Mở Đầu: Kịch Bản Lỗi Thực Tế Khiến Tôi Thức Trắng 3 Đêm
Tôi vẫn nhớ rõ cái đêm tháng 3 năm 2025. Lúc 2:47 sáng, Slack reo liên hồi — hàng loạt alert cảnh báo ConnectionError: timeout after 30s từ hệ thống AI API 中转站 của khách hàng. Đội ngũ devOps hơn 10 người vào cuộc, nhưng không ai xác định được nguyên nhân gốc. Kết quả: 3 tiếng downtime, 2000+ request thất bại, và tôi mất một tuần để khôi phục niềm tin từ khách hàng.
Sau sự cố đó, tôi đã xây dựng một hệ thống SLO (Service Level Objective) monitoring hoàn chỉnh cho AI API 中转站. Bài viết này chia sẻ toàn bộ kiến thức thực chiến — từ lý thuyết đến code có thể chạy ngay.
SLO Monitoring Là Gì và Tại Sao Cần Thiết?
SLO (Service Level Objective) là cam kết về chất lượng dịch vụ mà bạn đo lường được. Với AI API 中转站 như HolySheep AI, SLO thường bao gồm:
- Availability: Tỷ lệ uptime (target: 99.9%)
- Latency: Thời gian phản hồi trung bình (target: P99 < 500ms)
- Error Rate: Tỷ lệ lỗi (target: < 0.1%)
- Throughput: Số request/giây có thể xử lý
Kiến Trúc Hệ Thống Monitoring
┌─────────────────────────────────────────────────────────────┐
│ AI API 中转站 Architecture │
├─────────────────────────────────────────────────────────────┤
│ │
│ Client Request │
│ │ │
│ ▼ │
│ ┌─────────┐ ┌──────────┐ ┌─────────────────────┐ │
│ │ Load │───▶│ Rate │───▶│ HolySheep AI │ │
│ │ Balancer│ │ Limiter │ │ Proxy (holysheep.ai)│ │
│ └─────────┘ └──────────┘ └─────────────────────┘ │
│ │ │ │ │
│ │ │ ▼ │
│ │ │ ┌─────────────┐ │
│ │ │ │ SLO Monitor │ │
│ │ │ │ - Prometheus│ │
│ │ │ │ - Grafana │ │
│ │ │ │ - Alertmgr │ │
│ │ │ └─────────────┘ │
│ │ │ │ │
│ └──────────────┴────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ Alert Channel │ │
│ │ (Slack/PagerDuty)│ │
│ └─────────────────┘ │
└─────────────────────────────────────────────────────────────┘Code Implementation: Prometheus Metrics Exporter
Đầu tiên, tôi sẽ chia sẻ code Python để expose Prometheus metrics từ AI API 中转站:
# prometheus_exporter.py
AI API 中转站 SLO Metrics Exporter
Compatible with Prometheus + Grafana stack
from prometheus_client import Counter, Histogram, Gauge, start_http_server
import time
import random
from datetime import datetime
========== Define SLO Metrics ==========
Request Counter - đếm tổng số request
REQUEST_COUNT = Counter(
'ai_api_requests_total',
'Total AI API requests',
['endpoint', 'status_code', 'provider']
)
Latency Histogram - phân bố độ trễ
REQUEST_LATENCY = Histogram(
'ai_api_request_duration_seconds',
'AI API request latency in seconds',
['endpoint', 'provider'],
buckets=[0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0]
)
Error Counter - đếm lỗi
ERROR_COUNT = Counter(
'ai_api_errors_total',
'Total AI API errors',
['endpoint', 'error_type', 'provider']
)
Active Requests Gauge - số request đang xử lý
ACTIVE_REQUESTS = Gauge(
'ai_api_active_requests',
'Number of active requests',
['provider']
)
SLO Compliance Gauge - tỷ lệ đạt SLO
SLO_COMPLIANCE = Gauge(
'ai_api_slo_compliance_ratio',
'SLO compliance ratio (1.0 = 100%)',
['slo_type']
)
Token Usage Counter - đếm token usage
TOKEN_USAGE = Counter(
'ai_api_tokens_total',
'Total tokens used',
['model', 'type'] # type: prompt/completion
)
class AISLOService:
"""
AI API 中转站 SLO Monitoring Service
Thu thập metrics và tính toán SLO compliance
"""
def __init__(self):
self.provider = "holysheep"
self.total_requests = 0
self.successful_requests = 0
self.failed_requests = 0
self.total_latency = 0.0
def record_request(self, endpoint: str, status_code: int,
latency: float, provider: str = "holysheep"):
"""
Ghi nhận một request vào metrics
"""
self.total_requests += 1
# Record counters
REQUEST_COUNT.labels(
endpoint=endpoint,
status_code=str(status_code),
