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 for the purpose of Web indexing (web spidering).
Web search engines and some other sites use Web crawling or spidering software to update their web content or indices of others sites' web content. Web crawlers copy pages for processing by a search engine which indexes the downloaded pages so 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 robots.txt 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.
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 page 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 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 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:
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 stanford.edu 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 Ph.D. 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 .it 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 .gr and .cl 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.
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 .html, .htm, .asp, .aspx, .php, .jsp, .jspx 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 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 http://llama.org/hamster/monkey/page.html, 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.
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.
Two simple re-visiting policies were studied by Cho and Garcia-Molina:
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. Needless to say, if a single crawler is performing multiple requests per second and/or downloading large files, a server would 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:
A partial solution to these problems is the robots exclusion protocol, also known as the robots.txt 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 robots.txt 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.
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 robots.txt) 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.
It is important for Web crawlers to identify themselves so that Web site administrators 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.
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
<a href="URL"> 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.
A recent study based on a large scale analysis of robots.txt files showed that certain web crawlers were preferred over others, with Googlebot being the most preferred web crawler.
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" like Diffbot, outwithub, and import.io 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 methodology 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.
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:
Apache Nutch is a highly extensible and scalable open source web crawler software project.Archive site
In web archiving, an archive site is a website that stores information on webpages from the past for anyone to view.BTJunkie
BTJunkie was a BitTorrent web search engine operating between 2005 and 2012. It used a web crawler to search for torrent files from other torrent sites and store them on its database. It had nearly 4,000,000 active torrents and about 4,200 torrents added daily (compared to runner-up Torrent Portal with 1,500), making it the largest torrent site indexer on the web in 2006. During 2011, BTJunkie was the 5th most popular BitTorrent site.Crawler
Crawler may refer to:
Crawler (band), a British rock band
Web crawler, a computer program that gathers and categorizes information on the World Wide Web
A first-instar nymph of a scale insect that has legs and walks around before it attaches itself and becomes stationary
A fictional creature in the video game Fable III
A fictional creature in the movie The Descent
A type of crane on tracksDistributed web crawling
Distributed web crawling is a distributed computing technique whereby Internet search engines employ many computers to index the Internet via web crawling. Such systems may allow for users to voluntarily offer their own computing and bandwidth resources towards crawling web pages. By spreading the load of these tasks across many computers, costs that would otherwise be spent on maintaining large computing clusters are avoided.Focused crawler
A focused crawler is a web crawler that collects Web pages that satisfy some specific property, by carefully prioritizing the crawl frontier and managing the hyperlink exploration process. Some predicates may be based on simple, deterministic and surface properties. For example, a crawler's mission may be to crawl pages from only the .jp domain. Other predicates may be softer or comparative, e.g., "crawl pages about baseball", or "crawl pages with large PageRank". An important page property pertains to topics, leading to topical crawlers. For example, a topical crawler may be deployed to collect pages about solar power, swine flu, or even more abstract concepts like controversy while minimizing resources spent fetching pages on other topics. Crawl frontier management may not be the only device used by focused crawlers; they may use a Web directory, a Web text index, backlinks, or any other Web artifact.
A focused crawler must predict the probability that an unvisited page will be relevant before actually downloading the page. A possible predictor is the anchor text of links; this was the approach taken by Pinkerton in a crawler developed in the early days of the Web. Topical crawling was first introduced by Filippo Menczer Chakrabarti et al. coined the term focused crawler and used a text classifier to prioritize the crawl frontier. Andrew McCallum and co-authors also used reinforcement learning to focus crawlers. Diligenti et al. traced the context graph leading up to relevant pages, and their text content, to train classifiers. A form of online reinforcement learning has been used along with features extracted from the DOM tree and text of linking pages, to continually train classifiers that guide the crawl. In a review of topical crawling algorithms, Menczer et al. show that such simple strategies are very effective for short crawls, while more sophisticated techniques such as reinforcement learning and evolutionary adaptation can give the best performance over longer crawls. It has been shown that spatial
information is important to classify Web documents.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.
Crawlers are also focused on page properties other than topics. Cho et al. study a variety of crawl prioritization policies and their effects on the link popularity of fetched pages. Najork and Weiner show that breadth-first crawling, starting from popular seed pages, leads to collecting large-PageRank pages early in the crawl. Refinements involving detection of stale (poorly maintained) pages have been reported by Eiron et al..
