A web crawler is a program that automatically discovers and visits web pages by following links from one page to the next, used to index or collect content at scale. Search engines use crawlers to build their index of the web, and Googlebot is one well-known example. Data collection systems can use crawlers for a different reason: to find the pages that contain information they eventually want to collect.
A crawler and a scraper are not the same thing. A crawler’s job is to find pages. A web scraper’s job is to pull specific data out of a page once it has been found. Most large-scale collection systems use both: the crawler builds the map, the scraper does the extraction. A simple way to think about it is that the crawler builds the map, and the scraper uses that map to get the information you actually need.
[GRAPHIC 1: crawl loop] Alt Tag: a simple circular flow diagram showing the crawl loop in four steps, “Seed URL” to “Fetch page” to “Parse links” to “Queue new links,” with an arrow looping back from the last step to “Fetch page.” Plain, labeled, no more than four nodes. This is the single most useful graphic on the page, since the loop is the actual mechanism.
How does a web crawler work?
A crawler is assigned to index an ecommerce category page. It starts with one seed URL, the category’s main listing page. It fetches that page, then scans the HTML for links: product pages, pagination links to page 2, page 3, links to related categories, and so on. Each new link goes into a queue. The crawler then fetches the next URL in that queue, repeats the same process, and keeps going until the queue runs out or it hits a limit set by whoever is running it.
The basic loop has four steps: fetch, find links, queue, repeat. Crawling systems add controls around that loop. They may decide which URLs to follow, how deeply to crawl a site, how quickly to make requests, how to handle duplicate URLs, and what to do when a page fails to load. Sites can also publish a robots.txt file that specifies which URL paths compliant crawlers should or should not crawl.
What is the difference between a crawler and a scraper?
The simplest way to picture it is a library. A crawler is someone walking through the library and making a list of where the books are. A scraper is someone who already knows which book they need and copies the specific information they want from it.
| Web Crawler | Web Scraper | |
|---|---|---|
| What it does | Discovers web pages by starting with one or more URLs and following links to find additional pages. | Extracts specific information from pages, such as prices, product names, reviews, or article text. |
| What it starts with | A seed URL or list of starting URLs that gives the crawler somewhere to begin exploring. | A webpage or list of pages that have already been identified as containing useful data. |
| What it produces | A list or map of discovered URLs that can be indexed, monitored, or passed to another system for extraction. | Extracted web data that can be cleaned and organized for use in a dataset, database, API, or application. |
| Real example | Starts on a retailer’s category page and discovers 40,000 individual product URLs across categories and pagination. | Visits those product URLs and extracts the product name, price, SKU, availability, and rating from each one. |
| How it fails | Pages can be missed because of JavaScript-rendered links, crawl rules, duplicate URLs, pagination, or other discovery problems. | Fields can be missing or extracted incorrectly when page layouts change, content loads dynamically, or the scraper gets blocked. |
A crawler can operate without a scraper if the goal is simply to discover or index pages. A scraper can also operate without a crawler if the URLs to extract data from are already known. Large-scale web data collection often uses both. That is why the two are usually paired: the crawler answers “what pages exist,” and the scraper answers “what is actually on them.” For more on how a scraper works once it is pointed at a page, see What Is a Web Scraper?.
What are web crawlers used for?
Web crawling is used anywhere a system needs to discover pages across a website or a larger part of the web. Common uses include:
- Search engine indexing: Search engines crawl the web to discover pages that may be added to their indexes.
- Web data collection: Crawlers can discover the pages that contain the data a collection system needs.
- Website monitoring: Crawlers can revisit known areas of a site to identify new or changed pages.
- Site auditing: SEO and technical tools crawl websites to identify links, redirects, errors, metadata, and site structure.
- Content discovery: Crawlers can find articles, product pages, listings, or other content within a target site.
For large-scale data collection, discovery is only the first stage. Once the relevant pages are known, they still need to be accessed and the required information extracted.
What problems do web crawlers run into?