provider=provider
).inc()
REQUEST_LATENCY.labels(
endpoint=endpoint,
provider=provider
).observe(latency)
# Track success/failure
if 200 <= status_code < 300:
self.successful_requests += 1
else:
self.failed_requests += 1
ERROR_COUNT.labels(
endpoint=endpoint,
error_type=f"http_{status_code}",
provider=provider
).inc()
# Update SLO metrics
self._update_slo_metrics()
def _update_slo_metrics(self):
"""
Cập nhật SLO compliance metrics
"""
if self.total_requests > 0:
# Availability SLO (target: 99.9%)
availability = self.successful_requests / self.total_requests
SLO_COMPLIANCE.labels(slo_type='availability').set(availability)
# Error Rate SLO (target: < 0.1%)
error_rate = self.failed_requests / self.total_requests
SLO_COMPLIANCE.labels(slo_type='error_rate').set(1 - error_rate)
def check_slo_breach(self, slo_type: str, threshold: float) -> bool:
"""
Kiểm tra xem SLO có bị breach không
Args:
slo_type: 'availability' hoặc 'error_rate'
threshold: Ngưỡng tối thiểu (ví dụ: 0.999 cho availability)
Returns:
True nếu SLO bị breach
"""
current_value = None
if slo_type == 'availability':
if self.total_requests == 0:
return False
current_value = self.successful_requests / self.total_requests
elif slo_type == 'error_rate':
if self.total_requests == 0:
return False
current_value = 1 - (self.failed_requests / self.total_requests)
return current_value < threshold if current_value else False
def simulate_traffic(slo_service: AISLOService):
"""
Simulate traffic để test monitoring
"""
endpoints = ['/chat/completions', '/embeddings', '/images/generations']
status_codes = [200, 200, 200, 200, 200, 200, 200, 200, 401, 429, 500]
for i in range(100):
endpoint = random.choice(endpoints)
status = random.choice(status_codes)
latency = random.gauss(0.15, 0.05) # Mean: 150ms, SD: 50ms
slo_service.record_request(endpoint, status, latency)
time.sleep(0.1)
if __name__ == '__main__':
# Start Prometheus metrics server on port 9090
start_http_server(9090)
print("🚀 SLO Metrics Exporter started on http://localhost:9090")
print("📊 Metrics available at http://localhost:9090/metrics")
slo_service = AISLOService()
# Run simulation
print("📈 Running traffic simulation...")
simulate_traffic(slo_service)
# Check SLO breach
if slo_service.check_slo_breach('availability', 0.999):
print("⚠️ WARNING: Availability SLO breach detected!")
print(f"✅ Total requests: {slo_service.total_requests}")
print(f"✅ Success rate: {slo_service.successful_requests / slo_service.total_requests * 100:.2f}%")Alerting System Với Alertmanager
Tiếp theo, cấu hình Alertmanager để gửi cảnh báo khi SLO bị breach:
# alert_rules.yml
Prometheus Alert Rules cho AI API 中转站 SLO
groups:
- name: ai_api_slo_alerts
rules:
# Alert: Availability dưới 99.9%
- alert: AIAccessibilityBreach
expr: |
(
sum(rate(ai_api_requests_total{status_code=~"2.."}[5m])) by (provider)
/
sum(rate(ai_api_requests_total[5m])) by (provider)
) < 0.999
for: 2m
labels:
severity: critical
team: platform
annotations:
summary: "AI API Availability Breach"
description: |
Provider {{ $labels.provider }} availability is {{ $value | humanizePercentage }}
Target SLO: 99.9%
Duration: {{ $for }}
# Alert: Error Rate vượt 1%
- alert: AIHighErrorRate
expr: |
(
sum(rate(ai_api_requests_total{status_code=~"[45].."}[5m])) by (provider)
/
sum(rate(ai_api_requests_total[5m])) by (provider)
) > 0.01
for: 1m
labels:
severity: warning
team: platform
annotations:
summary: "AI API High Error Rate"
description: |
Provider {{ $labels.provider }} error rate is {{ $value | humanizePercentage }}
Threshold: 1%
# Alert: P99 Latency vượt 2 giây
- alert: AIHighLatency
expr: |
histogram_quantile(0.99,
sum(rate(ai_api_request_duration_seconds_bucket[5m])) by (le, provider)
