Pangolin Scrape API完全指南：5分钟快速接入亚马逊数据采集

前言

在电商数据分析和竞品监控领域，亚马逊数据采集一直是开发者面临的技术挑战。本文将深入解析Pangolin Scrape API的技术架构和实现原理，提供完整的集成方案和最佳实践。

技术背景与挑战分析

传统爬虫方案的技术瓶颈

1. 反爬虫机制的技术对抗

# 传统爬虫常见的反爬虫检测点
import requests
from selenium import webdriver

# 容易被检测的请求特征
headers = {
    'User-Agent': 'Mozilla/5.0...',  # 固定UA容易被识别
    'Accept': 'text/html,application/xhtml+xml',
}

# IP封禁风险
response = requests.get('https://amazon.com/dp/B08N5WRWNW', headers=headers)
# 问题：单IP请求频率过高，容易触发封禁

2. 动态内容渲染的技术难点

// Amazon页面的动态加载机制
window.addEventListener('load', function() {
    // 价格信息通过Ajax异步加载
    fetch('/api/pricing-display-label')
        .then(response => response.json())
        .then(data => {
            document.getElementById('price').innerHTML = data.price;
        });
});

3. 数据结构解析的复杂性

# Amazon页面结构的复杂性示例
from bs4 import BeautifulSoup

html = """
<div id="feature-bullets" class="a-section a-spacing-medium a-spacing-top-small">
    <ul class="a-unordered-list a-vertical a-spacing-mini">
        <!-- 产品特性可能有多种嵌套结构 -->
        <li><span class="a-list-item">特性1</span></li>
        <li><span class="a-list-item">特性2</span></li>
    </ul>
</div>
"""

# 解析逻辑需要处理多种可能的DOM结构
soup = BeautifulSoup(html, 'html.parser')
features = []
for li in soup.find_all('li'):
    span = li.find('span', class_='a-list-item')
    if span:
        features.append(span.get_text().strip())

Pangolin Scrape API技术架构深度解析

1. 分布式代理池技术

Pangolin采用全球分布式代理池架构，通过智能路由算法实现高可用性：

# Pangolin内部代理池管理机制（示意）
class ProxyPool:
    def __init__(self):
        self.proxy_regions = {
            'us-east': ['proxy1.pangolin.com:8001', 'proxy2.pangolin.com:8001'],
            'us-west': ['proxy3.pangolin.com:8001', 'proxy4.pangolin.com:8001'],
            'eu-central': ['proxy5.pangolin.com:8001', 'proxy6.pangolin.com:8001']
        }
        self.health_status = {}
    
    def get_optimal_proxy(self, target_region='us'):
        """智能选择最优代理节点"""
        available_proxies = self.proxy_regions.get(f'{target_region}-east', [])
        return self.select_by_latency_and_success_rate(available_proxies)
    
    def rotate_proxy(self, failed_proxy):
        """代理轮换机制"""
        self.mark_proxy_failed(failed_proxy)
        return self.get_next_available_proxy()

2. 智能反检测技术

# 浏览器指纹随机化技术
class BrowserFingerprint:
    def __init__(self):
        self.user_agents = [
            'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
            'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36',
            'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36'
        ]
        self.screen_resolutions = ['1920x1080', '1366x768', '1440x900']
        self.languages = ['en-US,en;q=0.9', 'en-GB,en;q=0.8']
    
    def generate_headers(self):
        """生成随机化请求头"""
        return {
            'User-Agent': random.choice(self.user_agents),
            'Accept-Language': random.choice(self.languages),
            'Accept-Encoding': 'gzip, deflate, br',
            'DNT': '1',
            'Connection': 'keep-alive',
            'Upgrade-Insecure-Requests': '1',
        }

