在电商的红海竞争中，数据是决胜的关键。特别是对于像沃尔玛这样拥有海量商品和频繁价格变动的平台，如何高效、实时地获取商品数据，成为了众多卖家、分析师和开发者面临的挑战。今天，我将带大家深入探讨如何使用Python，从基础搭建到高级优化，构建一个功能完善的沃尔玛商品数据采集系统，帮助你更好地洞察市场趋势，制定精准的商业策略。
沃尔玛爬虫完整构建指南：用Python打造高效商品数据采集系统
沃尔玛爬虫（Walmart Scraper）作为电商数据采集的重要工具，能够帮助卖家、分析师和开发者自动获取沃尔玛平台的商品信息、价格数据和市场趋势。在竞争激烈的电商环境中，掌握实时的商品数据对于制定营销策略、价格优化和竞品分析至关重要。本文将详细介绍如何使用Python构建一个功能完善的沃尔玛爬虫系统，涵盖从基础设置到高级优化的全过程。
为什么需要构建沃尔玛爬虫
在深入技术实现之前，我们先了解构建沃尔玛爬虫的核心价值。沃尔玛作为全球最大的零售商之一，其平台上包含数百万种商品，价格变化频繁，促销活动不断。对于电商从业者而言，及时获取这些数据能够：
竞品价格监控：实时跟踪竞争对手的价格策略
市场趋势分析：了解热销商品和消费者偏好
库存管理优化：基于供需数据调整采购计划
营销策略制定：根据促销信息制定相应策略
然而，手动收集这些数据不仅效率低下，而且容易出错。这就是Python沃尔玛数据抓取（Python Walmart Data Scraping）技术发挥作用的地方。
技术准备与环境搭建
- 开发环境配置 首先确保您的系统已安装Python 3.7或更高版本。我们将使用以下核心库来构建我们的沃尔玛商品信息采集器（Walmart Product Information Crawler）： # requirements.txt requests==2.31.0 beautifulsoup4==4.12.2 selenium==4.15.0 pandas==2.1.3 fake-useragent==1.4.0 python-dotenv==1.0.0 安装依赖： pip install -r requirements.txt
- 基础项目结构 walmart_scraper/ ├── config/ │ ├── init.py │ └── settings.py ├── scrapers/ │ ├── init.py │ ├── base_scraper.py │ └── walmart_scraper.py ├── utils/ │ ├── init.py │ ├── proxy_handler.py │ └── data_processor.py ├── data/ │ └── output/ ├── main.py └── requirements.txt 核心爬虫组件开发
- 基础爬虫类设计 让我们从创建一个基础的爬虫类开始： # scrapers/base_scraper.py import requests import time import random from fake_useragent import UserAgent from bs4 import BeautifulSoup import logging
class BaseScraper:
def init(self):
self.session = requests.Session()
self.ua = UserAgent()
self.setup_logging()
def setup_logging(self):
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('scraper.log'),
logging.StreamHandler()
]
)
self.logger = logging.getLogger(__name__)
def get_headers(self):
"""生成随机请求头"""
return {
'User-Agent': self.ua.random,
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Accept-Language': 'en-US,en;q=0.5',
'Accept-Encoding': 'gzip, deflate',
'Connection': 'keep-alive',
'Upgrade-Insecure-Requests': '1',
}
def random_delay(self, min_delay=1, max_delay=3):
"""随机延迟防止被识别"""
delay = random.uniform(min_delay, max_delay)
time.sleep(delay)
def make_request(self, url, max_retries=3):
"""发送HTTP请求with重试机制"""
for attempt in range(max_retries):
try:
headers = self.get_headers()
response = self.session.get(url, headers=headers, timeout=10)
response.raise_for_status()
return response
except requests.RequestException as e:
self.logger.warning(f"请求失败 (尝试 {attempt + 1}/{max_retries}): {e}")
if attempt < max_retries - 1:
self.random_delay(2, 5)
else:
self.logger.error(f"所有请求尝试失败: {url}")
raise
- 沃尔玛专用爬虫实现 接下来实现专门针对沃尔玛的爬虫类： # scrapers/walmart_scraper.py from .base_scraper import BaseScraper from bs4 import BeautifulSoup import json import re from urllib.parse import urljoin, urlparse, parse_qs
class WalmartScraper(BaseScraper):
def init(self):
super().init()
self.base_url = "https://www.walmart.com"
def search_products(self, keyword, page=1, max_results=50):
"""搜索商品列表"""
search_url = f"{self.base_url}/search?q={keyword}&page={page}"
try:
response = self.make_request(search_url)
soup = BeautifulSoup(response.content, 'html.parser')
# 提取商品列表
products = self.extract_product_list(soup)
self.logger.info(f"成功提取 {len(products)} 个商品信息")
return products[:max_results]
except Exception as e:
self.logger.error(f"搜索商品失败: {e}")
return []
def extract_product_list(self, soup):
"""从搜索结果页面提取商品信息"""
products = []
# 查找商品容器
product_containers = soup.find_all('div', {'data-automation-id': 'product-tile'})
for container in product_containers:
try:
product_data = self.extract_single_product(container)
if product_data:
products.append(product_data)
except Exception as e:
self.logger.warning(f"提取单个商品失败: {e}")
continue
return products
def extract_single_product(self, container):
