Vấn Đề Thực Tế: Tại Sao SLA API Lại Quan Trọng?
Đội ngũ backend của tôi từng trải qua một tuần kinh hoàng khi API chính thức của một nhà cung cấp lớn bị downtime liên tục 3 ngày liền. Hệ thống chatbot phục vụ 50,000 người dùng đồng thời gần như tê liệt. Khách hàng phàn nàn, đối tác yêu cầu bồi thường, và đội ngũ phải làm việc xuyên đêm để xây dựng fallback mechanism. Kinh nghiệm xương máu đó cho thấy: không có hệ thống theo dõi SLA chủ động, bạn sẽ luôn bị động trước mọi sự cố.
Trong bài viết này, tôi sẽ chia sẻ cách đội ngũ của bạn có thể xây dựng một hệ thống dashboard theo dõi SLA đạt tỷ lệ real-time, tích hợp trực tiếp với
HolySheep AI - nền tảng API AI với uptime 99.95% và độ trễ trung bình dưới 50ms.
HolySheep AI - Lựa Chọn Thay Thế Đáng Tin Cậy
Trước khi đi vào chi tiết kỹ thuật, cho phép tôi giải thích vì sao HolySheep AI trở thành lựa chọn số một của đội ngũ chúng tôi:
**Ưu điểm vượt trội:**
- Tỷ giá ¥1 = $1 — tiết kiệm chi phí lên tới 85%+ so với các nhà cung cấp phương Tây
- Hỗ trợ thanh toán qua WeChat và Alipay — thuận tiện cho doanh nghiệp châu Á
- Độ trễ trung bình dưới 50ms — nhanh hơn đáng kể so với nhiều đối thủ
- Tín dụng miễn phí khi đăng ký — dùng thử không rủi ro
- Uptime SLA 99.95% cam kết bằng hợp đồng
**Bảng giá tham khảo 2026 (USD/1M tokens):**
- GPT-4.1: $8.00
- Claude Sonnet 4.5: $15.00
- Gemini 2.5 Flash: $2.50
- DeepSeek V3.2: $0.42
Với mức giá DeepSeek V3.2 chỉ $0.42/MTok, việc chạy batch processing hàng triệu request mỗi ngày hoàn toàn trong tầm kiểm soát ngân sách.
Kiến Trúc Hệ Thống Monitoring SLA
Sơ Đồ Tổng Quan
┌─────────────────────────────────────────────────────────────┐
│ SLA Monitoring Architecture │
├─────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Your App │───▶│ HolySheep │───▶│ Response │ │
│ │ (Client) │ │ API │ │ + Metrics │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ Prometheus + Grafana Stack │ │
│ │ - Request latency histogram │ │
│ │ - Success/failure counters │ │
│ │ - SLA percentage calculation │ │
│ └──────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ AlertManager │ │
│ │ - Email/Slack/PagerDuty when SLA < 99.9% │ │
│ └──────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
Triển Khai Code Chi Tiết
Bước 1: Wrapper Class Cho API Requests
Dưới đây là implementation hoàn chỉnh bằng Python với session management, retry logic, và metrics recording tự động:
"""
HolySheep AI API Client with SLA Monitoring
Author: HolySheep AI Technical Team
Version: 1.0.0
"""
import time
import asyncio
import aiohttp
import logging
from dataclasses import dataclass, field
from typing import Optional, Dict, Any, List
from datetime import datetime, timedelta
from collections import defaultdict
from enum import Enum
import prometheus_client as prom
from prometheus_client import Counter, Histogram, Gauge
============== Prometheus Metrics Setup ==============
REQUEST_LATENCY = Histogram(
'holysheep_request_latency_seconds',
'Request latency in seconds',
['model', 'endpoint', 'status_code'],
buckets=[0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0]
)
REQUEST_COUNT = Counter(
'holysheep_requests_total',
'Total number of requests',
['model', 'endpoint', 'status_code', 'success']
)
SLA_UPTIME = Gauge(
'holysheep_sla_uptime_percentage',
'Current SLA uptime percentage (last 24h)'
)
ERROR_RATE = Gauge(
'holysheep_error_rate_percentage',
'Current error rate percentage',
['error_type']
)
class APIError(Exception):
"""Base exception for API errors"""
def __init__(self, message: str, status_code: int = 500):
self.message = message
self.status_code = status_code
super().__init__(self.message)
class RateLimitError(APIError):
"""Rate limit exceeded"""
pass
class AuthenticationError(APIError):
"""Invalid API key"""
pass
@dataclass
class SLAStats:
"""SLA statistics container"""
total_requests: int = 0
successful_requests: int = 0
failed_requests: int = 0
timeout_requests: int = 0
rate_limited_requests: int = 0
total_latency_ms: float = 0.0
min_latency_ms: float = float('inf')
max_latency_ms: float = 0.0
latency_history: List[float] = field(default_factory=list)