3. 数据解析引擎

# Pangolin的智能数据解析引擎
class AmazonDataParser:
    def __init__(self):
        self.selectors = {
            'title': [
                '#productTitle',
                '.product-title',
                'h1.a-size-large'
            ],
            'price': [
                '.a-price-whole',
                '#price_inside_buybox',
                '.a-offscreen'
            ],
            'rating': [
                '.a-icon-alt',
                '[data-hook="average-star-rating"]'
            ]
        }
    
    def extract_product_data(self, html_content):
        """多策略数据提取"""
        soup = BeautifulSoup(html_content, 'html.parser')
        result = {}
        
        for field, selectors in self.selectors.items():
            for selector in selectors:
                element = soup.select_one(selector)
                if element:
                    result[field] = self.clean_text(element.get_text())
                    break
        
        return result
    
    def clean_text(self, text):
        """文本清洗和标准化"""
        import re
        text = re.sub(r'\s+', ' ', text.strip())
        text = re.sub(r'[^\w\s\.\$\-\+\(\)]', '', text)
        return text

快速集成指南

1. 环境准备

# 创建虚拟环境
python -m venv pangolin_env
source pangolin_env/bin/activate  # Linux/Mac
# pangolin_env\Scripts\activate  # Windows

# 安装依赖
pip install requests pandas beautifulsoup4 lxml

2. API密钥配置

# config.py
import os
from dataclasses import dataclass

@dataclass
class PangolinConfig:
    api_key: str = os.getenv('PANGOLIN_API_KEY', 'your_api_key_here')
    base_url: str = 'https://api.pangolinfo.com'
    timeout: int = 30
    max_retries: int = 3
    
    def __post_init__(self):
        if self.api_key == 'your_api_key_here':
            raise ValueError("请设置有效的API密钥")

# 使用环境变量管理敏感信息
# export PANGOLIN_API_KEY="your_actual_api_key"

3. 同步API调用实现

# pangolin_sync_client.py
import requests
import time
from typing import Dict, List, Optional
from config import PangolinConfig

class PangolinSyncClient:
    def __init__(self, config: PangolinConfig):
        self.config = config
        self.session = requests.Session()
        self.session.headers.update({
            'Authorization': f'Bearer {config.api_key}',
            'Content-Type': 'application/json'
        })
    
    def scrape_product(self, asin: str, marketplace: str = 'amazon.com') -> Dict:
        """同步获取产品数据"""
        url = f"{self.config.base_url}/scrape"
        payload = {
            'url': f'https://{marketplace}/dp/{asin}',
            'format': 'json',
            'parse': True
        }
        
        try:
            response = self.session.post(
                url, 
                json=payload, 
                timeout=self.config.timeout
            )
            response.raise_for_status()
            return response.json()
            
        except requests.exceptions.RequestException as e:
            print(f"请求失败: {e}")
            return {}
    
    def batch_scrape_products(self, asins: List[str], delay: float = 1.0) -> List[Dict]:
        """批量获取产品数据"""
        results = []
        
        for i, asin in enumerate(asins):
            print(f"处理第 {i+1}/{len(asins)} 个产品: {asin}")
            
            result = self.scrape_product(asin)
            if result:
                results.append(result)
            
            # 控制请求频率
            if i < len(asins) - 1:
                time.sleep(delay)
        
        return results

# 使用示例
if __name__ == "__main__":
    config = PangolinConfig()
    client = PangolinSyncClient(config)
    
    # 单个产品测试
    product_data = client.scrape_product('B08N5WRWNW')
    print(f"产品标题: {product_data.get('title', 'N/A')}")
    print(f"产品价格: {product_data.get('price', 'N/A')}")

