What Is a Web Crawler?
Learn what web crawlers are, how they discover pages, how crawling differs from scraping, and how a Python crawler can collect links.
A web crawler is a program that systematically visits web pages and collects information available on the web. Crawlers follow links from page to page, building a record of the pages they discover.
Web crawler, web spider, and bot are commonly used names for this kind of automated program. In this lesson, you will learn how crawling works and how these ideas apply to building a crawler in Python.
What Is a Web Crawler?
A web crawler methodically browses web pages by making requests to URLs, receiving page content, and examining that content for useful information. A simple crawler may collect only links. A more advanced crawler may also collect page titles, headings, text, images, metadata, or other elements.
The crawler needs a starting point and a way to remember which pages it has already found or visited. It then repeats the process of visiting pages and discovering more links.
Important names
- Web crawler: A program that methodically browses web pages to collect information.
- Web spider: Another name for a web crawler.
- Bot: An automated program. In this context, a bot visits web pages automatically.
Why Are Web Crawlers Used?
Web crawling supports several types of work. The most recognizable use is search-engine discovery. Search engines need automated programs to find new pages and revisit existing pages when their content changes.
A crawler collects documents or page information. The search engine can process that information and add useful results to a search index. A search index is a searchable collection of documents or page information. When someone searches, the search engine uses its index to find pages that may answer the query.
Crawling is also useful for research, monitoring, link checking, catalog creation, and data collection. In many projects, crawling is the part that finds and visits pages, while another step extracts the specific information the project needs.
Googlebot as a real-world example
Googlebot is Google's web crawler. It visits web documents and gathers information that can be processed for Google Search. Googlebot illustrates the connection between crawling and indexing: crawling discovers documents, and indexing organizes information about those documents so they can be found through search.
Web Crawling Versus Web Scraping
Web crawling focuses on discovering and visiting pages. Web scraping focuses on extracting selected information or elements from pages.
| Activity | Primary goal | Typical output |
|---|---|---|
| Web crawling | Discover and visit pages | A list of URLs, visited-page records, or downloaded documents |
| Web scraping | Extract selected information from pages | Data such as product names, prices, headings, or article text |
The two activities often work together. For example, a crawler can identify every reachable page in a domain, and a scraping step can extract the page title from each visited page.
A crawler does not have to scrape detailed page data. It can collect links only. However, the same crawler can later be extended to gather other page elements after it downloads a page.
How the Crawl Process Works
A basic crawler follows a repeated discovery-and-visit cycle. It begins with one or more seed URLs, visits a URL, finds hyperlinks on the page, and saves eligible new URLs for later processing.
A seed URL is an initial URL from which a crawler begins its work. A hyperlink is a link on a page that points to another URL.
The collection of URLs waiting to be visited is called the crawl frontier. The frontier may be implemented as a list, set, queue, or another data structure. A crawler normally also tracks visited URLs so it does not repeatedly process the same page.
Basic crawl lifecycle
| Step | Input | Action | Result |
|---|---|---|---|
| Start with seed URLs | One or more starting addresses | Place the starting URLs into the pending collection | The crawl frontier contains initial work |
| Visit a pending URL | A URL from the frontier | Fetch the page and inspect its response | The crawler has page content to process |
| Extract hyperlinks | The visited page | Find links that point to other URLs | Potentially new URLs are discovered |
| Add eligible links to the frontier | Discovered links | Filter and record links that should be visited | New pending work is available |
| Continue until the frontier is exhausted | The updated frontier | Repeat visiting and discovery | The crawler stops when no eligible URLs remain |
Simple pseudocode
frontier = [seed_url]
visited = set()
while frontier:
url = frontier.pop(0)
if url in visited:
continue
page = fetch(url)
visited.add(url)
links = extract_links(page)
for link in links:
if link is eligible and link not in visited:
frontier.append(link)This example shows the central algorithm, not a complete production crawler. A real implementation must also handle failed requests, invalid links, duplicate URLs, response types, and the rules for which pages belong in the crawl.