@property
def success_rate(self) -> float:
if self.total_requests == 0:
return 0.0
return (self.successful_requests / self.total_requests) * 100
@property
def average_latency_ms(self) -> float:
if self.successful_requests == 0:
return 0.0
return self.total_latency_ms / self.successful_requests
@property
def p95_latency_ms(self) -> float:
if len(self.latency_history) == 0:
return 0.0
sorted_latencies = sorted(self.latency_history)
index = int(len(sorted_latencies) * 0.95)
return sorted_latencies[min(index, len(sorted_latencies) - 1)]
@property
def p99_latency_ms(self) -> float:
if len(self.latency_history) == 0:
return 0.0
sorted_latencies = sorted(self.latency_history)
index = int(len(sorted_latencies) * 0.99)
return sorted_latencies[min(index, len(sorted_latencies) - 1)]
class HolySheepAIClient:
"""
Production-ready HolySheep AI API client with comprehensive SLA monitoring.
Features:
- Automatic retry with exponential backoff
- Circuit breaker pattern
- Real-time SLA metrics
- Request queuing with priority
"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(
self,
api_key: str,
timeout: int = 30,
max_retries: int = 3,
circuit_breaker_threshold: int = 10,
circuit_breaker_timeout: int = 60
):
self.api_key = api_key
self.timeout = timeout
self.max_retries = max_retries
self._session: Optional[aiohttp.ClientSession] = None
self._stats = SLAStats()
self._last_request_time = {}
# Circuit breaker state
self._failure_count = 0
self._circuit_breaker_threshold = circuit_breaker_threshold
self._circuit_breaker_timeout = circuit_breaker_timeout
self._circuit_open_time: Optional[float] = None
self._is_circuit_open = False
self.logger = logging.getLogger(__name__)
async def _get_session(self) -> aiohttp.ClientSession:
"""Get or create aiohttp session with connection pooling"""
if self._session is None or self._session.closed:
connector = aiohttp.TCPConnector(
limit=100,
limit_per_host=20,
ttl_dns_cache=300,
enable_cleanup_closed=True
)
timeout = aiohttp.ClientTimeout(total=self.timeout)
self._session = aiohttp.ClientSession(
connector=connector,
timeout=timeout,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
)
return self._session
def _check_circuit_breaker(self) -> bool:
"""Check if circuit breaker should open"""
if not self._is_circuit_open:
return False
if time.time() - self._circuit_open_time >= self._circuit_breaker_timeout:
self.logger.info("Circuit breaker reset - attempting recovery")
self._is_circuit_open = False
self._failure_count = 0
return False
return True
def _record_success(self, latency_ms: float):
"""Record successful request metrics"""
self._stats.successful_requests += 1
self._stats.total_requests += 1
self._stats.total_latency_ms += latency_ms
self._stats.min_latency_ms = min(self._stats.min_latency_ms, latency_ms)
self._stats.max_latency_ms = max(self._stats.max_latency_ms, latency_ms)
self._stats.latency_history.append(latency_ms)
# Keep only last 10000 latencies for P95/P99 calculation
if len(self._stats.latency_history) > 10000:
self._stats.latency_history = self._stats.latency_history[-10000:]
# Reset failure count on success
self._failure_count = max(0, self._failure_count - 1)
# Update Prometheus metrics
SLA_UPTIME.set(self._stats.success_rate)
def _record_failure(self, error_type: str):
"""Record failed request metrics"""
self._stats.failed_requests += 1
self._stats.total_requests += 1
self._failure_count += 1
ERROR_RATE.labels(error_type=error_type).inc()
# Open circuit breaker if threshold exceeded
if self._failure_count >= self._circuit_breaker_threshold:
self._is_circuit_open = True
self._circuit_open_time = time.time()
self.logger.error(
f"Circuit breaker OPENED after {self._failure_count} consecutive failures"
)
SLA_UPTIME.set(self._stats.success_rate)
async def _request_with_retry(
self,
method: str,
endpoint: str,
payload: Optional[Dict[str, Any]] = None,
retry_count: int = 0
) -> Dict[str, Any]:
"""Execute request with exponential backoff retry"""
if self._check_circuit_breaker():
raise APIError(
"Circuit breaker is open - service temporarily unavailable",
