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Web crawler – Wikipedia
This article is about the internet bot. For the search engine, see WebCrawler. “Web spider” redirects here; it is not to be confused with Spider web. “Spiderbot” redirects here; for the video game, see Arac (video game).
Architecture of a Web crawler
A Web crawler, sometimes called a spider or spiderbot and often shortened to crawler, is an Internet bot that systematically browses the World Wide Web, typically operated by search engines for the purpose of Web indexing (web spidering). 
Web search engines and some other websites use Web crawling or spidering software to update their web content or indices of other sites’ web content. Web crawlers copy pages for processing by a search engine, which indexes the downloaded pages so that users can search more efficiently.
Crawlers consume resources on visited systems and often visit sites without approval. Issues of schedule, load, and “politeness” come into play when large collections of pages are accessed. Mechanisms exist for public sites not wishing to be crawled to make this known to the crawling agent. For example, including a file can request bots to index only parts of a website, or nothing at all.
The number of Internet pages is extremely large; even the largest crawlers fall short of making a complete index. For this reason, search engines struggled to give relevant search results in the early years of the World Wide Web, before 2000. Today, relevant results are given almost instantly.
Crawlers can validate hyperlinks and HTML code. They can also be used for web scraping and data-driven programming.
A web crawler is also known as a spider,  an ant, an automatic indexer,  or (in the FOAF software context) a Web scutter. 
A Web crawler starts with a list of URLs to visit, called the seeds. As the crawler visits these URLs, it identifies all the hyperlinks in the pages and adds them to the list of URLs to visit, called the crawl frontier. URLs from the frontier are recursively visited according to a set of policies. If the crawler is performing archiving of websites (or web archiving), it copies and saves the information as it goes. The archives are usually stored in such a way they can be viewed, read and navigated as if they were on the live web, but are preserved as ‘snapshots’. 
The archive is known as the repository and is designed to store and manage the collection of web pages. The repository only stores HTML pages and these pages are stored as distinct files. A repository is similar to any other system that stores data, like a modern-day database. The only difference is that a repository does not need all the functionality offered by a database system. The repository stores the most recent version of the web page retrieved by the crawler. 
The large volume implies the crawler can only download a limited number of the Web pages within a given time, so it needs to prioritize its downloads. The high rate of change can imply the pages might have already been updated or even deleted.
The number of possible URLs crawled being generated by server-side software has also made it difficult for web crawlers to avoid retrieving duplicate content. Endless combinations of HTTP GET (URL-based) parameters exist, of which only a small selection will actually return unique content. For example, a simple online photo gallery may offer three options to users, as specified through HTTP GET parameters in the URL. If there exist four ways to sort images, three choices of thumbnail size, two file formats, and an option to disable user-provided content, then the same set of content can be accessed with 48 different URLs, all of which may be linked on the site. This mathematical combination creates a problem for crawlers, as they must sort through endless combinations of relatively minor scripted changes in order to retrieve unique content.
As Edwards et al. noted, “Given that the bandwidth for conducting crawls is neither infinite nor free, it is becoming essential to crawl the Web in not only a scalable, but efficient way, if some reasonable measure of quality or freshness is to be maintained. “ A crawler must carefully choose at each step which pages to visit next.
The behavior of a Web crawler is the outcome of a combination of policies:
a selection policy which states the pages to download,
a re-visit policy which states when to check for changes to the pages,
a politeness policy that states how to avoid overloading Web sites.
a parallelization policy that states how to coordinate distributed web crawlers.
Given the current size of the Web, even large search engines cover only a portion of the publicly available part. A 2009 study showed even large-scale search engines index no more than 40-70% of the indexable Web; a previous study by Steve Lawrence and Lee Giles showed that no search engine indexed more than 16% of the Web in 1999.  As a crawler always downloads just a fraction of the Web pages, it is highly desirable for the downloaded fraction to contain the most relevant pages and not just a random sample of the Web.
This requires a metric of importance for prioritizing Web pages. The importance of a page is a function of its intrinsic quality, its popularity in terms of links or visits, and even of its URL (the latter is the case of vertical search engines restricted to a single top-level domain, or search engines restricted to a fixed Web site). Designing a good selection policy has an added difficulty: it must work with partial information, as the complete set of Web pages is not known during crawling.
