Nmb Scrapling

Scrapling

自适应Web爬虫框架，能过反爬、能大规模爬取、网站改版不崩。

Installation

# 基础安装（仅解析器）
pip install scrapling
完整安装（含fetchers和浏览器）
pip install "scrapling[all]"
scrapling install
或单独安装功能
pip install "scrapling[fetchers]"  # 抓取功能
pip install "scrapling[ai]"        # MCP服务
pip install "scrapling[shell]"     # 交互式shell

Quick Start

基础抓取

from scrapling.fetchers import Fetcher
page = Fetcher.get('https://quotes.toscrape.com/')
quotes = page.css('.quote .text::text').getall()
print(quotes)

过反爬（Cloudflare等）

from scrapling.fetchers import StealthyFetcher
自动过Cloudflare Turnstile
page = StealthyFetcher.fetch(
    'https://目标网站',
    headless=True,
    solve_cloudflare=True
)
data = page.css('.content::text').getall()

动态页面（JS渲染）

from scrapling.fetchers import DynamicFetcher
完整浏览器渲染
page = DynamicFetcher.fetch(
    'https://spa网站',
    headless=True,
    network_idle=True  # 等网络请求完成
)

Fetcher Types

| Fetcher | 用途 | 特点 | |---------|------|------| | Fetcher | 普通HTTP请求 | 最快，适合静态页面 | | StealthyFetcher | 隐身模式 | 过反爬，过Cloudflare | | DynamicFetcher | 浏览器模式 | JS渲染，SPA页面 |

Element Selection

page = Fetcher.get('https://example.com')
CSS选择器
items = page.css('.item')
title = page.css('h1::text').get()
titles = page.css('h2::text').getall()
XPath
items = page.xpath('//div[@class="item"]')
BeautifulSoup风格
items = page.find_all('div', class_='item')
items = page.find_by_text('关键词', tag='div')
链式选择
quote_text = page.css('.quote')[0].css('.text::text').get()
导航
first = page.css('.item')[0]
parent = first.parent
sibling = first.next_sibling
similar = first.find_similar()  # 找相似元素

Session Management

from scrapling.fetchers import FetcherSession, StealthySession
保持会话（cookie复用）
with StealthySession(headless=True, solve_cloudflare=True) as session:
    page1 = session.fetch('https://example.com/login')
    page2 = session.fetch('https://example.com/dashboard')  # 已登录状态
异步Session
from scrapling.fetchers import AsyncStealthySession
async with AsyncStealthySession(headless=True) as session:
    page = await session.fetch('https://example.com')

Building Spiders (大规模爬取)

from scrapling.spiders import Spider, Response
class MySpider(Spider):
    name = "products"
    start_urls = ["https://shop.example.com/"]
    concurrent_requests = 10  # 并发数
    async def parse(self, response: Response):
        for item in response.css('.product'):
            yield {
                "title": item.css('h2::text').get(),
                "price": item.css('.price::text').get(),
            }
        # 翻页
        next_page = response.css('.next a::attr(href)').get()
        if next_page:
            yield response.follow(next_page)
运行
result = MySpider().start()
print(f"爬取了 {len(result.items)} 条")
导出
result.items.to_json("output.json")
result.items.to_jsonl("output.jsonl")

断点续爬

# 指定crawl目录，支持暂停/恢复
MySpider(crawldir="./crawl_data").start()
Ctrl+C 暂停，再次运行从断点继续

多Session混用

from scrapling.spiders import Spider, Request
from scrapling.fetchers import FetcherSession, AsyncStealthySession
class MultiSpider(Spider):
    name = "multi"
    def configure_sessions(self, manager):
        # 普通请求 - 快
        manager.add("fast", FetcherSession(impersonate="chrome"))
        # 隐身请求 - 过反爬
        manager.add("stealth", AsyncStealthySession(headless=True), lazy=True)
    async def parse(self, response):
        for link in response.css('a::attr(href)').getall():
            if "protected" in link:
                yield Request(link, sid="stealth")  # 用隐身session
            else:
                yield Request(link, sid="fast")      # 用快速session

Adaptive Parsing (自适应解析)

网站改版后自动重新定位元素：

# 首次爬取，保存元素特征
products = page.css('.product', auto_save=True)
网站改版后，用adaptive=True自动重新定位
products = page.css('.product', adaptive=True)

Proxy Rotation

from scrapling.fetchers import StealthyFetcher, ProxyRotator
proxies = ProxyRotator([
    "http://proxy1:8080",
    "http://proxy2:8080",
])
page = StealthyFetcher.fetch(
    'https://example.com',
    proxy=proxies.next()
)

CLI Commands

# 交互式shell
scrapling shell
直接抓取（不用写代码）
scrapling extract get 'https://example.com' output.md
scrapling extract stealthy-fetch 'https://protected.com' output.html --solve-cloudflare
安装浏览器
scrapling install
scrapling install --force

MCP Server (AI集成)

让Claude/Cursor直接调Scrapling爬数据：

pip install "scrapling[ai]"
启动MCP服务
scrapling mcp

配置到Claude Desktop的config：

{
  "mcpServers": {
    "scrapling": {
      "command": "scrapling",
      "args": ["mcp"]
    }
  }
}

Common Use Cases

电商比价

from scrapling.fetchers import StealthyFetcher
page = StealthyFetcher.fetch('https://item.jd.com/12345.html', headless=True)
price = page.css('.price::text').get()
title = page.css('.sku-name::text').get()

招聘信息

from scrapling.spiders import Spider, Response
class JobsSpider(Spider):
    name = "jobs"
    start_urls = ["https://www.zhipin.com/job_detail/?query=Python"]
    async def parse(self, response: Response):
        for job in response.css('.job-list li'):
            yield {
                "title": job.css('.job-name::text').get(),
                "salary": job.css('.salary::text').get(),
                "company": job.css('.company-name::text').get(),
            }

竞品监控

from scrapling.fetchers import Fetcher
import json
def check_competitor(url):
    page = Fetcher.get(url)
    return {
        "products": len(page.css('.product')),
        "price_range": page.css('.price::text').getall(),
        "updated": page.css('.update-time::text').get(),
    }

Tips

1. 先测试后规模化：用scrapling shell调试选择器 2. 合理设置并发：concurrent_requests别太高，容易被封 3. 用Session复用：登录态、cookie保持用Session 4. 断点续爬：长时间爬取务必设置crawldir 5. 尊重robots.txt：合规爬取

References

基础抓取

from scrapling.fetchers import Fetcher
page = Fetcher.get('https://quotes.toscrape.com/')
quotes = page.css('.quote .text::text').getall()
print(quotes)

过反爬（Cloudflare等）

from scrapling.fetchers import StealthyFetcher
自动过Cloudflare Turnstile
page = StealthyFetcher.fetch(
    'https://目标网站',
    headless=True,
    solve_cloudflare=True
)
data = page.css('.content::text').getall()

动态页面（JS渲染）

from scrapling.fetchers import DynamicFetcher
完整浏览器渲染
page = DynamicFetcher.fetch(
    'https://spa网站',
    headless=True,
    network_idle=True  # 等网络请求完成
)

1. 先测试后规模化：用scrapling shell调试选择器 2. 合理设置并发：concurrent_requests别太高，容易被封 3. 用Session复用：登录态、cookie保持用Session 4. 断点续爬：长时间爬取务必设置crawldir 5. 尊重robots.txt：合规爬取