"""提取单个商品的详细信息"""
product = {}
try:
# 商品标题
title_elem = container.find('span', {'data-automation-id': 'product-title'})
product['title'] = title_elem.get_text(strip=True) if title_elem else ''
# 价格信息
price_elem = container.find('div', {'data-automation-id': 'product-price'})
if price_elem:
price_text = price_elem.get_text(strip=True)
product['price'] = self.clean_price(price_text)
# 商品链接
link_elem = container.find('a', href=True)
if link_elem:
product['url'] = urljoin(self.base_url, link_elem['href'])
# 从URL中提取商品ID
product['product_id'] = self.extract_product_id(product['url'])
# 评分信息
rating_elem = container.find('span', class_=re.compile(r'.*rating.*'))
if rating_elem:
rating_text = rating_elem.get('aria-label', '')
product['rating'] = self.extract_rating(rating_text)
# 图片
img_elem = container.find('img')
if img_elem:
product['image_url'] = img_elem.get('src', '')
# 供应商信息
seller_elem = container.find('span', string=re.compile(r'Sold by'))
if seller_elem:
product['seller'] = seller_elem.get_text(strip=True)
return product if product.get('title') else None
except Exception as e:
self.logger.warning(f"解析商品数据失败: {e}")
return None
def get_product_details(self, product_url):
"""获取商品详细页面信息"""
try:
response = self.make_request(product_url)
soup = BeautifulSoup(response.content, 'html.parser')
details = {}
# 从script标签中提取JSON数据
script_tags = soup.find_all('script', {'type': 'application/ld+json'})
for script in script_tags:
try:
json_data = json.loads(script.string)
if '@type' in json_data and json_data['@type'] == 'Product':
details.update(self.parse_product_json(json_data))
break
except json.JSONDecodeError:
continue
# 商品描述
desc_elem = soup.find('div', {'data-automation-id': 'product-highlights'})
if desc_elem:
details['description'] = desc_elem.get_text(strip=True)
# 库存状态
stock_elem = soup.find('div', {'data-automation-id': 'fulfillment-section'})
if stock_elem:
details['in_stock'] = 'in stock' in stock_elem.get_text().lower()
return details
except Exception as e:
self.logger.error(f"获取商品详情失败: {e}")
return {}
def clean_price(self, price_text):
"""清理价格文本"""
if not price_text:
return None
# 提取数字和小数点
price_match = re.search(r'\$?(\d+\.?\d*)', price_text.replace(',', ''))
return float(price_match.group(1)) if price_match else None
def extract_product_id(self, url):
"""从URL中提取商品ID"""
try:
parsed_url = urlparse(url)
path_parts = parsed_url.path.split('/')
for part in path_parts:
if part.isdigit():
return part
except:
pass
return None
def extract_rating(self, rating_text):
"""提取评分数值"""
rating_match = re.search(r'(\d+\.?\d*)', rating_text)
return float(rating_match.group(1)) if rating_match else None
def parse_product_json(self, json_data):
"""解析产品JSON数据"""
details = {}
if 'name' in json_data:
details['full_name'] = json_data['name']
if 'offers' in json_data:
offer = json_data['offers']
if isinstance(offer, list):
offer = offer[0]
details['availability'] = offer.get('availability', '')
details['currency'] = offer.get('priceCurrency', 'USD')
if 'price' in offer:
details['detailed_price'] = float(offer['price'])
if 'aggregateRating' in json_data:
rating_data = json_data['aggregateRating']
details['average_rating'] = float(rating_data.get('ratingValue', 0))
details['review_count'] = int(rating_data.get('reviewCount', 0))
return details
应对反爬虫策略
- IP代理池集成 现代电商网站都部署了先进的反爬虫系统。为了构建稳定的自动化沃尔玛爬虫系统（Automated Walmart Scraping System），我们需要集成IP代理池： # utils/proxy_handler.py import requests import random import threading from queue import Queue import time
class ProxyHandler:
def init(self, proxy_list=None):
self.proxy_queue = Queue()
self.failed_proxies = set()
self.proxy_stats = {}
self.lock = threading.Lock()
if proxy_list:
