von Thomas Müller, Senior Backend Architect bei HolySheep AI
Als ich vor zwei Jahren ein E-Commerce-KI-Kundenservice-System für einen großen chinesischen Online-Händler aufgebaut habe, standen wir vor einem kritischen Problem: Während des Singles' Day (11.11) musste unser System 50.000 gleichzeitige Kundenanfragen bewältigen. Die ursprüngliche Architektur brach bei 2.000 Requests pro Sekunde zusammen. Nach wochenlangem Experimentieren mit verschiedenen Threading-Strategien haben wir gelernt, dass die richtige API-Gateway-Konfiguration den Unterschied zwischen einem Systemausfall und einem reibungslosen Betrieb ausmacht.
Der konkrete Anwendungsfall: E-Commerce KI-Kundenservice-Peak
Unser Kunde, ein Mode-E-Commerce-Plattform mit 8 Millionen täglich aktiven Nutzern, benötigte einen KI-Chatbot für:
- Produktempfehlungen basierend auf Kundenanfragen
- Retourenabwicklung mit automatischer Bearbeitung
- 24/7 FAQ-Beantwortung mit RAG-Retrieval
Die Herausforderung: Lastspitzen von 08:00-10:00 Uhr und 20:00-22:00 Uhr mit dem 10-fachen des Normalbetriebs. Unsere HolySheep-API-Integration musste diese Last absorbieren, ohne dass die Antwortzeiten über 2 Sekunden stiegen.
Warum API-Gateway-Concurrency-Control entscheidend ist
Bei der Integration von KI-APIs wie HolySheep gibt es zwei kritische Ressourcen:
- Rate Limits: Die meisten KI-Provider limitieren Requests pro Minute (RPM) oder Tokens pro Minute (TPM)
- Backend-Kapazität: Ihre eigene Server-Infrastruktur hat endliche Thread-Pools und Verbindungslimits
Ohne properConcurrency-Management entstehen:
- HTTP 429 Too Many Requests (Ratelimit-Exceeding)
- Connection Timeout Errors
- Memory Leaks durch akkumulierte offene Verbindungen
- Inkonsistente Antworten durch Race Conditions
Architektur-Übersicht: HolySheep API mit Concurrency-Control
# Optimierte Architektur für High-Concurrency KI-Integration
#
┌─────────────────────────────────────┐
│ Load Balancer │
│ (Nginx/AWS ALB) │
└───────────────┬─────────────────────┘
│
┌───────────────▼─────────────────────┐
│ API Gateway Layer │
│ ┌────────────────────────────────┐ │
│ │ Rate Limiter (Token Bucket) │ │
│ │ Semaphore für Max-Parallel │ │
│ │ Request Queue mit Priority │ │
│ └────────────────────────────────┘ │
└───────────────┬─────────────────────┘
│
┌─────────────────────────┼─────────────────────────┐
│ │ │
┌────────▼────────┐ ┌────────▼────────┐ ┌────────▼────────┐
│ Thread Pool A │ │ Thread Pool B │ │ Thread Pool C │
│ (RAG Queries) │ │ (Completions) │ │ (Embeddings) │
│ Priority: HIGH │ │ Priority: MED │ │ Priority: LOW │
└────────┬────────┘ └────────┬────────┘ └────────┬────────┘
│ │ │
└─────────────────────────┼─────────────────────────┘
│
┌───────────────▼─────────────────────┐
│ HolySheep API Gateway │
│ https://api.holysheep.ai/v1 │
│ <50ms Latenz │
└─────────────────────────────────────┘
Grundlegende HolySheep API-Integration mit Python
import asyncio
import aiohttp
from typing import Optional, List, Dict, Any
from dataclasses import dataclass
from datetime import datetime
import json
============================================================
HolySheep AI API Client - Thread-Safe & Rate-Limited
============================================================
Base URL: https://api.holysheep.ai/v1
Dokumentation: https://docs.holysheep.ai
============================================================
@dataclass
class HolySheepConfig:
api_key: str
base_url: str = "https://api.holysheep.ai/v1"
max_concurrent_requests: int = 50
requests_per_minute: int = 1000
timeout_seconds: int = 30
retry_attempts: int = 3
retry_delay: float = 1.0
class HolySheepAIClient:
"""
Thread-safe HolySheep API Client mit integrierter
Concurrency-Control und automatischer Retry-Logik.
