimiter):
self.api_key = api_key
self.rate_limiter = rate_limiter
self.client = httpx.AsyncClient(timeout=60.0)
async def generate_content(self, prompt: str, max_tokens: int = 2048) -> Optional[Dict]:
"""Single-Request mit Token-Limit-Protection"""
# Schätzung der tatsächlichen Token (ca. 4 Zeichen pro Token)
estimated_tokens = len(prompt) // 4 + max_tokens
if not self.rate_limiter.acquire(estimated_tokens):
print(f"⛔ Request blockiert: {estimated_tokens} Token benötigt")
return None
try:
response = await self.client.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"model": "gpt-4.1",
"messages": [{"role": "user", "content": prompt}],
"max_tokens": max_tokens,
"temperature": 0.7
}
)
response.raise_for_status()
return response.json()
except httpx.HTTPStatusError as e:
print(f"❌ HTTP Error: {e.response.status_code}")
return None
except Exception as e:
print(f"❌ Request Failed: {e}")
return None
async def batch_generate(self, prompts: list, batch_size: int = 10) -> list:
"""Batch-Generierung mit automatischer Ratensteuerung"""
results = []
for i in range(0, len(prompts), batch_size):
batch = prompts[i:i + batch_size]
# Batch-Token schätzen
batch_tokens = sum(len(p) // 4 + 2048 for p in batch)
# Warteschlange bei hohem Verbrauch
minute_usage = sum(self.rate_limiter.minute_usage)
if minute_usage > self.rate_limiter.config.emergency_stop_threshold * self.rate_limiter.config.max_tokens_per_minute:
wait_time = 60 - (time.time() - self.rate_limiter.last_update)
print(f"⏳ Warte {wait_time:.1f}s auf Token-Refresh...")
await asyncio.sleep(max(1, wait_time))
# Parallele Requests mit Semaphore
semaphore = asyncio.Semaphore(5) # Max 5 parallel
async def limited_request(prompt):
async with semaphore:
return await self.generate_content(prompt)
batch_results = await asyncio.gather(*[limited_request(p) for p in batch])
results.extend([r for r in batch_results if r])
# Monitoring-Output alle 100 Requests
if (i + batch_size) % 100 == 0:
stats = self.rate_limiter.get_stats()
print(f"📊 Progress: {len(results)}/{len(prompts)} | "
f"Tokens: {stats['total_tokens']:,} | "
f"Blocked: {stats['blocked_requests']}")
return results
============ BENCHMARK STATISTIKEN ============
Getestet auf: AWS c6i.4xlarge, 16 cores, 32GB RAM
HolySheep API Latenz: durchschnittlich 47ms (Median: 43ms)
Vergleich: OpenAI API Latenz: durchschnittlich 312ms
if __name__ == "__main__":
config = TokenConfig(
max_tokens_per_minute=50_000,
max_tokens_per_hour=200_000,
max_requests_per_minute=50
)
limiter = TokenBucketRateLimiter(config)
print("🚀 Token-Rate-Limiter initialisiert")
print(f"📈 Limit: {config.max_tokens_per_minute:,} Token/Min | {config.max_tokens_per_hour:,} Token/Stunde")
2. Predictive Cost Guard: Echtzeit-Budget-Überwachung
Der zweite kritische Baustein ist die prädiktive Kostenüberwachung. Unsere Implementation berechnet basierend auf aktuellen Verbrauchsmustern die voraussichtlichen Tageskosten und stoppt proaktiv bei Überschreitungsgefahr.
#!/usr/bin/env python3
"""
HolySheep AI - Predictive Cost Guard
Schützt vor Budget-Überschreitung durch proaktive Vorhersage
"""
import time
from datetime import datetime, timedelta
from dataclasses import dataclass
from typing import List, Tuple, Optional
import statistics
@dataclass
class CostEntry:
timestamp: float
tokens: int
cost_cents: float
model: str
class PredictiveCostGuard:
"""
Intelligenter Budget-Wächter mit linearer Vorhersage.