self.load_proxies(proxy_list)
def load_proxies(self, proxy_list):
"""加载代理列表"""
for proxy in proxy_list:
self.proxy_queue.put(proxy)
self.proxy_stats[proxy] = {'success': 0, 'failed': 0}
def get_proxy(self):
"""获取可用代理"""
with self.lock:
while not self.proxy_queue.empty():
proxy = self.proxy_queue.get()
if proxy not in self.failed_proxies:
return proxy
return None
def test_proxy(self, proxy, test_url="http://httpbin.org/ip"):
"""测试代理是否可用"""
try:
proxies = {
'http': f'http://{proxy}',
'https': f'https://{proxy}'
}
response = requests.get(
test_url,
proxies=proxies,
timeout=10,
headers={'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'}
)
if response.status_code == 200:
self.mark_proxy_success(proxy)
return True
except requests.RequestException:
pass
self.mark_proxy_failed(proxy)
return False
def mark_proxy_success(self, proxy):
"""标记代理成功"""
with self.lock:
if proxy in self.proxy_stats:
self.proxy_stats[proxy]['success'] += 1
# 成功的代理重新放回队列
self.proxy_queue.put(proxy)
def mark_proxy_failed(self, proxy):
"""标记代理失败"""
with self.lock:
if proxy in self.proxy_stats:
self.proxy_stats[proxy]['failed'] += 1
# 失败次数过多的代理加入黑名单
if self.proxy_stats[proxy]['failed'] > 3:
self.failed_proxies.add(proxy)
集成代理的爬虫类
class WalmartScraperWithProxy(WalmartScraper):
def init(self, proxy_list=None):
super().init()
self.proxy_handler = ProxyHandler(proxy_list) if proxy_list else None
def make_request_with_proxy(self, url, max_retries=3):
"""使用代理发送请求"""
for attempt in range(max_retries):
proxy = self.proxy_handler.get_proxy() if self.proxy_handler else None
try:
headers = self.get_headers()
proxies = None
if proxy:
proxies = {
'http': f'http://{proxy}',
'https': f'https://{proxy}'
}
response = self.session.get(
url,
headers=headers,
proxies=proxies,
timeout=15
)
response.raise_for_status()
if proxy and self.proxy_handler:
self.proxy_handler.mark_proxy_success(proxy)
return response
except requests.RequestException as e:
if proxy and self.proxy_handler:
self.proxy_handler.mark_proxy_failed(proxy)
self.logger.warning(f"代理请求失败 {proxy}: {e}")
self.random_delay(3, 7)
raise Exception(f"所有代理请求都失败: {url}")
- 验证码识别与处理 沃尔玛网站可能会出现验证码挑战。我们需要集成验证码识别服务： # utils/captcha_solver.py import base64 import requests from PIL import Image import io
class CaptchaSolver:
def init(self, api_key=None, service='2captcha'):
self.api_key = api_key
self.service = service
self.base_url = 'http://2captcha.com' if service == '2captcha' else None
def solve_image_captcha(self, image_data):
"""解决图片验证码"""
if not self.api_key:
self.logger.warning("未配置验证码服务API密钥")
return None
try:
# 提交验证码
submit_url = f"{self.base_url}/in.php"
files = {'file': ('captcha.png', image_data, 'image/png')}
data = {
'key': self.api_key,
'method': 'post'
}
response = requests.post(submit_url, files=files, data=data)
result = response.text
if 'OK|' in result:
captcha_id = result.split('|')[1]
return self.get_captcha_result(captcha_id)
except Exception as e:
self.logger.error(f"验证码识别失败: {e}")
return None
def get_captcha_result(self, captcha_id, max_wait=120):
"""获取验证码识别结果"""
result_url = f"{self.base_url}/res.php"
for _ in range(max_wait // 5):
try:
response = requests.get(result_url, params={
'key': self.api_key,
'action': 'get',
'id': captcha_id
})
result = response.text
if result == 'CAPCHA_NOT_READY':
time.sleep(5)
continue
elif 'OK|' in result:
return result.split('|')[1]
else:
break
except Exception as e:
self.logger.error(f"获取验证码结果失败: {e}")
break
return None
数据处理与存储
- 数据清洗和标准化 # utils/data_processor.py import pandas as pd import re from datetime import datetime import json
class DataProcessor:
def init(self):
self.cleaned_data = []
def clean_product_data(self, raw_products):
"""清洗商品数据"""
