# AI智能面试平台架构设计

## 1. 系统架构概述

### 1.1 整体架构模式

**选择：微服务架构 + 云原生设计**

**选择理由：**
- **可扩展性**：支持百万级用户的水平扩展需求
- **高可用性**：单个服务故障不影响整体系统
- **技术多样性**：不同服务可选择最适合的技术栈
- **团队协作**：支持多团队并行开发
- **部署灵活性**：支持独立部署和版本管理

**架构特点：**
- 采用API网关统一入口
- 服务间通过消息队列异步通信
- 使用容器化部署和Kubernetes编排
- 实现服务发现和负载均衡

### 1.2 关键组件与交互图

```
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Web前端       │    │   移动端App     │    │   管理后台      │
│   (Vue.js)      │    │   (React Native)│    │   (React)       │
└─────────┬───────┘    └─────────┬───────┘    └─────────┬───────┘
          │                      │                      │
          └──────────────────────┼──────────────────────┘
                                 │
                    ┌─────────────┴─────────────┐
                    │      API网关              │
                    │   (Kong/Nginx Gateway)    │
                    └─────────────┬─────────────┘
                                 │
        ┌────────────────────────┼────────────────────────┐
        │                       │                       │
┌───────▼───────┐    ┌─────────▼─────────┐    ┌───────▼───────┐
│   用户服务     │    │    面试服务       │    │   企业服务     │
│ (User Service) │    │(Interview Service)│    │(Company Service)│
└───────┬───────┘    └─────────┬─────────┘    └───────┬───────┘
        │                      │                      │
┌───────▼───────┐    ┌─────────▼─────────┐    ┌───────▼───────┐
│   简历服务     │    │    AI分析服务     │    │   通知服务     │
│(Resume Service)│    │ (AI Analysis)     │    │(Notification)  │
└───────┬───────┘    └─────────┬─────────┘    └───────┬───────┘
        │                      │                      │
        └──────────────────────┼──────────────────────┘
                               │
                    ┌─────────▼─────────┐
                    │    消息队列       │
                    │  (Apache Kafka)   │
                    └───────────────────┘
```

**核心服务说明：**
- **用户服务**：用户注册、登录、权限管理
- **企业服务**：企业信息、职位管理
- **简历服务**：简历上传、解析、存储
- **面试服务**：AI面试流程控制、视频处理
- **AI分析服务**：简历筛选、面试分析、报告生成
- **通知服务**：邮件、短信、站内消息

### 1.3 系统边界与外部接口

**外部服务集成：**
- **云存储服务**：阿里云OSS/AWS S3（简历文件、视频存储）
- **AI服务**：讯飞星火/OpenAI API（自然语言处理）
- **视频处理**：阿里云视频点播（视频转码、截图）
- **邮件服务**：阿里云邮件推送/SendGrid
- **短信服务**：阿里云短信服务
- **第三方登录**：微信、钉钉、LinkedIn OAuth
- **支付服务**：支付宝、微信支付

## 2. 前端架构

### 2.1 推荐框架与库

**选择：Vue.js 3 + TypeScript**

**选择理由：**
- **学习曲线平缓**：团队快速上手
- **生态完善**：丰富的组件库和工具链
- **性能优秀**：Composition API和响应式系统
- **TypeScript支持**：类型安全和开发体验
- **社区活跃**：持续更新和维护

**技术栈：**
```javascript
// 核心框架
Vue.js 3.3+
TypeScript 5.0+
Vite 4.0+

// 路由和状态管理
Vue Router 4
Pinia

// UI组件库
Element Plus
Tailwind CSS

// 工具库
Axios (HTTP客户端)
Day.js (日期处理)
Lodash (工具函数)
```

### 2.2 状态管理策略

**选择：Pinia**

**优势：**
- Vue 3官方推荐
- TypeScript原生支持
- 模块化设计
- 开发工具支持

**状态结构：**
```typescript
// stores/user.ts
export const useUserStore = defineStore('user', {
  state: () => ({
    currentUser: null,
    isAuthenticated: false,
    permissions: []
  })
})

// stores/interview.ts
export const useInterviewStore = defineStore('interview', {
  state: () => ({
    currentInterview: null,
    questions: [],
    answers: [],
    status: 'idle'
  })
})
```

### 2.3 UI组件库建议

**主要选择：Element Plus + Tailwind CSS**

**理由：**
- **Element Plus**：企业级组件库，功能完整
- **Tailwind CSS**：原子化CSS，快速样式开发
- **自定义组件**：业务特定组件的封装

### 2.4 模块组织与性能优化

**项目结构：**
```
src/
├── components/          # 通用组件
│   ├── common/         # 基础组件
│   ├── business/       # 业务组件
│   └── layout/         # 布局组件
├── views/              # 页面组件
│   ├── auth/          # 认证相关
│   ├── interview/     # 面试相关
│   ├── resume/        # 简历相关
│   └── dashboard/     # 仪表板
├── stores/             # 状态管理
├── composables/        # 组合式函数
├── utils/              # 工具函数
├── types/              # TypeScript类型
└── assets/             # 静态资源
```

**性能优化措施：**
- **代码分割**：路由级别的懒加载
- **组件懒加载**：大型组件按需加载
- **资源优化**：图片压缩、CDN加速
- **缓存策略**：HTTP缓存、本地存储
- **虚拟滚动**：大列表性能优化

## 3. 后端架构

### 3.1 推荐技术栈

**选择：Node.js + NestJS + TypeScript**

**选择理由：**
- **统一语言栈**：前后端使用相同语言，降低学习成本
- **高性能**：事件驱动、非阻塞I/O
- **生态丰富**：NPM包生态完善
- **微服务友好**：轻量级、易于容器化
- **实时通信**：WebSocket支持优秀

**技术栈详情：**
```typescript
// 核心框架
Node.js 18+
NestJS 10+
TypeScript 5.0+

// 数据库ORM
TypeORM / Prisma

// 验证和安全
class-validator
Passport.js
JWT

// 消息队列
Bull (Redis-based)

// 文档和测试
Swagger
Jest
```

**备选方案：**
- **Java + Spring Boot**：企业级应用首选，生态成熟
- **Python + FastAPI**：AI集成友好，开发效率高
- **Go + Gin**：高性能、低资源消耗

### 3.2 API设计原则

**选择：RESTful API + GraphQL混合**

**RESTful API用于：**
- CRUD操作
- 文件上传下载
- 第三方集成

**GraphQL用于：**
- 复杂数据查询
- 前端数据聚合
- 实时订阅

**API设计规范：**
```typescript
// RESTful API示例
GET    /api/v1/users/:id
POST   /api/v1/users
PUT    /api/v1/users/:id
DELETE /api/v1/users/:id

// GraphQL Schema示例
type User {
  id: ID!
  email: String!
  profile: UserProfile
  interviews: [Interview!]!
}

type Query {
  user(id: ID!): User
  interviews(filter: InterviewFilter): [Interview!]!
}
```