A kind of semantic focused crawler, making use of the idea of reinforcement learning has been introduced by Meusel et al. using online-based classification algorithms in combination with a bandit-based selection strategy to efficiently crawl pages with markup languages like RDFa, Microformats, and Microdata.
The performance of a focused crawler depends on the richness of links in the specific topic being searched, and focused crawling usually relies on a general web search engine for providing starting points. Davison presented studies on Web links and text that explain why focused crawling succeeds on broad topics; similar studies were presented by Chakrabarti et al.. Seed selection can be important for focused crawlers and significantly influence the crawling efficiency. A whitelist strategy is to start the focus crawl from a list of high quality seed URLs and limit the crawling scope to the domains of these URLs. These high quality seeds should be selected based on a list of URL candidates which are accumulated over a sufficient long period of general web crawling. The whitelist should be updated periodically after it is created.Gigablast
Gigablast is a free and open-source web search engine and directory. Founded in 2000, it is an independent engine and web crawler based in New Mexico, developed and maintained by Matt Wells, a former Infoseek employee and New Mexico Tech graduate.The search engine source code is written in the programming languages C and C++. It was released as open-source software under the Apache License version 2, in July 2013. In 2015, Gigablast claimed to have indexed over 12 billion web pages, and received billions of queries per month.Gigablast has provided, and provides, search results to other companies, such as Ixquick, Clusty, Zuula, Snap, Blingo, and Internet Archive.Googlebot
Googlebot is the web crawler software used by Google, which collects documents from the web to build a searchable index for the Google Search engine. This name is actually used to refer to two different types of web crawlers: a desktop crawler (to simulate desktop users) and a mobile crawler (to simulate a mobile user).A website will probably be crawled by both Googlebot Desktop and Googlebot Mobile. The subtype of Googlebot can be identified by looking at the user agent string in the request. However, both crawler types obey the same product token (useent token) in robots.txt, and so a developer cannot selectively target either Googlebot mobile or Googlebot desktop using robots.txt.
A problem that webmasters have often noted with the Googlebot is that it takes up an enormous amount of bandwidth. This can cause websites to exceed their bandwidth limit and be taken down temporarily. This is especially troublesome for mirror sites which host many gigabytes of data. Google provides "Search Console" that allow website owners to throttle the crawl rate.How often Googlebot will crawl a site depends on the crawl budget. Crawl budget is an estimation of how often a website is updated. A site's crawl budget is determined by how many incoming links it has and how frequently the site is updated.Technically, Googlebot's development team (Crawling and Indexing team) uses several defined terms internally to takes over what "crawl budget" stands for.HTTrack
HTTrack is a free and open-source Web crawler and offline browser, developed by Xavier Roche and licensed under the GNU General Public License Version 3.
HTTrack allows users to download World Wide Web sites from the Internet to a local computer. By default, HTTrack arranges the downloaded site by the original site's relative link-structure. The downloaded (or "mirrored") website can be browsed by opening a page of the site in a browser.
HTTrack can also update an existing mirrored site and resume interrupted downloads. HTTrack is configurable by options and by filters (include/exclude), and has an integrated help system. There is a basic command line version and two GUI versions (WinHTTrack and WebHTTrack); the former can be part of scripts and cron jobs.
Heritrix is a web crawler designed for web archiving. It was written by the Internet Archive. It is available under a free software license and written in Java. The main interface is accessible using a web browser, and there is a command-line tool that can optionally be used to initiate crawls.
Heritrix was developed jointly by the Internet Archive and the Nordic national libraries on specifications written in early 2003. The first official release was in January 2004, and it has been continually improved by employees of the Internet Archive and other interested parties.
Heritrix was not the main crawler used to crawl content for the Internet Archive's web collection for many years. The largest contributor to the collection, as of 2011, is Alexa Internet. Alexa crawls the web for its own purposes, using a crawler named ia_archiver. Alexa then donates the material to the Internet Archive. The Internet Archive itself did some of its own crawling using Heritrix, but only on a smaller scale.Starting in 2008, the Internet Archive began performance improvements to do its own wide scale crawling, and now does collect most of its content.Internet bot
An Internet bot, also known as a web robot, WWW robot or simply bot, is a software application that runs automated tasks (scripts) over the Internet. Typically, bots perform tasks that are both simple and structurally repetitive, at a much higher rate than would be possible for a human alone. The largest use of bots is in web spidering (web crawler), in which an automated script fetches, analyzes and files information from web servers at many times the speed of a human. More than half of all web traffic is made up of bots.Efforts by servers hosting websites to counteract bots vary. Servers may choose to outline rules on the behaviour of internet bots by implementing a robots.txt file: this file is simply text stating the rules governing a bot's behaviour on that server. Any bot that does not follow these rules when interacting with (or 'spidering') any server should, in theory, be denied access to, or removed from, the affected website. If the only rule implementation by a server is a posted text file with no associated program/software/app, then adhering to those rules is entirely voluntary – in reality there is no way to enforce those rules, or even to ensure that a bot's creator or implementer acknowledges, or even reads, the robots.txt file contents. Some bots are "good" – e.g. search engine spiders – while others can be used to launch malicious and harsh attacks, most notably, in political campaigns.Mediabot
Mediabot is the web crawler that Google uses for analysing the content so Google AdSense can serve contextually relevant advertising to a web page. Mediabot identifies itself with the user agent string "Mediapartners-Google/2.1".