cleaned_products = []
for product in raw_products:
cleaned_product = {}
# 标题清洗
title = product.get('title', '').strip()
cleaned_product['title'] = self.clean_title(title)
# 价格标准化
price = product.get('price')
cleaned_product['price_usd'] = self.standardize_price(price)
# URL标准化
url = product.get('url', '')
cleaned_product['product_url'] = self.clean_url(url)
# 评分标准化
rating = product.get('rating')
cleaned_product['rating_score'] = self.standardize_rating(rating)
# 添加时间戳
cleaned_product['scraped_at'] = datetime.now().isoformat()
# 商品ID
cleaned_product['product_id'] = product.get('product_id', '')
# 图片URL
cleaned_product['image_url'] = product.get('image_url', '')
# 供应商
cleaned_product['seller'] = product.get('seller', 'Walmart')
if cleaned_product['title']: # 只保留有标题的商品
cleaned_products.append(cleaned_product)
return cleaned_products
def clean_title(self, title):
"""清洗商品标题"""
if not title:
return ''
# 移除多余空白字符
title = re.sub(r'\s+', ' ', title).strip()
# 移除特殊字符但保留基本标点
title = re.sub(r'[^\w\s\-\(\)\[\]&,.]', '', title)
return title[:200] # 限制长度
def standardize_price(self, price):
"""标准化价格"""
if price is None:
return None
if isinstance(price, str):
# 移除货币符号和逗号
price_clean = re.sub(r'[$,]', '', price)
try:
return float(price_clean)
except ValueError:
return None
return float(price) if price else None
def clean_url(self, url):
"""清洗URL"""
if not url:
return ''
# 移除追踪参数
if '?' in url:
base_url = url.split('?')[0]
return base_url
return url
def standardize_rating(self, rating):
"""标准化评分"""
if rating is None:
return None
try:
rating_float = float(rating)
# 确保评分在0-5范围内
return max(0, min(5, rating_float))
except (ValueError, TypeError):
return None
def save_to_excel(self, products, filename):
"""保存到Excel文件"""
if not products:
self.logger.warning("没有数据要保存")
return
df = pd.DataFrame(products)
# 重新排序列
column_order = [
'product_id', 'title', 'price_usd', 'rating_score',
'seller', 'product_url', 'image_url', 'scraped_at'
]
df = df.reindex(columns=column_order)
# 保存到Excel
with pd.ExcelWriter(filename, engine='openpyxl') as writer:
df.to_excel(writer, sheet_name='Products', index=False)
# 添加统计信息
stats_df = pd.DataFrame({
'统计项': ['总商品数', '平均价格', '最高价格', '最低价格', '平均评分'],
'数值': [
len(df),
df['price_usd'].mean() if df['price_usd'].notna().any() else 0,
df['price_usd'].max() if df['price_usd'].notna().any() else 0,
df['price_usd'].min() if df['price_usd'].notna().any() else 0,
df['rating_score'].mean() if df['rating_score'].notna().any() else 0
]
})
stats_df.to_excel(writer, sheet_name='Statistics', index=False)
print(f"数据已保存到 {filename}")
def save_to_json(self, products, filename):
"""保存到JSON文件"""
with open(filename, 'w', encoding='utf-8') as f:
json.dump(products, f, ensure_ascii=False, indent=2)
print(f"JSON数据已保存到 {filename}")
- 完整的主程序实现 现在让我们把所有组件整合到一个完整的沃尔玛商品列表抓取工具（Walmart Product List Scraping Tool）中： # main.py import argparse import sys import os from datetime import datetime from scrapers.walmart_scraper import WalmartScraperWithProxy from utils.data_processor import DataProcessor from utils.captcha_solver import CaptchaSolver import logging
class WalmartScrapingManager:
def init(self, proxy_list=None, captcha_api_key=None):
self.scraper = WalmartScraperWithProxy(proxy_list)
self.data_processor = DataProcessor()
self.captcha_solver = CaptchaSolver(captcha_api_key) if captcha_api_key else None
self.logger = logging.getLogger(name)
def scrape_products(self, keywords, max_products_per_keyword=50, output_format='excel'):
"""批量抓取商品数据"""
all_products = []
for keyword in keywords:
self.logger.info(f"开始抓取关键词: {keyword}")
try:
# 搜索商品列表
products = self.scraper.search_products(
keyword=keyword,
max_results=max_products_per_keyword