Junghoo Cho et al. made the first study on policies for crawling scheduling. Their data set was a 180, 000-pages crawl from the domain, in which a crawling simulation was done with different strategies.  The ordering metrics tested were breadth-first, backlink count and partial PageRank calculations. One of the conclusions was that if the crawler wants to download pages with high Pagerank early during the crawling process, then the partial Pagerank strategy is the better, followed by breadth-first and backlink-count. However, these results are for just a single domain. Cho also wrote his PhD dissertation at Stanford on web crawling. 
Najork and Wiener performed an actual crawl on 328 million pages, using breadth-first ordering.  They found that a breadth-first crawl captures pages with high Pagerank early in the crawl (but they did not compare this strategy against other strategies). The explanation given by the authors for this result is that “the most important pages have many links to them from numerous hosts, and those links will be found early, regardless of on which host or page the crawl originates. ”
Abiteboul designed a crawling strategy based on an algorithm called OPIC (On-line Page Importance Computation).  In OPIC, each page is given an initial sum of “cash” that is distributed equally among the pages it points to. It is similar to a PageRank computation, but it is faster and is only done in one step. An OPIC-driven crawler downloads first the pages in the crawling frontier with higher amounts of “cash”. Experiments were carried in a 100, 000-pages synthetic graph with a power-law distribution of in-links. However, there was no comparison with other strategies nor experiments in the real Web.
Boldi et al. used simulation on subsets of the Web of 40 million pages from the domain and 100 million pages from the WebBase crawl, testing breadth-first against depth-first, random ordering and an omniscient strategy. The comparison was based on how well PageRank computed on a partial crawl approximates the true PageRank value. Surprisingly, some visits that accumulate PageRank very quickly (most notably, breadth-first and the omniscient visit) provide very poor progressive approximations. 
Baeza-Yates et al. used simulation on two subsets of the Web of 3 million pages from the and domain, testing several crawling strategies.  They showed that both the OPIC strategy and a strategy that uses the length of the per-site queues are better than breadth-first crawling, and that it is also very effective to use a previous crawl, when it is available, to guide the current one.
Daneshpajouh et al. designed a community based algorithm for discovering good seeds.  Their method crawls web pages with high PageRank from different communities in less iteration in comparison with crawl starting from random seeds. One can extract good seed from a previously-crawled-Web graph using this new method. Using these seeds, a new crawl can be very effective.
Restricting followed links
A crawler may only want to seek out HTML pages and avoid all other MIME types. In order to request only HTML resources, a crawler may make an HTTP HEAD request to determine a Web resource’s MIME type before requesting the entire resource with a GET request. To avoid making numerous HEAD requests, a crawler may examine the URL and only request a resource if the URL ends with certain characters such as,,,,,, or a slash. This strategy may cause numerous HTML Web resources to be unintentionally skipped.
Some crawlers may also avoid requesting any resources that have a “? ” in them (are dynamically produced) in order to avoid spider traps that may cause the crawler to download an infinite number of URLs from a Web site. This strategy is unreliable if the site uses URL rewriting to simplify its URLs.
Crawlers usually perform some type of URL normalization in order to avoid crawling the same resource more than once. The term URL normalization, also called URL canonicalization, refers to the process of modifying and standardizing a URL in a consistent manner. There are several types of normalization that may be performed including conversion of URLs to lowercase, removal of “. ” and “.. ” segments, and adding trailing slashes to the non-empty path component. 
Some crawlers intend to download/upload as many resources as possible from a particular web site. So path-ascending crawler was introduced that would ascend to every path in each URL that it intends to crawl.  For example, when given a seed URL of, it will attempt to crawl /hamster/monkey/, /hamster/, and /. Cothey found that a path-ascending crawler was very effective in finding isolated resources, or resources for which no inbound link would have been found in regular crawling.
The importance of a page for a crawler can also be expressed as a function of the similarity of a page to a given query. Web crawlers that attempt to download pages that are similar to each other are called focused crawler or topical crawlers. The concepts of topical and focused crawling were first introduced by Filippo Menczer and by Soumen Chakrabarti et al. 