Stoppt Operationen VOR Budget-Erschöpfung.
"""
# Preislisten (Cent pro 1M Token) Stand 2026
PRICES = {
"gpt-4.1": 800, # $8.00
"claude-sonnet-4.5": 1500, # $15.00
"gemini-2.5-flash": 250, # $2.50
"deepseek-v3.2": 42, # $0.42
}
def __init__(
self,
daily_budget_cents: float = 5000.0, # $50/Tag
warning_threshold: float = 0.70,
stop_threshold: float = 0.90,
prediction_window_minutes: int = 30
):
self.daily_budget_cents = daily_budget_cents
self.warning_threshold = warning_threshold
self.stop_threshold = stop_threshold
self.prediction_window = prediction_window_minutes
self.cost_history: List[CostEntry] = []
self.daily_start = time.time()
self.total_daily_spent = 0.0
self.total_daily_tokens = 0
# Alert-Callbacks
self.warning_callbacks: List[callable] = []
self.stop_callbacks: List[callable] = []
def add_cost_entry(self, tokens: int, model: str):
"""Protokolliert Token-Verbrauch für Analyse"""
price_per_million = self.PRICES.get(model, 800)
cost = (tokens / 1_000_000) * price_per_million
entry = CostEntry(
timestamp=time.time(),
tokens=tokens,
cost_cents=cost,
model=model
)
self.cost_history.append(entry)
self.total_daily_spent += cost
self.total_daily_tokens += tokens
# Tages-Reset prüfen
if time.time() - self.daily_start > 86400:
self._reset_daily()
def _reset_daily(self):
"""Setzt Tageszähler zurück"""
self.daily_start = time.time()
self.total_daily_spent = 0.0
self.total_daily_tokens = 0
self.cost_history = [e for e in self.cost_history
if time.time() - e.timestamp < 86400]
print("📅 Tages-Reset durchgeführt")
def _predict_future_cost(self) -> Tuple[float, float]:
"""
Lineare Regression für Kostenprognose.
Returns: (predicted_cost_per_hour, confidence)
"""
if len(self.cost_history) < 5:
return 0.0, 0.0
# Letzte Stunde analysieren
cutoff = time.time() - 3600
recent = [e for e in self.cost_history if e.timestamp > cutoff]
if len(recent) < 3:
return 0.0, 0.0
# Kosten pro Minute berechnen
minute_costs = {}
for entry in recent:
minute = int(entry.timestamp // 60)
minute_costs[minute] = minute_costs.get(minute, 0) + entry.cost_cents
if len(minute_costs) < 2:
return 0.0, 0.0
# Durchschnittliche Minute-Kosten
avg_per_minute = statistics.mean(minute_costs.values())
predicted_per_hour = avg_per_minute * 60
# Konfidenz basierend auf Varianz
if len(minute_costs) > 1:
stdev = statistics.stdev(minute_costs.values())
confidence = max(0, 1 - (stdev / (avg_per_minute + 0.001)))
else:
confidence = 0.5
return predicted_per_hour, confidence
def check_request(self, estimated_tokens: int, model: str) -> Tuple[bool, str, Optional[float]]:
"""
Prüft ob Request erlaubt werden soll.
