Web Scrape Plus

Enhanced web scraping with anti-detection, proxy support, rate limiting, data validation, and multiple extraction methods. Supports static pages, dynamic content, login-gated sites, and pagination.

Enhanced web scraping with anti-detection, proxy support, rate limiting, and data validation.

Features

• Multiple Extraction Methods: Static HTML, dynamic DOM, API endpoints
• Anti-Detection: User agent rotation, random delays, fingerprint evasion
• Proxy Support: HTTP/SOCKS5 proxy routing
• Rate Limiting: Configurable request throttling
• Data Validation: Schema validation and deduplication
• Error Recovery: Retry logic with exponential backoff

Approach Selection

Scenario	Method	Tool
Static HTML	CSS/XPath selectors	web_fetch
Dynamic content	Browser automation	browser
API endpoints	Direct HTTP requests	http-client
Login required	Session management	browser + cookies
Pagination	Sequential/parallel	browser + loops

Anti-Detection Techniques

User Agent Rotation

python

import random

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",
]

headers = {"User-Agent": random.choice(USER_AGENTS)}

Random Delays

python

import time
import random

def random_delay(min_sec=1, max_sec=5):
    time.sleep(random.uniform(min_sec, max_sec))

Viewport Randomization

python

VIEWPORTS = [
    {"width": 1920, "height": 1080},
    {"width": 1366, "height": 768},
    {"width": 1536, "height": 864},
]

viewport = random.choice(VIEWPORTS)

Proxy Support

python

import requests

proxies = {
    "http": "http://proxy:port",
    "https": "http://proxy:port",
}

response = requests.get(url, proxies=proxies)

Rate Limiting

python

import time
from collections import deque

class RateLimiter:
    def __init__(self, max_requests, time_window):
        self.max_requests = max_requests
        self.time_window = time_window
        self.timestamps = deque()
    
    def wait_if_needed(self):
        now = time.time()
        while self.timestamps and self.timestamps[0] < now - self.time_window:
            self.timestamps.popleft()
        if len(self.timestamps) >= self.max_requests:
            sleep_time = self.timestamps[0] + self.time_window - now
            if sleep_time > 0:
                time.sleep(sleep_time)
        self.timestamps.append(time.time())

Error Recovery

python

import time
from functools import wraps

def retry(max_retries=3, backoff_factor=2):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == max_retries - 1:
                        raise
                    wait_time = backoff_factor ** attempt
                    time.sleep(wait_time)
            return None
        return wrapper
    return decorator

Data Validation

python

from pydantic import BaseModel, validator
from typing import List, Optional

class ScrapedItem(BaseModel):
    title: str
    url: str
    price: Optional[float]
    description: Optional[str]
    
    @validator("title")
    def title_must_not_be_empty(cls, v):
        if not v.strip():
            raise ValueError("title cannot be empty")
        return v.strip()

def validate_items(items: List[dict]) -> List[ScrapedItem]:
    return [ScrapedItem(**item) for item in items]

Deduplication

python

def deduplicate(items, key="url"):
    seen = set()
    unique = []
    for item in items:
        item_key = item.get(key)
        if item_key not in seen:
            seen.add(item_key)
            unique.append(item)
    return unique

Workflow

1. Test one page first - Verify extraction works
2. Choose method - Static vs dynamic vs API
3. Apply anti-detection - User agents, delays, proxies
4. Rate limit - Respect server limits
5. Validate data - Schema validation
6. Deduplicate - Remove duplicates
7. Save results - JSON/CSV output
8. Cleanup - Close browsers, clear state

Best Practices

1. Respect robots.txt - Check before scraping
2. Rate limit - Don't overload servers
3. Use proxies - Rotate IPs for large jobs
4. Validate data - Ensure quality
5. Handle errors - Retry logic
6. Cache responses - Avoid re-fetching
7. Log requests - Debug issues
8. Clean up - Close browsers, clear state

Common Issues

Issue	Solution
CAPTCHA	Switch proxy, use manual solving
Rate limited	Increase delays, rotate proxies
Dynamic content	Use browser automation
Login required	Use session cookies
Anti-bot detection	Use stealth mode

Output Formats

JSON

json

[
  {
    "title": "...",
    "url": "...",
    "price": 29.99,
    "description": "..."
  }
]

CSV

csv

title,url,price,description
"Product 1",https://example.com/1,29.99,"Description 1"