For exceptionally huge or complex websites, a typical scraping process won’t be sufficient.

The internet is an ever-expanding realm, and with that expansion comes an increase in the volume of crucial data that you might need to extract for a variety of purposes. If you didn’t already know, online web data scraping is the quickest and most effective way to gather freely accessible web data and transform it into a structured format that can be used for research.

There are times, though, when the amount of data that must be gathered and the rate at which it must be gathered exceed the capabilities of the typical web scraping tool. If you need to collect information from 1,000 or even 10,000 web pages, scraping will be sufficient. What happens, though, if there are millions of pages? For this, extensive scraping is needed.

Wide-scale scraping is the term for gathering information on a massive scale from complex or large websites. Millions of pages might be extracted each month, week, or even day as a result of extensive scraping. This calls for a different strategy. Therefore, we’ll explain how large-scale scraping operates and how to overcome the challenges presented by scraping complex or large websites.

Is Data Scraping on a Large Scale Legal?

Is Data Scraping on a Large Scale Legal?

Be mindful of the target website’s restrictions. Scraping a large website like Amazon is very different than scraping a tiny, neighbourhood shop. Unaccustomed to heavy traffic, a website might not be able to handle a high volume of crawler requests. Not only would this have an impact on the company’s user base, but it might also cause the website to lag or even crash. Be polite and refrain from overtaxing your target website as a result. If you’re unsure, do some internet research to find out how many people visit the website.

How Does Typical Web Scraping Work in Steps?

So how can you tell whether a web scraping task requires extensive scraping? We’ll start with a common web scraping technique to demonstrate that.

Step 1: Access the desired website.

Here, we’ll use the website fashionphile as our aim.

Step 2: Add top-level categories to the queue.

Then select Shop All Bag under the Bag category.

We can see that there are 21,477 different types of bags available on the website. The largest number of scraped objects, according to our research so far, is 21,387.

Step 3: Gather all product information.

You may now extract product details including names of brands, the colours of bags, and price points, among other things. For instance, Louis Vuitton handbags range in price from $1,050 to $2,100.

Step 4: The game is run on a solitary server.

With the help of this knowledge, you may launch an actor on the Logicwis platform and retrieve the needed data.

Why then does this not apply to really large or complicated websites?

Why Is Massive (Large-Scale) Scraping Necessary?

Dealing with really large websites, like Amazon, poses three problems.

  • There is a cap on how many pages pagination can display.
  • One server is not enough.
  • Proxies by default might not be scalable.

The Total Number of Possible Solutions is Limited by Pagination.

It’s common to set the pagination limit between 1,000 and 10,000 items. Three steps can be taken to get around this restriction:

  • Use search filters and subcategories to navigate.
  • Sort them according to price ranges (for instance, $0-10, $10-100).
  • Recursively divide the price ranges in half (for instance, divide the $0–10 price range into $0–5 and $5–10).

A Workaround for Memory and CPU Limitations

There is a maximum size for the server (vertical scaling), thus you will need to add extra servers (horizontal scraping). As a result, you will need to distribute your runs among a number of servers, each of which will run them concurrently. How to do it is as follows:

  • Assign things to servers after gathering them.
  • Make servers as necessary.
  • Use the Merge, Dedup& Transform Datasets actor to combine and deduplicate the results after merging them into a single dataset.

Proxy Services

Your web scraping costs are influenced by the proxy you choose. Data centre proxies are likely to get blacklisted if you scrape extensively. Residential proxies are expensive. Therefore, the best strategy involves using a combination of home proxies, data centre proxies, and third-party API providers.


Keeping the following in mind can help you with challenging large-scale data scraping:

  • Plan your strategy before you start.
  • Make web servers as little of a burden as you can.
  • Only accurate data ought to be taken out.
  • When it comes to handling the problems associated with extensive scraping, Logicwis is quite knowledgeable.

For a customised solution if you need extensive data extraction, please get in touch with Logicwis.

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