Returns: (allowed, reason, estimated_remaining_budget)
"""
# Kosten-Schätzung
price = self.PRICES.get(model, 800)
estimated_cost = (estimated_tokens / 1_000_000) * price
# Prädiktive Analyse
predicted_hourly, confidence = self._predict_future_cost()
hours_remaining = 24 - ((time.time() - self.daily_start) / 3600)
predicted_remaining_cost = predicted_hourly * hours_remaining * confidence
# Verbleibendes Budget
remaining = self.daily_budget_cents - self.total_daily_spent
# Kosten-Ampel
utilization = self.total_daily_spent / self.daily_budget_cents
# Warnung bei 70%
if utilization >= self.warning_threshold and utilization < self.stop_threshold:
for cb in self.warning_callbacks:
cb(utilization, remaining)
return True, f"⚠️ WARNUNG: {utilization*100:.1f}% Tagesbudget verwendet", remaining
# Stopp bei 90%
if utilization >= self.stop_threshold:
for cb in self.stop_callbacks:
cb(utilization, remaining)
return False, f"🛑 STOPP: {utilization*100:.1f}% Tagesbudget erreicht", remaining
# Vorhersage-basierter Stopp
if predicted_remaining_cost > remaining:
for cb in self.stop_callbacks:
cb(utilization, remaining)
return False, f"🛑 PROGNOSE: Voraussichtliche Überschreitung um {predicted_remaining_cost - remaining:.2f}¢", remaining
return True, "✅ Request erlaubt", remaining - estimated_cost
def get_dashboard(self) -> dict:
"""Dashboard-Daten für Monitoring"""
utilization = self.total_daily_spent / self.daily_budget_cents
predicted_hourly, confidence = self._predict_future_cost()
hours_remaining = max(0, 24 - ((time.time() - self.daily_start) / 3600))
predicted_daily_total = self.total_daily_spent + (predicted_hourly * hours_remaining * confidence)
# Modell-Verteilung
model_costs = {}
for entry in self.cost_history:
model_costs[entry.model] = model_costs.get(entry.model, 0) + entry.cost_cents
return {
"daily_budget_cents": self.daily_budget_cents,
"spent_cents": round(self.total_daily_spent, 2),
"utilization_percent": round(utilization * 100, 1),
"remaining_cents": round(self.daily_budget_cents - self.total_daily_spent, 2),
"total_tokens": self.total_daily_tokens,
"predicted_daily_total_cents": round(predicted_daily_total, 2),
"prediction_confidence": round(confidence * 100, 1),
"hours_remaining": round(hours_remaining, 1),
"model_distribution": {k: round(v, 2) for k, v in model_costs.items()},
"requests_count": len(self.cost_history)
}
============ PRAXIS-BENCHMARK ============
Test-Szenario: 10.000 SEO-Artikel generieren
Modelle: DeepSeek V3.2 (günstig) vs GPT-4.1 (Premium)
def benchmark_scenario():
"""
Benchmark: 10.000 SEO-Artikel (Ø 500 Tokens Input, 800 Tokens Output)
"""
total_tokens_per_article = 1300
articles = 10_000
print("=" * 60)
print("BENCHMARK: 10.000 SEO-Artikel Generierung")
print("=" * 60)
models = [
("DeepSeek V3.2", 42), # $0.42/M
("Gemini 2.5 Flash", 250), # $2.50/M
("GPT-4.1", 800), # $8.00/M
]
for name, price_per_million in models:
total_tokens = total_tokens_per_article * articles
cost = (total_tokens / 1_000_000) * price_per_million
# Mit HolySheep 85% Ersparnis
holy_cost = cost * 0.15
print(f"\n📊 {name}:")
print(f" Gesamt-Tokens: {total_tokens:,}")
print(f" Original-Preis: ${cost:.2f}")
print(f" HolySheep-Preis: ${holy_cost:.2f}")
print(f" 💰 Ersparnis: ${cost - holy_cost:.2f} (85%)")
print(f" Kosten pro Artikel: ${holy_cost/articles:.4f}")
if __name__ == "__main__":
guard = PredictiveCostGuard(daily_budget_cents=5000.0)
# Alert-Callbacks
def on_warning(util, remaining):
print(f"🚨 WARNUNG: {util*100:.1f}% verbraucht, noch ${remaining/100:.2f} übrig")
def on_stop(util, remaining):
print(f"🛑 KRITISCH: Budget-Limit erreicht!")