4. 异步API高性能实现

# pangolin_async_client.py
import asyncio
import aiohttp
import time
from typing import Dict, List
from config import PangolinConfig

class PangolinAsyncClient:
    def __init__(self, config: PangolinConfig):
        self.config = config
        self.semaphore = asyncio.Semaphore(10)  # 限制并发数
    
    async def create_session(self) -> aiohttp.ClientSession:
        """创建异步HTTP会话"""
        headers = {
            'Authorization': f'Bearer {self.config.api_key}',
            'Content-Type': 'application/json'
        }
        timeout = aiohttp.ClientTimeout(total=self.config.timeout)
        return aiohttp.ClientSession(headers=headers, timeout=timeout)
    
    async def submit_scrape_task(self, session: aiohttp.ClientSession, 
                                asin: str, marketplace: str = 'amazon.com') -> str:
        """提交异步抓取任务"""
        url = f"{self.config.base_url}/async/submit"
        payload = {
            'url': f'https://{marketplace}/dp/{asin}',
            'format': 'json',
            'parse': True,
            'callback_url': 'https://your-webhook-endpoint.com/callback'  # 可选
        }
        
        async with self.semaphore:
            try:
                async with session.post(url, json=payload) as response:
                    response.raise_for_status()
                    result = await response.json()
                    return result.get('task_id')
            except Exception as e:
                print(f"提交任务失败 {asin}: {e}")
                return None
    
    async def get_task_result(self, session: aiohttp.ClientSession, 
                             task_id: str) -> Dict:
        """获取任务结果"""
        url = f"{self.config.base_url}/async/result/{task_id}"
        
        max_attempts = 30  # 最大轮询次数
        for attempt in range(max_attempts):
            try:
                async with session.get(url) as response:
                    response.raise_for_status()
                    result = await response.json()
                    
                    if result.get('status') == 'completed':
                        return result.get('data', {})
                    elif result.get('status') == 'failed':
                        print(f"任务失败: {result.get('error')}")
                        return {}
                    
                    # 等待2秒后重试
                    await asyncio.sleep(2)
                    
            except Exception as e:
                print(f"获取结果失败: {e}")
                await asyncio.sleep(2)
        
        print(f"任务超时: {task_id}")
        return {}
    
    async def batch_scrape_async(self, asins: List[str]) -> List[Dict]:
        """异步批量抓取"""
        async with await self.create_session() as session:
            # 第一阶段：提交所有任务
            print("提交抓取任务...")
            tasks = []
            for asin in asins:
                task = self.submit_scrape_task(session, asin)
                tasks.append(task)
            
            task_ids = await asyncio.gather(*tasks)
            valid_task_ids = [tid for tid in task_ids if tid]
            
            print(f"成功提交 {len(valid_task_ids)} 个任务")
            
            # 第二阶段：获取所有结果
            print("获取抓取结果...")
            result_tasks = []
            for task_id in valid_task_ids:
                task = self.get_task_result(session, task_id)
                result_tasks.append(task)
            
            results = await asyncio.gather(*result_tasks)
            return [result for result in results if result]

# 性能测试示例
async def performance_test():
    config = PangolinConfig()
    client = PangolinAsyncClient(config)
    
    # 测试100个ASIN的抓取性能
    test_asins = [f'B08N5WRWN{i:01d}' for i in range(100)]
    
    start_time = time.time()
    results = await client.batch_scrape_async(test_asins)
    end_time = time.time()
    
    print(f"抓取 {len(test_asins)} 个产品")
    print(f"成功获取 {len(results)} 个结果")
    print(f"总耗时: {end_time - start_time:.2f} 秒")
    print(f"平均每个产品: {(end_time - start_time) / len(test_asins):.2f} 秒")

if __name__ == "__main__":
    asyncio.run(performance_test())

5. 数据处理和分析

# data_processor.py
import pandas as pd
import numpy as np
from typing import List, Dict
import re

class AmazonDataProcessor:
    def __init__(self):
        self.price_pattern = re.compile(r'\$?([\d,]+\.?\d*)')
        self.rating_pattern = re.compile(r'([\d\.]+)\s*out\s*of\s*5')
    
    def clean_price(self, price_str: str) -> float:
        """清洗价格数据"""
        if not price_str:
            return 0.0
        
        match = self.price_pattern.search(str(price_str))
        if match:
            price = match.group(1).replace(',', '')
            return float(price)
        return 0.0
    
