將平台升級為雲原生架構，支持多雲部署和智能運維能力。
1. 雲原生架構重構
# kubernetes_manager.py
from kubernetes import client, config
from typing import Dict, List
import yaml
import asyncio
class KubernetesManager:
def __init__(self):
try:
config.load_incluster_config() # 在集羣內運行
except:
config.load_kube_config() # 本地開發
self.apps_v1 = client.AppsV1Api()
self.core_v1 = client.CoreV1Api()
self.networking_v1 = client.NetworkingV1Api()
async def deploy_application(self, app_spec: Dict) -> str:
"""部署應用到Kubernetes"""
namespace = app_spec.get("namespace", "default")
app_name = app_spec["name"]
# 創建命名空間
await self._create_namespace(namespace)
# 部署配置映射
await self._create_config_map(app_spec, namespace)
# 部署Secret
await self._create_secrets(app_spec, namespace)
# 部署Deployment
deployment = self._generate_deployment(app_spec)
await self.apps_v1.create_namespaced_deployment(
namespace=namespace,
body=deployment
)
# 部署Service
service = self._generate_service(app_spec)
await self.core_v1.create_namespaced_service(
namespace=namespace,
body=service
)
# 部署Ingress（如果需要）
if app_spec.get("ingress"):
ingress = self._generate_ingress(app_spec)
await self.networking_v1.create_namespaced_ingress(
namespace=namespace,
body=ingress
)
return f"{app_name}.{namespace}"
def _generate_deployment(self, app_spec: Dict) -> Dict:
"""生成Kubernetes Deployment配置"""
return {
"apiVersion": "apps/v1",
"kind": "Deployment",
"metadata": {
"name": app_spec["name"],
"labels": {"app": app_spec["name"]}
},
"spec": {
"replicas": app_spec.get("replicas", 2),
"selector": {
"matchLabels": {"app": app_spec["name"]}
},
"template": {
"metadata": {
"labels": {"app": app_spec["name"]}
},
"spec": {
"containers": [{
"name": app_spec["name"],
"image": app_spec["image"],
"ports": [{
"containerPort": app_spec.get("port", 5000)
}],
"env": self._generate_env_vars(app_spec),
"resources": {
"requests": {
"cpu": "100m",
"memory": "128Mi"
},
"limits": {
"cpu": "500m",
"memory": "512Mi"
}
},
"livenessProbe": {
"httpGet": {
"path": "/health",
"port": app_spec.get("port", 5000)
},
"initialDelaySeconds": 30,
"periodSeconds": 10
}
}]
}
}
}
}
class MultiCloudManager:
"""多雲管理"""
def __init__(self):
self.cloud_providers = {
"aws": AWSCloudManager(),
"azure": AzureCloudManager(),
"gcp": GCPCloudManager()
}
async def deploy_to_cloud(self, app_spec: Dict, cloud_provider: str) -> Dict:
"""部署應用到指定雲平台"""
provider = self.cloud_providers[cloud_provider]
# 創建雲資源
resources = await provider.create_infrastructure(app_spec)
# 部署應用到Kubernetes
k8s_manager = KubernetesManager()
endpoint = await k8s_manager.deploy_application(app_spec)
return {
"cloud_provider": cloud_provider,
"resources_created": resources,
"application_endpoint": endpoint,
"status": "deployed"
}
async def auto_scale_application(self, app_name: str, metrics: Dict):
"""基於指標自動擴縮容"""
# 分析指標決定擴縮容策略
scaling_decision = await self._analyze_scaling_needs(metrics)
if scaling_decision["action"] == "scale_up":
await self._scale_application(app_name, scaling_decision["replicas"])
elif scaling_decision["action"] == "scale_down":
await self._scale_application(app_name, scaling_decision["replicas"])
# 使用示例
k8s_manager = KubernetesManager()
cloud_manager = MultiCloudManager()
# 部署應用到AWS
app_spec = {
"name": "employee-management",
"image": "my-registry/employee-app:latest",
"port": 5000,
"replicas": 3,
"ingress": True,
"env_vars": {
"DATABASE_URL": "postgresql://...",
"REDIS_URL": "redis://..."
