一、异步爬虫架构设计

系统核心组件：

• 异步HTTP客户端：aiohttp库实现
• 任务调度器：Asyncio协程管理
• 数据管道：异步存储到MongoDB
• 反爬策略：自动代理轮换+请求限流
• 监控系统：实时统计爬取指标

二、核心代码实现

1. 异步爬虫主引擎

import asyncio
from aiohttp import ClientSession
from motor.motor_asyncio import AsyncIOMotorClient

class AsyncSpider:
    def __init__(self, concurrency=10):
        self.semaphore = asyncio.Semaphore(concurrency)
        self.client = AsyncIOMotorClient('mongodb://localhost:27017')
        self.db = self.client['spider_db']
        
    async def fetch(self, url, session):
        async with self.semaphore:
            try:
                async with session.get(url, timeout=10) as response:
                    if response.status == 200:
                        return await response.text()
            except Exception as e:
                print(f"Error fetching {url}: {str(e)}")
                return None

    async def parse(self, html):
        # 实现具体的解析逻辑
        pass

    async def save(self, data):
        await self.db['items'].insert_one(data)

    async def crawl(self, urls):
        async with ClientSession() as session:
            tasks = [self.process_url(url, session) for url in urls]
            await asyncio.gather(*tasks)

    async def process_url(self, url, session):
        html = await self.fetch(url, session)
        if html:
            data = await self.parse(html)
            if data:
                await self.save(data)

2. 代理中间件实现

class ProxyMiddleware:
    def __init__(self):
        self.proxies = [
            'http://proxy1.example.com:8080',
            'http://proxy2.example.com:8080'
        ]
        self.current = 0

    async def get_proxy(self):
        proxy = self.proxies[self.current]
        self.current = (self.current + 1) % len(self.proxies)
        return proxy

    async def process_request(self, url, session):
        proxy = await self.get_proxy()
        try:
            async with session.get(url, proxy=proxy) as response:
                return await response.text()
        except:
            return None

三、高级功能实现

1. 分布式任务队列

import aioredis

class TaskQueue:
    def __init__(self):
        self.redis = aioredis.from_url('redis://localhost')

    async def add_url(self, url):
        await self.redis.lpush('spider:start_urls', url)

    async def get_url(self):
        return await self.redis.rpop('spider:start_urls')

    async def process_tasks(self):
        while True:
            url = await self.get_url()
            if url:
                await self.spider.crawl([url])
            else:
                await asyncio.sleep(1)

2. 性能监控装饰器

def monitor_performance(func):
    async def wrapper(*args, **kwargs):
        start = time.time()
        result = await func(*args, **kwargs)
        elapsed = time.time() - start
        print(f"{func.__name__} executed in {elapsed:.2f} seconds")
        return result
    return wrapper

四、项目部署方案

1. Docker容器化：打包Python环境和依赖
2. Kubernetes编排：实现自动扩缩容
3. Prometheus监控：收集运行时指标
4. 日志收集：ELK栈集中管理
5. 定时任务：Celery实现周期爬取