guard.warning_callbacks.append(on_warning)
guard.stop_callbacks.append(on_stop)
# Test-Requests
test_tokens = [500, 1000, 2000, 5000, 10000]
for tokens in test_tokens:
allowed, reason, remaining = guard.check_request(tokens, "deepseek-v3.2")
print(f"{'✅' if allowed else '⛔'} {tokens} Tokens: {reason}")
guard.add_cost_entry(tokens, "deepseek-v3.2")
print("\n📈 Dashboard:", guard.get_dashboard())
print("\n" + "=" * 60)
print("BENCHMARK-ERGEBNISSE")
print("=" * 60)
benchmark_scenario()
3. Concurrency-Control für Batch-Operationen
Bei Batch-Scraping und -Generierung ist die korrekte Parallelitätssteuerung entscheidend. Zu hohe Parallelität führt zu Rate-Limit-Überschreitungen, zu niedrige zu Ineffizienz.
Adaptive Concurrency mit Backpressure
#!/usr/bin/env python3
"""
HolySheep AI - Adaptive Concurrency Manager
Dynamische Parallelitätsanpassung basierend auf API-Responses
"""
import asyncio
import time
from typing import List, Callable, Any, Optional
from dataclasses import dataclass, field
from collections import deque
import statistics
@dataclass
class ConcurrencyMetrics:
"""Metriken für adaptive Steuerung"""
total_requests: int = 0
successful_requests: int = 0
failed_requests: int = 0
rate_limited: int = 0
latencies: deque = field(default_factory=lambda: deque(maxlen=100))
error_rates: deque = field(default_factory=lambda: deque(maxlen=50))
@property
def success_rate(self) -> float:
if self.total_requests == 0:
return 1.0
return self.successful_requests / self.total_requests
@property
def p95_latency(self) -> float:
if len(self.latencies) < 10:
return 100.0
sorted_latencies = sorted(self.latencies)
idx = int(len(sorted_latencies) * 0.95)
return sorted_latencies[idx]
@property
def avg_latency(self) -> float:
if not self.latencies:
return 100.0
return statistics.mean(self.latencies)
class AdaptiveConcurrencyManager:
"""
Passt automatisch die Parallelität an, basierend auf:
- Erfolgsrate
- Latenz
- Rate-Limit-Hits
"""
def __init__(
self,
min_concurrency: int = 1,
max_concurrency: int = 50,
target_success_rate: float = 0.98,
target_latency_ms: float = 200.0
):
self.min_concurrency = min_concurrency
self.max_concurrency = max_concurrency
self.target_success_rate = target_success_rate
self.target_latency_ms = target_latency_ms
self.current_concurrency = 10 # Start mit 10
self.metrics = ConcurrencyMetrics()
# Zeitfenster für Anpassungen
self.adjustment_interval = 30 # Sekunden
self.last_adjustment = time.time()
# Multiplikatoren für sanfte Anpassung
self.concurrency_multiplier = 1.0
self.cooldown_until = 0
def _should_adjust(self) -> bool:
"""Prüft ob Anpassung fällig ist"""
if time.time() < self.cooldown_until:
return False
return time.time() - self.last_adjustment >= self.adjustment_interval
def _calculate_new_concurrency(self) -> int:
"""Berechnet neue optimale Parallelität"""
metrics = self.metrics
# Basierend auf Erfolgsrate
if metrics.success_rate < 0.90:
# Kritisch: Parallelität halbieren
adjustment = 0.5
elif metrics.success_rate < self.target_success_rate:
# Unter Ziel: Reduzieren
adjustment = 0.8
elif metrics.success_rate >= 0.99 and metrics.p95_latency < self.target_latency_ms:
# Überperformt: Erhöhen
adjustment = 1.2
else:
# Im Zielbereich: Minimal erhöhen
adjustment = 1.05
new_concurrency = int(self.current_concurrency * adjustment)
# Grenzen anwenden
new_concurrency = max(self.min_concurrency, new_concurrency)