### 3.3 身份验证与授权

**设计方案：JWT + RBAC**

**认证流程：**
```typescript
// JWT Token结构
interface JWTPayload {
  userId: string
  email: string
  roles: string[]
  permissions: string[]
  exp: number
}

// 权限控制装饰器
@UseGuards(JwtAuthGuard, RolesGuard)
@Roles('hr', 'admin')
@Controller('interviews')
export class InterviewController {
  @Post()
  @RequirePermissions('interview:create')
  createInterview(@Body() dto: CreateInterviewDto) {
    // 创建面试逻辑
  }
}
```

**安全措施：**
- **Token刷新机制**：Access Token + Refresh Token
- **多因素认证**：短信验证码、邮箱验证
- **设备管理**：登录设备记录和管理
- **异常检测**：异常登录行为监控

### 3.4 业务逻辑组织

**采用：领域驱动设计(DDD) + 分层架构**

**分层结构：**
```typescript
// 领域层 (Domain Layer)
export class Interview {
  constructor(
    private readonly id: InterviewId,
    private readonly candidateId: UserId,
    private readonly jobId: JobId,
    private status: InterviewStatus
  ) {}

  public start(): void {
    if (this.status !== InterviewStatus.SCHEDULED) {
      throw new Error('Interview cannot be started');
    }
    this.status = InterviewStatus.IN_PROGRESS;
  }
}

// 应用层 (Application Layer)
@Injectable()
export class InterviewService {
  constructor(
    private readonly interviewRepo: InterviewRepository,
    private readonly aiService: AIAnalysisService
  ) {}

  async conductInterview(command: ConductInterviewCommand): Promise<void> {
    const interview = await this.interviewRepo.findById(command.interviewId);
    interview.start();
    await this.interviewRepo.save(interview);
    
    // 发布领域事件
    this.eventBus.publish(new InterviewStartedEvent(interview.id));
  }
}

// 基础设施层 (Infrastructure Layer)
@Injectable()
export class TypeOrmInterviewRepository implements InterviewRepository {
  constructor(
    @InjectRepository(InterviewEntity)
    private readonly repo: Repository<InterviewEntity>
  ) {}

  async findById(id: InterviewId): Promise<Interview> {
    const entity = await this.repo.findOne({ where: { id: id.value } });
    return InterviewMapper.toDomain(entity);
  }
}
```

### 3.5 异步任务处理

**选择：Redis + Bull队列**

**队列设计：**
```typescript
// 队列定义
export enum QueueNames {
  RESUME_PROCESSING = 'resume-processing',
  VIDEO_ANALYSIS = 'video-analysis',
  EMAIL_NOTIFICATION = 'email-notification',
  REPORT_GENERATION = 'report-generation'
}

// 任务处理器
@Processor(QueueNames.RESUME_PROCESSING)
export class ResumeProcessor {
  @Process('parse-resume')
  async parseResume(job: Job<ParseResumeData>) {
    const { resumeId, fileUrl } = job.data;
    
    // 1. 下载文件
    const fileBuffer = await this.fileService.download(fileUrl);
    
    // 2. AI解析
    const parsedData = await this.aiService.parseResume(fileBuffer);
    
    // 3. 保存结果
    await this.resumeService.updateParsedData(resumeId, parsedData);
    
    // 4. 更新进度
    job.progress(100);
  }
}

// 任务调度
@Injectable()
export class TaskScheduler {
  constructor(
    @InjectQueue(QueueNames.RESUME_PROCESSING)
    private resumeQueue: Queue
  ) {}

  async scheduleResumeProcessing(resumeId: string, fileUrl: string) {
    await this.resumeQueue.add('parse-resume', {
      resumeId,
      fileUrl
    }, {
      attempts: 3,
      backoff: 'exponential',
      delay: 2000
    });
  }
}
```

## 4. 深度数据架构

### 4.1 数据库类型选择

**混合架构设计**

**关系型数据库：PostgreSQL**
- **用途**：用户信息、企业数据、职位信息、面试记录
- **优势**：ACID特性、复杂查询、数据一致性
- **版本**：PostgreSQL 15+

**文档数据库：MongoDB**
- **用途**：简历解析结果、面试分析报告、日志数据
- **优势**：灵活schema、水平扩展、JSON原生支持
- **版本**：MongoDB 6.0+

**搜索引擎：Elasticsearch**
- **用途**：简历全文搜索、职位匹配、数据分析
- **优势**：全文搜索、实时分析、高性能
- **版本**：Elasticsearch 8.0+

**缓存数据库：Redis**
- **用途**：会话存储、热点数据缓存、消息队列
- **优势**：高性能、丰富数据结构、持久化
- **版本**：Redis 7.0+

**CAP理论权衡：**
- **用户核心数据**：选择CP（一致性+分区容错），使用PostgreSQL
- **搜索和分析**：选择AP（可用性+分区容错），使用Elasticsearch
- **缓存数据**：选择AP，使用Redis集群

### 4.2 详细数据模型设计

**PostgreSQL核心实体设计：**

```sql
-- 用户表
CREATE TABLE users (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    email VARCHAR(255) UNIQUE NOT NULL,
    password_hash VARCHAR(255) NOT NULL,
    user_type VARCHAR(20) NOT NULL CHECK (user_type IN ('candidate', 'hr', 'admin')),
    status VARCHAR(20) DEFAULT 'active',
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    last_login_at TIMESTAMP WITH TIME ZONE
);

-- 企业表
CREATE TABLE companies (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    industry VARCHAR(100),
    size_range VARCHAR(50),
    website VARCHAR(255),
    description TEXT,
    logo_url VARCHAR(500),
    status VARCHAR(20) DEFAULT 'active',
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- 职位表
CREATE TABLE jobs (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    company_id UUID NOT NULL REFERENCES companies(id),
    title VARCHAR(255) NOT NULL,
    description TEXT NOT NULL,
    requirements TEXT,
    salary_min INTEGER,
    salary_max INTEGER,
    location VARCHAR(255),
    employment_type VARCHAR(50),
    experience_level VARCHAR(50),
    skills JSONB,
    status VARCHAR(20) DEFAULT 'active',
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- 简历表
CREATE TABLE resumes (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    user_id UUID NOT NULL REFERENCES users(id),
    original_filename VARCHAR(255),
    file_url VARCHAR(500),
    file_size INTEGER,
    mime_type VARCHAR(100),
    parsing_status VARCHAR(20) DEFAULT 'pending',
    parsed_data_id VARCHAR(100), -- MongoDB文档ID
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- 面试表
CREATE TABLE interviews (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    job_id UUID NOT NULL REFERENCES jobs(id),
    candidate_id UUID NOT NULL REFERENCES users(id),
    hr_id UUID NOT NULL REFERENCES users(id),
    type VARCHAR(20) NOT NULL CHECK (type IN ('ai', 'human', 'hybrid')),
    status VARCHAR(20) DEFAULT 'scheduled',
    scheduled_at TIMESTAMP WITH TIME ZONE,
    started_at TIMESTAMP WITH TIME ZONE,
    completed_at TIMESTAMP WITH TIME ZONE,
    duration_minutes INTEGER,
    video_url VARCHAR(500),
    analysis_result_id VARCHAR(100), -- MongoDB文档ID
    score INTEGER CHECK (score >= 0 AND score <= 100),
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- 面试问题表
CREATE TABLE interview_questions (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    interview_id UUID NOT NULL REFERENCES interviews(id),
    question_text TEXT NOT NULL,
    question_type VARCHAR(50),
    order_index INTEGER NOT NULL,
    asked_at TIMESTAMP WITH TIME ZONE,
    answer_text TEXT,
    answer_duration_seconds INTEGER,
    answer_score INTEGER CHECK (answer_score >= 0 AND answer_score <= 100),
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
```

