A standard scraping procedure will not be enough for crawling particularly large or complicated websites.
The internet is a constantly expanding universe, and as it expands, so does the amount of critical data you may need to extract for a variety of reasons. Online web data scraping is the fastest and most efficient means of obtaining publicly available web data and converting it into a structured format that can be utilized for analysis if you didn't previously know.
However, there are occasions when the volume of data and the speed with which it must be obtained exceeds the capabilities of the usual online scraping tool. Scraping will suffice if you need to extract data from a thousand or even tens of thousands of web pages. But what if the number of pages is in the millions? Scraping on a huge scale is required for this.
Scraping data from large or sophisticated websites on a large scale is known as large-scale scraping. Scraping on a wide scale might result in millions of pages being extracted monthly, weekly, or even daily. This necessitates a different approach. So, we'll show you how big-scale scraping works and how to handle the difficulties that arise with scraping huge or sophisticated websites.
Is it Legal to Scrape Large-Scale Data?
Be careful of the limitations of the target website. Scraping a major website like Amazon differs significantly from scraping a small, local business's website. A website that isn't used to a lot of traffic might not be able to handle a lot of crawler queries. Not only would this affect the company's user numbers, but it may also slow down or even crash the website. As a result, be courteous and avoid overburdening your target website. If you're not sure, conduct some internet research to see how much traffic the website gets.
Steps of How Normal Web Scraping Works?
So, how can you recognize whether a web scraping assignment necessitates large-scale scraping? To show that, we'll start with a standard web scraping approach.
Step 1: Open the targeted webpage
Here, we will take fashionphile as our targeted webpage.
Step 2: Add top-level categories to the queue.
Then click on the bag category and select shop all bag.
The total number of bag goods on the site is 21,477, as we can see. So far, we've determined that the maximum number of scraped objects is 21,387.
Step 3: Extract every product detail
You now can extract product information including brand names, bag colors, and pricing ranges, among other things. For example, Louis Vuitton purses cost between $1,050 and $2,100.
Step 4: A single server is used to run the game.
You may use this information to run an actor on the Scraping Intelligence platform to extract the data you require.
So, why doesn't this work for websites that are exceedingly vast or complex?
Why Large-Scale Scraping is Required?
Dealing with really big websites, such as Amazon, presents three issues
- The number of pages displayed in pagination has a limit.
- A single server is insufficient.
- Default proxies may not be scalable.
Pagination Restricts the Number of Possible Solutions.
The pagination limit is often set from 1,000 to 10,000 items. This constraint may be overcome in three steps:
- Go to subcategories and use search filters.
- Divide them into pricing categories (for example, $0-10, $10-100).
- Break the price ranges in half recursively (for example, split the $0-10 price range into $0-5 and $5-10)
An Alternative for Memory and CPU Constraints
You'll need to add more servers because there's a limit to how big the server can become (vertical scaling) (horizontal scraping). This means you'll need to split your runs among many servers, each of which will execute in simultaneously. Here's how to go about it:
- Gather items and disperse them among servers.
- As needed, create servers.
- Merge the findings into a single dataset, then use the Merge, Dedup & Transform Datasets actor to unify and deduplicate them.
Proxy Solutions
Your proxy selection influences on your web scraping charges. If you scrape on a massive scale, data center proxies are likely to be blacklisted. Proxies for residential use are costly. As a result, a mix of data center proxies, residential proxies, and external API providers is the optimum approach.
Conclusion
Here are a few points to remember for complicated large-scale data scraping:
- Before you begin, make a plan.
- Reduce the burden on web servers as much as possible.
- Only correct data should be extracted.
- Scraping Intelligence has a lot of expertise in dealing with the issues of large-scale scraping.
If you want large-scale data extraction, please contact Scraping Intelligence for a tailored solution.
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