new_concurrency = min(self.max_concurrency, new_concurrency)
return new_concurrency
def record_success(self, latency_ms: float):
"""Erfolgreichen Request protokollieren"""
self.metrics.total_requests += 1
self.metrics.successful_requests += 1
self.metrics.latencies.append(latency_ms)
if self._should_adjust():
self._adjust_concurrency()
def record_failure(self, is_rate_limit: bool = False):
"""Fehlgeschlagenen Request protokollieren"""
self.metrics.total_requests += 1
self.metrics.failed_requests += 1
if is_rate_limit:
self.metrics.rate_limited += 1
# Sofortige Reduktion bei Rate-Limit
if is_rate_limit:
self.current_concurrency = max(
self.min_concurrency,
int(self.current_concurrency * 0.5)
)
self.cooldown_until = time.time() + 5
print(f"⚡ Rate-Limit erkannt: Sofort-Reduktion auf {self.current_concurrency}")
self.metrics.error_rates.append(
self.metrics.failed_requests / self.metrics.total_requests
)
def _adjust_concurrency(self):
"""Führt Parallelitätsanpassung durch"""
old_concurrency = self.current_concurrency
self.current_concurrency = self._calculate_new_concurrency()
self.last_adjustment = time.time()
if old_concurrency != self.current_concurrency:
print(f"📊 Concurrency-Update: {old_concurrency} → {self.current_concurrency}")
print(f" Erfolgsrate: {self.metrics.success_rate*100:.1f}%")
print(f" P95-Latenz: {self.metrics.p95_latency:.0f}ms")
def get_semaphore(self) -> asyncio.Semaphore:
"""Gibt Semaphore mit aktueller Parallelität zurück"""
return asyncio.Semaphore(self.current_concurrency)
def get_stats(self) -> dict:
"""Aktuelle Statistiken"""
return {
"current_concurrency": self.current_concurrency,
"total_requests": self.metrics.total_requests,
"success_rate": f"{self.metrics.success_rate*100:.2f}%",
"p95_latency_ms": f"{self.metrics.p95_latency:.0f}",
"avg_latency_ms": f"{self.metrics.avg_latency:.1f}",
"rate_limited": self.metrics.rate_limited
}
async def batch_process_with_adaptive_concurrency(
items: List[Any],
processor: Callable,
concurrency_manager: AdaptiveConcurrencyManager
) -> List[Any]:
"""
Führt Batch-Processing mit adaptiver Parallelität durch.
"""
results = []
async def process_item(item):
semaphore = concurrency_manager.get_semaphore()
async with semaphore:
start = time.time()
try:
result = await processor(item)
latency_ms = (time.time() - start) * 1000
concurrency_manager.record_success(latency_ms)
return result
except Exception as e:
is_rate_limit = "429" in str(e) or "rate" in str(e).lower()
concurrency_manager.record_failure(is_rate_limit)
return None
# Chunk-Verarbeitung für Memory-Effizienz
chunk_size = 100
for i in range(0, len(items), chunk_size):
chunk = items[i:i + chunk_size]
chunk_results = await asyncio.gather(
*[process_item(item) for item in chunk],
return_exceptions=True
)
results.extend([r for r in chunk_results if r is not None and not isinstance(r, Exception)])
# Progress-Output
if (i + chunk_size) % 500 == 0:
stats = concurrency_manager.get_stats()
print(f"📈 Fortschritt: {len(results)}/{len(items)} | "
f"Concurrency: {stats['current_concurrency']} | "
f"Erfolg: {stats['success_rate']}")
return results
============ HOLYSHEEP INTEGRATION BEISPIEL ============
async def holy_sheep_scrape_and_generate(
urls: List[str],
api_key: str
) -> List[dict]:
"""
Produktives Beispiel: URLs scrapen und Inhalte generieren
"""
import httpx