**MongoDB文档结构：**

```javascript
// 简历解析结果集合
db.parsed_resumes.insertOne({
  _id: ObjectId(),
  resume_id: "uuid",
  parsing_version: "1.0",
  parsed_at: new Date(),
  personal_info: {
    name: "张三",
    email: "zhangsan@example.com",
    phone: "+86-138-0000-0000",
    location: "北京市朝阳区",
    birth_date: "1990-01-01",
    gender: "male"
  },
  education: [{
    institution: "清华大学",
    degree: "本科",
    major: "计算机科学与技术",
    start_date: "2008-09",
    end_date: "2012-06",
    gpa: 3.8
  }],
  work_experience: [{
    company: "阿里巴巴",
    position: "高级软件工程师",
    start_date: "2020-03",
    end_date: "2023-08",
    description: "负责电商平台后端开发",
    achievements: ["优化系统性能提升30%", "主导微服务架构改造"]
  }],
  skills: {
    programming_languages: ["Java", "Python", "JavaScript"],
    frameworks: ["Spring Boot", "React", "Vue.js"],
    databases: ["MySQL", "Redis", "MongoDB"],
    tools: ["Git", "Docker", "Kubernetes"]
  },
  projects: [{
    name: "电商推荐系统",
    description: "基于机器学习的商品推荐系统",
    technologies: ["Python", "TensorFlow", "Redis"],
    start_date: "2022-01",
    end_date: "2022-06"
  }],
  certifications: [{
    name: "AWS认证解决方案架构师",
    issuer: "Amazon",
    issue_date: "2022-03",
    expiry_date: "2025-03"
  }],
  languages: [{
    language: "英语",
    proficiency: "流利"
  }],
  extracted_keywords: ["Java", "微服务", "高并发", "分布式系统"],
  confidence_score: 0.95
});

// 面试分析结果集合
db.interview_analysis.insertOne({
  _id: ObjectId(),
  interview_id: "uuid",
  analysis_version: "2.0",
  analyzed_at: new Date(),
  overall_score: 85,
  dimensions: {
    technical_skills: {
      score: 88,
      details: {
        programming_knowledge: 90,
        system_design: 85,
        problem_solving: 90
      }
    },
    communication: {
      score: 82,
      details: {
        clarity: 85,
        fluency: 80,
        confidence: 80
      }
    },
    behavioral: {
      score: 85,
      details: {
        teamwork: 88,
        leadership: 82,
        adaptability: 85
      }
    }
  },
  question_analysis: [{
    question_id: "uuid",
    question_text: "请介绍一下你的项目经验",
    answer_analysis: {
      content_relevance: 0.9,
      technical_depth: 0.85,
      communication_clarity: 0.8,
      keywords_mentioned: ["微服务", "高并发", "Redis"],
      sentiment_score: 0.7,
      confidence_level: 0.8
    },
    transcription: "我在阿里巴巴主要负责...",
    duration_seconds: 120
  }],
  video_analysis: {
    facial_expressions: {
      confidence_avg: 0.75,
      engagement_avg: 0.8,
      stress_indicators: 0.2
    },
    voice_analysis: {
      pace_consistency: 0.8,
      volume_stability: 0.85,
      filler_words_count: 5
    }
  },
  recommendations: [
    "候选人技术能力强，建议进入下一轮",
    "沟通表达可以更加简洁明了",
    "对系统设计有深入理解"
  ],
  red_flags: [],
  processing_metadata: {
    ai_model_version: "gpt-4",
    processing_time_ms: 15000,
    confidence_threshold: 0.7
  }
});
```

### 4.3 规范化与反规范化

**关系型数据规范化（第三范式）：**
- **用户表**：符合3NF，避免数据冗余
- **企业-职位关系**：通过外键关联，保持数据一致性
- **面试-问题关系**：一对多关系，规范化存储

**性能优化的反规范化：**
```sql
-- 在jobs表中冗余company_name，避免频繁JOIN
ALTER TABLE jobs ADD COLUMN company_name VARCHAR(255);

-- 在interviews表中冗余candidate_name和job_title
ALTER TABLE interviews ADD COLUMN candidate_name VARCHAR(255);
ALTER TABLE interviews ADD COLUMN job_title VARCHAR(255);

-- 创建物化视图用于复杂查询
CREATE MATERIALIZED VIEW interview_summary AS
SELECT 
    i.id,
    i.status,
    i.score,
    u.email as candidate_email,
    j.title as job_title,
    c.name as company_name,
    i.completed_at
FROM interviews i
JOIN users u ON i.candidate_id = u.id
JOIN jobs j ON i.job_id = j.id
JOIN companies c ON j.company_id = c.id;
```

### 4.4 查询优化与缓存

**索引策略：**
```sql
-- 复合索引用于常见查询
CREATE INDEX idx_interviews_status_date ON interviews(status, created_at);
CREATE INDEX idx_jobs_company_status ON jobs(company_id, status);
CREATE INDEX idx_resumes_user_status ON resumes(user_id, parsing_status);

-- 部分索引用于特定条件
CREATE INDEX idx_active_jobs ON jobs(created_at) WHERE status = 'active';

-- 表达式索引
CREATE INDEX idx_users_email_lower ON users(LOWER(email));

-- JSONB索引用于技能搜索
CREATE INDEX idx_jobs_skills_gin ON jobs USING GIN(skills);
```