"""
def __init__(self, config: HolySheepConfig):
self.config = config
self._semaphore = asyncio.Semaphore(config.max_concurrent_requests)
self._rate_limiter = TokenBucket(
capacity=config.requests_per_minute,
refill_rate=config.requests_per_minute / 60.0
)
self._session: Optional[aiohttp.ClientSession] = None
async def __aenter__(self):
connector = aiohttp.TCPConnector(
limit=config.max_concurrent_requests,
limit_per_host=config.max_concurrent_requests,
ttl_dns_cache=300
)
timeout = aiohttp.ClientTimeout(total=self.config.timeout_seconds)
self._session = aiohttp.ClientSession(
connector=connector,
timeout=timeout
)
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
if self._session:
await self._session.close()
async def chat_completion(
self,
messages: List[Dict[str, str]],
model: str = "gpt-4.1",
temperature: float = 0.7,
max_tokens: int = 2048
) -> Dict[str, Any]:
"""
Sendet eine Chat-Completion-Anfrage an HolySheep.
Thread-safe mit automatischer Rate-Limiting.
"""
async with self._semaphore:
await self._rate_limiter.acquire()
headers = {
"Authorization": f"Bearer {self.config.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens
}
for attempt in range(self.config.retry_attempts):
try:
async with self._session.post(
f"{self.config.base_url}/chat/completions",
headers=headers,
json=payload
) as response:
if response.status == 200:
return await response.json()
elif response.status == 429:
# Rate Limited - warte und retry
retry_after = int(response.headers.get("Retry-After", 60))
await asyncio.sleep(retry_after)
continue
else:
error_body = await response.text()
raise HolySheepAPIError(
f"API Error {response.status}: {error_body}"
)
except aiohttp.ClientError as e:
if attempt == self.config.retry_attempts - 1:
raise
await asyncio.sleep(self.config.retry_delay * (2 ** attempt))
raise HolySheepAPIError("Max retry attempts exceeded")
@dataclass
class TokenBucket:
"""Token Bucket Algorithmus für Rate Limiting"""
capacity: float
refill_rate: float
_tokens: float = None
_last_refill: datetime = None
def __post_init__(self):
self._tokens = self.capacity
self._last_refill = datetime.now()
async def acquire(self):
while True:
now = datetime.now()
elapsed = (now - self._last_refill).total_seconds()
self._tokens = min(
self.capacity,
self._tokens + elapsed * self.refill_rate
)
self._last_refill = now
if self._tokens >= 1:
self._tokens -= 1
return
await asyncio.sleep(0.1)
class HolySheepAPIError(Exception):
"""Custom Exception für HolySheep API Fehler"""
passThread-Pool-Konfiguration für verschiedene Workload-Typen
In Enterprise-Anwendungen unterscheiden wir typischerweise drei Workload-Kategorien:
- RAG-Queries: Retrieval + Generation, hohe Latenz-Toleranz
- Stream-Chat: Interaktive Chats, niedrige Latenz kritisch
- Batch-Processing: Bulk-Textverarbeitung, Durchsatz priorisiert
import concurrent.futures
from queue import PriorityQueue, Empty
from threading import Lock, Event
from typing import Callable, Any, Optional
import time
from dataclasses import dataclass, field
from enum import IntEnum
class WorkloadPriority(IntEnum):
CRITICAL = 0 # Streaming, User-facing
HIGH = 1 # RAG Queries
MEDIUM = 2 # Standard Completions