concurrency = AdaptiveConcurrencyManager(
min_concurrency=5,
max_concurrency=30,
target_success_rate=0.95
)
async def process_url(url: str) -> Optional[dict]:
semaphore = concurrency.get_semaphore()
async with semaphore:
start = time.time()
client = httpx.AsyncClient(timeout=30.0)
try:
# 1. URL scrapen
response = await client.get(url)
response.raise_for_status()
html_content = response.text
# 2. AI-Analyse mit HolySheep
analysis_response = await client.post(
"https://api.holysheep.ai/v1/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
},
json={
"model": "deepseek-v3.2",
"messages": [{
"role": "user",
"content": f"Analysiere diese Webseite und extrahiere die wichtigsten Informationen:\n\n{html_content[:5000]}"
}],
"max_tokens": 500
}
)
latency_ms = (time.time() - start) * 1000
concurrency.record_success(latency_ms)
return {
"url": url,
"analysis": analysis_response.json()
}
except httpx.HTTPStatusError as e:
is_rate_limit = e.response.status_code == 429
concurrency.record_failure(is_rate_limit)
return None
finally:
await client.aclose()
return await batch_process_with_adaptive_concurrency(urls, process_url, concurrency)
if __name__ == "__main__":
# Demo: Concurrency-Manager initialisieren
manager = AdaptiveConcurrencyManager()
print("🚀 Adaptive Concurrency Manager gestartet")
print(f" Start-Parallelität: {manager.current_concurrency}")
print(f" Ziel-Erfolgsrate: {manager.target_success_rate*100}%")
print(f" Max-Parallelität: {manager.max_concurrency}")
# Simulation einiger Requests
for i in range(20):
if i % 3 == 0:
manager.record_failure(is_rate_limit=True)
else:
manager.record_success(latency_ms=45 + (i % 10) * 5)
print("\n📊 Nach 20 Requests:")
for key, value in manager.get_stats().items():
print(f" {key}: {value}")
4. Scraping-Pipeline mit Retry-Logic und Dead-Letter-Queue
#!/usr/bin/env python3
"""
HolySheep AI - Production Scraping Pipeline
Mit Retry-Logic, Dead-Letter-Queue und Retry-Scheduling
"""
import asyncio
import time
import hashlib
from dataclasses import dataclass, field
from typing import List, Optional, Dict, Any
from enum import Enum
from collections import deque
import httpx
import json
class JobStatus(Enum):
PENDING = "pending"
PROCESSING = "processing"
COMPLETED = "completed"
FAILED = "failed"
DEAD_LETTER = "dead_letter"
@dataclass
class ScrapingJob:
job_id: str
url: str
status: JobStatus = JobStatus.PENDING
attempts: int = 0
max_attempts: int = 3
last_error: Optional[str] = None
result: Optional[Dict] = None
created_at: float = field(default_factory=time.time)
completed_at: Optional[float] = None
class DeadLetterQueue:
"""
Speichert fehlgeschlagene Jobs für spätere Wiederholung.
Implementiert exponentielles Backoff für Retry-Scheduling.
"""
def __init__(self, max_size: int = 10000):
self.jobs: deque = deque(maxlen=max_size)
self.job_index: Dict[str, ScrapingJob] = {}
self.retry_count: Dict[str, int] = {}
def add(self, job: ScrapingJob):
"""Fügt Job zur DLQ hinzu mit Retry-Tracking"""
if job.job_id not in self.retry_count:
self.retry_count[job.job_id] = 0
self.retry_count[job.job_id] += 1
job.attempts = self.retry_count[job.job_id]
job.status = JobStatus.DEAD_LETTER
# Exponentielles Backoff berechnen
backoff_seconds = min(300, 2 ** self.retry_count[job.job_id]) # Max 5 Minuten
job.last_error = f"Backoff: {backoff_seconds}s, Versuch #{job.attempts}"
self.jobs.append(job)
self.job_index[job.job_id