) > 2
for: 3m
labels:
severity: warning
team: platform
annotations:
summary: "AI API High Latency"
description: |
Provider {{ $labels.provider }} P99 latency is {{ $value }}s
Threshold: 2s
# Alert: Rate Limit Hit
- alert: AIRateLimitHit
expr: |
sum(rate(ai_api_errors_total{error_type="http_429"}[5m])) by (provider) > 10
for: 30s
labels:
severity: warning
team: platform
annotations:
summary: "AI API Rate Limit Being Hit"
description: |
Provider {{ $labels.provider }} is hitting rate limits
Error rate: {{ $value }} errors/second
# Alert: API Key Invalid
- alert: AIAuthenticationError
expr: |
sum(rate(ai_api_errors_total{error_type="http_401"}[5m])) by (provider) > 5
for: 1m
labels:
severity: critical
team: platform
annotations:
summary: "AI API Authentication Errors"
description: |
Possible invalid API key for provider {{ $labels.provider }}
Error rate: {{ $value }} errors/secondFile cấu hình Alertmanager để gửi notification:
# alertmanager.yml
Alertmanager Configuration cho AI API 中转站
global:
resolve_timeout: 5m
smtp_smarthost: 'smtp.gmail.com:587'
smtp_from: '[email protected]'
smtp_auth_username: '[email protected]'
route:
group_by: ['alertname', 'provider']
group_wait: 10s
group_interval: 10s
repeat_interval: 12h
receiver: 'default-receiver'
routes:
# Critical alerts - PagerDuty immediately
- match:
severity: critical
receiver: 'pagerduty-critical'
continue: true
# Warning alerts - Slack
- match:
severity: warning
receiver: 'slack-warnings'
continue: true
receivers:
- name: 'default-receiver'
email_configs:
- to: '[email protected]'
headers:
subject: 'AI API Alert: {{ .GroupLabels.alertname }}'
- name: 'pagerduty-critical'
pagerduty_configs:
- service_key: 'YOUR_PAGERDUTY_SERVICE_KEY'
severity: critical
event_action: 'trigger'
description: |
AI API 中转站 Critical Alert
{{ range .Alerts }}
{{ .Annotations.summary }}
{{ .Annotations.description }}
{{ end }}
- name: 'slack-warnings'
slack_configs:
- api_url: 'https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK'
channel: '#ai-api-alerts'
username: 'AI SLO Bot'
title: |
🚨 AI API 中转站 Alert: {{ .GroupLabels.alertname }}
text: |
{{ range .Alerts }}
*Alert:* {{ .Annotations.summary }}
*Provider:* {{ .Labels.provider }}
*Description:* {{ .Annotations.description }}
*Time:* {{ .StartsAt }}
{{ end }}
color: '{{ if eq .Status "firing" }}danger{{ else }}good{{ end }}'
send_resolved: true
inhibit_rules:
# Suppress less severe alerts when critical is firing
- source_match:
severity: critical
target_match:
severity: warning
equal: ['alertname', 'provider']Tích Hợp Với HolySheep AI API - Code Thực Chiến
Đây là phần quan trọng nhất — tích hợp SLO monitoring với HolySheep AI API 中转站:
# holy_sheep_slo_monitor.py
AI API 中转站 SLO Monitor với HolySheep AI Integration
base_url: https://api.holysheep.ai/v1
import requests
import time
import logging
from datetime import datetime, timedelta
from dataclasses import dataclass
from typing import Optional, Dict, List
from prometheus_client import Counter, Histogram, Gauge, start_http_server
Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
Prometheus metrics
API_REQUESTS = Counter('holysheep_requests_total', 'Total requests', ['model', 'status'])
API_LATENCY = Histogram('holysheep_latency_seconds', 'Request latency', ['model'])
API_COST = Counter('holysheep_cost_usd', 'Total cost in USD', ['model'])
@dataclass
class SLOConfig:
"""SLO Configuration"""
availability_target: float = 0.999 # 99.9%
latency_p99_target: float = 2.0 # 2 seconds
latency_p95_target: float = 1.0 # 1 second
error_rate_max: float = 0.001 # 0.1%
@dataclass
class SLOReport:
"""SLO Compliance Report"""
timestamp: datetime
total_requests: int
successful_requests: int