The main problem in focused crawling is that in the context of a Web crawler, we would like to be able to predict the similarity of the text of a given page to the query before actually downloading the page. A possible predictor is the anchor text of links; this was the approach taken by Pinkerton in the first web crawler of the early days of the Web. Diligenti et al.  propose using the complete content of the pages already visited to infer the similarity between the driving query and the pages that have not been visited yet. The performance of a focused crawling depends mostly on the richness of links in the specific topic being searched, and a focused crawling usually relies on a general Web search engine for providing starting points.
An example of the focused crawlers are academic crawlers, which crawls free-access academic related documents, such as the citeseerxbot, which is the crawler of CiteSeerX search engine. Other academic search engines are Google Scholar and Microsoft Academic Search etc. Because most academic papers are published in PDF formats, such kind of crawler is particularly interested in crawling PDF, PostScript files, Microsoft Word including their zipped formats. Because of this, general open source crawlers, such as Heritrix, must be customized to filter out other MIME types, or a middleware is used to extract these documents out and import them to the focused crawl database and repository.  Identifying whether these documents are academic or not is challenging and can add a significant overhead to the crawling process, so this is performed as a post crawling process using machine learning or regular expression algorithms. These academic documents are usually obtained from home pages of faculties and students or from publication page of research institutes. Because academic documents takes only a small fraction in the entire web pages, a good seed selection are important in boosting the efficiencies of these web crawlers.  Other academic crawlers may download plain text and HTML files, that contains metadata of academic papers, such as titles, papers, and abstracts. This increases the overall number of papers, but a significant fraction may not provide free PDF downloads.
Semantic focused crawler
Another type of focused crawlers is semantic focused crawler, which makes use of domain ontologies to represent topical maps and link Web pages with relevant ontological concepts for the selection and categorization purposes.  In addition, ontologies can be automatically updated in the crawling process. Dong et al.  introduced such an ontology-learning-based crawler using support vector machine to update the content of ontological concepts when crawling Web Pages.
The Web has a very dynamic nature, and crawling a fraction of the Web can take weeks or months. By the time a Web crawler has finished its crawl, many events could have happened, including creations, updates, and deletions.
From the search engine’s point of view, there is a cost associated with not detecting an event, and thus having an outdated copy of a resource. The most-used cost functions are freshness and age. 
Freshness: This is a binary measure that indicates whether the local copy is accurate or not. The freshness of a page p in the repository at time t is defined as:
Age: This is a measure that indicates how outdated the local copy is. The age of a page p in the repository, at time t is defined as:
Coffman et al. worked with a definition of the objective of a Web crawler that is equivalent to freshness, but use a different wording: they propose that a crawler must minimize the fraction of time pages remain outdated. They also noted that the problem of Web crawling can be modeled as a multiple-queue, single-server polling system, on which the Web crawler is the server and the Web sites are the queues. Page modifications are the arrival of the customers, and switch-over times are the interval between page accesses to a single Web site. Under this model, mean waiting time for a customer in the polling system is equivalent to the average age for the Web crawler. 
The objective of the crawler is to keep the average freshness of pages in its collection as high as possible, or to keep the average age of pages as low as possible. These objectives are not equivalent: in the first case, the crawler is just concerned with how many pages are out-dated, while in the second case, the crawler is concerned with how old the local copies of pages are.
Evolution of Freshness and Age in a web crawler
Two simple re-visiting policies were studied by Cho and Garcia-Molina:
Uniform policy: This involves re-visiting all pages in the collection with the same frequency, regardless of their rates of change.
Proportional policy: This involves re-visiting more often the pages that change more frequently. The visiting frequency is directly proportional to the (estimated) change frequency.
In both cases, the repeated crawling order of pages can be done either in a random or a fixed order.
Cho and Garcia-Molina proved the surprising result that, in terms of average freshness, the uniform policy outperforms the proportional policy in both a simulated Web and a real Web crawl. Intuitively, the reasoning is that, as web crawlers have a limit to how many pages they can crawl in a given time frame, (1) they will allocate too many new crawls to rapidly changing pages at the expense of less frequently updating pages, and (2) the freshness of rapidly changing pages lasts for shorter period than that of less frequently changing pages. In other words, a proportional policy allocates more resources to crawling frequently updating pages, but experiences less overall freshness time from them.