LOW = 3 # Batch Processing
@dataclass
class WorkloadTask:
priority: WorkloadPriority
timestamp: float
task_id: str
func: Callable
args: tuple = field(default_factory=tuple)
kwargs: dict = field(default_factory=dict)
def __lt__(self, other):
if self.priority != other.priority:
return self.priority < other.priority
return self.timestamp < other.timestamp
class MultiPoolExecutor:
"""
Multi-Thread-Pool Executor mit Priority-Queuing
Optimiert für HolySheep API Integration
"""
def __init__(
self,
critical_workers: int = 10,
high_workers: int = 25,
medium_workers: int = 50,
low_workers: int = 100
):
self.pools = {
WorkloadPriority.CRITICAL: ThreadPoolWithMetrics(
max_workers=critical_workers,
name="critical-stream"
),
WorkloadPriority.HIGH: ThreadPoolWithMetrics(
max_workers=high_workers,
name="high-priority-rag"
),
WorkloadPriority.MEDIUM: ThreadPoolWithMetrics(
max_workers=medium_workers,
name="medium-completions"
),
WorkloadPriority.LOW: ThreadPoolWithMetrics(
max_workers=low_workers,
name="low-batch"
),
}
self.queues = {
priority: PriorityQueue()
for priority in WorkloadPriority
}
self._shutdown = Event()
self._metrics = {
"submitted": 0,
"completed": 0,
"rejected": 0,
"avg_latency_ms": 0
}
self._metrics_lock = Lock()
def submit(
self,
func: Callable,
priority: WorkloadPriority,
*args,
**kwargs
) -> Optional[concurrent.futures.Future]:
"""Submit a task with specified priority"""
if self._shutdown.is_set():
raise RuntimeError("Executor is shut down")
task = WorkloadTask(
priority=priority,
timestamp=time.time(),
task_id=f"task_{self._metrics['submitted']}",
func=func,
args=args,
kwargs=kwargs
)
self.queues[priority].put(task)
self._update_metric("submitted", 1)
return self._dispatch(task)
def _dispatch(self, task: WorkloadTask) -> concurrent.futures.Future:
"""Dispatch task to appropriate pool"""
pool = self.pools[task.priority]
def wrapped_func():
start = time.time()
try:
result = task.func(*task.args, **task.kwargs)
self._update_latency((time.time() - start) * 1000)
self._update_metric("completed", 1)
return result
except Exception as e:
self._update_metric("rejected", 1)
raise
return pool.submit(wrapped_func)
def _update_metric(self, key: str, value: int):
with self._metrics_lock:
self._metrics[key] += value
def _update_latency(self, latency_ms: float):
with self._metrics_lock:
current = self._metrics["avg_latency_ms"]
completed = self._metrics["completed"]
self._metrics["avg_latency_ms"] = (
(current * (completed - 1) + latency_ms) / completed
)
def get_metrics(self) -> dict:
"""Gibt aktuelle Performance-Metriken zurück"""
with self._metrics_lock:
result = self._metrics.copy()
for priority, pool in self.pools.items():
pool_metrics = pool.get_metrics()
result[f"pool_{priority.name}"] = {
"active": pool_metrics["active_workers"],
"queue_size": self.queues[priority].qsize(),
"completed": pool_metrics["completed_tasks"]
}
return result
def shutdown(self, wait: bool = True):
"""Graceful Shutdown aller Pools"""
self._shutdown.set()
for pool in self.pools.values():
pool.shutdown(wait=wait)
class ThreadPoolWithMetrics(concurrent.futures.ThreadPoolExecutor):
"""ThreadPool mit integrierten Metriken"""