**多级缓存策略：**

```typescript
// L1缓存：应用内存缓存
@Injectable()
export class CacheService {
  private readonly memoryCache = new Map<string, any>();
  
  // L2缓存：Redis缓存
  constructor(
    @Inject('REDIS_CLIENT') private redis: Redis
  ) {}
  
  async get<T>(key: string): Promise<T | null> {
    // 1. 检查内存缓存
    if (this.memoryCache.has(key)) {
      return this.memoryCache.get(key);
    }
    
    // 2. 检查Redis缓存
    const cached = await this.redis.get(key);
    if (cached) {
      const data = JSON.parse(cached);
      this.memoryCache.set(key, data);
      return data;
    }
    
    return null;
  }
  
  async set(key: string, value: any, ttl: number = 3600): Promise<void> {
    // 同时设置内存和Redis缓存
    this.memoryCache.set(key, value);
    await this.redis.setex(key, ttl, JSON.stringify(value));
  }
}

// 缓存装饰器
export function Cacheable(ttl: number = 3600) {
  return function (target: any, propertyName: string, descriptor: PropertyDescriptor) {
    const method = descriptor.value;
    
    descriptor.value = async function (...args: any[]) {
      const cacheKey = `${target.constructor.name}:${propertyName}:${JSON.stringify(args)}`;
      const cacheService = this.cacheService;
      
      let result = await cacheService.get(cacheKey);
      if (!result) {
        result = await method.apply(this, args);
        await cacheService.set(cacheKey, result, ttl);
      }
      
      return result;
    };
  };
}

// 使用示例
@Injectable()
export class JobService {
  @Cacheable(1800) // 30分钟缓存
  async getActiveJobs(companyId: string): Promise<Job[]> {
    return this.jobRepository.findActiveByCompany(companyId);
  }
}
```

### 4.5 可扩展性设计

**数据库分片策略：**

```typescript
// 用户数据按用户ID分片
class UserShardingStrategy {
  getShardKey(userId: string): string {
    const hash = crypto.createHash('md5').update(userId).digest('hex');
    const shardIndex = parseInt(hash.substring(0, 2), 16) % 4;
    return `user_shard_${shardIndex}`;
  }
}

// 面试数据按时间分片
class InterviewShardingStrategy {
  getShardKey(date: Date): string {
    const year = date.getFullYear();
    const month = date.getMonth() + 1;
    return `interview_${year}_${month.toString().padStart(2, '0')}`;
  }
}

// 分片路由器
@Injectable()
export class ShardingRouter {
  private readonly shards = new Map<string, DataSource>();
  
  constructor(
    private userStrategy: UserShardingStrategy,
    private interviewStrategy: InterviewShardingStrategy
  ) {
    this.initializeShards();
  }
  
  getUserShard(userId: string): DataSource {
    const shardKey = this.userStrategy.getShardKey(userId);
    return this.shards.get(shardKey)!;
  }
  
  getInterviewShard(date: Date): DataSource {
    const shardKey = this.interviewStrategy.getShardKey(date);
    return this.shards.get(shardKey)!;
  }
}
```

**读写分离配置：**
```typescript
// 数据源配置
@Module({
  imports: [
    TypeOrmModule.forRoot({
      name: 'master',
      type: 'postgres',
      host: 'master.db.example.com',
      replication: {
        master: {
          host: 'master.db.example.com',
          port: 5432,
          username: 'master_user',
          password: 'master_pass',
          database: 'interview_platform'
        },
        slaves: [
          {
            host: 'slave1.db.example.com',
            port: 5432,
            username: 'slave_user',
            password: 'slave_pass',
            database: 'interview_platform'
          },
          {
            host: 'slave2.db.example.com',
            port: 5432,
            username: 'slave_user',
            password: 'slave_pass',
            database: 'interview_platform'
          }
        ]
      }
    })
  ]
})
export class DatabaseModule {}
```

### 4.6 备份与恢复

**备份策略：**

```bash
#!/bin/bash
# 全量备份脚本
BACKUP_DIR="/backup/$(date +%Y%m%d)"
mkdir -p $BACKUP_DIR

# PostgreSQL备份
pg_dump -h $PG_HOST -U $PG_USER -d $PG_DATABASE | gzip > $BACKUP_DIR/postgres_full.sql.gz

# MongoDB备份
mongodump --host $MONGO_HOST --db $MONGO_DATABASE --out $BACKUP_DIR/mongodb/

# Redis备份
redis-cli --rdb $BACKUP_DIR/redis_dump.rdb

# 上传到云存储
aws s3 sync $BACKUP_DIR s3://backup-bucket/$(date +%Y%m%d)/

# 清理本地备份（保留7天）
find /backup -type d -mtime +7 -exec rm -rf {} +
```

**增量备份：**
```bash
#!/bin/bash
# PostgreSQL WAL归档
archive_command = 'aws s3 cp %p s3://wal-archive-bucket/%f'

# MongoDB Oplog备份
mongodump --host $MONGO_HOST --db local --collection oplog.rs --query '{"ts": {"$gte": Timestamp('$(date -d "1 hour ago" +%s)', 0)}}' --out $BACKUP_DIR/oplog/
```

**恢复策略：**
```bash
#!/bin/bash
# 点时间恢复(PITR)
function restore_to_point_in_time() {
  local target_time=$1
  
  # 1. 恢复基础备份
  pg_restore -h $PG_HOST -U $PG_USER -d $PG_DATABASE $BACKUP_DIR/postgres_full.sql.gz
  
  # 2. 应用WAL日志到指定时间点
  pg_ctl start -D $PGDATA
  psql -c "SELECT pg_wal_replay_resume();"
  
  # 3. 验证数据一致性
  psql -c "SELECT count(*) FROM users;"
}
```

## 5. 基础设施与部署架构

### 5.1 部署环境

**推荐：阿里云 + Kubernetes**

**选择理由：**
- **本土化优势**：国内访问速度快，合规性好
- **生态完整**：提供全套云原生服务
- **成本效益**：相比AWS更具价格优势
- **技术支持**：中文技术支持，响应及时

**云服务选择：**
```yaml
# 核心服务
Compute: 
  - ECS (弹性计算服务)
  - ACK (容器服务Kubernetes版)
  - Serverless App Engine

Storage:
  - OSS (对象存储)
  - NAS (文件存储)
  - CPFS (并行文件系统)

Database:
  - RDS PostgreSQL
  - MongoDB Atlas
  - Redis Enterprise
  - AnalyticDB (数据仓库)

Networking:
  - VPC (专有网络)
  - SLB (负载均衡)
  - CDN (内容分发)
  - API Gateway

Security:
  - WAF (Web应用防火墙)
  - Anti-DDoS
  - SSL证书服务
  - RAM (访问控制)

Monitoring:
  - CloudMonitor
  - Log Service
  - Application Real-Time Monitoring Service (ARMS)
```