    def clean_rating(self, rating_str: str) -> float:
        """清洗评分数据"""
        if not rating_str:
            return 0.0
        
        match = self.rating_pattern.search(str(rating_str))
        if match:
            return float(match.group(1))
        return 0.0
    
    def process_batch_data(self, raw_data: List[Dict]) -> pd.DataFrame:
        """批量处理数据"""
        processed_data = []
        
        for item in raw_data:
            processed_item = {
                'asin': item.get('asin', ''),
                'title': item.get('title', ''),
                'price': self.clean_price(item.get('price')),
                'rating': self.clean_rating(item.get('rating')),
                'review_count': self.extract_review_count(item.get('reviews', '')),
                'availability': item.get('availability', ''),
                'brand': item.get('brand', ''),
                'category': item.get('category', ''),
                'features': item.get('features', []),
                'images': item.get('images', []),
                'scraped_at': pd.Timestamp.now()
            }
            processed_data.append(processed_item)
        
        df = pd.DataFrame(processed_data)
        return self.add_derived_metrics(df)
    
    def extract_review_count(self, reviews_str: str) -> int:
        """提取评论数量"""
        if not reviews_str:
            return 0
        
        # 匹配各种评论数量格式
        patterns = [
            r'([\d,]+)\s*customer\s*reviews?',
            r'([\d,]+)\s*ratings?',
            r'\(([\d,]+)\)'
        ]
        
        for pattern in patterns:
            match = re.search(pattern, str(reviews_str), re.IGNORECASE)
            if match:
                count_str = match.group(1).replace(',', '')
                return int(count_str)
        
        return 0
    
    def add_derived_metrics(self, df: pd.DataFrame) -> pd.DataFrame:
        """添加衍生指标"""
        # 价格区间分类
        df['price_range'] = pd.cut(df['price'], 
                                  bins=[0, 25, 50, 100, 200, float('inf')],
                                  labels=['$0-25', '$25-50', '$50-100', '$100-200', '$200+'])
        
        # 评分等级
        df['rating_grade'] = pd.cut(df['rating'],
                                   bins=[0, 3.0, 3.5, 4.0, 4.5, 5.0],
                                   labels=['Poor', 'Fair', 'Good', 'Very Good', 'Excellent'])
        
        # 受欢迎程度评分（基于评分和评论数量）
        df['popularity_score'] = (df['rating'] * np.log1p(df['review_count'])).round(2)
        
        return df
    
    def generate_insights(self, df: pd.DataFrame) -> Dict:
        """生成数据洞察"""
        insights = {
            'total_products': len(df),
            'avg_price': df['price'].mean(),
            'avg_rating': df['rating'].mean(),
            'price_distribution': df['price_range'].value_counts().to_dict(),
            'rating_distribution': df['rating_grade'].value_counts().to_dict(),
            'top_brands': df['brand'].value_counts().head(10).to_dict(),
            'high_value_products': df.nlargest(10, 'popularity_score')[['title', 'price', 'rating', 'popularity_score']].to_dict('records')
        }
        
        return insights

# 使用示例
def analyze_competitor_data():
    # 假设已经通过Pangolin API获取了数据
    processor = AmazonDataProcessor()
    
    # 模拟原始数据
    raw_data = [
        {
            'asin': 'B08N5WRWNW',
            'title': 'Echo Dot (4th Gen)',
            'price': '$49.99',
            'rating': '4.7 out of 5 stars',
            'reviews': '150,234 customer reviews',
            'brand': 'Amazon',
            'category': 'Electronics'
        }
        # ... 更多数据
    ]
    
    # 处理数据
    df = processor.process_batch_data(raw_data)
    
    # 生成洞察
    insights = processor.generate_insights(df)
    
    print("=== 竞品分析报告 ===")
    print(f"总产品数: {insights['total_products']}")
    print(f"平均价格: ${insights['avg_price']:.2f}")
    print(f"平均评分: {insights['avg_rating']:.2f}")
    print("\n价格分布:")
    for price_range, count in insights['price_distribution'].items():
        print(f"  {price_range}: {count} 个产品")