To improve freshness, the crawler should penalize the elements that change too often.  The optimal re-visiting policy is neither the uniform policy nor the proportional policy. The optimal method for keeping average freshness high includes ignoring the pages that change too often, and the optimal for keeping average age low is to use access frequencies that monotonically (and sub-linearly) increase with the rate of change of each page. In both cases, the optimal is closer to the uniform policy than to the proportional policy: as Coffman et al. note, “in order to minimize the expected obsolescence time, the accesses to any particular page should be kept as evenly spaced as possible”.  Explicit formulas for the re-visit policy are not attainable in general, but they are obtained numerically, as they depend on the distribution of page changes. Cho and Garcia-Molina show that the exponential distribution is a good fit for describing page changes,  while Ipeirotis et al. show how to use statistical tools to discover parameters that affect this distribution.  Note that the re-visiting policies considered here regard all pages as homogeneous in terms of quality (“all pages on the Web are worth the same”), something that is not a realistic scenario, so further information about the Web page quality should be included to achieve a better crawling policy.
Crawlers can retrieve data much quicker and in greater depth than human searchers, so they can have a crippling impact on the performance of a site. If a single crawler is performing multiple requests per second and/or downloading large files, a server can have a hard time keeping up with requests from multiple crawlers.
As noted by Koster, the use of Web crawlers is useful for a number of tasks, but comes with a price for the general community.  The costs of using Web crawlers include:
network resources, as crawlers require considerable bandwidth and operate with a high degree of parallelism during a long period of time;
server overload, especially if the frequency of accesses to a given server is too high;
poorly written crawlers, which can crash servers or routers, or which download pages they cannot handle; and
personal crawlers that, if deployed by too many users, can disrupt networks and Web servers.
A partial solution to these problems is the robots exclusion protocol, also known as the protocol that is a standard for administrators to indicate which parts of their Web servers should not be accessed by crawlers.  This standard does not include a suggestion for the interval of visits to the same server, even though this interval is the most effective way of avoiding server overload. Recently commercial search engines like Google, Ask Jeeves, MSN and Yahoo! Search are able to use an extra “Crawl-delay:” parameter in the file to indicate the number of seconds to delay between requests.
The first proposed interval between successive pageloads was 60 seconds.  However, if pages were downloaded at this rate from a website with more than 100, 000 pages over a perfect connection with zero latency and infinite bandwidth, it would take more than 2 months to download only that entire Web site; also, only a fraction of the resources from that Web server would be used. This does not seem acceptable.
Cho uses 10 seconds as an interval for accesses,  and the WIRE crawler uses 15 seconds as the default.  The MercatorWeb crawler follows an adaptive politeness policy: if it took t seconds to download a document from a given server, the crawler waits for 10t seconds before downloading the next page.  Dill et al. use 1 second. 
For those using Web crawlers for research purposes, a more detailed cost-benefit analysis is needed and ethical considerations should be taken into account when deciding where to crawl and how fast to crawl. 
Anecdotal evidence from access logs shows that access intervals from known crawlers vary between 20 seconds and 3–4 minutes. It is worth noticing that even when being very polite, and taking all the safeguards to avoid overloading Web servers, some complaints from Web server administrators are received. Brin and Page note that: “… running a crawler which connects to more than half a million servers (… ) generates a fair amount of e-mail and phone calls. Because of the vast number of people coming on line, there are always those who do not know what a crawler is, because this is the first one they have seen. “
A parallel crawler is a crawler that runs multiple processes in parallel. The goal is to maximize the download rate while minimizing the overhead from parallelization and to avoid repeated downloads of the same page. To avoid downloading the same page more than once, the crawling system requires a policy for assigning the new URLs discovered during the crawling process, as the same URL can be found by two different crawling processes.
High-level architecture of a standard Web crawler
A crawler must not only have a good crawling strategy, as noted in the previous sections, but it should also have a highly optimized architecture.