### 5.2 容器化与编排

**Docker容器化：**

```dockerfile
# Node.js应用Dockerfile
FROM node:18-alpine AS builder

WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production

FROM node:18-alpine AS runtime
WORKDIR /app

# 创建非root用户
RUN addgroup -g 1001 -S nodejs
RUN adduser -S nextjs -u 1001

COPY --from=builder /app/node_modules ./node_modules
COPY --chown=nextjs:nodejs . .

USER nextjs
EXPOSE 3000

HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
  CMD curl -f http://localhost:3000/health || exit 1

CMD ["npm", "start"]
```

**Kubernetes部署配置：**

```yaml
# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: interview-api
  namespace: production
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  selector:
    matchLabels:
      app: interview-api
  template:
    metadata:
      labels:
        app: interview-api
    spec:
      containers:
      - name: api
        image: registry.cn-hangzhou.aliyuncs.com/interview/api:v1.0.0
        ports:
        - containerPort: 3000
        env:
        - name: NODE_ENV
          value: "production"
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: db-secret
              key: url
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health
            port: 3000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 3000
          initialDelaySeconds: 5
          periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
  name: interview-api-service
spec:
  selector:
    app: interview-api
  ports:
  - protocol: TCP
    port: 80
    targetPort: 3000
  type: ClusterIP
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: interview-api-ingress
  annotations:
    kubernetes.io/ingress.class: nginx
    cert-manager.io/cluster-issuer: letsencrypt-prod
    nginx.ingress.kubernetes.io/rate-limit: "100"
spec:
  tls:
  - hosts:
    - api.interview-platform.com
    secretName: api-tls
  rules:
  - host: api.interview-platform.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: interview-api-service
            port:
              number: 80
```

**Helm Chart配置：**

```yaml
# values.yaml
replicaCount: 3

image:
  repository: registry.cn-hangzhou.aliyuncs.com/interview/api
  tag: "v1.0.0"
  pullPolicy: IfNotPresent

service:
  type: ClusterIP
  port: 80

ingress:
  enabled: true
  className: nginx
  annotations:
    cert-manager.io/cluster-issuer: letsencrypt-prod
  hosts:
    - host: api.interview-platform.com
      paths:
        - path: /
          pathType: Prefix
  tls:
    - secretName: api-tls
      hosts:
        - api.interview-platform.com

resources:
  limits:
    cpu: 500m
    memory: 512Mi
  requests:
    cpu: 250m
    memory: 256Mi

autoscaling:
  enabled: true
  minReplicas: 3
  maxReplicas: 10
  targetCPUUtilizationPercentage: 80
  targetMemoryUtilizationPercentage: 80

env:
  NODE_ENV: production
  LOG_LEVEL: info

secrets:
  database:
    url: "postgresql://user:pass@host:5432/db"
  redis:
    url: "redis://redis-cluster:6379"
  jwt:
    secret: "your-jwt-secret"
```

### 5.3 CI/CD流程

**GitLab CI/CD Pipeline：**

```yaml
# .gitlab-ci.yml
stages:
  - test
  - build
  - security
  - deploy-staging
  - deploy-production

variables:
  DOCKER_REGISTRY: registry.cn-hangzhou.aliyuncs.com
  IMAGE_NAME: interview/api
  KUBECONFIG_FILE: $KUBECONFIG_STAGING

# 测试阶段
test:unit:
  stage: test
  image: node:18-alpine
  script:
    - npm ci
    - npm run test:unit
    - npm run test:coverage
  coverage: '/Lines\s*:\s*(\d+\.?\d*)%/'
  artifacts:
    reports:
      coverage_report:
        coverage_format: cobertura
        path: coverage/cobertura-coverage.xml

test:integration:
  stage: test
  image: node:18-alpine
  services:
    - postgres:13
    - redis:7-alpine
  variables:
    POSTGRES_DB: test_db
    POSTGRES_USER: test_user
    POSTGRES_PASSWORD: test_pass
  script:
    - npm ci
    - npm run test:integration

# 构建阶段
build:docker:
  stage: build
  image: docker:20.10.16
  services:
    - docker:20.10.16-dind
  before_script:
    - echo $DOCKER_PASSWORD | docker login -u $DOCKER_USERNAME --password-stdin $DOCKER_REGISTRY
  script:
    - docker build -t $DOCKER_REGISTRY/$IMAGE_NAME:$CI_COMMIT_SHA .
    - docker build -t $DOCKER_REGISTRY/$IMAGE_NAME:latest .
    - docker push $DOCKER_REGISTRY/$IMAGE_NAME:$CI_COMMIT_SHA
    - docker push $DOCKER_REGISTRY/$IMAGE_NAME:latest
  only:
    - main
    - develop

# 安全扫描
security:container-scan:
  stage: security
  image: aquasec/trivy:latest
  script:
    - trivy image --exit-code 0 --severity HIGH,CRITICAL $DOCKER_REGISTRY/$IMAGE_NAME:$CI_COMMIT_SHA
  allow_failure: true

security:sast:
  stage: security
  image: node:18-alpine
  script:
    - npm ci
    - npm audit --audit-level high
    - npx eslint . --ext .ts,.js --format gitlab
  artifacts:
    reports:
      sast: gl-sast-report.json

# 部署到测试环境
deploy:staging:
  stage: deploy-staging
  image: bitnami/kubectl:latest
  script:
    - echo $KUBECONFIG_STAGING | base64 -d > kubeconfig
    - export KUBECONFIG=kubeconfig
    - helm upgrade --install interview-api-staging ./helm-chart \
        --namespace staging \
        --set image.tag=$CI_COMMIT_SHA \
        --set ingress.hosts[0].host=staging-api.interview-platform.com \
        --values ./helm-chart/values-staging.yaml
  environment:
    name: staging
    url: https://staging-api.interview-platform.com
  only:
    - develop

# 部署到生产环境
deploy:production:
  stage: deploy-production
  image: bitnami/kubectl:latest
  script:
    - echo $KUBECONFIG_PRODUCTION | base64 -d > kubeconfig
    - export KUBECONFIG=kubeconfig
    - helm upgrade --install interview-api ./helm-chart \
        --namespace production \
        --set image.tag=$CI_COMMIT_SHA \
        --values ./helm-chart/values-production.yaml
  environment:
    name: production
    url: https://api.interview-platform.com
  when: manual
  only:
    - main
```