}
}
result = await cloud_manager.deploy_to_cloud(app_spec, "aws")
print(f"應用已部署: {result['application_endpoint']}")2. 智能運維與監控
# ai_ops.py
import prometheus_client
from prometheus_client import Counter, Histogram, Gauge
import time
from typing import Dict, List
import asyncio
class AIOpsMonitor:
def __init__(self):
# Prometheus指標
self.request_count = Counter('http_requests_total', 'Total HTTP requests', ['method', 'endpoint', 'status'])
self.request_duration = Histogram('http_request_duration_seconds', 'HTTP request duration')
self.error_count = Counter('http_errors_total', 'Total HTTP errors', ['type'])
self.system_metrics = Gauge('system_metrics', 'System metrics', ['metric_name'])
# AI運維模型
self.anomaly_detector = AnomalyDetector()
self.incident_predictor = IncidentPredictor()
async def monitor_application(self, app_name: str, metrics_stream):
"""監控應用性能"""
async for metrics in metrics_stream:
# 記錄指標
self._record_metrics(app_name, metrics)
# 異常檢測
anomalies = await self.anomaly_detector.detect(metrics)
if anomalies:
await self._handle_anomalies(app_name, anomalies)
# 事件預測
predictions = await self.incident_predictor.predict(metrics)
if predictions.get("incident_risk") > 0.8:
await self._prevent_incident(app_name, predictions)
async def auto_remediate_issues(self, issue: Dict):
"""自動修復問題"""
issue_type = issue["type"]
if issue_type == "high_memory_usage":
await self._handle_memory_issue(issue)
elif issue_type == "high_cpu_usage":
await self._handle_cpu_issue(issue)
elif issue_type == "network_latency":
await self._handle_network_issue(issue)
elif issue_type == "database_connection":
await self._handle_database_issue(issue)
class AnomalyDetector:
"""AI異常檢測"""
async def detect(self, metrics: Dict) -> List[Dict]:
"""檢測指標異常"""
anomalies = []
# CPU使用率異常
if metrics.get("cpu_usage", 0) > 80: # 閾值
anomalies.append({
"type": "high_cpu_usage",
"metric": "cpu_usage",
"value": metrics["cpu_usage"],
"severity": "high"
})
# 內存使用率異常
if metrics.get("memory_usage", 0) > 85:
anomalies.append({
"type": "high_memory_usage",
"metric": "memory_usage",
"value": metrics["memory_usage"],
"severity": "high"
})
# 響應時間異常
if metrics.get("response_time_p95", 0) > 1000: # 1秒
anomalies.append({
"type": "high_response_time",
"metric": "response_time_p95",
"value": metrics["response_time_p95"],
"severity": "medium"
})
return anomalies
class IncidentPredictor:
"""事件預測"""
async def predict(self, metrics: Dict) -> Dict:
"""預測潛在事件"""
risk_score = 0.0
predictions = []
# 基於趨勢預測
if metrics.get("memory_growth_rate", 0) > 10: # 內存增長率
risk_score += 0.3
predictions.append("內存使用快速增長，可能即將耗盡")
if metrics.get("error_rate", 0) > 5: # 錯誤率
risk_score += 0.4
predictions.append("錯誤率上升，可能出現服務中斷")
if metrics.get("connection_pool_usage", 0) > 90:
risk_score += 0.3
predictions.append("數據庫連接池即將耗盡")
return {
"incident_risk": min(risk_score, 1.0),
"predictions": predictions,
"suggested_actions": self._generate_actions(risk_score, predictions)
}3. GitOps工作流
# gitops_controller.py
import git
import yaml
import asyncio
from typing import Dict
from pathlib import Path
class GitOpsController:
def __init__(self, repo_url: str, workdir: str = "/tmp/gitops"):
self.repo_url = repo_url
self.workdir = Path(workdir)
self.repo = None
async def clone_or_pull_repo(self):
"""克隆或拉取Git倉庫"""
if not self.workdir.exists():
self.workdir.mkdir(parents=True)
self.repo = git.Repo.clone_from(self.repo_url, self.workdir)
else:
self.repo = git.Repo(self.workdir)
self.repo.remotes.origin.pull()
async def watch_for_changes(self):
"""監聽Git倉庫變化"""
while True:
try:
# 檢查是否有新提交
current_commit = self.repo.head.commit.hexsha
self.repo.remotes.origin.pull()
if current_commit != self.repo.head.commit.hexsha:
await self._handle_config_changes()
await asyncio.sleep(30) # 每30秒檢查一次
except Exception as e:
print(f"GitOps監聽錯誤: {e}")
await asyncio.sleep(60)
async def _handle_config_changes(self):
"""處理配置變化"""
# 掃描Kubernetes配置文件
k8s_files = list(self.workdir.glob("**/*.yaml")) + list(self.workdir.glob("**/*.yml"))
for file_path in k8s_files:
with open(file_path, 'r') as f:
config = yaml.safe_load(f)
if config.get("kind") == "Application":
await self._deploy_application(config)
async def _deploy_application(self, app_config: Dict):
"""部署應用配置"""
k8s_manager = KubernetesManager()
try:
# 應用Kubernetes配置
await k8s_manager.apply_manifest(app_config)
print(f"應用 {app_config['metadata']['name']} 部署成功")
# 提交部署狀態
await self._commit_deployment_status(app_config, "success")
except Exception as e:
print(f"應用部署失敗: {e}")
await self._commit_deployment_status(app_config, "failed", str(e))
class ApplicationSetManager:
"""管理應用集合"""
def __init__(self):
self.gitops_controller = GitOpsController("https://github.com/company/gitops-repo.git")
async def deploy_application_set(self, environment: str, apps: List[Dict]):
"""部署應用集合到指定環境"""
# 生成環境特定的配置
env_configs = await self._generate_environment_configs(environment, apps)
# 提交到Git倉庫
await self._commit_to_gitops(env_configs, f"Deploy to {environment}")
# GitOps會自動同步部署
# 使用示例
gitops = GitOpsController("https://github.com/my-org/gitops-repo.git")
await gitops.clone_or_pull_repo()
asyncio.create_task(gitops.watch_for_changes())4. 服務網格集成
# service_mesh.py
from typing import Dict, List
import aiohttp
import json
class ServiceMeshManager:
def __init__(self, mesh_type: str = "istio"):
self.mesh_type = mesh_type
self.base_url = self._get_control_plane_url()
async def configure_traffic_routing(self, service_name: str, rules: Dict):
"""配置流量路由規則"""
if self.mesh_type == "istio":
await self._configure_istio_virtual_service(service_name, rules)
elif self.mesh_type == "linkerd":
await self._configure_linkerd_service_profile(service_name, rules)
async def setup_canary_release(self, service_name: str,
canary_config: Dict):
"""設置金絲雀發佈"""
virtual_service = {
"apiVersion": "networking.istio.io/v1alpha3",
"kind": "VirtualService",
"metadata": {"name": service_name},
"spec": {
"hosts": [service_name],
"http": [{
"route": [
{
"destination": {
"host": service_name,
"subset": "stable"
},
"weight": canary_config.get("stable_weight", 90)
},
{
"destination": {
"host": service_name,
"subset": "canary"
},
"weight": canary_config.get("canary_weight", 10)
}
]
}]
}
}
await self._apply_istio_config(virtual_service)
async def configure_resilience(self, service_name: str, policies: Dict):
"""配置彈性策略"""
destination_rule = {
"apiVersion": "networking.istio.io/v1alpha3",
"kind": "DestinationRule",
"metadata": {"name": service_name},
"spec": {
"host": service_name,
"trafficPolicy": {
"outlierDetection": {
"consecutiveErrors": policies.get("max_errors", 5),
"interval": f"{policies.get('interval_seconds', 10)}s",
"baseEjectionTime": f"{policies.get('ejection_time', 30)}s",
"maxEjectionPercent": policies.get("max_ejection_percent", 10)
},
"connectionPool": {
"tcp": {
"maxConnections": policies.get("max_connections", 100)
},