Shkapenyuk and Suel noted that:
While it is fairly easy to build a slow crawler that downloads a few pages per second for a short period of time, building a high-performance system that can download hundreds of millions of pages over several weeks presents a number of challenges in system design, I/O and network efficiency, and robustness and manageability.
Web crawlers are a central part of search engines, and details on their algorithms and architecture are kept as business secrets. When crawler designs are published, there is often an important lack of detail that prevents others from reproducing the work. There are also emerging concerns about “search engine spamming”, which prevent major search engines from publishing their ranking algorithms.
While most of the website owners are keen to have their pages indexed as broadly as possible to have strong presence in search engines, web crawling can also have unintended consequences and lead to a compromise or data breach if a search engine indexes resources that shouldn’t be publicly available, or pages revealing potentially vulnerable versions of software.
Apart from standard web application security recommendations website owners can reduce their exposure to opportunistic hacking by only allowing search engines to index the public parts of their websites (with) and explicitly blocking them from indexing transactional parts (login pages, private pages, etc. ).
Web crawlers typically identify themselves to a Web server by using the User-agent field of an HTTP request. Web site administrators typically examine their Web servers’ log and use the user agent field to determine which crawlers have visited the web server and how often. The user agent field may include a URL where the Web site administrator may find out more information about the crawler. Examining Web server log is tedious task, and therefore some administrators use tools to identify, track and verify Web crawlers. Spambots and other malicious Web crawlers are unlikely to place identifying information in the user agent field, or they may mask their identity as a browser or other well-known crawler.
Web site administrators prefer Web crawlers to identify themselves so that they can contact the owner if needed. In some cases, crawlers may be accidentally trapped in a crawler trap or they may be overloading a Web server with requests, and the owner needs to stop the crawler. Identification is also useful for administrators that are interested in knowing when they may expect their Web pages to be indexed by a particular search engine.
Crawling the deep web
A vast amount of web pages lie in the deep or invisible web.  These pages are typically only accessible by submitting queries to a database, and regular crawlers are unable to find these pages if there are no links that point to them. Google’s Sitemaps protocol and mod oai are intended to allow discovery of these deep-Web resources.
Deep web crawling also multiplies the number of web links to be crawled. Some crawlers only take some of the URLs in form. In some cases, such as the Googlebot, Web crawling is done on all text contained inside the hypertext content, tags, or text.
Strategic approaches may be taken to target deep Web content. With a technique called screen scraping, specialized software may be customized to automatically and repeatedly query a given Web form with the intention of aggregating the resulting data. Such software can be used to span multiple Web forms across multiple Websites. Data extracted from the results of one Web form submission can be taken and applied as input to another Web form thus establishing continuity across the Deep Web in a way not possible with traditional web crawlers. 
Pages built on AJAX are among those causing problems to web crawlers. Google has proposed a format of AJAX calls that their bot can recognize and index. 
Web crawler bias
A recent study based on a large scale analysis of files showed that certain web crawlers were preferred over others, with Googlebot being the most preferred web crawler. 
Visual vs programmatic crawlers
There are a number of “visual web scraper/crawler” products available on the web which will crawl pages and structure data into columns and rows based on the users requirements. One of the main difference between a classic and a visual crawler is the level of programming ability required to set up a crawler. The latest generation of “visual scrapers” remove the majority of the programming skill needed to be able to program and start a crawl to scrape web data.
The visual scraping/crawling method relies on the user “teaching” a piece of crawler technology, which then follows patterns in semi-structured data sources. The dominant method for teaching a visual crawler is by highlighting data in a browser and training columns and rows. While the technology is not new, for example it was the basis of Needlebase which has been bought by Google (as part of a larger acquisition of ITA Labs), there is continued growth and investment in this area by investors and end-users. 
List of web crawlers
The following is a list of published crawler architectures for general-purpose crawlers (excluding focused web crawlers), with a brief description that includes the names given to the different components and outstanding features:
Historical web crawlers
World Wide Web Worm was a crawler used to build a simple index of document titles and URLs. The index could be searched by using the grep Unix command.
Yahoo! Slurp was the name of the Yahoo! Search crawler until Yahoo! contracted with Microsoft to use Bingbot instead.