Crawling a Site or Domain
A simple project commonly restricts its work to one target website or domain. Here, the domain is the website scope that the crawler is configured to stay within.
For example, a crawler that starts at a site's homepage may collect links from that page, visit eligible links, discover more links, and continue until all reachable in-scope pages have been processed. This is called domain-scoped crawling.
Domain scope is a practical constraint. Without it, a crawler could follow external links indefinitely across the wider web. A domain filter lets a beginner project focus on gathering pages that belong to the selected site.
Example: link discovery from one starting page
- Use the site's homepage as a seed URL.
- Fetch the homepage.
- Extract its hyperlinks.
- Keep links that meet the project's scope rules.
- Add previously unseen links to the crawl frontier.
- Visit a pending link and repeat the process.
The result is a growing set of reachable, eligible URLs. The crawler may not find every possible page if a page is not linked from the starting point or if the project deliberately excludes certain links.
Crawler and Scraper Working Together
Consider a project that needs the title from every reachable page in a domain. The crawler first discovers and visits the pages. The scraper then extracts the title from each page.
The division of work can look like this:
- Crawler: Finds pages by following hyperlinks.
- Fetcher: Retrieves the content for a URL.
- Scraper: Selects and extracts the required page elements.
- Storage: Saves URLs and extracted results for later use.
These roles may be implemented in one program, but separating the concepts makes the design easier to understand and extend.
Introducing the Python Crawler Project
The course project is a functional Python crawler. Its initial behavior is deliberately focused: visit pages in a target site and gather links from them.
That first version establishes the main crawling workflow:
- Maintain a collection of URLs waiting to be visited.
- Fetch a pending page.
- Parse the page to find hyperlinks.
- Reject links outside the selected scope or links already seen.
- Add new eligible links to the pending collection.
Once link collection works, the crawler can be adapted to gather information besides links. For example, it could record page titles, headings, text, or selected attributes.
For a practical implementation path, see Create a Web Crawler in Python. Related lessons cover web crawler requirements, parsing HTML, and creating the crawler.
Why use multithreading?
Multithreading means using multiple threads of execution so a program can handle more than one waiting task over time. Network requests often spend time waiting for a server response. With several worker threads, a crawler can process multiple pending pages rather than leaving the whole program idle during each wait.
For example, one worker may be waiting for a page response while another worker handles a different pending URL. This can reduce the time required to collect links compared with a single-worker crawler. Multithreading also adds design concerns, such as coordinating the frontier, preventing duplicate visits, and safely recording results.
Web Crawler Terminology
| Term | Meaning | Role in the crawl process |
|---|---|---|
| Web crawler | A program that methodically browses web pages to collect information | Fetches pages and drives the discovery process |
| Spider or bot | Alternative names for an automated page-visiting program | Describes the automated actor performing the crawl |
| Seed URL | An initial URL from which a crawler begins | Provides the first item or items for the frontier |
| Crawl frontier | The set, queue, or list of discovered URLs still waiting to be visited | Stores pending crawl work |
| Hyperlink | A link on a page that points to another URL | Provides new URLs for discovery |
| Web scraping | Extracting information or specific elements from web pages | Processes visited pages to produce selected data |
| Search index | A searchable collection of documents or page information | Stores processed information that a search engine can query |
Key Takeaways
- A web crawler, web spider, or bot is software that systematically visits web pages.
- Crawlers discover content for search engines and support data-collection projects.
- Crawling finds and visits pages; scraping extracts selected information from those pages.
- Seed URLs start the process, and the crawl frontier holds discovered URLs waiting for processing.
- The basic cycle is fetch, extract hyperlinks, add eligible new links, and repeat.
- Domain-scoped crawling keeps a simple crawler focused on one target website.
- Googlebot is a real-world crawler that collects documents for Google Search.
- The planned Python crawler begins with link collection and can later be extended to gather other data.
- Multithreading can help process network-bound crawling work faster, while requiring careful coordination.