"http": {
"http1MaxPendingRequests": policies.get("max_pending_requests", 50),
"maxRequestsPerConnection": policies.get("max_requests_per_conn", 10)
}
}
}
}
}
await self._apply_istio_config(destination_rule)
class DistributedTracing:
"""分佈式追蹤"""
def __init__(self):
self.tracing_backend = "jaeger" # 或 zipkin, tempo
async def setup_tracing(self, service_name: str, sampling_rate: float = 0.1):
"""設置分佈式追蹤"""
tracing_config = {
"apiVersion": "v1",
"kind": "ConfigMap",
"metadata": {
"name": f"{service_name}-tracing",
"labels": {"app": service_name}
},
"data": {
"tracing.json": json.dumps({
"sampler": {
"type": "probabilistic",
"param": sampling_rate
},
"reporter": {
"localAgentHostPort": "jaeger-agent:6831"
}
})
}
}
await self._apply_config(tracing_config)5. 成本優化器
# cost_optimizer.py
from typing import Dict, List
from datetime import datetime, timedelta
import asyncio
class CostOptimizer:
def __init__(self):
self.cloud_cost_apis = {
"aws": AWSCostExplorer(),
"azure": AzureCostManagement(),
"gcp": GCBCostAnalysis()
}
async def analyze_cost_optimization(self, cluster_name: str) -> Dict:
"""分析成本優化機會"""
cost_data = await self._get_cluster_costs(cluster_name)
resource_usage = await self._get_resource_usage(cluster_name)
recommendations = []
# 識別空閒資源
idle_resources = await self._identify_idle_resources(resource_usage)
if idle_resources:
recommendations.append({
"type": "idle_resources",
"resources": idle_resources,
"estimated_savings": await self._calculate_idle_savings(idle_resources),
"action": "scale_down_or_delete"
})
# 識別過度配置
over_provisioned = await self._identify_over_provisioned(resource_usage)
if over_provisioned:
recommendations.append({
"type": "over_provisioned",
"resources": over_provisioned,
"estimated_savings": await self._calculate_over_provision_savings(over_provisioned),
"action": "right_size_resources"
})
# 識別可用的節省計劃
savings_plans = await self._identify_savings_plans(cost_data)
if savings_plans:
recommendations.append({
"type": "savings_plans",
"plans": savings_plans,
"estimated_savings": await self._calculate_plan_savings(savings_plans),
"action": "purchase_savings_plan"
})
return {
"total_monthly_cost": cost_data.get("total_cost", 0),
"optimization_opportunities": recommendations,
"potential_savings": sum(rec["estimated_savings"] for rec in recommendations)
}
async def auto_apply_optimizations(self, recommendations: List[Dict]):
"""自動應用優化建議"""
for recommendation in recommendations:
if recommendation["type"] == "idle_resources":
await self._scale_down_resources(recommendation["resources"])
elif recommendation["type"] == "over_provisioned":
await self._right_size_resources(recommendation["resources"])
class SpotInstanceManager:
"""Spot實例管理"""
async def migrate_to_spot(self, workload_type: str,
interruption_tolerance: str) -> Dict:
"""遷移到Spot實例"""
suitable_instances = await self._find_suitable_spot_instances(workload_type)
migration_plan = {
"current_instances": await self._get_current_instances(),
"spot_instances": suitable_instances,
"estimated_savings": await self._calculate_spot_savings(suitable_instances),
"migration_strategy": self._determine_migration_strategy(interruption_tolerance),
"rollback_plan": await self._create_rollback_plan()
}
return migration_plan這個雲原生版AI低代碼平台提供：
- Kubernetes原生部署 - 容器化、自動擴縮容
- 多雲支持 - AWS、Azure、GCP統一管理
- 智能運維 - AI驅動的監控和自動修復
- GitOps工作流 - 聲明式配置、自動同步
- 服務網格 - 流量管理、安全、可觀測性
- 成本優化 - 自動識別節省機會
實現企業級的雲原生應用管理和運維。