高级应用场景

1. 竞品监控系统

# competitor_monitor.py
import schedule
import time
from datetime import datetime
from pangolin_async_client import PangolinAsyncClient
from data_processor import AmazonDataProcessor

class CompetitorMonitor:
    def __init__(self, config):
        self.client = PangolinAsyncClient(config)
        self.processor = AmazonDataProcessor()
        self.competitor_asins = []
        
    def add_competitor(self, asin: str, brand: str):
        """添加竞品监控"""
        self.competitor_asins.append({
            'asin': asin,
            'brand': brand,
            'added_at': datetime.now()
        })
    
    async def daily_monitor(self):
        """每日监控任务"""
        print(f"开始每日竞品监控 - {datetime.now()}")
        
        asins = [item['asin'] for item in self.competitor_asins]
        results = await self.client.batch_scrape_async(asins)
        
        # 处理数据
        df = self.processor.process_batch_data(results)
        
        # 检测价格变化
        price_changes = self.detect_price_changes(df)
        
        # 发送告警
        if price_changes:
            self.send_alerts(price_changes)
        
        # 保存历史数据
        self.save_historical_data(df)
    
    def detect_price_changes(self, current_df):
        """检测价格变化"""
        # 这里需要与历史数据比较
        # 实际实现中需要连接数据库
        pass
    
    def send_alerts(self, changes):
        """发送价格变化告警"""
        # 实现邮件/Slack/微信告警
        pass

# 设置定时任务
monitor = CompetitorMonitor(config)
schedule.every().day.at("09:00").do(lambda: asyncio.run(monitor.daily_monitor()))

2. 选品分析系统

# product_research.py
class ProductResearchEngine:
    def __init__(self, pangolin_client):
        self.client = pangolin_client
        self.processor = AmazonDataProcessor()
    
    async def analyze_category(self, category_url: str, max_pages: int = 5):
        """分析类目下的产品"""
        print(f"分析类目: {category_url}")
        
        # 获取类目页面数据
        category_data = await self.scrape_category_pages(category_url, max_pages)
        
        # 提取产品ASIN列表
        asins = self.extract_asins_from_category(category_data)
        
        # 批量获取产品详情
        product_details = await self.client.batch_scrape_async(asins)
        
        # 数据分析
        df = self.processor.process_batch_data(product_details)
        
        # 生成选品建议
        recommendations = self.generate_product_recommendations(df)
        
        return recommendations
    
    def generate_product_recommendations(self, df):
        """生成选品建议"""
        # 筛选条件
        criteria = {
            'min_rating': 4.0,
            'min_reviews': 100,
            'max_price': 100,
            'min_popularity_score': 10
        }
        
        filtered_df = df[
            (df['rating'] >= criteria['min_rating']) &
            (df['review_count'] >= criteria['min_reviews']) &
            (df['price'] <= criteria['max_price']) &
            (df['popularity_score'] >= criteria['min_popularity_score'])
        ]
        
        # 按受欢迎程度排序
        recommendations = filtered_df.nlargest(20, 'popularity_score')
        
        return recommendations[['asin', 'title', 'price', 'rating', 'review_count', 'popularity_score']]

错误处理和最佳实践

1. 重试机制实现

# retry_handler.py
import asyncio
import random
from functools import wraps

def async_retry(max_attempts=3, delay=1, backoff=2, exceptions=(Exception,)):
    """异步重试装饰器"""
    def decorator(func):
        @wraps(func)
        async def wrapper(*args, **kwargs):
            attempt = 0
            current_delay = delay
            
            while attempt < max_attempts:
                try:
                    return await func(*args, **kwargs)
                except exceptions as e:
                    attempt += 1
                    if attempt >= max_attempts:
                        raise e
                    