failed_requests: int
availability: float
p50_latency: float
p95_latency: float
p99_latency: float
avg_cost_per_request: float
slo_met: bool
class HolySheepSLOClient:
"""
HolySheep AI API Client với SLO Monitoring
Đảm bảo tích hợp với https://api.holysheep.ai/v1
"""
BASE_URL = "https://api.holysheep.ai/v1" # LUÔN LUÔN dùng base_url này
def __init__(self, api_key: str, slo_config: Optional[SLOConfig] = None):
"""
Initialize HolySheep AI SLO Client
Args:
api_key: HolySheep AI API key (đăng ký tại https://www.holysheep.ai/register)
slo_config: SLO configuration
"""
self.api_key = api_key
self.slo_config = slo_config or SLOConfig()
# Metrics tracking
self.latencies: List[float] = []
self.request_count = 0
self.success_count = 0
self.error_count = 0
self.total_cost = 0.0
# Session với retry
self.session = requests.Session()
self.session.headers.update({
'Authorization': f'Bearer {api_key}',
'Content-Type': 'application/json'
})
def _calculate_cost(self, model: str, tokens: int) -> float:
"""
Tính chi phí theo model (HolySheep AI pricing 2026)
HolySheep AI Pricing:
- GPT-4.1: $8.00/1M tokens
- Claude Sonnet 4.5: $15.00/1M tokens
- Gemini 2.5 Flash: $2.50/1M tokens
- DeepSeek V3.2: $0.42/1M tokens
"""
pricing = {
'gpt-4.1': 8.00,
'gpt-4.1-turbo': 8.00,
'claude-sonnet-4.5': 15.00,
'claude-3-5-sonnet': 15.00,
'gemini-2.5-flash': 2.50,
'deepseek-v3.2': 0.42,
}
rate = pricing.get(model.lower(), 8.00)
return (tokens / 1_000_000) * rate
def chat_completions(self, model: str, messages: List[Dict],
temperature: float = 0.7, max_tokens: int = 1000) -> Dict:
"""
Gọi chat completions API với SLO tracking
Args:
model: Model name (gpt-4.1, claude-sonnet-4.5, etc.)
messages: Chat messages
temperature: Sampling temperature
max_tokens: Maximum tokens to generate
Returns:
API response dict
Raises:
Exception: Khi API call thất bại
"""
start_time = time.time()
self.request_count += 1
try:
response = self.session.post(
f"{self.BASE_URL}/chat/completions",
json={
'model': model,
'messages': messages,
'temperature': temperature,
'max_tokens': max_tokens
},
timeout=30
)
latency = time.time() - start_time
self.latencies.append(latency)
API_LATENCY.labels(model=model).observe(latency)
if response.status_code == 200:
self.success_count += 1
API_REQUESTS.labels(model=model, status='success').inc()
result = response.json()
# Calculate cost
prompt_tokens = result.get('usage', {}).get('prompt_tokens', 0)
completion_tokens = result.get('usage', {}).get('completion_tokens', 0)
total_tokens = prompt_tokens + completion_tokens
cost = self._calculate_cost(model, total_tokens)
self.total_cost += cost
API_COST.labels(model=model).inc(cost)
logger.info(
f"✅ Request successful | Model: {model} | "
f"Latency: {latency:.3f}s | Cost: ${cost:.4f}"
)
return result
elif response.status_code == 401:
self.error_count += 1
API_REQUESTS.labels(model=model, status='auth_error').inc()
raise Exception(f"401 Unauthorized - Invalid API Key. "
f"Check your key at https://www.holysheep.ai/register")
elif response.status_code == 429:
self.error_count += 1
API_REQUESTS.labels(model=model, status='rate_limited').inc()
raise Exception("429 Rate Limited - Retry after backoff")
else:
self.error_count += 1
API_REQUESTS.labels(model=model, status='error').inc()
raise Exception(f"API Error {response.status_code}: {response.text}")
except requests.exceptions.Timeout:
self.error_count += 1
API_REQUESTS.labels(model=model, status='timeout').inc()
logger.error(f"⏰ Request timeout after 30s")
raise Exception("ConnectionError: timeout after 30s")
except requests.exceptions.ConnectionError as e:
self.error_count += 1
API_REQUESTS.labels(model=model, status='connection_error').inc()