)
# 获取详细信息
detailed_products = []
for i, product in enumerate(products):
if product.get('url'):
try:
details = self.scraper.get_product_details(product['url'])
product.update(details)
detailed_products.append(product)
# 添加关键词标签
product['search_keyword'] = keyword
self.logger.info(f"已处理 {i+1}/{len(products)} 个商品")
# 随机延迟
self.scraper.random_delay(1, 3)
except Exception as e:
self.logger.warning(f"获取商品详情失败: {e}")
continue
all_products.extend(detailed_products)
self.logger.info(f"关键词 '{keyword}' 抓取完成，获得 {len(detailed_products)} 个商品")
except Exception as e:
self.logger.error(f"抓取关键词 '{keyword}' 失败: {e}")
continue
# 数据清洗
cleaned_products = self.data_processor.clean_product_data(all_products)
# 保存数据
self.save_results(cleaned_products, output_format)
return cleaned_products
def save_results(self, products, output_format):
"""保存抓取结果"""
if not products:
self.logger.warning("没有数据需要保存")
return
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
if output_format.lower() == 'excel':
filename = f"data/output/walmart_products_{timestamp}.xlsx"
self.data_processor.save_to_excel(products, filename)
elif output_format.lower() == 'json':
filename = f"data/output/walmart_products_{timestamp}.json"
self.data_processor.save_to_json(products, filename)
else:
# 同时保存两种格式
excel_filename = f"data/output/walmart_products_{timestamp}.xlsx"
json_filename = f"data/output/walmart_products_{timestamp}.json"
self.data_processor.save_to_excel(products, excel_filename)
self.data_processor.save_to_json(products, json_filename)
def main():
parser = argparse.ArgumentParser(description='沃尔玛商品数据抓取工具')
parser.add_argument('--keywords', nargs='+', required=True, help='搜索关键词列表')
parser.add_argument('--max-products', type=int, default=50, help='每个关键词最大抓取商品数')
parser.add_argument('--output-format', choices=['excel', 'json', 'both'], default='excel', help='输出格式')
parser.add_argument('--proxy-file', help='代理列表文件路径')
parser.add_argument('--captcha-api-key', help='验证码识别服务API密钥')
args = parser.parse_args()
# 确保输出目录存在
os.makedirs('data/output', exist_ok=True)
# 加载代理列表
proxy_list = None
if args.proxy_file and os.path.exists(args.proxy_file):
with open(args.proxy_file, 'r') as f:
proxy_list = [line.strip() for line in f if line.strip()]
# 创建爬虫管理器
scraper_manager = WalmartScrapingManager(
proxy_list=proxy_list,
captcha_api_key=args.captcha_api_key
)
# 开始抓取
try:
products = scraper_manager.scrape_products(
keywords=args.keywords,
max_products_per_keyword=args.max_products,
output_format=args.output_format
)
print(f"\n抓取完成！总共获得 {len(products)} 个商品数据")
# 显示统计信息
if products:
prices = [p['price_usd'] for p in products if p.get('price_usd')]
ratings = [p['rating_score'] for p in products if p.get('rating_score')]
print(f"价格统计: 平均 ${sum(prices)/len(prices):.2f}" if prices else "无价格数据")
print(f"评分统计: 平均 {sum(ratings)/len(ratings):.2f}" if ratings else "无评分数据")
except KeyboardInterrupt:
print("\n用户中断抓取过程")
except Exception as e:
print(f"抓取过程出现错误: {e}")
sys.exit(1)
if name == "main":
main()
常见挑战与解决方案
- 动态内容加载 现代电商网站大量使用JavaScript动态加载内容。对于这种情况，我们需要使用Selenium来处理： # scrapers/selenium_scraper.py from selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.chrome.options import Options import undetected_chromedriver as uc
class SeleniumWalmartScraper:
def init(self, headless=True, proxy=None):
self.setup_driver(headless, proxy)
def setup_driver(self, headless=True, proxy=None):
"""配置浏览器驱动"""
options = uc.ChromeOptions()
if headless:
options.add_argument('--headless')
# 反检测设置
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument('--disable-blink-features=AutomationControlled')
options.add_experimental_option("excludeSwitches", ["enable-automation"])