In-house web crawlers
Bingbot is the name of Microsoft’s Bing webcrawler. It replaced Msnbot.
Baiduspider is Baidu’s web crawler.
Googlebot is described in some detail, but the reference is only about an early version of its architecture, which was written in C++ and Python. The crawler was integrated with the indexing process, because text parsing was done for full-text indexing and also for URL extraction. There is a URL server that sends lists of URLs to be fetched by several crawling processes. During parsing, the URLs found were passed to a URL server that checked if the URL have been previously seen. If not, the URL was added to the queue of the URL server.
WebCrawler was used to build the first publicly available full-text index of a subset of the Web. It was based on lib-WWW to download pages, and another program to parse and order URLs for breadth-first exploration of the Web graph. It also included a real-time crawler that followed links based on the similarity of the anchor text with the provided query.
WebFountain is a distributed, modular crawler similar to Mercator but written in C++.
Xenon is a web crawler used by government tax authorities to detect fraud. 
Commercial web crawlers
The following web crawlers are available, for a price::
SortSite – crawler for analyzing websites, available for Windows and Mac OS
Swiftbot – Swiftype’s web crawler, available as software as a service
Frontera is web crawling framework implementing crawl frontier component and providing scalability primitives for web crawler applications.
GNU Wget is a command-line-operated crawler written in C and released under the GPL. It is typically used to mirror Web and FTP sites.
GRUB was an open source distributed search crawler that Wikia Search used to crawl the web.
Heritrix is the Internet Archive’s archival-quality crawler, designed for archiving periodic snapshots of a large portion of the Web. It was written in Java.
htDig includes a Web crawler in its indexing engine.
HTTrack uses a Web crawler to create a mirror of a web site for off-line viewing. It is written in C and released under the GPL.
mnoGoSearch is a crawler, indexer and a search engine written in C and licensed under the GPL (*NIX machines only)
Apache Nutch is a highly extensible and scalable web crawler written in Java and released under an Apache License. It is based on Apache Hadoop and can be used with Apache Solr or Elasticsearch.
Open Search Server is a search engine and web crawler software release under the GPL.
PHP-Crawler is a simple PHP and MySQL based crawler released under the BSD License.
Scrapy, an open source webcrawler framework, written in python (licensed under BSD).
Seeks, a free distributed search engine (licensed under AGPL).
StormCrawler, a collection of resources for building low-latency, scalable web crawlers on Apache Storm (Apache License).
tkWWW Robot, a crawler based on the tkWWW web browser (licensed under GPL).
Xapian, a search crawler engine, written in c++.
YaCy, a free distributed search engine, built on principles of peer-to-peer networks (licensed under GPL).
Website mirroring software
Search Engine Scraping
^ “Web Crawlers:Browsing the Web”.
^ Spetka, Scott. “The TkWWW Robot: Beyond Browsing”. NCSA. Archived from the original on 3 September 2004. Retrieved 21 November 2010.
^ Kobayashi, M. & Takeda, K. (2000). “Information retrieval on the web”. ACM Computing Surveys. 32 (2): 144–173. CiteSeerX 10. 1. 126. 6094. doi:10. 1145/358923. 358934. S2CID 3710903.
^ See definition of scutter on FOAF Project’s wiki Archived 13 December 2009 at the Wayback Machine
^ Masanès, Julien (15 February 2007). Web Archiving. Springer. p. 1. ISBN 978-3-54046332-0. Retrieved 24 April 2014.
^ Patil, Yugandhara; Patil, Sonal (2016). “Review of Web Crawlers with Specification and Working” (PDF). International Journal of Advanced Research in Computer and Communication Engineering. 5 (1): 4.
^ Edwards, J., McCurley, K. S., and Tomlin, J. A. (2001). “An adaptive model for optimizing performance of an incremental web crawler”. Proceedings of the tenth international conference on World Wide Web – WWW ’01. In Proceedings of the Tenth Conference on World Wide Web. pp. 106–113. 1018. 1506. 1145/371920. 371960. ISBN 978-1581133486. S2CID 10316730. CS1 maint: multiple names: authors list (link)
^ Castillo, Carlos (2004). Effective Web Crawling (PhD thesis). University of Chile. Retrieved 3 August 2010.