status_code=503
)
session = await self._get_session()
url = f"{self.BASE_URL}/{endpoint.lstrip('/')}"
start_time = time.time()
try:
async with session.request(
method,
url,
json=payload
) as response:
latency_ms = (time.time() - start_time) * 1000
REQUEST_LATENCY.labels(
model=payload.get('model', 'unknown') if payload else 'unknown',
endpoint=endpoint,
status_code=str(response.status)
).observe(latency_ms / 1000)
if response.status == 200:
self._record_success(latency_ms)
result = await response.json()
REQUEST_COUNT.labels(
model=payload.get('model', 'unknown') if payload else 'unknown',
endpoint=endpoint,
status_code="200",
success="true"
).inc()
return result
elif response.status == 429:
self._record_failure("rate_limit")
REQUEST_COUNT.labels(
model=payload.get('model', 'unknown') if payload else 'unknown',
endpoint=endpoint,
status_code="429",
success="false"
).inc()
retry_after = int(response.headers.get('Retry-After', 60))
self.logger.warning(f"Rate limited - retry after {retry_after}s")
raise RateLimitError("Rate limit exceeded", status_code=429)
elif response.status == 401:
self._record_failure("auth_error")
raise AuthenticationError("Invalid API key", status_code=401)
else:
self._record_failure("http_error")
error_text = await response.text()
raise APIError(f"API error: {error_text}", status_code=response.status)
except aiohttp.ClientError as e:
latency_ms = (time.time() - start_time) * 1000
self._record_failure("network_error")
if retry_count < self.max_retries:
wait_time = min(2 ** retry_count * 0.5, 10)
self.logger.warning(
f"Request failed (attempt {retry_count + 1}/{self.max_retries}): {e}. "
f"Retrying in {wait_time}s..."
)
await asyncio.sleep(wait_time)
return await self._request_with_retry(
method, endpoint, payload, retry_count + 1
)
raise APIError(f"Request failed after {self.max_retries} retries: {e}")
async def chat_completions(
self,
model: str,
messages: List[Dict[str, str]],
temperature: float = 0.7,
max_tokens: int = 1000,
**kwargs
) -> Dict[str, Any]:
"""
Send chat completion request to HolySheep AI.
Args:
model: Model identifier (e.g., 'gpt-4.1', 'claude-sonnet-4.5',
'gemini-2.5-flash', 'deepseek-v3.2')
messages: List of message dictionaries with 'role' and 'content'
temperature: Sampling temperature (0.0 to 2.0)
max_tokens: Maximum tokens in response
Returns:
API response dictionary
"""
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens,
**kwargs
}
self.logger.info(f"Sending request to {model} via HolySheep AI")
return await self._request_with_retry("POST", "chat/completions", payload)
async def embeddings(
self,
model: str,
input_text: str
) -> Dict[str, Any]:
"""Generate embeddings for text"""
payload = {
"model": model,
"input": input_text
}
return await self._request_with_retry("POST", "embeddings", payload)
def get_sla_report(self) -> Dict[str, Any]:
"""Generate comprehensive SLA report"""
uptime_percentage = self._stats.success_rate
target_sla = 99.95
sla_met = uptime_percentage >= target_sla
return {
"period": "Last 24 hours",
"generated_at": datetime.now().isoformat(),
"sla_target": f"{target_sla}%",
"sla_achieved": f"{uptime_percentage:.4f}%",
"sla_met": sla_met,
"status": "✓ MET" if sla_met else "✗ NOT MET",
"metrics": {
"total_requests": self._stats.total_requests,
"successful_requests": self._stats.successful_requests,
"failed_requests": self._stats.failed_requests,
"success_rate": f"{uptime_percentage:.4f}%",
"average_latency_ms": f"{self._stats.average_latency_ms:.2f}",
"p95_latency_ms": f"{self._stats.p95_latency_ms:.2f}",
"p99_latency_ms": f"{self._stats.p99_latency_ms:.2f}",
"min_latency_ms": f"{self._stats.min_latency_ms:.2f}" if self._stats.min_latency_ms != float('inf') else "N/A",
"max_latency_ms": f"{self._stats.max_latency_ms:.2f}",
"circuit_breaker_status": "OPEN" if self._is_circuit_open else "CLOSED",
"failure_count": self._failure_count
}
}
async def close(self):
"""Close the client session"""
if self._session and not self._session.closed:
await self._session.close()
async def __aenter__(self):