options.add_experimental_option('useAutomationExtension', False)
# 代理设置
if proxy:
options.add_argument(f'--proxy-server={proxy}')
# 用户代理
options.add_argument('--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36')
self.driver = uc.Chrome(options=options)
# 执行反检测脚本
self.driver.execute_script("Object.defineProperty(navigator, 'webdriver', {get: () => undefined})")
def scrape_with_javascript(self, url, wait_selector=None):
"""使用Selenium抓取动态内容"""
try:
self.driver.get(url)
# 等待特定元素加载
if wait_selector:
WebDriverWait(self.driver, 10).until(
EC.presence_of_element_located((By.CSS_SELECTOR, wait_selector))
)
# 滚动页面触发懒加载
self.scroll_page()
# 获取页面源码
html_content = self.driver.page_source
return html_content
except Exception as e:
print(f"Selenium抓取失败: {e}")
return None
def scroll_page(self):
"""滚动页面以触发懒加载"""
last_height = self.driver.execute_script("return document.body.scrollHeight")
while True:
# 滚动到页面底部
self.driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
# 等待新内容加载
time.sleep(2)
# 计算新的页面高度
new_height = self.driver.execute_script("return document.body.scrollHeight")
if new_height == last_height:
break
last_height = new_height
def close(self):
"""关闭浏览器"""
if hasattr(self, 'driver'):
self.driver.quit()
- 分布式爬虫架构 对于大规模数据抓取，我们可以实现分布式爬虫： # distributed/task_manager.py import redis import json import uuid from datetime import datetime, timedelta
class TaskManager:
def init(self, redis_host='localhost', redis_port=6379, redis_db=0):
self.redis_client = redis.Redis(host=redis_host, port=redis_port, db=redis_db)
self.task_queue = 'walmart_scrape_tasks'
self.result_queue = 'walmart_scrape_results'
def add_task(self, keyword, max_products=50, priority=1):
"""添加抓取任务"""
task_id = str(uuid.uuid4())
task_data = {
'task_id': task_id,
'keyword': keyword,
'max_products': max_products,
'priority': priority,
'created_at': datetime.now().isoformat(),
'status': 'pending'
}
# 使用优先级队列
self.redis_client.zadd(self.task_queue, {json.dumps(task_data): priority})
return task_id
def get_task(self):
"""获取待处理任务"""
# 获取最高优先级任务
task_data = self.redis_client.zpopmax(self.task_queue)
if task_data:
task_json = task_data[0][0].decode('utf-8')
return json.loads(task_json)
return None
def save_result(self, task_id, products, status='completed'):
"""保存抓取结果"""
result_data = {
'task_id': task_id,
'products': products,
'status': status,
'completed_at': datetime.now().isoformat(),
'product_count': len(products)
}
self.redis_client.lpush(self.result_queue, json.dumps(result_data))
def get_results(self, limit=10):
"""获取抓取结果"""
results = []
for _ in range(limit):
result_data = self.redis_client.rpop(self.result_queue)
if result_data:
results.append(json.loads(result_data.decode('utf-8')))
else:
break
return results
distributed/worker.py
import time
import logging
from task_manager import TaskManager
from scrapers.walmart_scraper import WalmartScraperWithProxy
class ScrapingWorker:
def init(self, worker_id, proxy_list=None):
self.worker_id = worker_id
self.task_manager = TaskManager()
self.scraper = WalmartScraperWithProxy(proxy_list)
self.logger = logging.getLogger(f'Worker-{worker_id}')
def run(self):
"""工作进程主循环"""
self.logger.info(f"工作进程 {self.worker_id} 启动")
while True:
try:
# 获取任务
task = self.task_manager.get_task()
if task:
self.logger.info(f"处理任务: {task['task_id']}")
self.process_task(task)
else:
# 没有任务时休眠
time.sleep(5)
except KeyboardInterrupt:
self.logger.info("工作进程停止")
break
except Exception as e:
self.logger.error(f"工作进程异常: {e}")
time.sleep(10)
def process_task(self, task):
"""处理单个抓取任务"""
try:
keyword = task['keyword']
max_products = task['max_products']
# 执行抓取
products = self.scraper.search_products(keyword, max_results=max_products)