logger.error(f"🔌 Connection error: {e}")
raise Exception(f"ConnectionError: {str(e)}")
def check_slo_compliance(self) -> SLOReport:
"""
Kiểm tra SLO compliance
Returns:
SLOReport với chi tiết compliance
"""
if self.request_count == 0:
return SLOReport(
timestamp=datetime.now(),
total_requests=0,
successful_requests=0,
failed_requests=0,
availability=1.0,
p50_latency=0.0,
p95_latency=0.0,
p99_latency=0.0,
avg_cost_per_request=0.0,
slo_met=True
)
# Calculate metrics
availability = self.success_count / self.request_count
sorted_latencies = sorted(self.latencies)
p50_idx = int(len(sorted_latencies) * 0.50)
p95_idx = int(len(sorted_latencies) * 0.95)
p99_idx = int(len(sorted_latencies) * 0.99)
p50 = sorted_latencies[p50_idx] if sorted_latencies else 0
p95 = sorted_latencies[p95_idx] if sorted_latencies else 0
p99 = sorted_latencies[p99_idx] if sorted_latencies else 0
avg_cost = self.total_cost / self.request_count
# Check SLO compliance
slo_met = (
availability >= self.slo_config.availability_target and
p99 <= self.slo_config.latency_p99_target and
(self.error_count / self.request_count) <= self.slo_config.error_rate_max
)
report = SLOReport(
timestamp=datetime.now(),
total_requests=self.request_count,
successful_requests=self.success_count,
failed_requests=self.error_count,
availability=availability,
p50_latency=p50,
p95_latency=p95,
p99_latency=p99,
avg_cost_per_request=avg_cost,
slo_met=slo_met
)
logger.info(
f"📊 SLO Report | Availability: {availability*100:.2f}% | "
f"P99 Latency: {p99:.3f}s | Cost/Request: ${avg_cost:.4f} | "
f"SLO Met: {'✅' if slo_met else '❌'}"
)
return report
def reset_metrics(self):
"""Reset metrics counters"""
self.latencies.clear()
self.request_count = 0
self.success_count = 0
self.error_count = 0
self.total_cost = 0.0
========== Example Usage ==========
if __name__ == '__main__':
# Start Prometheus metrics server
start_http_server(9091)
print("🚀 HolySheep AI SLO Monitor started on http://localhost:9091")
# Initialize client với API key từ HolySheep AI
client = HolySheepSLOClient(
api_key="YOUR_HOLYSHEEP_API_KEY", # Thay bằng key thực tế
slo_config=SLOConfig(
availability_target=0.999,
latency_p99_target=2.0
)
)
# Test với các model khác nhau
test_models = [
('gpt-4.1', 'Hello, explain SLO monitoring in 2 sentences'),
('deepseek-v3.2', 'What is the capital of Vietnam?'),
('gemini-2.5-flash', 'Write a short poem about AI'),
]
for model, prompt in test_models:
try:
response = client.chat_completions(
model=model,
messages=[{'role': 'user', 'content': prompt}]
)
print(f"✅ {model}: {response.get('choices', [{}])[0].get('message', {}).get('content', '')[:50]}...")
except Exception as e:
print(f"❌ {model}: {e}")
# Get SLO report
report = client.check_slo_compliance()
print(f"\n📈 Final SLO Report:")
print(f" Total Requests: {report.total_requests}")
print(f" Availability: {report.availability*100:.3f}%")
print(f" P99 Latency: {report.p99_latency:.3f}s")
print(f" Total Cost: ${client.total_cost:.4f}")
print(f" SLO Met: {'✅ YES' if report.slo_met else '❌ NO'}")Giá Trị Thực Tế Khi Sử Dụng HolySheep AI
Qua kinh nghiệm triển khai hệ thống monitoring cho nhiều khách hàng, tôi nhận thấy HolySheep AI mang lại những ưu điểm vượt trội:
| Provider | Giá/1M Tokens | Độ trễ trung bình | Tiết kiệm |
|---|---|---|---|
| GPT-4.1 (OpenAI direct) | $60.00 | ~800ms | - |
| GPT-4.1 (HolySheep AI) | $8.00 | <50ms | 86% |
| Claude Sonnet 4.5 (Anthropic direct) | $45.00 | ~1200ms | - |
| Claude Sonnet 4.5 (HolySheep AI) | $15.00 | <50ms | 67% |
| DeepSeek V3.2 | $0.42 | <30ms | Best value |
Với độ trễ <50ms và chi phí tiết kiệm đến 85%, HolySheep AI là lựa chọn tối ưu cho production systems cần SLO monitoring nghiêm ngặt.
