Adding more URLs
Previously you've built a very simple crawler that downloads HTML of a single page, reads its title and prints it to the console. This is the original source code:
import asyncio
from crawlee.beautifulsoup_crawler import BeautifulSoupCrawler, BeautifulSoupCrawlingContext
async def main() -> None:
crawler = BeautifulSoupCrawler()
@crawler.router.default_handler
async def request_handler(context: BeautifulSoupCrawlingContext) -> None:
url = context.request.url
title = context.soup.title.string if context.soup.title else ''
context.log.info(f'The title of {url} is: {title}.')
await crawler.run(['https://crawlee.dev/'])
if __name__ == '__main__':
asyncio.run(main())
Now you'll use the example from the previous section and improve on it. You'll add more URLs to the queue and thanks to that the crawler will keep going, finding new links, enqueuing them into the RequestQueue and then scraping them.
How crawling works
The process is simple:
- Find new links on the page.
- Filter only those pointing to the same domain, in this case crawlee.dev.
- Enqueue (add) them to the
RequestQueue. - Visit the newly enqueued links.
- Repeat the process.
In the following paragraphs you will learn about the enqueue_links function which simplifies crawling to a single function call.
The enqueue_links function is context aware. It means that it will read the information about the currently crawled page from the context, and you don't need to explicitly provide any arguments. However, you can specify filtering criteria or an enqueuing strategy if desired. It will find the links and automatically add the links to the running crawler's RequestQueue.
Limit your crawls
When you're just testing your code or when your crawler could potentially find millions of links, it's very useful to set a maximum limit of crawled pages. The option is called max_requests_per_crawl, is available in all crawlers, and you can set it like this:
crawler = BeautifulSoupCrawler(max_requests_per_crawl=20)
This means that no new requests will be started after the 20th request is finished. The actual number of processed requests might be a little higher thanks to parallelization, because the running requests won't be forcefully aborted. It's not even possible in most cases.
Finding new links
There are numerous approaches to finding links to follow when crawling the web. For our purposes, we will be looking for <a> elements that contain the href attribute because that's what you need in most cases. For example:
<a href="https://crawlee.dev/docs/introduction">This is a link to Crawlee introduction</a>
Since this is the most common case, it is also the enqueue_links default.
import asyncio
from crawlee.beautifulsoup_crawler import BeautifulSoupCrawler, BeautifulSoupCrawlingContext
async def main() -> None:
# Let's limit our crawls to make our tests shorter and safer.
crawler = BeautifulSoupCrawler(max_requests_per_crawl=20)
@crawler.router.default_handler
async def request_handler(context: BeautifulSoupCrawlingContext) -> None:
url = context.request.url
title = context.soup.title.string if context.soup.title else ''
context.log.info(f'The title of {url} is: {title}.')
# The enqueue_links function is available as one of the fields of the context.
# It is also context aware, so it does not require any parameters.
await context.enqueue_links()
await crawler.run(['https://crawlee.dev/'])
if __name__ == '__main__':
asyncio.run(main())
If you need to override the default selection of elements in enqueue_links, you can use the selector argument.
await context.enqueue_links(selector='a.article-link')
Filtering links to same domain
Websites typically contain a lot of links that lead away from the original page. This is normal, but when crawling a website, we usually want to crawl that one site and not let our crawler wander away to Google, Facebook and Twitter. Therefore, we need to filter out the off-domain links and only keep the ones that lead to the same domain.
# The default behavior of enqueue_links is to stay on the same hostname, so it does not require
# any parameters. This will ensure the subdomain stays the same.
await context.enqueue_links()
The default behavior of enqueue_links is to stay on the same hostname. This does not include subdomains. To include subdomains in your crawl, use the strategy argument.
# See the EnqueueStrategy object for more strategy options.
await enqueue_links(strategy='same-domain')
When you run the code, you will see the crawler log the title of the first page, then the enqueueing message showing number of URLs, followed by the title of the first enqueued page and so on and so on.
Skipping duplicate URLs
Skipping of duplicate URLs is critical, because visiting the same page multiple times would lead to duplicate results. This is automatically handled by the RequestQueue which deduplicates requests using their unique_key. This unique_key is automatically generated from the request's URL by lowercasing the URL, lexically ordering query parameters, removing fragments and a few other tweaks that ensure the queue only includes unique URLs.
Advanced filtering arguments
While the defaults for enqueue_links can be often exactly what you need, it also gives you fine-grained control over which URLs should be enqueued. One way we already mentioned above. It is using the EnqueueStrategy. You can use the all strategy if you want to follow every single link, regardless of its domain, or you can enqueue links that target the same domain name with the same_domain strategy.
# Wanders the internet.
await enqueue_links(strategy='all')
Filter URLs with patterns
For even more control, you can use the include or exclude parameters, either as glob patterns or regular expressions, to filter the URLs. Refer to the API documentation for enqueue_links for detailed information on these and other available options.
from crawlee import Glob
await enqueue_links(
include=[Glob('https://someplace.com/**/cats')],
exclude=[Glob('https://**/archive/**')],
)
Next steps
Next, you will start your project of scraping a production website and learn some more Crawlee tricks in the process.