def __init__(self, max_workers: int, name: str):
super().__init__(max_workers=max_workers)
self.pool_name = name
self._active_count = 0
self._completed = 0
self._lock = Lock()
def submit(self, fn, *args, **kwargs):
with self._lock:
self._active_count += 1
future = super().submit(self._wrapper(fn), *args, **kwargs)
future.add_done_callback(self._done_callback)
return future
def _wrapper(self, fn):
def wrapped(*args, **kwargs):
try:
return fn(*args, **kwargs)
finally:
with self._lock:
self._active_count -= 1
return wrapped
def _done_callback(self, future):
with self._lock:
self._completed += 1
def get_metrics(self) -> dict:
with self._lock:
return {
"name": self.pool_name,
"active_workers": self._active_count,
"completed_tasks": self._completed,
"max_workers": self._max_workers
}Praxis-Beispiel: RAG-System mit optimierter Concurrency
Basierend auf meiner Erfahrung bei der Migration von drei Enterprise-RAG-Systemen zur HolySheep API zeige ich nun die optimale Konfiguration für verschiedene Szenarien:
"""
Production RAG-System mit HolySheep API
Optimiert für 10.000+ Requests/Stunde
"""
import asyncio
import hashlib
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
import json
HolySheep API Client importieren
from holysheep_client import HolySheepAIClient, HolySheepConfig, WorkloadPriority
@dataclass
class RAGConfig:
# HolySheep API Einstellungen
api_key: str = "YOUR_HOLYSHEEP_API_KEY" # Ersetzen Sie mit Ihrem Key
base_url: str = "https://api.holysheep.ai/v1"
# Concurrency Settings
max_concurrent_rag_queries: int = 30
max_concurrent_streaming: int = 50
max_concurrent_embeddings: int = 100
# Rate Limiting (basierend auf Ihrem HolySheep-Plan)
requests_per_minute: int = 3000
tokens_per_minute: int = 150000
# Retry Settings
max_retries: int = 3
timeout_seconds: int = 60
class RAGPipeline:
"""
Production-ready RAG Pipeline mit HolySheep API
"""
def __init__(self, config: RAGConfig):
self.config = config
self.client = HolySheepAIClient(
HolySheepConfig(
api_key=config.api_key,
base_url=config.base_url,
max_concurrent_requests=config.max_concurrent_rag_queries,
requests_per_minute=config.requests_per_minute,
timeout_seconds=config.timeout_seconds,
retry_attempts=config.max_retries
)
)
# Embedding Cache für häufige Queries
self._embedding_cache: Dict[str, List[float]] = {}
self._cache_lock = asyncio.Lock()
# Metriken
self._stats = {
"total_queries": 0,
"cache_hits": 0,
"avg_retrieval_ms": 0,
"avg_generation_ms": 0
}
async def query(
self,
question: str,
context_docs: List[str],
use_cache: bool = True,
stream: bool = False
) -> Dict[str, Any]:
"""
Führt eine RAG-Query aus
Args:
question: Die Benutzerfrage
context_docs: Relevante Dokument-Kontexte
use_cache: Ob Embeddings gecacht werden sollen
stream: Ob Streaming verwendet werden soll
Returns:
Dict mit Antwort und Metriken
"""
import time
self._stats["total_queries"] += 1
# 1. Kontext vorbereiten
context = self._prepare_context(context_docs)
# 2. System-Prompt mit RAG-Kontext
system_message = f"""Du bist ein hilfreicher KI-Assistent.
Nutze ausschließlich die folgenden Informationen, um die Frage zu beantworten.
Wenn die Information nicht ausreicht, sage das ehrlich.
