Production-Ready Python Web Scraping: Advanced Techniques for Dynamic Sites and Data Collection

#programming #devto #python #softwareengineering

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Collecting web data efficiently requires navigating modern complexities. I've found that combining several Python techniques creates robust scrapers capable of handling dynamic content while maintaining ethical standards. Here's what works reliably in production environments.

JavaScript-heavy sites often require full browser rendering. I use Playwright because it handles single-page applications effectively. Here's how I extract content after client-side rendering completes:

from playwright.sync_api import sync_playwright

def extract_dynamic_content(url):
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        context = browser.new_context(viewport={"width": 1920, "height": 1080})
        page = context.new_page()

        try:
            page.goto(url, timeout=60000)
            page.wait_for_selector("#dynamic-content", state="visible", timeout=15000)

            # Handle lazy-loaded elements
            page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
            page.wait_for_timeout(2000)

            return page.inner_html("#content-container")
        finally:
            browser.close()

# Usage
html_content = extract_dynamic_content("https://modern-web-app.com/data-feed")
print(f"Extracted {len(html_content)} characters of rendered content")

Server defenses often block repetitive requests. Rotating headers helps significantly. I combine user agent rotation with proxy switching for better results:

from fake_useragent import UserAgent
import requests
from itertools import cycle

proxies = cycle([
    "http://user:pass@192.168.1.1:8080",
    "http://user:pass@192.168.1.2:8080"
])

def safe_request(url):
    ua = UserAgent()
    headers = {
        "User-Agent": ua.random,
        "Accept-Language": "en-US,en;q=0.9",
        "Referer": "https://google.com"
    }

    try:
        response = requests.get(url, 
            headers=headers,
            proxies={"http": next(proxies)},
            timeout=15)
        response.raise_for_status()
        return response.text
    except requests.exceptions.RequestException as e:
        print(f"Request failed: {str(e)}")
        return None

# Usage
content = safe_request("https://protected-site.com/inventory")

For precise element targeting, XPath consistently outperforms CSS selectors in complex documents. This approach handles deeply nested structures:

from lxml import html, etree

def parse_product_page(html_content):
    tree = html.fromstring(html_content)

    # Extract variant prices using XPath axes
    results = []
    for product in tree.xpath('//div[contains(@class, "product-card")]'):
        name = product.xpath('.//h2[@itemprop="name"]/text()')[0].strip()

        # Handle price variations
        base_price = product.xpath('.//span[@class="base-price"]/text()')
        sale_price = product.xpath('.//span[@class="sale-price"]/text()')
        price = sale_price[0] if sale_price else base_price[0]

        # Extract metadata using sibling selectors
        sku = product.xpath('.//dt[text()="SKU"]/following-sibling::dd/text()')[0]

        results.append({"name": name, "price": price, "sku": sku})

    return results

# Usage
products = parse_product_page(html_content)
print(f"Found {len(products)} product listings")

CAPTCHAs require specialized services. I integrate solvers directly into automation scripts:

from twocaptcha import TwoCaptcha
from selenium.webdriver.common.by import By

def solve_captcha(driver):
    solver = TwoCaptcha("YOUR_API_KEY")

    # Identify CAPTCHA parameters
    sitekey = driver.find_element(By.CSS_SELECTOR, ".h-captcha").get_attribute("data-sitekey")
    page_url = driver.current_url

    # Solve and inject solution
    result = solver.hcaptcha(sitekey=sitekey, url=page_url)
    driver.execute_script(
        f"document.querySelector('[name=h-captcha-response]').value = '{result['code']}'"
    )
    driver.find_element(By.ID, "submit-btn").click()

    return "Verification Success" in driver.page_source

# Usage in Selenium
# driver.get("https://secure-site.com/login")
# if "CAPTCHA" in driver.page_source:
#     solve_captcha(driver)

Large projects demand distributed systems. Scrapy-Redis handles queue management effectively:

# Scrapy project structure
# ├── scrapy.cfg
# ├── myproject/
# │   ├── __init__.py
# │   ├── items.py
# │   ├── middlewares.py
# │   ├── pipelines.py
# │   ├── settings.py
# │   └── spiders/
# │       └── distributed_spider.py

# settings.py
SCHEDULER = "scrapy_redis.scheduler.Scheduler"
DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"
REDIS_URL = "redis://:password@server-ip:6379/0"
ITEM_PIPELINES = {
    'myproject.pipelines.DatabasePipeline': 300,
}

# distributed_spider.py
import scrapy
from scrapy_redis.spiders import RedisSpider

class MyDistributedSpider(RedisSpider):
    name = "distributed_crawler"
    redis_key = "crawler:start_urls"

    def parse(self, response):
        # Extraction logic here
        yield {"url": response.url, "data": response.css("title::text").get()}

Database integration keeps pipelines flowing. This PostgreSQL loader handles continuous inserts:

import psycopg2
from contextlib import contextmanager

@contextmanager
def db_connection():
    conn = psycopg2.connect(
        dbname="scraped_data",
        user="loader",
        password="securepass",
        host="db-server.com"
    )
    try:
        yield conn
    finally:
        conn.close()

class DatabasePipeline:
    def process_item(self, item, spider):
        with db_connection() as conn:
            with conn.cursor() as cur:
                cur.execute("""
                    INSERT INTO scraped_items (url, content, timestamp)
                    VALUES (%s, %s, NOW())
                    ON CONFLICT (url) DO UPDATE
                    SET content = EXCLUDED.content,
                        timestamp = NOW()
                """, (item["url"], item["content"]))
        return item

Respecting website rules is non-negotiable. This robots.txt checker prevents policy violations:

from urllib.robotparser import RobotFileParser
from urllib.parse import urlparse

def check_crawl_permission(target_url):
    parsed = urlparse(target_url)
    robots_url = f"{parsed.scheme}://{parsed.netloc}/robots.txt"

    rp = RobotFileParser()
    rp.set_url(robots_url)
    rp.read()

    crawl_delay = rp.crawl_delay("*")
    if crawl_delay:
        print(f"Respecting crawl delay: {crawl_delay} seconds")

    return rp.can_fetch("MyBot/1.0", target_url)

# Usage
if check_crawl_permission("https://example.com/restricted-area"):
    print("Proceeding with extraction")
else:
    print("Access prohibited by robots.txt")

Monitoring site changes prevents unexpected extraction failures. This version tracker detects layout modifications:

import hashlib
import requests
from difflib import HtmlDiff

class SiteMonitor:
    def __init__(self, url):
        self.url = url
        self.last_hash = None

    def fetch_snapshot(self):
        response = requests.get(self.url)
        content = response.text

        # Normalize HTML for consistent comparison
        content = content.replace(" ", "").replace("\n", "")
        current_hash = hashlib.sha256(content.encode()).hexdigest()

        return content, current_hash

    def detect_changes(self):
        content, current_hash = self.fetch_snapshot()

        if not self.last_hash:
            print("Initial version stored")
            self.last_hash = current_hash
            return False

        if current_hash != self.last_hash:
            print("Structure change detected")
            # Generate change report
            diff = HtmlDiff().make_file(
                self.previous_content.splitlines(),
                content.splitlines(),
                context=True
            )
            with open("change_report.html", "w") as f:
                f.write(diff)
            return True

        return False

# Usage
monitor = SiteMonitor("https://frequently-updated.com")
if monitor.detect_changes():
    print("Site structure modified - update selectors")

These methods form a comprehensive approach to modern web data collection. Each addresses specific challenges I've encountered in real projects. The key is balancing extraction capability with respectful crawling practices. Always verify legality and respect website terms before scraping. Proper rate limiting and error handling make the difference between sustainable data collection and blocked IPs. Start with small requests and scale gradually while monitoring server responses.

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