### 5.4 监控与警报

**Prometheus + Grafana监控栈：**

```yaml
# prometheus-config.yaml
global:
  scrape_interval: 15s
  evaluation_interval: 15s

rule_files:
  - "alert_rules.yml"

alerting:
  alertmanagers:
    - static_configs:
        - targets:
          - alertmanager:9093

scrape_configs:
  - job_name: 'kubernetes-apiservers'
    kubernetes_sd_configs:
    - role: endpoints
    scheme: https
    tls_config:
      ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt
    bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token
    relabel_configs:
    - source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name]
      action: keep
      regex: default;kubernetes;https

  - job_name: 'interview-api'
    kubernetes_sd_configs:
    - role: pod
    relabel_configs:
    - source_labels: [__meta_kubernetes_pod_label_app]
      action: keep
      regex: interview-api
    - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
      action: keep
      regex: true
    - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
      action: replace
      target_label: __metrics_path__
      regex: (.+)
```

**告警规则：**

```yaml
# alert_rules.yml
groups:
- name: interview-platform
  rules:
  - alert: HighErrorRate
    expr: rate(http_requests_total{status=~"5.."}[5m]) > 0.1
    for: 5m
    labels:
      severity: critical
    annotations:
      summary: "High error rate detected"
      description: "Error rate is {{ $value }} errors per second"

  - alert: HighResponseTime
    expr: histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m])) > 1
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: "High response time detected"
      description: "95th percentile response time is {{ $value }} seconds"

  - alert: DatabaseConnectionHigh
    expr: pg_stat_activity_count > 80
    for: 2m
    labels:
      severity: warning
    annotations:
      summary: "High database connection count"
      description: "Database has {{ $value }} active connections"

  - alert: PodCrashLooping
    expr: rate(kube_pod_container_status_restarts_total[15m]) > 0
    for: 5m
    labels:
      severity: critical
    annotations:
      summary: "Pod is crash looping"
      description: "Pod {{ $labels.pod }} in namespace {{ $labels.namespace }} is crash looping"
```

**Grafana仪表板配置：**

```json
{
  "dashboard": {
    "title": "Interview Platform Overview",
    "panels": [
      {
        "title": "Request Rate",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(http_requests_total[5m])",
            "legendFormat": "{{method}} {{status}}"
          }
        ]
      },
      {
        "title": "Response Time",
        "type": "graph",
        "targets": [
          {
            "expr": "histogram_quantile(0.50, rate(http_request_duration_seconds_bucket[5m]))",
            "legendFormat": "50th percentile"
          },
          {
            "expr": "histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))",
            "legendFormat": "95th percentile"
          }
        ]
      },
      {
        "title": "Database Performance",
        "type": "graph",
        "targets": [
          {
            "expr": "pg_stat_database_tup_fetched",
            "legendFormat": "Rows fetched"
          },
          {
            "expr": "pg_stat_database_tup_inserted",
            "legendFormat": "Rows inserted"
          }
        ]
      }
    ]
  }
}
```

## 6. 安全架构

### 6.1 数据安全

**传输加密(TLS)：**

```nginx
# Nginx TLS配置
server {
    listen 443 ssl http2;
    server_name api.interview-platform.com;
    
    # TLS证书配置
    ssl_certificate /etc/ssl/certs/api.interview-platform.com.crt;
    ssl_certificate_key /etc/ssl/private/api.interview-platform.com.key;
    
    # TLS安全配置
    ssl_protocols TLSv1.2 TLSv1.3;
    ssl_ciphers ECDHE-RSA-AES256-GCM-SHA512:DHE-RSA-AES256-GCM-SHA512:ECDHE-RSA-AES256-GCM-SHA384:DHE-RSA-AES256-GCM-SHA384;
    ssl_prefer_server_ciphers off;
    ssl_session_cache shared:SSL:10m;
    ssl_session_timeout 10m;
    
    # HSTS
    add_header Strict-Transport-Security "max-age=31536000; includeSubDomains" always;
    
    # 其他安全头
    add_header X-Frame-Options DENY;
    add_header X-Content-Type-Options nosniff;
    add_header X-XSS-Protection "1; mode=block";
    add_header Referrer-Policy "strict-origin-when-cross-origin";
    
    location / {
        proxy_pass http://interview-api-service;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
    }
}
```

**存储加密：**

```typescript
// 数据库字段加密
@Entity()
export class User {
  @PrimaryGeneratedColumn('uuid')
  id: string;
  
  @Column()
  email: string;
  
  @Column({ transformer: new EncryptionTransformer() })
  phone: string; // 加密存储手机号
  
  @Column({ transformer: new EncryptionTransformer() })
  idCard: string; // 加密存储身份证号
}

// 加密转换器
export class EncryptionTransformer implements ValueTransformer {
  private readonly algorithm = 'aes-256-gcm';
  private readonly key = Buffer.from(process.env.ENCRYPTION_KEY!, 'hex');
  
  to(value: string): string {
    if (!value) return value;
    
    const iv = crypto.randomBytes(16);
    const cipher = crypto.createCipher(this.algorithm, this.key);
    cipher.setAAD(Buffer.from('interview-platform'));
    
    let encrypted = cipher.update(value, 'utf8', 'hex');
    encrypted += cipher.final('hex');
    
    const authTag = cipher.getAuthTag();
    
    return iv.toString('hex') + ':' + authTag.toString('hex') + ':' + encrypted;
  }
  
  from(value: string): string {
    if (!value) return value;
    
    const [ivHex, authTagHex, encrypted] = value.split(':');
    const iv = Buffer.from(ivHex, 'hex');
    const authTag = Buffer.from(authTagHex, 'hex');
    
    const decipher = crypto.createDecipher(this.algorithm, this.key);
    decipher.setAAD(Buffer.from('interview-platform'));
    decipher.setAuthTag(authTag);
    
    let decrypted = decipher.update(encrypted, 'hex', 'utf8');
    decrypted += decipher.final('utf8');
    
    return decrypted;
  }
}
```