# 保存结果
self.task_manager.save_result(
task['task_id'],
products,
'completed'
)
self.logger.info(f"任务 {task['task_id']} 完成，抓取 {len(products)} 个商品")
except Exception as e:
self.logger.error(f"任务处理失败: {e}")
self.task_manager.save_result(
task['task_id'],
[],
'failed'
)
- 监控和告警系统 # monitoring/scraper_monitor.py import psutil import time import smtplib from email.mime.text import MimeText from datetime import datetime, timedelta
class ScraperMonitor:
def init(self, email_config=None):
self.email_config = email_config
self.performance_log = []
def monitor_performance(self):
"""监控系统性能"""
cpu_percent = psutil.cpu_percent(interval=1)
memory_percent = psutil.virtual_memory().percent
disk_percent = psutil.disk_usage('/').percent
performance_data = {
'timestamp': datetime.now(),
'cpu_percent': cpu_percent,
'memory_percent': memory_percent,
'disk_percent': disk_percent
}
self.performance_log.append(performance_data)
# 检查是否需要告警
if cpu_percent > 80 or memory_percent > 80:
self.send_alert(f"系统资源使用率过高: CPU {cpu_percent}%, 内存 {memory_percent}%")
return performance_data
def send_alert(self, message):
"""发送告警邮件"""
if not self.email_config:
print(f"告警: {message}")
return
try:
msg = MimeText(f"沃尔玛爬虫系统告警\n\n{message}\n\n时间: {datetime.now()}")
msg['Subject'] = '爬虫系统告警'
msg['From'] = self.email_config['from']
msg['To'] = self.email_config['to']
server = smtplib.SMTP(self.email_config['smtp_server'], self.email_config['smtp_port'])
server.starttls()
server.login(self.email_config['username'], self.email_config['password'])
server.send_message(msg)
server.quit()
print(f"告警邮件已发送: {message}")
except Exception as e:
print(f"发送告警邮件失败: {e}")
高级优化技巧
- 智能重试机制 # utils/retry_handler.py import time import random from functools import wraps
def smart_retry(max_retries=3, base_delay=1, backoff_factor=2, jitter=True):
"""智能重试装饰器"""
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
last_exception = None
for attempt in range(max_retries):
try:
return func(*args, **kwargs)
except Exception as e:
last_exception = e
if attempt < max_retries - 1:
# 计算延迟时间
delay = base_delay * (backoff_factor ** attempt)
# 添加随机抖动
if jitter:
delay += random.uniform(0, delay * 0.1)
print(f"重试 {attempt + 1}/{max_retries}，{delay:.2f}秒后重试")
time.sleep(delay)
else:
print(f"所有重试都失败，最后异常: {e}")
raise last_exception
return wrapper
return decorator
- 数据去重和缓存 # utils/cache_manager.py import hashlib import json import os from datetime import datetime, timedelta
class CacheManager:
def init(self, cache_dir='cache', expire_hours=24):
self.cache_dir = cache_dir
self.expire_hours = expire_hours
os.makedirs(cache_dir, exist_ok=True)
def get_cache_key(self, url):
"""生成缓存键"""
return hashlib.md5(url.encode()).hexdigest()
def get_cache_file(self, cache_key):
"""获取缓存文件路径"""
return os.path.join(self.cache_dir, f"{cache_key}.json")
def is_cache_valid(self, cache_file):
"""检查缓存是否有效"""
if not os.path.exists(cache_file):
return False
file_time = datetime.fromtimestamp(os.path.getmtime(cache_file))
expire_time = datetime.now() - timedelta(hours=self.expire_hours)
return file_time > expire_time
def get_cached_data(self, url):
"""获取缓存数据"""
cache_key = self.get_cache_key(url)
cache_file = self.get_cache_file(cache_key)
if self.is_cache_valid(cache_file):
try:
with open(cache_file, 'r', encoding='utf-8') as f:
return json.load(f)
except Exception:
pass
return None
def save_to_cache(self, url, data):
"""保存数据到缓存"""
cache_key = self.get_cache_key(url)
cache_file = self.get_cache_file(cache_key)
try:
with open(cache_file, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=2)
except Exception as e:
print(f"保存缓存失败: {e}")
class DataDeduplicator:
def init(self):
self.seen_products = set()
def is_duplicate(self, product):