Kontext:
{context}
"""
messages = [
{"role": "system", "content": system_message},
{"role": "user", "content": question}
]
# 3. Generierung mit HolySheep
gen_start = time.time()
# Für Production: DeepSeek V3.2 für Kosteneffizienz
# Für komplexe Aufgaben: GPT-4.1
model = "deepseek-v3.2" if not stream else "gpt-4.1"
response = await self.client.chat_completion(
messages=messages,
model=model,
temperature=0.3,
max_tokens=2048
)
gen_time = (time.time() - gen_start) * 1000
self._stats["avg_generation_ms"] = (
(self._stats["avg_generation_ms"] * (self._stats["total_queries"] - 1) + gen_time)
/ self._stats["total_queries"]
)
return {
"answer": response["choices"][0]["message"]["content"],
"model": response.get("model", model),
"usage": response.get("usage", {}),
"latency_ms": {
"generation": gen_time
}
}
def _prepare_context(self, docs: List[str]) -> str:
"""Bereitet Kontext aus Dokumenten vor"""
context_parts = []
for i, doc in enumerate(docs, 1):
context_parts.append(f"[Dokument {i}]\n{doc[:2000]}")
return "\n\n".join(context_parts)
async def batch_process(
self,
questions: List[str],
contexts: List[List[str]]
) -> List[Dict[str, Any]]:
"""
Batch-Verarbeitung für mehrere Queries
Mit automatischer Concurrency-Limitierung
"""
tasks = []
semaphore = asyncio.Semaphore(10) # Max 10 parallel
async def bounded_query(q: str, ctx: List[str]):
async with semaphore:
return await self.query(q, ctx)
for q, ctx in zip(questions, contexts):
tasks.append(bounded_query(q, ctx))
# Mit asyncio.gather für parallele Ausführung
results = await asyncio.gather(*tasks, return_exceptions=True)
return [
r if not isinstance(r, Exception) else {"error": str(r)}
for r in results
]
def get_stats(self) -> Dict[str, Any]:
"""Gibt aktuelle Performance-Statistiken zurück"""
return self._stats.copy()
============================================================
Production Usage Example
============================================================
async def main():
config = RAGConfig(
api_key="YOUR_HOLYSHEEP_API_KEY", # HolySheep API Key
max_concurrent_rag_queries=30,
requests_per_minute=3000
)
pipeline = RAGPipeline(config)
# Einzelne Query
result = await pipeline.query(
question="Was sind die Rückgaberichtlinien?",
context_docs=[
"Unsere Rückgaberichtlinie erlaubt Rücksendungen innerhalb von 30 Tagen.",
"Produkte müssen unbenutzt und in Originalverpackung sein.",
"Sale-Artikel sind von der Rückgabe ausgeschlossen."
]
)
print(f"Antwort: {result['answer']}")
print(f"Modell: {result['model']}")
print(f"Latenz: {result['latency_ms']['generation']:.2f}ms")
print(f"Token-Nutzung: {result['usage']}")
# Batch Processing
questions = [
"Wie kann ich bezahlen?",
"Wie lange dauert die Lieferung?",
"Wie kontaktiere ich den Support?"
]
contexts = [
["Akzeptierte Zahlungsmethoden: Kreditkarte, PayPal, WeChat Pay, Alipay"],
["Standardlieferung: 3-5 Werktage, Express: 1-2 Werktage"],
["Support: [email protected], Mo-Fr 9-18 Uhr"]
]
batch_results = await pipeline.batch_process(questions, contexts)
for i, res in enumerate(batch_results):
print(f"\n--- Frage {i+1} ---")
if "error" in res:
print(f"Fehler: {res['error']}")
else:
print(f"Antwort: {res['answer']}")
if __name__ == "__main__":
asyncio.run(main())Rate-Limiting-Strategien für HolySheep API
Je nach HolySheep-Tarif gelten unterschiedliche Limits. Hier die optimalen Strategien:
"""
Adaptive Rate Limiter für HolySheep API
Implementiert Token Bucket + Sliding Window
"""
import asyncio
import time
from typing import Dict, Optional
from dataclasses import dataclass, field
from collections import deque
from threading import Lock
import logging
logger = logging.getLogger(__name__)
@dataclass
class RateLimitConfig:
requests_per_minute: int
tokens_per_minute: int
burst_size: int = 10
class SlidingWindowRateLimiter:
"""
Sliding Window Rate Limiter mit Tokenrefill
Optimiert für HolySheep API Rate Limits
"""
