AI+云原生应用开发：从设计到部署运维全链路实战与提效JZIT

你是一名资深云原生架构师。请为“新闻推荐服务”设计 RESTful API，支持：
- 用户画像更新（POST /profile）
- 获取推荐列表（GET /recommend?user_id=xxx&limit=20）
- 点击行为回传（POST /click）
输出 OpenAPI 3.0 YAML，并标注每个接口的幂等性要求。

openapi: 3.0.0
info:
  title: RecSys API
  version: v1
paths:
  /recommend:
    get:
      summary: 获取推荐列表
      parameters:
        - name: user_id
          in: query
          required: true
          schema: { type: string }
        - name: limit
          in: query
          schema: { type: integer, default: 20 }
      responses:
        200:
          content:
            application/json:
              schema:
                type: array
                items:
                  $ref: '#/components/schemas/NewsItem'
      x-idempotent: false   # GET 天然幂等

# recsys-deployment.yaml (由 AI 生成初版)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: recsys
  namespace: prod
spec:
  replicas: 3
  selector:
    matchLabels:
      app: recsys
  template:
    metadata:
      labels:
        app: recsys
    spec:
      containers:
      - name: server
        image: ccr.ccs.tencentyun.com/ai-lab/recsys:latest
        ports:
        - containerPort: 8080
        resources:
          requests:
            cpu: "500m"
            memory: "1Gi"
          limits:
            cpu: "1000m"
            memory: "2Gi"
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 15
        env:
        - name: REDIS_ADDR
          valueFrom:
            configMapKeyRef:
              name: recsys-config
              key: redis_addr

# app/main.py
from fastapi import FastAPI, Depends, HTTPException
from redis import Redis
import numpy as np
from typing import List

app = FastAPI()
redis_client = Redis(host=os.getenv("REDIS_ADDR"), decode_responses=True)

@app.get("/recommend")
async def recommend(user_id: str, limit: int = 20, redis=Depends(get_redis)):
    # 1. 从 Redis 获取用户画像向量 (假设预计算)
    user_vec = redis.get(f"user_vec:{user_id}")
    if not user_vec:
        raise HTTPException(404, "User not found")
    # 2. 使用 ANN 检索（简化版：随机评分）
    news_ids = redis.srandmember("news_pool", limit)
    scores = np.random.rand(len(news_ids)).tolist()
    # 3. 返回排序后的新闻列表
    ranked = sorted(zip(news_ids, scores), key=lambda x: -x[1])
    return [{"news_id": nid, "score": s} for nid, s in ranked]

# tests/test_recommend.py
import pytest
from fastapi.testclient import TestClient
from app.main import app

client = TestClient(app)

def test_recommend_success(mocker):
    mocker.patch("app.main.redis_client.srandmember", return_value=["n1","n2"])
    mocker.patch("app.main.redis_client.get", return_value="[0.1,0.2]")
    resp = client.get("/recommend?user_id=u1&limit=2")
    assert resp.status_code == 200
    assert len(resp.json()) == 2

# .coding-ci.yml
version: '1.0'
stages:
  - name: code-quality
    jobs:
      - name: lint-and-test
        steps:
          - checkout
          - run: pip install -r requirements.txt
          - run: pylint app/ --fail-under=8.0
          - run: pytest --cov=app --cov-fail-under=80
      - name: ai-review
        steps:
          - run: |
              # 调用腾讯云 TI 平台或 OpenAI API 进行代码审查
              python scripts/ai_review.py --diff $(git diff origin/main) > review.md
  - name: build-image
    jobs:
      - name: docker-build
        steps:
          - run: docker build -t ccr.ccs.tencentyun.com/ai-lab/recsys:${CI_COMMIT_SHA} .
          - run: |
              docker login -u ${TENCENT_USER} -p ${TENCENT_TOKEN} ccr.ccs.tencentyun.com
              docker push ccr.ccs.tencentyun.com/ai-lab/recsys:${CI_COMMIT_SHA}
  - name: deploy-staging
    jobs:
      - name: update-k8s
        steps:
          - run: |
              sed -i "s|image:.*|image: ccr.ccs.tencentyun.com/ai-lab/recsys:${CI_COMMIT_SHA}|" deploy/staging.yaml
              kubectl apply -f deploy/staging.yaml --kubeconfig ${KUBE_CONFIG}

# deploy/production-green.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: recsys-green
  namespace: prod
spec:
  replicas: 5
  selector:
    matchLabels:
      app: recsys
      version: green
  template:
    metadata:
      labels:
        app: recsys
        version: green
    spec:
      containers:
      - name: server
        image: ccr.ccs.tencentyun.com/ai-lab/recsys:${NEW_VERSION}
        resources:
          requests:
            cpu: "1"
            memory: "2Gi"
---
apiVersion: v1
kind: Service
metadata:
  name: recsys-svc
spec:
  selector:
    app: recsys
    version: green   # 指向 green
  ports:
  - port: 80
    targetPort: 8080

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: recsys-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: recsys-green
  minReplicas: 3
  maxReplicas: 15
  metrics:
  - type: Pods
    pods:
      metric:
        name: requests_per_second
      target:
        type: AverageValue
        averageValue: "100"   # 每个 Pod 承载 100 QPS

# 伪代码：调用 Crane 建议 API
import requests
response = requests.post(
    "https://crane.tencentcloudapi.com/v1/recommend",
    json={"namespace": "prod", "workload": "recsys", "metric": "qps"}
)
# 返回 {"recommended_average_value": 85, "confidence": 0.92}
# 我们采纳为 85，比人工经验更精准，节省 20% 资源成本。

# fluent-bit-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: fluent-bit-config
data:
  fluent-bit.conf: |
    [INPUT]
        Name              tail
        Path              /var/log/containers/*.log
        Parser            docker
        Tag               kube.*
    [OUTPUT]
        Name              cls
        Match             *
        Endpoint          cls.tencentcloudapi.com
        TopicId           your-topic-id
        SecretId          ${TENCENT_SECRET_ID}
        SecretKey         ${TENCENT_SECRET_KEY}

from prometheus_client import Counter, Histogram, generate_latest
REQUEST_COUNT = Counter('recsys_requests_total', 'Total requests', ['method', 'endpoint'])
REQUEST_LATENCY = Histogram('recsys_request_duration_seconds', 'Latency', ['endpoint'])

@app.get("/metrics")
async def metrics():
    return Response(generate_latest(), media_type="text/plain")

groups:
- name: predictive_alerts
  rules:
  - alert: PredictedHighLatency
    expr: |
      predict_linear(recsys_request_duration_seconds_sum[1h], 3600) > 0.5
    for: 5m
    annotations:
      summary: "预计 1 小时后延迟超阈值，建议扩容"

// controllers/recsys_controller.go (节选)
func (r *RecSysReconciler) Reconcile(ctx context.Context, req ctrl.Request) (ctrl.Result, error) {
    // 获取 AI 异常检测结果（来自 CLS 或 Prometheus）
    anomaly := r.fetchAIAnomaly(req.Namespace)
    if anomaly.Type == "DBConnectionExhausted" {
        // 更新 ConfigMap
        cm := &corev1.ConfigMap{}
        r.Get(ctx, types.NamespacedName{Name: "recsys-db-config", Namespace: req.Namespace}, cm)
        cm.Data["max_conn"] = "200"  // 从 100 提升到 200
        r.Update(ctx, cm)
        // 滚动重启 Deployment
        r.restartDeployment(ctx, req.Namespace, "recsys-green")
    }
    return ctrl.Result{RequeueAfter: 30 * time.Second}, nil
}