Category:Scraping Basics

How to Speed Up Web Scraping: 7 Proven Strategies

4 Mins Read

Created Date: December 01, 2025

Updated Date: December 01, 2025

Raif Tekin

R&D Engineer

Is your scraper taking days to finish a job that should take hours?

In the world of data, speed isn't just a luxury—it's a requirement. Whether you're monitoring stock prices in real-time or aggregating millions of product listings, a slow scraper means stale data and missed opportunities.

But speeding up a scraper isn't just about writing better code. It's about overcoming the inherent bottlenecks of the web: sequential requests, heavy browser rendering, and aggressive rate limits.

Here are 7 proven strategies to turbocharge your web scraping performance.

1. Switch to Asynchronous Requests (AsyncIO)

The biggest bottleneck in most scrapers is waiting.

When you use a standard library like Python's requests, your code sends a request and then sits idle, doing absolutely nothing until the server responds. This is called "blocking" I/O.

Asynchronous code allows your scraper to send a request and, while waiting for the response, move on to send the next one.

Python Example (aiohttp vs requests):

# The Slow Way (Sequential)
import requests
for url in urls:
    requests.get(url) # Waits for each one to finish

# The Fast Way (Async)
import aiohttp
import asyncio

async def fetch(session, url):
    async with session.get(url) as response:
        return await response.text()

async def main():
    async with aiohttp.ClientSession() as session:
        tasks = [fetch(session, url) for url in urls]
        await asyncio.gather(*tasks)

By switching to aiohttp, you can process hundreds of requests in the time it takes to process one sequentially.

2. Drop the Headless Browser (When Possible)

Tools like Selenium, Puppeteer, and Playwright are powerful because they render JavaScript. But they are also heavy.

Launching a browser instance consumes significant RAM and CPU. If you are scraping 10,000 pages, opening 10,000 browser tabs (even sequentially) is incredibly slow.

The Fix: Always check if the data is available in the raw HTML source first. If it is, use a lightweight HTTP library (like requests or aiohttp) instead of a browser.

Pro Tip: Check the "Network" tab in your browser's Developer Tools. Often, websites load data via a hidden JSON API. If you can find that API endpoint, you can scrape it directly, bypassing the HTML entirely.

3. Use Multithreading / Multiprocessing

If you can't use AsyncIO (perhaps due to library limitations) or if your bottleneck is parsing the data (CPU-bound) rather than fetching it (I/O-bound), use parallelism.

Multithreading: Good for I/O bound tasks (waiting for network).
Multiprocessing: Good for CPU bound tasks (parsing huge HTML files).

Python's concurrent.futures module makes this easy:

import concurrent.futures

with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
    executor.map(scrape_function, urls)

4. Optimize Your Parsing Logic

Not all parsers are created equal.

If you are using Python's BeautifulSoup, the default parser (html.parser) is written in Python and can be slow.

The Fix: Install and use lxml. It's a high-performance parser written in C.

# Slower
soup = BeautifulSoup(html, 'html.parser')

# Faster
soup = BeautifulSoup(html, 'lxml')

Also, only parse what you need. Don't render the entire DOM tree if you only need the page title. Use string manipulation or regex for simple extractions where appropriate (but be careful, regex and HTML don't always mix well!).

5. Disable Unnecessary Assets

If you absolutely must use a headless browser (e.g., the site is a Single Page Application heavily reliant on JS), strip it down.

A typical webpage loads images, CSS, custom fonts, and third-party tracking scripts. You don't need any of these to scrape text data.

The Fix: Configure your browser to block these resource types. This can reduce page load times by 50-80%.

Playwright Example:

await page.route("**/*", lambda route: route.abort()
    if route.request.resource_type in ["image", "stylesheet", "font"]
    else route.continue_())

6. Handle Rate Limits Smartly

It's a paradox: Going too fast can slow you down.

If you hammer a server with requests, you will get rate-limited (HTTP 429) or banned. Once banned, your speed drops to zero.

Strategy:

Respect robots.txt: It often specifies a crawl delay.
Implement Backoff: If you get a 429 error, wait exponentially longer before retrying (1s, 2s, 4s, 8s).
Randomize Delays: Don't hit the server exactly every 1.0 seconds. Use a random delay between 0.5s and 1.5s to look more human.

7. Scale Horizontally with Proxies (The Game Changer)

Even with optimized code, your local machine has a limit: your IP address.

Websites track requests by IP. If you send 1,000 requests/minute from a single IP, you look like a bot.

The Solution: Rotating Proxies.

By routing your traffic through a pool of millions of IPs, you can send thousands of concurrent requests without any single IP triggering a rate limit.

The Scrape.do Way

This is where Scrape.do changes the game.

Instead of managing your own proxy pool (which is expensive and complex), you simply send your requests to our API. We route them through our network of 100M+ residential and mobile IPs.

Unlimited Concurrency: On our supported plans, you can send as many concurrent requests as your infrastructure can handle.
Auto-Rotation: We automatically rotate IPs for every request.
Success Guarantee: We only charge for successful requests (HTTP 200).

Example: With Scrape.do, you can spin up 500 threads and scrape 500 pages simultaneously, effectively making your scraper 500x faster than a sequential script.