def __init__(self, config: RateLimitConfig):
self.config = config
# Request-based limiting
self._request_times: deque = deque(maxlen=config.requests_per_minute)
self._request_lock = Lock()
# Token-based limiting (für API-Response-Tracking)
self._token_usage: deque = deque(maxlen=1000)
self._token_bucket: float = config.tokens_per_minute
self._token_lock = Lock()
self._last_token_refill = time.time()
# Metrics
self._total_requests = 0
self._total_rejected = 0
def can_make_request(self, estimated_tokens: int = 1000) -> bool:
"""
Prüft ob ein Request erlaubt ist
"""
now = time.time()
# Request-Rate prüfen
with self._request_lock:
# Entferne Requests außerhalb des 60-Sekunden-Fensters
cutoff = now - 60
while self._request_times and self._request_times[0] < cutoff:
self._request_times.popleft()
if len(self._request_times) >= self.config.requests_per_minute:
self._total_rejected += 1
return False
# Token-Limit prüfen
with self._token_lock:
# Refill Tokens
elapsed = now - self._last_token_refill
refill = elapsed * (self.config.tokens_per_minute / 60)
self._token_bucket = min(
self.config.tokens_per_minute,
self._token_bucket + refill
)
self._last_token_refill = now
if self._token_bucket < estimated_tokens:
self._total_rejected += 1
return False
return True
def record_request(self, tokens_used: int):
"""Zeichnet einen erfolgreichen Request auf"""
now = time.time()
with self._request_lock:
self._request_times.append(now)
self._total_requests += 1
with self._token_lock:
self._token_bucket -= tokens_used
self._token_usage.append({
"timestamp": now,
"tokens": tokens_used
})
def get_wait_time(self) -> float:
"""Berechnet Wartezeit bis zum nächsten erlaubten Request"""
now = time.time()
with self._request_lock:
if not self._request_times:
return 0
oldest = self._request_times[0]
time_since_oldest = now - oldest
if time_since_oldest >= 60:
return 0
return max(0, 60 - time_since_oldest)
def get_metrics(self) -> Dict:
"""Gibt aktuelle Metriken zurück"""
with self._request_lock:
current_requests = len(self._request_times)
return {
"current_rpm": current_requests,
"max_rpm": self.config.requests_per_minute,
"total_requests": self._total_requests,
"total_rejected": self._total_rejected,
"rejection_rate": (
self._total_rejected / self._total_requests
if self._total_requests > 0 else 0
),
"estimated_wait_ms": self.get_wait_time() * 1000
}
class HolySheepRateLimiter:
"""
High-Level Rate Limiter speziell für HolySheep API
"""
def __init__(
self,
rpm_limit: int = 3000,
tpm_limit: int = 150000,
max_retries: int = 5
):
self.config = RateLimitConfig(
requests_per_minute=rpm_limit,
tokens_per_minute=tpm_limit,
burst_size=rpm_limit // 10
)
self.limiter = SlidingWindowRateLimiter(self.config)
self.max_retries = max_retries
# Exponential backoff state
self._current_backoff = 1.0
self._backoff_multiplier = 1.5
self._max_backoff = 60.0
async def acquire(self, estimated_tokens: int = 1000) -> bool:
"""
Acquired permission für einen Request
Blockiert falls nötig mit Backoff
"""
for attempt in range(self.max_retries):
if self.limiter.can_make_request(estimated_tokens):
return True
wait_time = self.limiter.get_wait_time()
if attempt < self.max_retries - 1:
logger.info(
f"Rate limit reached. Waiting {wait_time:.2f}s "
f"(attempt {attempt + 1}/{self.max_retries})"
)
await asyncio.sleep(wait_time)
else:
# Erhöhe Backoff für nächsten Burst
self._current_backoff = min(
self._current_backoff * self._backoff_multiplier,
self._max_backoff
)
return False
return False
def release(self, tokens_used: int):
"""Gibt Request-Belegung frei"""
self.limiter.record_request(tokens_used)
# Backoff zurücksetzen bei erfolgreichem Request
self._current_backoff = max(1.0, self._current_backoff / 2)
def get_stats(self) -> Dict:
"""Gibt detaillierte Statistiken zurück"""
return self.limiter.get_metrics()
Usage Example
async def example_usage():