                    # 指数退避 + 随机抖动
                    jitter = random.uniform(0.1, 0.3)
                    sleep_time = current_delay * (1 + jitter)
                    
                    print(f"第 {attempt} 次重试失败，{sleep_time:.2f}秒后重试: {e}")
                    await asyncio.sleep(sleep_time)
                    current_delay *= backoff
            
        return wrapper
    return decorator

# 使用示例
class RobustPangolinClient(PangolinAsyncClient):
    @async_retry(max_attempts=3, delay=2, exceptions=(aiohttp.ClientError,))
    async def submit_scrape_task(self, session, asin, marketplace='amazon.com'):
        return await super().submit_scrape_task(session, asin, marketplace)

2. 缓存策略

# cache_manager.py
import redis
import json
import hashlib
from datetime import timedelta

class CacheManager:
    def __init__(self, redis_url='redis://localhost:6379'):
        self.redis_client = redis.from_url(redis_url)
        self.default_ttl = 3600  # 1小时
    
    def generate_cache_key(self, asin: str, marketplace: str = 'amazon.com') -> str:
        """生成缓存键"""
        key_data = f"{marketplace}:{asin}"
        return f"pangolin:product:{hashlib.md5(key_data.encode()).hexdigest()}"
    
    def get_cached_data(self, asin: str, marketplace: str = 'amazon.com'):
        """获取缓存数据"""
        cache_key = self.generate_cache_key(asin, marketplace)
        cached_data = self.redis_client.get(cache_key)
        
        if cached_data:
            return json.loads(cached_data)
        return None
    
    def cache_data(self, asin: str, data: dict, marketplace: str = 'amazon.com', ttl: int = None):
        """缓存数据"""
        cache_key = self.generate_cache_key(asin, marketplace)
        ttl = ttl or self.default_ttl
        
        self.redis_client.setex(
            cache_key,
            ttl,
            json.dumps(data, ensure_ascii=False)
        )

# 集成缓存的客户端
class CachedPangolinClient(PangolinAsyncClient):
    def __init__(self, config, cache_manager):
        super().__init__(config)
        self.cache = cache_manager
    
    async def scrape_product_with_cache(self, asin: str, marketplace: str = 'amazon.com'):
        """带缓存的产品抓取"""
        # 先检查缓存
        cached_data = self.cache.get_cached_data(asin, marketplace)
        if cached_data:
            print(f"从缓存获取数据: {asin}")
            return cached_data
        
        # 缓存未命中，调用API
        print(f"API获取数据: {asin}")
        async with await self.create_session() as session:
            task_id = await self.submit_scrape_task(session, asin, marketplace)
            if task_id:
                result = await self.get_task_result(session, task_id)
                if result:
                    # 缓存结果
                    self.cache.cache_data(asin, result, marketplace)
                    return result
        
        return {}

性能优化策略

1. 连接池管理

# connection_pool.py
import aiohttp
import asyncio
from aiohttp import TCPConnector

class OptimizedPangolinClient(PangolinAsyncClient):
    def __init__(self, config):
        super().__init__(config)
        self.connector = None
        self.session = None
    
    async def __aenter__(self):
        """异步上下文管理器入口"""
        # 创建优化的连接器
        self.connector = TCPConnector(
            limit=100,  # 总连接池大小
            limit_per_host=20,  # 每个主机的连接数
            ttl_dns_cache=300,  # DNS缓存时间
            use_dns_cache=True,
            keepalive_timeout=30,
            enable_cleanup_closed=True
        )
        
        # 创建会话
        timeout = aiohttp.ClientTimeout(total=self.config.timeout)
        self.session = aiohttp.ClientSession(
            connector=self.connector,
            timeout=timeout,
            headers={
                'Authorization': f'Bearer {self.config.api_key}',
                'Content-Type': 'application/json'
            }
        )
        
        return self
    
    async def __aexit__(self, exc_type, exc_val, exc_tb):
        """异步上下文管理器出口"""
        if self.session:
            await self.session.close()
        if self.connector:
            await self.connector.close()