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
await self.close()
============== Usage Example ==============
async def main():
# Initialize client with your API key
client = HolySheepAIClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
timeout=30,
max_retries=3
)
try:
# Example: Chat completion request
response = await client.chat_completions(
model="deepseek-v3.2",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain SLA monitoring in simple terms."}
],
temperature=0.7,
max_tokens=500
)
print(f"Response: {response['choices'][0]['message']['content']}")
# Generate SLA report
sla_report = client.get_sla_report()
print(f"\n=== SLA Report ===")
print(f"Status: {sla_report['status']}")
print(f"Success Rate: {sla_report['metrics']['success_rate']}")
print(f"Avg Latency: {sla_report['metrics']['average_latency_ms']}")
print(f"P99 Latency: {sla_report['metrics']['p99_latency_ms']}")
finally:
await client.close()
if __name__ == "__main__":
asyncio.run(main())
Bước 2: Dashboard Prometheus + Grafana
Để visualize SLA metrics real-time, cấu hình Prometheus scrape config và Grafana dashboard sau:
# prometheus.yml
global:
scrape_interval: 15s
evaluation_interval: 15s
scrape_configs:
- job_name: 'holysheep-api-monitor'
static_configs:
- targets: ['localhost:8000']
metrics_path: /metrics
scrape_interval: 10s
- job_name: 'holysheep-api-health'
static_configs:
- targets: ['localhost:8000']
metrics_path: /health
scrape_interval: 30s
alerting:
alertmanagers:
- static_configs:
- targets: ['alertmanager:9093']
rule_files:
- 'sla_alerts.yml'
# sla_alerts.yml
groups:
- name: holySheep_sla_alerts
rules:
# Alert when SLA drops below 99.9%
- alert: HolySheepSLABelowTarget
expr: holysheep_sla_uptime_percentage < 99.9
for: 5m
labels:
severity: critical
team: platform
annotations:
summary: "HolySheep API SLA below target"
description: "SLA is {{ $value }}%, below 99.9% target for 5 minutes"
# Alert when error rate exceeds 1%
- alert: HolySheepHighErrorRate
expr: rate(holysheep_requests_total{success="false"}[5m]) / rate(holysheep_requests_total[5m]) > 0.01
for: 2m
labels:
severity: warning
team: platform
annotations:
summary: "High error rate detected"
description: "Error rate is {{ $value | humanizePercentage }}"
# Alert when P99 latency exceeds 2 seconds
- alert: HolySheepHighLatency
expr: histogram_quantile(0.99, rate(holysheep_request_latency_seconds_bucket[5m])) > 2
for: 3m
labels:
severity: warning
team: platform
annotations:
summary: "High P99 latency detected"
description: "P99 latency is {{ $value }}s, exceeding 2s threshold"
# Alert when circuit breaker opens
- alert: HolySheepCircuitBreakerOpen
expr: holysheep_circuit_breaker_status == 1
labels:
severity: critical
team: platform
annotations:
summary: "Circuit breaker is OPEN"
description: "HolySheep API circuit breaker has opened - requests are failing"
# Alert when no requests for 10 minutes (potential outage)
- alert: HolySheepNoTraffic
expr: rate(holysheep_requests_total[5m]) == 0
for: 10m
labels:
severity: warning
team: platform
annotations:
summary: "No traffic to HolySheep API"
description: "No requests detected for 10 minutes - verify service availability"
Bước 3: Flask API Server Cho Metrics Endpoint
Để Prometheus có thể scrape metrics, tạo một Flask server đơn giản:
"""
Flask API Server exposing Prometheus metrics for HolySheep SLA monitoring
"""
from flask import Flask, jsonify, Response
from prometheus_client import generate_latest, CONTENT_TYPE_LATEST
import logging
app = Flask(__name__)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
Global client instance (initialized elsewhere)
holysheep_client = None
@app.route('/health')
def health_check():
"""Kubernetes-style health check endpoint"""
return jsonify({
"status": "healthy",
"service": "holysheep-api-monitor",
"version": "1.0.0"
})
@app.route('/health/live')
def liveness():
"""Liveness probe - returns 200 if process is alive"""
return jsonify({"status": "alive"})
@app.route('/health/ready')
def readiness():
"""Readiness probe - returns 200 if service can handle requests"""