### 6.2 威胁防护

**API安全中间件：**

```typescript
// 速率限制
@Injectable()
export class RateLimitMiddleware implements NestMiddleware {
  private readonly limiter = rateLimit({
    windowMs: 15 * 60 * 1000, // 15分钟
    max: 100, // 最多100个请求
    message: 'Too many requests from this IP',
    standardHeaders: true,
    legacyHeaders: false,
  });
  
  use(req: Request, res: Response, next: NextFunction) {
    this.limiter(req, res, next);
  }
}

// SQL注入防护
@Injectable()
export class SqlInjectionGuard implements CanActivate {
  private readonly sqlInjectionPatterns = [
    /('|(\-\-)|(;)|(\||\|)|(\*|\*))/i,
    /(exec(\s|\+)+(s|x)p\w+)/i,
    /union.*select/i,
    /insert.*into/i,
    /delete.*from/i,
    /update.*set/i,
    /drop.*table/i
  ];
  
  canActivate(context: ExecutionContext): boolean {
    const request = context.switchToHttp().getRequest();
    const { query, body, params } = request;
    
    const allInputs = { ...query, ...body, ...params };
    
    for (const [key, value] of Object.entries(allInputs)) {
      if (typeof value === 'string' && this.containsSqlInjection(value)) {
        throw new BadRequestException(`Potential SQL injection detected in ${key}`);
      }
    }
    
    return true;
  }
  
  private containsSqlInjection(input: string): boolean {
    return this.sqlInjectionPatterns.some(pattern => pattern.test(input));
  }
}

// XSS防护
@Injectable()
export class XssProtectionInterceptor implements NestInterceptor {
  intercept(context: ExecutionContext, next: CallHandler): Observable<any> {
    const request = context.switchToHttp().getRequest();
    
    // 清理请求数据
    this.sanitizeObject(request.body);
    this.sanitizeObject(request.query);
    
    return next.handle().pipe(
      map(data => {
        // 清理响应数据
        return this.sanitizeObject(data);
      })
    );
  }
  
  private sanitizeObject(obj: any): any {
    if (typeof obj === 'string') {
      return DOMPurify.sanitize(obj);
    }
    
    if (Array.isArray(obj)) {
      return obj.map(item => this.sanitizeObject(item));
    }
    
    if (obj && typeof obj === 'object') {
      const sanitized = {};
      for (const [key, value] of Object.entries(obj)) {
        sanitized[key] = this.sanitizeObject(value);
      }
      return sanitized;
    }
    
    return obj;
  }
}

// CSRF防护
@Injectable()
export class CsrfProtectionMiddleware implements NestMiddleware {
  use(req: Request, res: Response, next: NextFunction) {
    // 验证CSRF Token
    const token = req.headers['x-csrf-token'] || req.body._token;
    const sessionToken = req.session?.csrfToken;
    
    if (req.method !== 'GET' && token !== sessionToken) {
      throw new ForbiddenException('Invalid CSRF token');
    }
    
    next();
  }
}
```

**API访问控制：**

```typescript
// API密钥验证
@Injectable()
export class ApiKeyGuard implements CanActivate {
  constructor(private readonly apiKeyService: ApiKeyService) {}
  
  async canActivate(context: ExecutionContext): Promise<boolean> {
    const request = context.switchToHttp().getRequest();
    const apiKey = request.headers['x-api-key'];
    
    if (!apiKey) {
      throw new UnauthorizedException('API key required');
    }
    
    const isValid = await this.apiKeyService.validateKey(apiKey);
    if (!isValid) {
      throw new UnauthorizedException('Invalid API key');
    }
    
    // 记录API使用情况
    await this.apiKeyService.recordUsage(apiKey, request.path);
    
    return true;
  }
}

// IP白名单
@Injectable()
export class IpWhitelistGuard implements CanActivate {
  private readonly allowedIps = new Set([
    '192.168.1.0/24',
    '10.0.0.0/8',
    '172.16.0.0/12'
  ]);
  
  canActivate(context: ExecutionContext): boolean {
    const request = context.switchToHttp().getRequest();
    const clientIp = request.ip || request.connection.remoteAddress;
    
    if (!this.isIpAllowed(clientIp)) {
      throw new ForbiddenException('IP address not allowed');
    }
    
    return true;
  }
  
  private isIpAllowed(ip: string): boolean {
    return Array.from(this.allowedIps).some(range => {
      return this.ipInRange(ip, range);
    });
  }
}
```

### 6.3 合规性考虑

**数据隐私保护：**

```typescript
// 个人信息保护法合规
@Injectable()
export class PrivacyComplianceService {
  // 数据最小化原则
  async collectUserData(userData: Partial<User>): Promise<User> {
    // 只收集必要的数据字段
    const allowedFields = ['email', 'name', 'phone'];
    const filteredData = Object.keys(userData)
      .filter(key => allowedFields.includes(key))
      .reduce((obj, key) => {
        obj[key] = userData[key];
        return obj;
      }, {});
    
    return this.userService.create(filteredData);
  }
  
  // 用户同意管理
  async recordConsent(userId: string, consentType: string): Promise<void> {
    await this.consentService.record({
      userId,
      consentType,
      timestamp: new Date(),
      ipAddress: this.request.ip,
      userAgent: this.request.headers['user-agent']
    });
  }
  
  // 数据删除权（被遗忘权）
  async deleteUserData(userId: string): Promise<void> {
    // 1. 删除个人身份信息
    await this.userService.anonymize(userId);
    
    // 2. 删除关联数据
    await this.resumeService.deleteByUser(userId);
    await this.interviewService.anonymizeByUser(userId);
    
    // 3. 记录删除操作
    await this.auditService.logDataDeletion(userId);
  }
  
  // 数据导出权
  async exportUserData(userId: string): Promise<any> {
    const userData = await this.userService.findById(userId);
    const resumes = await this.resumeService.findByUser(userId);
    const interviews = await this.interviewService.findByUser(userId);
    
    return {
      personal_info: userData,
      resumes: resumes,
      interviews: interviews,
      exported_at: new Date().toISOString()
    };
  }
}
```

**审计日志：**

```typescript
@Injectable()
export class AuditService {
  async logUserAction(action: AuditAction): Promise<void> {
    await this.auditRepository.save({
      userId: action.userId,
      action: action.type,
      resource: action.resource,
      details: action.details,
      ipAddress: action.ipAddress,
      userAgent: action.userAgent,
      timestamp: new Date()
    });
  }
  
  // 敏感操作审计
  @AuditLog('DATA_ACCESS')
  async accessSensitiveData(userId: string, dataType: string): Promise<any> {
    // 记录敏感数据访问
    return this.dataService.getSensitiveData(userId, dataType);
  }
}

// 审计装饰器
export function AuditLog(actionType: string) {
  return function (target: any, propertyName: string, descriptor: PropertyDescriptor) {
    const method = descriptor.value;
    
    descriptor.value = async function (...args: any[]) {
      const result = await method.apply(this, args);
      
      // 记录审计日志
      await this.auditService.logUserAction({
        type: actionType,
        resource: `${target.constructor.name}.${propertyName}`,
        details: { args: args.slice(0, 2) }, // 只记录前两个参数
        userId: this.getCurrentUserId(),
        ipAddress: this.getClientIp(),
        userAgent: this.getUserAgent()
      });
      
      return result;
    };
  };
}
```