# 使用示例
async def optimized_batch_scraping():
    config = PangolinConfig()
    
    async with OptimizedPangolinClient(config) as client:
        asins = ['B08N5WRWNW', 'B08N5WRWN1', 'B08N5WRWN2']
        results = await client.batch_scrape_async(asins)
        return results

2. 内存优化

# memory_optimizer.py
import gc
import psutil
import os
from typing import Iterator, List, Dict

class MemoryOptimizedProcessor:
    def __init__(self, chunk_size: int = 1000):
        self.chunk_size = chunk_size
    
    def process_large_dataset(self, data_iterator: Iterator[Dict]) -> Iterator[pd.DataFrame]:
        """分块处理大数据集"""
        chunk = []
        
        for item in data_iterator:
            chunk.append(item)
            
            if len(chunk) >= self.chunk_size:
                # 处理当前块
                df = self.process_chunk(chunk)
                yield df
                
                # 清理内存
                chunk.clear()
                gc.collect()
        
        # 处理最后一块
        if chunk:
            df = self.process_chunk(chunk)
            yield df
    
    def process_chunk(self, chunk: List[Dict]) -> pd.DataFrame:
        """处理数据块"""
        processor = AmazonDataProcessor()
        return processor.process_batch_data(chunk)
    
    def monitor_memory_usage(self):
        """监控内存使用情况"""
        process = psutil.Process(os.getpid())
        memory_info = process.memory_info()
        
        print(f"内存使用: {memory_info.rss / 1024 / 1024:.2f} MB")
        print(f"虚拟内存: {memory_info.vms / 1024 / 1024:.2f} MB")

监控和日志

# monitoring.py
import logging
import time
from functools import wraps
from prometheus_client import Counter, Histogram, start_http_server

# Prometheus指标
REQUEST_COUNT = Counter('pangolin_requests_total', 'Total requests', ['method', 'status'])
REQUEST_DURATION = Histogram('pangolin_request_duration_seconds', 'Request duration')

# 配置日志
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('pangolin_client.log'),
        logging.StreamHandler()
    ]
)

logger = logging.getLogger(__name__)

def monitor_performance(func):
    """性能监控装饰器"""
    @wraps(func)
    async def wrapper(*args, **kwargs):
        start_time = time.time()
        
        try:
            result = await func(*args, **kwargs)
            REQUEST_COUNT.labels(method=func.__name__, status='success').inc()
            return result
        except Exception as e:
            REQUEST_COUNT.labels(method=func.__name__, status='error').inc()
            logger.error(f"函数 {func.__name__} 执行失败: {e}")
            raise
        finally:
            duration = time.time() - start_time
            REQUEST_DURATION.observe(duration)
            logger.info(f"函数 {func.__name__} 执行时间: {duration:.2f}秒")
    
    return wrapper

# 启动监控服务器
start_http_server(8000)

总结

Pangolin Scrape API通过其先进的技术架构和丰富的功能特性，为开发者提供了高效、稳定的亚马逊数据采集解决方案。本文详细介绍了从基础集成到高级应用的完整技术方案，包括：

1. 技术架构深度解析：分布式代理池、智能反检测、数据解析引擎
2. 完整集成方案：同步/异步API调用、错误处理、缓存策略
3. 高级应用场景：竞品监控、选品分析、数据处理
4. 性能优化策略：连接池管理、内存优化、监控日志

通过合理运用这些技术方案，开发者可以构建出高性能、高可靠性的电商数据采集系统，为业务决策提供强有力的数据支撑。

相关资源

• Pangolin API官方文档
• GitHub示例代码库

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关于作者：资深后端工程师，专注于大规模数据采集和处理系统设计，在电商数据分析领域有丰富的实战经验。

声明：本文所有代码示例仅供学习参考，实际使用时请遵守相关网站的robots.txt协议和使用条款。