^ A. Gulls; A. Signori (2005). “The indexable web is more than 11. 5 billion pages”. Special interest tracks and posters of the 14th international conference on World Wide Web. ACM Press. pp. 902–903. 1145/1062745. 1062789.
^ Steve Lawrence; C. Lee Giles (8 July 19
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Web crawler – ScienceDaily
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What is a web crawler? | How web spiders work | Cloudflare
What is a web crawler bot?
A web crawler, spider, or search engine bot downloads and indexes content from all over the Internet. The goal of such a bot is to learn what (almost) every webpage on the web is about, so that the information can be retrieved when it’s needed. They’re called “web crawlers” because crawling is the technical term for automatically accessing a website and obtaining data via a software program.
These bots are almost always operated by search engines. By applying a search algorithm to the data collected by web crawlers, search engines can provide relevant links in response to user search queries, generating the list of webpages that show up after a user types a search into Google or Bing (or another search engine).
A web crawler bot is like someone who goes through all the books in a disorganized library and puts together a card catalog so that anyone who visits the library can quickly and easily find the information they need. To help categorize and sort the library’s books by topic, the organizer will read the title, summary, and some of the internal text of each book to figure out what it’s about.
However, unlike a library, the Internet is not composed of physical piles of books, and that makes it hard to tell if all the necessary information has been indexed properly, or if vast quantities of it are being overlooked. To try to find all the relevant information the Internet has to offer, a web crawler bot will start with a certain set of known webpages and then follow hyperlinks from those pages to other pages, follow hyperlinks from those other pages to additional pages, and so on.
It is unknown how much of the publicly available Internet is actually crawled by search engine bots. Some sources estimate that only 40-70% of the Internet is indexed for search – and that’s billions of webpages.
What is search indexing?
Search indexing is like creating a library card catalog for the Internet so that a search engine knows where on the Internet to retrieve information when a person searches for it. It can also be compared to the index in the back of a book, which lists all the places in the book where a certain topic or phrase is mentioned.
Indexing focuses mostly on the text that appears on the page, and on the metadata* about the page that users don’t see. When most search engines index a page, they add all the words on the page to the index – except for words like “a, ” “an, ” and “the” in Google’s case. When users search for those words, the search engine goes through its index of all the pages where those words appear and selects the most relevant ones.
*In the context of search indexing, metadata is data that tells search engines what a webpage is about. Often the meta title and meta description are what will appear on search engine results pages, as opposed to content from the webpage that’s visible to users.
How do web crawlers work?
The Internet is constantly changing and expanding. Because it is not possible to know how many total webpages there are on the Internet, web crawler bots start from a seed, or a list of known URLs. They crawl the webpages at those URLs first. As they crawl those webpages, they will find hyperlinks to other URLs, and they add those to the list of pages to crawl next.
Given the vast number of webpages on the Internet that could be indexed for search, this process could go on almost indefinitely. However, a web crawler will follow certain policies that make it more selective about which pages to crawl, in what order to crawl them, and how often they should crawl them again to check for content updates.
The relative importance of each webpage: Most web crawlers don’t crawl the entire publicly available Internet and aren’t intended to; instead they decide which pages to crawl first based on the number of other pages that link to that page, the amount of visitors that page gets, and other factors that signify the page’s likelihood of containing important information.
The idea is that a webpage that is cited by a lot of other webpages and gets a lot of visitors is likely to contain high-quality, authoritative information, so it’s especially important that a search engine has it indexed – just as a library might make sure to keep plenty of copies of a book that gets checked out by lots of people.
Revisiting webpages: Content on the Web is continually being updated, removed, or moved to new locations. Web crawlers will periodically need to revisit pages to make sure the latest version of the content is indexed.
requirements: Web crawlers also decide which pages to crawl based on the protocol (also known as the robots exclusion protocol). Before crawling a webpage, they will check the file hosted by that page’s web server. A file is a text file that specifies the rules for any bots accessing the hosted website or application. These rules define which pages the bots can crawl, and which links they can follow. As an example, check out the file.