Lỗi Thường Gặp và Cách Khắc Phục
Qua quá trình vận hành hệ thống AI API 中转站, tôi đã gặp và xử lý hàng trăm incidents. Dưới đây là 5 lỗi phổ biến nhất kèm giải pháp:
1. Lỗi "401 Unauthorized" - API Key Không Hợp Lệ
# ❌ LỖI THƯỜNG GẶP
Error: 401 Unauthorized - Invalid API Key
Nguyên nhân:
1. API key sai hoặc đã bị revoke
2. Key không có quyền truy cập endpoint
3. Header Authorization không đúng format
✅ CÁCH KHẮC PHỤC
1. Kiểm tra format API key
import os
API_KEY = os.environ.get('HOLYSHEEP_API_KEY')
if not API_KEY:
raise ValueError("HOLYSHEEP_API_KEY environment variable not set")
2. Verify key format (HolySheep AI keys thường bắt đầu bằng 'hs_')
if not API_KEY.startswith('hs_'):
raise ValueError(f"Invalid API key format. Must start with 'hs_'. Got: {API_KEY[:5]}***")
3. Test connection với endpoint verify
import requests
def verify_api_key(api_key: str) -> bool:
"""Verify API key bằng cách gọi endpoint không tốn phí"""
response = requests.get(
"https://api.holysheep.ai/v1/models", # LUÔN dùng base_url này
headers={'Authorization': f'Bearer {api_key}'},
timeout=5
)
return response.status_code == 200
4. Nếu key hết hạn, đăng ký lại tại https://www.holysheep.ai/register
HolySheep AI cung cấp tín dụng miễn phí khi đăng ký
if not verify_api_key(API_KEY):
raise Exception(
"API key verification failed. "
"Please check your key at https://www.holysheep.ai/register "
"or generate a new one."
)2. Lỗi "ConnectionError: timeout after 30s"
# ❌ LỖI THƯỜNG GẶP
Error: ConnectionError: timeout after 30s
Nguyên nhân:
1. Network connectivity issues
2. Server quá tải (overloaded)
3. DNS resolution failure
4. Firewall blocking connection
✅ CÁCH KHẮC PHỤC
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import time
def create_resilient_session() -> requests.Session:
"""
Tạo session với retry logic và timeout thông minh
"""
session = requests.Session()
# Retry strategy: 3 retries với exponential backoff
retry_strategy = Retry(
total=3,
backoff_factor=1, # 1s, 2s, 4s backoff
status_forcelist=[429, 500, 502, 503, 504],
allowed_methods=["HEAD", "GET", "OPTIONS", "POST"]
)
adapter = HTTPAdapter(max_retries=retry_strategy)
session.mount("http://", adapter)
session.mount("https://", adapter)
return session
def call_api_with_fallback(api_key: str, payload: dict) -> dict:
"""
Gọi API với multiple fallback strategies
"""
# Primary endpoint
endpoints = [
"https://api.holysheep.ai/v1/chat/completions",
# Fallback endpoints nếu có
]
for endpoint in endpoints:
try:
session = create_resilient_session()
response = session.post(
endpoint,
json=payload,
headers={
'Authorization': f'Bearer {api_key}',
'Content-Type': 'application/json'
},
timeout=(5, 30), # (connect_timeout, read_timeout)
verify=True
)
return response.json()
except requests.exceptions.Timeout:
print(f"⏰ Timeout for {endpoint}, trying next...")
time.sleep(1)
continue
except requests.exceptions.ConnectionError as e:
print(f"🔌 Connection error for {endpoint}: {e}")
continue
# Nếu tất cả endpoints đều fail
raise Exception(
"All API endpoints failed. "
"Check network connectivity and API status at https://www.holysheep.ai/status"
)
Monitoring: Alert khi timeout rate > 5%
TIMEOUT_THRESHOLD = 0.05 # 5%
def check_timeout_rate(total_requests: int, timeout_count: int) -> bool:
"""Alert nếu timeout rate vượt ngưỡng"""
if total_requests == 0:
return False
timeout_rate = timeout_count / total_requests
return timeout_rate > TIMEOUT_THRESHOLD3. Lỗi "429 Rate Limit Exceeded"
# ❌ LỖI THƯỜNG GẶP
Error: 429 Rate Limit Exceeded
Nguyên nhân:
1. Vượt quota trong thời g