# API Mocking > 【API Mock】设计和实现 API Mock 服务，支持前后端并行开发、测试环境模拟、延迟/错误注入。 触发时机： - 用户要求"Mock API"、"模拟接口" - 后端接口未就绪，前端需要开发 - 测试需要模拟各种响应场景 支持 Mock Server 搭建和代码级 Mock。 - Skill: `afine907/api-mocking` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add afine907/api-mocking` - Raw SKILL.md: https://api.skillmd.com/api/skills/afine907/api-mocking/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Integrations & APIs - Author: afine907 (https://skillmd.com/u/afine907) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/afine907/api-mocking --- # API Mock — API Mock 技能 设计 Mock 服务，支持前后端并行开发和测试场景模拟。 ## Goal 设计和实现 API Mock 服务，支持前后端并行开发、测试环境模拟、延迟/错误注入 ## Trigger - 用户要求"Mock API"、"模拟接口" - 后端接口未就绪，前端需要开发 - 测试需要模拟各种响应场景 ## Workflow 1. **选择方案** — 根据场景选择 Mock Server / 代码级 Mock / API 网关 2. **定义数据** — 设计 Mock 数据结构和场景 3. **实现 Mock** — 搭建 Mock Server 或编写代码级 Mock 4. **注入场景** — 配置延迟、错误率、边界情况 5. **验证集成** — 前端/测试集成验证 ## Mock 方案对比 | 方案 | 适用场景 | 优势 | 劣势 | |------|----------|------|------| | Mock Server | 前端独立开发 | 真实 HTTP 请求 | 需要维护服务 | | 代码级 Mock | 单元测试 | 灵活、快速 | 不测试网络层 | | API 网关 Mock | 微服务开发 | 无需改代码 | 配置复杂 | | 录制回放 | 回归测试 | 真实数据 | 数据可能过期 | ## Mock Server 实现 ### Python + FastAPI ```python from fastapi import FastAPI, HTTPException from pydantic import BaseModel from typing import Optional, List import random import time app = FastAPI(title="Mock Server") # Mock 数据 USERS_DB = [ {"id": "1", "name": "张三", "email": "zhangsan@example.com"}, {"id": "2", "name": "李四", "email": "lisi@example.com"}, {"id": "3", "name": "王五", "email": "wangwu@example.com"}, ] # 配置 MOCK_DELAY = 0.1 # 模拟延迟 ERROR_RATE = 0.05 # 错误率 @app.middleware("http") async def mock_delay(request, call_next): """模拟网络延迟""" await asyncio.sleep(MOCK_DELAY + random.uniform(0, 0.05)) # 随机错误注入 if random.random() < ERROR_RATE: return JSONResponse( status_code=500, content={"error": "Internal Server Error"} ) return await call_next(request) @app.get("/api/v1/users") async def list_users(page: int = 1, page_size: int = 10): """获取用户列表""" start = (page - 1) * page_size end = start + page_size return { "data": USERS_DB[start:end], "pagination": { "page": page, "page_size": page_size, "total": len(USERS_DB), "total_pages": (len(USERS_DB) + page_size - 1) // page_size } } @app.get("/api/v1/users/{user_id}") async def get_user(user_id: str): """获取用户详情""" user = next((u for u in USERS_DB if u["id"] == user_id), None) if not user: raise HTTPException(status_code=404, detail="User not found") return {"data": user} @app.post("/api/v1/users") async def create_user(user: dict): """创建用户""" new_user = {**user, "id": str(len(USERS_DB) + 1)} USERS_DB.append(new_user) return {"data": new_user}, 201 ``` ### Node.js + json-server ```bash # 安装 npm install -g json-server # db.json { "users": [ {"id": 1, "name": "张三", "email": "zhangsan@example.com"}, {"id": 2, "name": "李四", "email": "lisi@example.com"} ], "posts": [ {"id": 1, "title": "文章1", "userId": 1}, {"id": 2, "title": "文章2", "userId": 2} ] } # 启动 json-server --watch db.json --port 3001 ``` ## 代码级 Mock ### Python pytest ```python from unittest.mock import patch, MagicMock import pytest # Mock HTTP 请求 @patch('httpx.AsyncClient.get') async def test_get_user(mock_get): mock_response = MagicMock() mock_response.status_code = 200 mock_response.json.return_value = { "data": {"id": "1", "name": "张三"} } mock_get.return_value = mock_response result = await get_user("1") assert result["name"] == "张三" # Mock 数据库 @patch('app.db.execute') async def test_create_user(mock_execute): mock_execute.return_value = [{"id": 1}] result = await create_user({"name": "张三"}) assert result["id"] == 1 ``` ### JavaScript MSW (Mock Service Worker) ```javascript // mocks/handlers.js import { http, HttpResponse } from 'msw'; export const handlers = [ http.get('/api/v1/users', () => { return HttpResponse.json({ data: [ { id: '1', name: '张三', email: 'zhangsan@example.com' }, { id: '2', name: '李四', email: 'lisi@example.com' } ] }); }), http.get('/api/v1/users/:id', ({ params }) => { const { id } = params; return HttpResponse.json({ data: { id, name: '张三', email: 'zhangsan@example.com' } }); }), http.post('/api/v1/users', async ({ request }) => { const body = await request.json(); return HttpResponse.json({ data: { id: '3', ...body } }, { status: 201 }); }), ]; ``` ## 场景 Mock ### 延迟模拟 ```python # 不同场景的延迟配置 SCENARIOS = { "fast": {"delay": 0.05, "description": "快速响应"}, "normal": {"delay": 0.2, "description": "正常延迟"}, "slow": {"delay": 2.0, "description": "慢响应"}, "timeout": {"delay": 30, "description": "超时"}, } @app.get("/api/v1/scenario/{scenario}") async def scenario_endpoint(scenario: str): config = SCENARIOS.get(scenario) if not config: raise HTTPException(404, "Scenario not found") await asyncio.sleep(config["delay"]) return {"scenario": scenario, "delay": config["delay"]} ``` ### 错误模拟 ```python # 随机错误 @app.get("/api/v1/flaky") async def flaky_endpoint(): if random.random() < 0.3: # 30% 错误率 raise HTTPException(500, "Random error") return {"status": "ok"} # 指定状态码 @app.get("/api/v1/status/{code}") async def status_endpoint(code: int): raise HTTPException(code, f"Simulated {code} error") ``` ### 数据生成 ```python from faker import Faker import factory fake = Faker('zh_CN') class UserFactory(factory.Factory): class Meta: model = dict id = factory.Sequence(lambda n: str(n + 1)) name = factory.LazyFunction(fake.name) email = factory.LazyFunction(fake.email) phone = factory.LazyFunction(fake.phone_number) avatar = factory.LazyFunction(fake.image_url) # 生成测试数据 @app.get("/api/v1/users/generated") async def generated_users(count: int = 10): return {"data": [UserFactory() for _ in range(count)]} ``` ## OpenAPI Mock ### Prism Mock Server ```bash # 安装 npm install -g @stoplight/prism-cli # 从 OpenAPI 规范启动 Mock prism mock openapi.yaml # 支持动态响应 prism mock openapi.yaml --dynamic ``` ## Example ``` 用户: 为用户管理 API 创建 Mock 服务，支持错误注入 输出: 1. 创建 FastAPI Mock Server 2. 配置 /api/v1/users 端点 3. 添加 5% 随机错误率 4. 添加 100ms 模拟延迟 5. 生成 50 条测试数据 ``` ## 参考资料 - MSW 文档: [references/msw.md](references/msw.md) - 测试数据生成: [references/faker.md](references/faker.md)