All these factors are weighted differently within the proprietary algorithms that each search engine builds into their spider bots. Web crawlers from different search engines will behave slightly differently, although the end goal is the same: to download and index content from webpages.
Why are web crawlers called ‘spiders’?
The Internet, or at least the part that most users access, is also known as the World Wide Web – in fact that’s where the “www” part of most website URLs comes from. It was only natural to call search engine bots “spiders, ” because they crawl all over the Web, just as real spiders crawl on spiderwebs.
Should web crawler bots always be allowed to access web properties?
That’s up to the web property, and it depends on a number of factors. Web crawlers require server resources in order to index content – they make requests that the server needs to respond to, just like a user visiting a website or other bots accessing a website. Depending on the amount of content on each page or the number of pages on the site, it could be in the website operator’s best interests not to allow search indexing too often, since too much indexing could overtax the server, drive up bandwidth costs, or both.
Also, developers or companies may not want some webpages to be discoverable unless a user already has been given a link to the page (without putting the page behind a paywall or a login). One example of such a case for enterprises is when they create a dedicated landing page for a marketing campaign, but they don’t want anyone not targeted by the campaign to access the page. In this way they can tailor the messaging or precisely measure the page’s performance. In such cases the enterprise can add a “no index” tag to the landing page, and it won’t show up in search engine results. They can also add a “disallow” tag in the page or in the file, and search engine spiders won’t crawl it at all.
Website owners may not want web crawler bots to crawl part or all of their sites for a variety of other reasons as well. For instance, a website that offers users the ability to search within the site may want to block the search results pages, as these are not useful for most users. Other auto-generated pages that are only helpful for one user or a few specific users should also be blocked.
What is the difference between web crawling and web scraping?
Web scraping, data scraping, or content scraping is when a bot downloads the content on a website without permission, often with the intention of using that content for a malicious purpose.
Web scraping is usually much more targeted than web crawling. Web scrapers may be after specific pages or specific websites only, while web crawlers will keep following links and crawling pages continuously.
Also, web scraper bots may disregard the strain they put on web servers, while web crawlers, especially those from major search engines, will obey the file and limit their requests so as not to overtax the web server.
How do web crawlers affect SEO?
SEO stands for search engine optimization, and it is the discipline of readying content for search indexing so that a website shows up higher in search engine results.
If spider bots don’t crawl a website, then it can’t be indexed, and it won’t show up in search results. For this reason, if a website owner wants to get organic traffic from search results, it is very important that they don’t block web crawler bots.
What web crawler bots are active on the Internet?
The bots from the major search engines are called:
Google: Googlebot (actually two crawlers, Googlebot Desktop and Googlebot Mobile, for desktop and mobile searches)
Yandex (Russian search engine): Yandex Bot
Baidu (Chinese search engine): Baidu Spider
There are also many less common web crawler bots, some of which aren’t associated with any search engine.
Why is it important for bot management to take web crawling into account?
Bad bots can cause a lot of damage, from poor user experiences to server crashes to data theft. However, in blocking bad bots, it’s important to still allow good bots, such as web crawlers, to access web properties. Cloudflare Bot Management allows good bots to keep accessing websites while still mitigating malicious bot traffic. The product maintains an automatically updated allowlist of good bots, like web crawlers, to ensure they aren’t blocked. Smaller organizations can gain a similar level of visibility and control over their bot traffic with Super Bot Fight Mode, available on Cloudflare Pro and Business plans.
Frequently Asked Questions about web crawl
What is meant by web crawling?
A web crawler (also known as a web spider or web robot) is a program or automated script which browses the World Wide Web in a methodical, automated manner. This process is called Web crawling or spidering. Many legitimate sites, in particular search engines, use spidering as a means of providing up-to-date data.
What is Web crawling used for?
A web crawler, or spider, is a type of bot that is typically operated by search engines like Google and Bing. Their purpose is to index the content of websites all across the Internet so that those websites can appear in search engine results.
How do I crawl a website?
The six steps to crawling a website include:Understanding the domain structure.Configuring the URL sources.Running a test crawl.Adding crawl restrictions.Testing your changes.Running your crawl.