Background requests power much of the modern web and can be a powerful opportunity for web scraping. In this comprehensive guide, we'll cover how to leverage headless browser scraping techniques like Playwright, Selenium, and Puppeteer to scrape data from background requests using Python.
Introduction to Web Scraping Techniques
There are a few primary approaches to web scraping:
This involves using a HTTP client to download a page's HTML and then extracting the desired data using parsers like BeautifulSoup. Pros are it's fast and simple. Cons are it can break easily if the site changes and is limited to data in the HTML source.
For sites with open APIs, you can reverse engineer and call them directly to scrape structured data. Pros are it's robust and fast. Cons are APIs can be difficult to find and scrape at scale.
Capturing Background Requests
This technique loads pages in a real browser like Chrome and captures AJAX/XHR requests made in the background. Pros are it's robust and can access hidden data. Cons are it requires more complex setup and can have performance limitations.
Each approach has tradeoffs but scraping background requests strikes a nice balance of speed, power, and reliability.
Headless Browsers for Scraping
headless browser scraping technique provide an automated way to scrape web pages using real browser engines like Chromium and Firefox. Here are some popular options:
- Created by Microsoft as an open source alternative to Puppeteer.
- Very fast and reliable browser control.
- Strong community and documentation.
- The most established browser automation tool.
- Supports many languages like Python, Java, C#, etc.
- Very extensible via a wide range of drivers.
- Large user base and support available.
- Created by Google specifically for Chrome.
- Fast and lightweight browser automation.
- Largest ecosystem of related tools and libraries.
All provide capabilities to intercept background requests which we'll leverage for web scraping. They also allow executing actions like clicks, scrolls, and form fills to trigger XHRs.
Benefits of Scraping Background Requests
Here are some of the advantages of using headless browser scraping technique and capturing background requests for web scraping:
Access Hidden Data
Often the HTML of a page will only contain a portion of the available data. Background requests can reveal additional data that's loaded asynchronously. This opens up new scraping possibilities.
Robust to Site Changes
Unlike parsing fixed HTML, background requests are a fluid way to obtain data that works even if parts of the site change. The scrapers follow user flows instead of fixed parsing rules.
Mimic User Actions
Headless browsers allow simulating user actions like clicks, scrolls, and more to trigger additional requests. This makes scraping reactive sites with endless scrolling, popups, etc. much more reliable.
How to Capture Background Requests
Here is the high-level approach to intercept background requests using headless browser scraping technique:
1. Initiate the Browser
Launch a browser instance like Chromium using Playwright, Selenium, or Puppeteer and navigate to the target page.
2. Enable Request Interception
Call the browser's request interception API to set up a callback handler.
3. Load Target Page
Navigate the browser to the target URL to scrape. Wait for page load if required.
4. Execute Actions
Perform user actions like clicks, scrolls, form fills, etc. to trigger requests.
5. Parse Requests
In the request handler callback, inspect responses and extract the data.
Now let's see how this works with Python examples.
Scraping Example Projects
To demonstrate scraping background requests, we'll walk through two common examples using both Playwright and Selenium with Python. The examples use web-scraping.dev, a site designed specifically for demonstrating and testing scrapers.
Button Click to Load More Reviews
For this example, we'll scrape a product page that uses “Load More” button clicks to fetch additional reviews via background requests. Here's how it looks:
# Playwright Example import json from playwright.sync_api import sync_playwright url = "https://web-scraping.dev/product/1" with sync_playwright() as p: browser = p.chromium.launch() page = browser.new_page() page.on("response", lambda r: print(r.url)) # print all requests page.goto(url) # click load more until no new requests while True: prev_requests = page.request.all_urls() page.click("[id^=load-more-reviews]") page.wait_for_timeout(1000) new_requests = page.request.all_urls() if prev_requests == new_requests: break browser.close() # Selenium Example from selenium import webdriver from selenium.webdriver.common.by import By options = webdriver.ChromeOptions() options.add_experimental_option("excludeSwitches", ["enable-logging"]) driver = webdriver.Chrome(options=options) url = "https://web-scraping.dev/product/1" driver.get(url) while True: prev_requests = driver.execute_script("return performance.getEntriesByType('resource')") driver.find_element(By.ID, "load-more-reviews").click() new_requests = driver.execute_script("return performance.getEntriesByType('resource')") if prev_requests == new_requests: break driver.quit()
This handles clicking the button until no new requests are captured, scraping all the review data.