if holysheep_client is None:
return jsonify({
"status": "not_ready",
"reason": "Client not initialized"
}), 503
sla_report = holysheep_client.get_sla_report()
success_rate = float(sla_report['metrics']['success_rate'].rstrip('%'))
if success_rate >= 99.0:
return jsonify({
"status": "ready",
"sla_achieved": f"{success_rate:.2f}%"
})
else:
return jsonify({
"status": "not_ready",
"sla_achieved": f"{success_rate:.2f}%",
"reason": "SLA below acceptable threshold"
}), 503
@app.route('/metrics')
def metrics():
"""Prometheus metrics endpoint"""
return Response(
generate_latest(),
mimetype=CONTENT_TYPE_LATEST
)
@app.route('/sla/report')
def sla_report():
"""Human-readable SLA report"""
if holysheep_client is None:
return jsonify({"error": "Client not initialized"}), 503
report = holysheep_client.get_sla_report()
return jsonify(report)
@app.route('/sla/report/detailed')
def sla_report_detailed():
"""Detailed SLA report with historical data"""
if holysheep_client is None:
return jsonify({"error": "Client not initialized"}), 503
report = holysheep_client.get_sla_report()
# Add additional detailed metrics
detailed_report = {
**report,
"historical": {
"last_1h_success_rate": calculate_historical_rate(1),
"last_6h_success_rate": calculate_historical_rate(6),
"last_24h_success_rate": calculate_historical_rate(24),
},
"cost_analysis": {
"estimated_daily_cost_usd": estimate_daily_cost(),
"savings_vs_competitors": calculate_savings()
}
}
return jsonify(detailed_report)
def calculate_historical_rate(hours: int) -> float:
"""Calculate success rate for historical period"""
# Implementation depends on your metrics storage
# For demo, return current rate
return holysheep_client.get_sla_report()['metrics']['success_rate']
def estimate_daily_cost() -> float:
"""Estimate daily API cost in USD"""
stats = holysheep_client.get_sla_report()['metrics']
requests_today = stats['total_requests']
# Average tokens per request estimate
avg_tokens_per_request = 500
avg_cost_per_1k_requests = 0.50 # Based on DeepSeek V3.2 pricing
return (requests_today / 1000) * avg_cost_per_1k_requests
def calculate_savings() -> dict:
"""Calculate savings compared to major US providers"""
stats = holysheep_client.get_sla_report()['metrics']
requests_today = stats['total_requests']
avg_tokens_per_request = 500
# HolySheep pricing (DeepSeek V3.2)
holySheep_cost = (requests_today * avg_tokens_per_request / 1_000_000) * 0.42
# Competitor pricing (GPT-4.1)
gpt4_cost = (requests_today * avg_tokens_per_request / 1_000_000) * 8.00
return {
"holySheep_cost_usd": round(holySheep_cost, 2),
"competitor_cost_usd": round(gpt4_cost, 2),
"savings_usd": round(gpt4_cost - holySheep_cost, 2),
"savings_percentage": round((gpt4_cost - holySheep_cost) / gpt4_cost * 100, 1)
}
if __name__ == '__main__':
app.run(host='0.0.0.0', port=8000, debug=False)
Kế Hoạch Rollback & Rủi Ro
Chiến Lược Migration An Toàn
Trước khi complete migration, đội ngũ cần chuẩn bị kế hoạch rollback chi tiết:
"""
Production Migration Manager for HolySheep AI
Implements canary deployment and instant rollback
"""
import asyncio
import hashlib
from typing import Callable, Optional, List
from dataclasses import dataclass
from enum import Enum
import logging
class MigrationPhase(Enum):
"""Migration phases"""
STAGING = "staging"
CANARY_10 = "canary_10"
CANARY_50 = "canary_50"
FULL_ROLLOUT = "full_rollout"
ROLLBACK = "rollback"
@dataclass
class MigrationConfig:
"""Configuration for migration process"""
canary_percentage: int = 10
canary_duration_minutes: int = 30
health_check_interval_seconds: int = 60
rollback_threshold_sla: float = 99.0
rollback_threshold_error_rate: float = 5.0
primary_provider: str = "holysheep"
fallback_provider: str = "legacy"
@dataclass
class MigrationStatus:
"""Current migration status"""
phase: MigrationPhase
started_at: Optional[str] = None
current_sla: float = 0.0
requests_routed: int = 0
errors_encountered: int = 0
rollback_triggered: bool = False
rollback_reason: Optional[str] = None
class MigrationManager:
"""
Manages safe migration between API providers with automatic rollback.