"""检查商品是否重复"""
# 使用商品ID和标题创建唯一标识
identifier = f"{product.get('product_id', '')}-{product.get('title', '')}"
identifier_hash = hashlib.md5(identifier.encode()).hexdigest()
if identifier_hash in self.seen_products:
return True
self.seen_products.add(identifier_hash)
return False
def deduplicate_products(self, products):
"""去重商品列表"""
unique_products = []
for product in products:
if not self.is_duplicate(product):
unique_products.append(product)
print(f"去重前: {len(products)} 个商品，去重后: {len(unique_products)} 个商品")
return unique_products
性能优化与扩展
- 异步并发处理 # async_scraper.py import asyncio import aiohttp from aiohttp import ClientTimeout import async_timeout
class AsyncWalmartScraper:
def init(self, max_concurrent=10):
self.max_concurrent = max_concurrent
self.semaphore = asyncio.Semaphore(max_concurrent)
async def fetch_page(self, session, url):
"""异步获取页面"""
async with self.semaphore:
try:
timeout = ClientTimeout(total=30)
async with session.get(url, timeout=timeout) as response:
if response.status == 200:
return await response.text()
else:
print(f"HTTP错误 {response.status}: {url}")
except Exception as e:
print(f"请求失败: {e}")
return None
async def scrape_multiple_urls(self, urls):
"""并发抓取多个URL"""
async with aiohttp.ClientSession() as session:
tasks = [self.fetch_page(session, url) for url in urls]
results = await asyncio.gather(*tasks, return_exceptions=True)
# 过滤成功的结果
successful_results = [r for r in results if isinstance(r, str)]
print(f"成功抓取 {len(successful_results)}/{len(urls)} 个页面")
return successful_results
实际应用场景示例
使用示例
基本使用
python main.py --keywords "wireless headphones" "bluetooth speaker" --max-products 30
使用代理
python main.py --keywords "laptop" --proxy-file proxies.txt --output-format both
大批量抓取
python main.py --keywords "electronics" "home garden" "sports" --max-products 100 --output-format json
配置代理文件示例 (proxies.txt)
192.168.1.100:8080
203.123.45.67:3128
104.248.63.15:30588
167.172.180.46:41258
为什么选择专业的API服务
虽然我们已经详细介绍了如何构建一个功能完善的沃尔玛爬虫系统，但在实际业务应用中，构建和维护自己的爬虫系统面临诸多挑战：
技术维护成本高：电商网站频繁更新反爬虫策略，需要持续投入技术资源进行适配和优化。
法律合规风险：不当的爬虫行为可能面临法律风险，需要专业的合规指导。
基础设施投入大：稳定的代理服务、验证码识别、分布式架构都需要大量资金投入。
数据质量保证难：确保数据的准确性、完整性和时效性需要专业的质量控制体系。
Pangolin Scrape API：专业的电商数据解决方案
如果您专注于沃尔玛运营和选品，希望将专业的数据采集工作交给专业团队，Pangolin Scrape API是您的理想选择。
核心优势
免维护智能解析：Pangolin Scrape API采用智能识别算法，自动适配沃尔玛等电商平台的页面结构变化，开发者无需关注DOM结构更新。
丰富的数据字段：支持抓取商品ID、图片、标题、评分、评论数、尺寸、颜色、描述、价格、库存状态等全面的商品信息。
多种调用方式：提供同步和异步两种API调用方式，满足不同业务场景需求。
快速集成示例
使用Pangolin Scrape API抓取沃尔玛商品信息非常简单：
import requests
import json
认证获取token
auth_url = "http://scrapeapi.pangolinfo.com/api/v1/auth"
auth_data = {
"email": "your_email@gmail.com",
"password": "your_password"
}
response = requests.post(auth_url, json=auth_data)
token = response.json()['data']
抓取沃尔玛商品详情
scrape_url = "http://scrapeapi.pangolinfo.com/api/v1"
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {token}"
}
scrape_data = {
"url": "https://www.walmart.com/ip/your-product-url",
"parserName": "walmProductDetail",
"formats": ["json"]
}
result = requests.post(scrape_url, headers=headers, json=scrape_data)
product_data = result.json()
服务特色
7x24小时稳定服务：专业运维团队保障服务稳定性
智能反爬虫应对：内置IP轮换、请求头随机化等反检测机制
数据质量保证：多重验证确保数据准确性和完整性
灵活的输出格式：支持JSON、Markdown、原始HTML多种格式
按需付费：根据实际使用量付费，降低成本
通过Pangolin Scrape API，您可以将更多精力投入到核心业务逻辑中，而无需担心复杂的技术实现和维护工作。
总结
本文全面介绍了如何使用Python构建一个专业级的沃尔玛爬虫系统，涵盖了从基础环境搭建到高级优化技巧的完整流程。我们详细讲解了应对反爬虫策略、数据处理、分布式架构等关键技术点，并提供了丰富的代码示例。
构建自己的爬虫系统虽然能够深度定制，但也面临着技术维护、合规风险、成本投入等诸多挑战。对于专注业务发展的企业而言，选择像Pangolin Scrape API这样的专业服务，能够更高效地获取所需数据，同时避免技术陷阱。
无论选择自建还是使用专业服务，关键是要根据自己的业务需求、技术能力和资源投入来做出明智的决策。数据驱动的电商时代，掌握准确、及时的市场信息就是掌握了竞争的主动权。
正如古人云："工欲善其事，必先利其器"——选择合适的数据采集方案，让您在电商征途中事半功倍，决胜千里。
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