# Für Enterprise-Plan: 3000 RPM, 150k TPM
limiter = HolySheepRateLimiter(rpm_limit=3000, tpm_limit=150000)
for i in range(100):
if await limiter.acquire(estimated_tokens=500):
print(f"Request {i + 1} erlaubt")
limiter.release(500) # Token-Nutzung aus Response
else:
print(f"Request {i + 1} abgelehnt - Rate Limit")
await asyncio.sleep(0.01) # 100 Requests/Sekunde simulieren
print("\n=== Rate Limiter Stats ===")
stats = limiter.get_stats()
print(f"RPM verwendet: {stats['current_rpm']}/{stats['max_rpm']}")
print(f"Anfrage gesamt: {stats['total_requests']}")
print(f"Abgelehnt: {stats['total_rejected']}")
print(f"Ablehnungsrate: {stats['rejection_rate']:.2%}")Monitoring und Performance-Optimierung
Für die kontinuierliche Optimierung Ihrer HolySheep-API-Integration empfehle ich folgende Metriken:
- Request-Latenz: P50, P95, P99 Percentile
- Throughput: Requests pro Sekunde, Tokens pro Minute
- Fehlerrate: HTTP 4xx, 5xx, Timeouts
- Cache-Hit-Rate: Für Embedding-Caching
- Queue-Depth: Wartezeit in Priority-Queues
"""
Performance Monitoring Dashboard Data Collector
Exportiert Metriken für Prometheus/Grafana
"""
import asyncio
import psutil
import time
from typing import Dict, List
from dataclasses import dataclass, asdict
from datetime import datetime
import json
@dataclass
class SystemMetrics:
timestamp: float
cpu_percent: float
memory_percent: float
memory_used_mb: float
active_connections: int
@dataclass
class APIMetrics:
timestamp: float
total_requests: int
successful_requests: int
failed_requests: int
avg_latency_ms: float
p95_latency_ms: float
p99_latency_ms: float
rpm_current: int
tpm_current: int
class PerformanceMonitor:
"""
Sammelt kontinuierlich Performance-Daten
für HolySheep API Integration
"""
def __init__(self, collection_interval: int = 10):
self.interval = collection_interval
self._running = False
self._history: List[Dict] = []
# Latenz-Tracking
self._latencies: List[float] = []
self._latency_lock = asyncio.Lock()
async def record_latency(self, latency_ms: float):
"""Zeichnet Request-Latenz auf"""
async with self._latency_lock:
self._latencies.append(latency_ms)
# Behalte nur letzte 10.000 Latenzen
if len(self._latencies) > 10000:
self._latencies = self._latencies[-10000:]
async def collect_metrics(
self,
api_limiter, # HolySheepRateLimiter
rag_pipeline # RAGPipeline
) -> Dict:
"""Sammelt System- und API-Metriken"""
# System Metrics
process = psutil.Process()
sys_metrics = SystemMetrics(
timestamp=time.time(),
cpu_percent=process.cpu_percent(),
memory_percent=process.memory_percent(),
memory_used_mb=process.memory_info().rss / 1024 / 1024,
active_connections=len(process.connections())
)
# Latenz-Perzentile berechnen
async with self._latency_lock:
latencies_sorted = sorted(self._latencies)
n = len(latencies_sorted)
p50 = latencies_sorted[int(n * 0.50)] if n > 0 else 0
p95 = latencies_sorted[int(n * 0.95)] if n > 0 else 0
p99 = latencies_sorted[int(n * 0.99)] if n > 0 else 0
avg = sum(latencies_sorted) / n if n > 0 else 0
# API Metrics
limiter_stats = api_limiter.get_stats()
api_metrics = APIMetrics(
timestamp=time.time(),
total_requests=limiter_stats["total_requests"],
successful_requests=limiter_stats["total_requests"] - limiter_stats["total_rejected"],
failed_requests=limiter_stats["total_rejected"],
avg_latency_ms=avg,
p95_latency_ms=p95,
p99_latency_ms=p99,
rpm_current=limiter_stats["current_rpm"],
tpm_current=int(limiter_stats.get("current_tpm", 0))
)
metrics = {
"system": asdict(sys_metrics),
"api": asdict(api_metrics),
"collection_time": datetime.now().isoformat()
}
self._history.append(metrics)
# History auf 1000 Einträge begrenzen
if len(self._history) > 1000:
self._history = self._history[-1000:]
return metrics
def export_prometheus_format(self) -> str:
"""Exportiert Metriken im Prometheus-Format"""
if not self._history:
return ""
latest = self._history[-1]
sys_m = latest["system"]
api_m