"""
def __init__(self, config: MigrationConfig):
self.config = config
self.status = MigrationStatus(phase=MigrationPhase.STAGING)
self.logger = logging.getLogger(__name__)
self._metrics_history: List[dict] = []
async def execute_migration(
self,
health_check_fn: Callable,
route_request_fn: Callable
) -> bool:
"""
Execute full migration with automatic health checks and rollback.
Args:
health_check_fn: Async function that returns SLA metrics
route_request_fn: Async function to route traffic between providers
Returns:
True if migration successful, False if rollback triggered
"""
phases = [
MigrationPhase.CANARY_10,
MigrationPhase.CANARY_50,
MigrationPhase.FULL_ROLLOUT
]
for phase in phases:
self.logger.info(f"Starting migration phase: {phase.value}")
self.status.phase = phase
# Calculate traffic percentage for this phase
traffic_percentage = self._get_traffic_percentage(phase)
# Execute health check monitoring
is_healthy = await self._monitor_phase(
phase,
traffic_percentage,
health_check_fn,
route_request_fn
)
if not is_healthy:
self.logger.error(f"Health check failed - triggering rollback")
await self._execute_rollback(route_request_fn)
return False
self.logger.info(f"Phase {phase.value} completed successfully")
self.logger.info("Migration completed successfully!")
return True
def _get_traffic_percentage(self, phase: MigrationPhase) -> int:
"""Get traffic percentage for migration phase"""
percentages = {
MigrationPhase.CANARY_10: 10,
MigrationPhase.CANARY_50: 50,
MigrationPhase.FULL_ROLLOUT: 100
}
return percentages.get(phase, 0)
async def _monitor_phase(
self,
phase: MigrationPhase,
traffic_percentage: int,
health_check_fn: Callable,
route_request_fn: Callable
) -> bool:
"""
Monitor a migration phase with health checks.
Returns:
True if phase passes health checks, False otherwise
"""
duration = self.config.canary_duration_minutes * 60
check_interval = self.config.health_check_interval_seconds
iterations = duration // check_interval
for i in range(iterations):
self.logger.info(
f"Health check {i+1}/{iterations} "
f"(traffic: {traffic_percentage}%)"
)
# Get current metrics
metrics = await health_check_fn()
current_sla = metrics.get('success_rate', 0)
error_rate = metrics.get('error_rate', 0)
self._metrics_history.append({
'phase': phase.value,
'timestamp': asyncio.get_event_loop().time(),
'sla': current_sla,
'error_rate': error_rate
})
self.status.current_sla = current_sla
# Check thresholds
if current_sla < self.config.rollback_threshold_sla:
self.status.rollback_triggered = True
self.status.rollback_reason = (
f"SLA {current_sla:.2f}% below threshold "
f"{self.config.rollback_threshold_sla}%"
)
return False
if error_rate > self.config.rollback_threshold_error_rate:
self.status.rollback_triggered = True
self.status.rollback_reason = (
f"Error rate {error_rate:.2f}% above threshold "
f"{self.config.rollback_threshold_error_rate}%"
)
return False
await asyncio.sleep(check_interval)
return True
async def _execute_rollback(self, route_request_fn: Callable):
"""
Execute instant rollback to previous provider.
"""
self.logger.warning("EXECUTING ROLLBACK")
self.status.rollback_triggered = True
self.status.phase = MigrationPhase.ROLLBACK
# Route 100% traffic to fallback provider
await route_request_fn(100, provider="fallback")
# Log rollback details for post-mortem
self.logger.error(
f"Rollback completed. Reason: {self.status.rollback_reason}\n"
f"Last metrics: {self._metrics_history[-5:] if self._metrics_history else 'N/A'}"
)
# In production, this should trigger alerts and notifications
await self._notify_rollback()
async def _notify_rollback(self):
"""Send rollback notifications"""
# Implementation for Slack, PagerD
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