## 7. 技术栈总结

### 7.1 技术组合一览表

| 层级 | 技术选择 | 版本 | 用途 | 备选方案 |
|------|----------|------|------|----------|
| **前端框架** | Vue.js | 3.3+ | 用户界面开发 | React 18, Angular 16 |
| **前端语言** | TypeScript | 5.0+ | 类型安全开发 | JavaScript ES2022 |
| **前端构建** | Vite | 4.0+ | 快速构建打包 | Webpack 5, Rollup |
| **状态管理** | Pinia | 2.1+ | 应用状态管理 | Vuex 4, Zustand |
| **UI组件库** | Element Plus | 2.3+ | 企业级组件 | Ant Design Vue, Vuetify |
| **CSS框架** | Tailwind CSS | 3.3+ | 原子化样式 | Bootstrap 5, Bulma |
| **后端框架** | NestJS | 10+ | 企业级Node.js框架 | Express.js, Fastify |
| **后端语言** | Node.js | 18+ | 服务端JavaScript | Java 17, Python 3.11, Go 1.20 |
| **ORM框架** | TypeORM | 0.3+ | 数据库对象映射 | Prisma, Sequelize |
| **主数据库** | PostgreSQL | 15+ | 关系型数据存储 | MySQL 8.0, MariaDB 10.6 |
| **文档数据库** | MongoDB | 6.0+ | 非结构化数据 | CouchDB, Amazon DocumentDB |
| **搜索引擎** | Elasticsearch | 8.0+ | 全文搜索分析 | Apache Solr, Algolia |
| **缓存数据库** | Redis | 7.0+ | 高性能缓存 | Memcached, Hazelcast |
| **消息队列** | Apache Kafka | 3.4+ | 分布式消息流 | RabbitMQ, Apache Pulsar |
| **任务队列** | Bull | 4.10+ | 异步任务处理 | Agenda, Kue |
| **容器化** | Docker | 20.10+ | 应用容器化 | Podman, containerd |
| **容器编排** | Kubernetes | 1.27+ | 容器集群管理 | Docker Swarm, Nomad |
| **服务网格** | Istio | 1.18+ | 微服务通信 | Linkerd, Consul Connect |
| **API网关** | Kong | 3.3+ | API统一入口 | Nginx Gateway, Zuul |
| **监控系统** | Prometheus | 2.45+ | 指标监控收集 | InfluxDB, Datadog |
| **可视化** | Grafana | 10.0+ | 监控数据可视化 | Kibana, New Relic |
| **日志管理** | ELK Stack | 8.8+ | 日志收集分析 | Fluentd + InfluxDB |
| **CI/CD** | GitLab CI | 16.0+ | 持续集成部署 | Jenkins, GitHub Actions |
| **云服务商** | 阿里云 | - | 云基础设施 | AWS, Azure, 腾讯云 |
| **CDN** | 阿里云CDN | - | 内容分发网络 | CloudFlare, AWS CloudFront |
| **对象存储** | 阿里云OSS | - | 文件对象存储 | AWS S3, 腾讯云COS |

### 7.2 优缺点与备选方案

**核心技术选择分析：**

#### Vue.js vs React vs Angular

**Vue.js (推荐)**
- ✅ 学习曲线平缓，团队快速上手
- ✅ 中文文档完善，社区活跃
- ✅ 性能优秀，包体积小
- ✅ TypeScript支持良好
- ❌ 企业级生态相对较小
- ❌ 大型项目架构指导较少

**React (备选)**
- ✅ 生态最丰富，社区最大
- ✅ 企业采用率最高
- ✅ 性能优化方案成熟
- ❌ 学习曲线较陡峭
- ❌ 配置复杂度较高

**Angular (备选)**
- ✅ 企业级框架，功能完整
- ✅ TypeScript原生支持
- ✅ 架构规范性强
- ❌ 学习成本最高
- ❌ 包体积较大

#### Node.js vs Java vs Python vs Go

**Node.js (推荐)**
- ✅ 前后端技术栈统一
- ✅ 开发效率高
- ✅ 实时应用支持优秀
- ✅ 微服务架构友好
- ❌ CPU密集型任务性能较差
- ❌ 单线程模型限制

**Java Spring Boot (备选)**
- ✅ 企业级应用首选
- ✅ 生态最成熟
- ✅ 性能稳定可靠
- ✅ 人才储备充足
- ❌ 开发效率相对较低
- ❌ 内存占用较大

**Python FastAPI (备选)**
- ✅ AI集成最友好
- ✅ 开发效率极高
- ✅ 数据处理能力强
- ❌ 性能相对较低
- ❌ 并发处理能力有限

**Go (备选)**
- ✅ 性能最优秀
- ✅ 并发处理能力强
- ✅ 部署简单
- ❌ 生态相对较小
- ❌ 学习成本较高

#### PostgreSQL vs MySQL vs MongoDB

**PostgreSQL (推荐)**
- ✅ 功能最丰富
- ✅ JSON支持优秀
- ✅ 扩展性强
- ✅ 数据一致性保证
- ❌ 学习成本较高
- ❌ 运维复杂度较大

**MySQL (备选)**
- ✅ 使用最广泛
- ✅ 运维成本低
- ✅ 性能优秀
- ❌ 功能相对简单
- ❌ JSON支持较弱

**MongoDB (混合使用)**
- ✅ 灵活的文档模型
- ✅ 水平扩展能力强
- ✅ 开发效率高
- ❌ 数据一致性较弱
- ❌ 复杂查询能力有限

**最终推荐架构组合：**

```
前端：Vue.js 3 + TypeScript + Vite + Pinia + Element Plus
后端：Node.js + NestJS + TypeScript + TypeORM
数据：PostgreSQL + MongoDB + Redis + Elasticsearch
基础设施：Docker + Kubernetes + 阿里云
监控：Prometheus + Grafana + ELK
```

**适用场景：**
- ✅ 中小型技术团队（10-50人）
- ✅ 快速迭代开发需求
- ✅ 实时交互应用
- ✅ AI功能集成
- ✅ 云原生部署

**不适用场景：**
- ❌ 超大规模企业级应用（建议Java）
- ❌ CPU密集型计算（建议Go/C++）
- ❌ 强一致性金融应用（建议Java）
- ❌ 传统企业环境（建议.NET）

---

## 总结

本架构设计为AI智能面试平台提供了一套完整、可扩展、安全的技术解决方案。通过微服务架构、云原生部署、多数据库混合使用等现代化技术栈，能够支撑百万级用户的业务需求，同时保证系统的高可用性、高性能和高安全性。

**关键优势：**
1. **技术先进性**：采用最新的云原生技术栈
2. **可扩展性**：支持水平扩展和业务增长
3. **开发效率**：统一技术栈，提高开发效率
4. **运维友好**：容器化部署，自动化运维
5. **安全合规**：全面的安全防护和合规设计

**实施建议：**
1. **分阶段实施**：先实现核心功能，再逐步完善
2. **技术选型验证**：通过POC验证关键技术选择
3. **团队培训**：确保团队掌握相关技术栈
4. **监控先行**：优先建立监控和日志体系
5. **安全优先**：从设计阶段就考虑安全因素

该架构设计为AI智能面试平台的成功实施提供了坚实的技术基础。