Scrolling to Trigger Pagination
Another common pattern is endless scroll pagination, where sites load content as you scroll down. Here's an example script to scrape paginated testimonials using scrolling:
# Playwright import json from playwright.sync_api import sync_playwright url = "https://web-scraping.dev/testimonials" with sync_playwright() as p: browser = p.chromium.launch() page = browser.new_page() page.goto(url) page.on("response", lambda r: print(r.url)) # print all requests prev_height = -1 while True: curr_height = page.evaluate('document.body.scrollHeight') page.evaluate('window.scrollTo(0, document.body.scrollHeight)') page.wait_for_timeout(1000) if curr_height == prev_height: break prev_height = curr_height browser.close() # Selenium from selenium import webdriver options = webdriver.ChromeOptions() driver = webdriver.Chrome(options=options) url = "https://web-scraping.dev/testimonials" driver.get(url) prev_height = -1 while True: curr_height = driver.execute_script("return document.body.scrollHeight") driver.execute_script("window.scrollTo(0, document.body.scrollHeight)") if curr_height == prev_height: break prev_height = curr_height driver.quit()
This handles scrolling until no new content is loaded, capturing all background requests along the way.
There are a couple of common challenges you may encounter when scraping background requests:
Waiting for Page Load
Browsers load content asynchronously, so you need to properly wait for elements or requests to finish loading before interacting with the page. Each browser tool provides robust wait/timeout APIs to handle this.
To trigger scroll-based pagination, you need to replicate the browser's native scrolling closely. Just using
window.scrollTo() often isn't sufficient to simulate a user's scroll behavior. Libraries like Autoscroll.js can help here, providing a more realistic scroll motion.
Here are some tips for smoothly scraping background requests at scale:
- Block unnecessary requests – Improves speed and reduces bandwidth costs. Browser tools allow blocking CSS, media, fonts, etc.
- Use proxy rotation – Rotate IPs to avoid blocks and scrape more reliably. Integrations like Bright DataSmartproxyProxy-SellerSoax work well.
- Retry failed requests – Network errors are common. Add retry logic and exponential backoff to handle flakiness.
- Export results incrementally – For large scrapes, save results as you go instead of keeping them all in memory.
You can further optimize and scale background request scraping using:
- Scraping SPAs – Use Puppeteer, Playwright, or Selenium to render client-side pages and XHR.
- Calling APIs directly – Reverse engineer and directly call REST or GraphQL APIs for higher throughput.
- Performance tuning – Tweak browser instances for higher concurrency and throughput via flags and configs.
- Distributed scraping – Spread jobs across scrapers on multiple machines for faster crawling.
Comparing Playwright, Selenium, and Puppeteer
While Playwright, Selenium, and Puppeteer all provide the ability to intercept requests, there are some key differences between the tools:
Speed and Rendering
Playwright and Puppeteer use Chromium directly so they tend to have faster page loads and resource usage. Selenium supports many browsers which can impact performance.
Languages and Platforms
Selenium has a very rich ecosystem of browser drivers and extensions. Playwright and Puppeteer provide robust APIs that cover most use cases.
Documentation and Community
Due to its longevity, Selenium has more available content and support. But Playwright and Puppeteer have excellent official docs and growing communities.
What is the difference between XHR and API?
XHR (XMLHTTPRequest) is the front-end technique used to call APIs. The API (Application Programming Interface) is the server-side endpoint that returns data.
What are the pros and cons of Playwright vs Selenium?
Playwright is faster while Selenium supports more languages. Playwright's API covers most common needs while Selenium provides more extensibility.
How do I parse JSON data from a request?
In Python you can use the
json module to parse:
import json json_data = json.loads(request.text())
What status codes should I retry requests for?
5XX errors indicate server issues, so those are good candidates for retries. 429 Too Many Requests responses may also benefit from retrying after delaying.
How can I prevent getting blocked while scraping?
Using proxies and random user agents helps distribute the load, which looks less bot-like. Starting with low concurrency and slowly ramping up request volume can also help avoid spikes that trigger blocks.
Capturing network traffic from headless browsers is a powerful technique to build robust web scrapers in Python. Libraries like Playwright, Selenium, and Puppeteer include many tools to intercept requests and retrieve data loaded in the background.
While more complex than parsing HTML, this approach provides access to hidden page data and maintains resilience even if sites change over time. With the right patterns and tools, you can scrape complex user flows and extract data not otherwise available.