Web crawlers run into a handful of recurring problems.
| Problem | Why it happens | How it is usually handled |
|---|---|---|
| Pages that do not show their links until they have loaded | Many modern websites create links and page content with JavaScript after the initial HTML arrives. A crawler that only reads the first HTML response may never see those links. | Try first: use a crawler or browser automation tool that can load JavaScript before collecting links. Open-source tools such as Playwright or Puppeteer can do this, and some crawler frameworks can be configured to work with them. At larger scale: teams often use browser automation services, scraping APIs, or managed data collection providers that handle rendering, browser sessions, retries, and infrastructure across many pages. |
| Infinite pagination and duplicate URLs | Filters, sort orders, tracking parameters, session IDs, and faceted navigation can create many different URLs that lead to the same or very similar content. | Try first: use a crawler framework such as Scrapy, Crawlee, or Apache Nutch that can track visited URLs and enforce crawl limits. You can also normalize URLs, remove unnecessary tracking parameters, and define pagination rules. At larger scale: teams may use managed crawling or web scraping services that track crawl state, deduplicate URLs, schedule recurring jobs, and prevent repeated requests across very large collections. |
| Rules the site itself sets, on purpose | Websites can publish a robots.txt file that indicates which parts of the site crawlers should or should not access. | Try first: configure your crawler to read robots.txt and avoid disallowed paths. Frameworks such as Scrapy, Crawlee, and Apache Nutch can be configured to respect these rules while also controlling crawl speed, allowed domains, and page depth At larger scale: managed crawling platforms can apply these rules consistently across many domains and collection jobs, reducing the amount of manual configuration needed for each source. |
| Rate limits and blocking | Crawlers may be throttled or blocked when they send too many requests, come from a limited set of IPs, or exhibit patterns that a site’s defenses recognize as automated traffic. | Try first: use built-in controls in tools such as Scrapy or Crawlee to slow request rates, add retries and backoff, monitor response codes, and reduce unnecessary requests. Browser tools such as Playwright or Selenium can also help when pages require browser-like interaction At larger scale: teams may need distributed IP access, browser automation, session management, monitoring, and other access infrastructure. These capabilities are often provided by specialized web scraping infrastructure or full-service data collection providers. |
Which tools are needed depends on the scale and complexity of the crawl. A small project may only need a crawler and basic retry logic. Larger collection systems often depend on professional web scraping or managed data collection services that combine browser rendering, distributed IP access, session management, monitoring, retries, and other access infrastructure in one workflow.
These capabilities become especially important when websites start limiting or challenging automated traffic. Why Do Web Scrapers Get Blocked? explains how rate limits, fingerprinting, access controls, and blocking affect large-scale collection and what infrastructure is typically used to manage them.
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FAQs
- What is a web crawler? A web crawler is a program that automatically discovers and visits web pages by following links, typically to build an index or a list of URLs for later use.
- How do you crawl a website? At a basic level, a crawler starts from one URL, extracts the links on that page, and repeats the process on each new link it finds, usually with rules limiting how deep or how fast it goes, and respecting the site’s robots.txt rules along the way. For a full walkthrough of building this at production scale, see Large-Scale Data Collection: Complete Guide.
- Are Googlebot and a web crawler the same thing?Googlebot is Google’s own web crawler, the specific program it uses to discover and index pages for search results. It is one example of a web crawler, not a separate category of thing. The same mechanics, fetching a page, following its links, repeating, apply whether it is Googlebot indexing the web or a company crawling its own set of target sites. Google runs both a mobile and a desktop version of Googlebot, though for most sites today the mobile crawler does the majority of the work.
- Do you need a crawler for web scraping? Not always. If the target URLs are already known, a scraper can work directly from that list. Crawling becomes useful when the pages first need to be discovered.
- Is web crawling legal? Crawling publicly accessible pages is generally treated the same as any other automated access to public content, though the legal picture depends on the specific site, its terms, and what the data is used for afterward. See [Is Web Scraping Legal?] for the fuller answer, and Why Do Web Scrapers Get Blocked? for how the 2026 legal picture around scraping for AI training specifically is playing out in real cases.
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