Unlike other crawlers, Mediabot does not follow links to discover new crawlable URLs, instead only visiting URLs that have included the AdSense code. Where that content resides behind a login, the crawler can be given a login so that it is able to crawl protected content.Mediabot will usually first visit a page within seconds of AdSense code first being called from that page. Thereafter it revisits pages on a regular but unpredictable basis. Changes made to a page therefore do not immediately cause changes to the ads displayed on the page.
Ads can still be shown on a page even if the Mediabot has not yet visited it. In this instance ads chosen will be based on a combination of the overall domain theme and keywords appearing in the URL string. If no ads can be matched to the page, either public service ads, blank space, or a solid color are shown, depending on the settings for that ad unit.PowerMapper
PowerMapper is a web crawler that automatically creates a site map of a website using thumbnails of each web page. A number of map styles are available, although the cheaper Standard edition has fewer styles than the Professional edition.Scrapy
Scrapy ( SKRAY-pee) is a free and open-source web-crawling framework written in Python. Originally designed for web scraping, it can also be used to extract data using APIs or as a general-purpose web crawler. It is currently maintained by Scrapinghub Ltd., a web-scraping development and services company.
Scrapy project architecture is built around "spiders", which are self-contained crawlers that are given a set of instructions. Following the spirit of other don't repeat yourself frameworks, such as Django, it makes it easier to build and scale large crawling projects by allowing developers to reuse their code. Scrapy also provides a web-crawling shell, which can be used by developers to test their assumptions on a site’s behavior.Some well-known companies and products using Scrapy are: Lyst, Parse.ly, Sayone Technologies, Sciences Po Medialab, Data.gov.uk’s World Government Data site.SortSite
SortSite is a web crawler that scans entire websites for quality issues including: accessibility; browser compatibility; broken links; legal compliance; search optimization; usability and web standards compliance.Spider trap
A spider trap (or crawler trap) is a set of web pages that may intentionally or unintentionally be used to cause a web crawler or search bot to make an infinite number of requests or cause a poorly constructed crawler to crash. Web crawlers are also called web spiders, from which the name is derived. Spider traps may be created to "catch" spambots or other crawlers that waste a website's bandwidth. They may also be created unintentionally by calendars that use dynamic pages with links that continually point to the next day or year.
Common techniques used are:
creation of indefinitely deep directory structures like http://example.com/bar/foo/bar/foo/bar/foo/bar/.....
Dynamic pages that produce an unbounded number of documents for a web crawler to follow. Examples include calendars and algorithmically generated language poetry.
documents filled with a large number of characters, crashing the lexical analyzer parsing the document.
documents with session-id's based on required cookies.There is no algorithm to detect all spider traps. Some classes of traps can be detected automatically, but new, unrecognized traps arise quickly.Vertical search
A vertical search engine is distinct from a general web search engine, in that it focuses on a specific segment of online content. They are also called specialty or topical search engines. The vertical content area may be based on topicality, media type, or genre of content. Common verticals include shopping, the automotive industry, legal information, medical information, scholarly literature, job search and travel. Examples of vertical search engines include the Library of Congress, Mocavo, Nuroa, Trulia and Yelp.
In contrast to general web search engines, which attempt to index large portions of the World Wide Web using a web crawler, vertical search engines typically use a focused crawler which attempts to index only relevant web pages to a pre-defined topic or set of topics. Some vertical search sites focus on individual verticals, while other sites include multiple vertical searches within one search engine.WWWW
WWWW may refer to:
WWWW-FM, a radio station (102.9 FM) licensed to Ann Arbor, Michigan, United States
WLLZ (FM), a radio station (106.7 FM) licensed to Detroit, Michigan, United States, which used the call signs WWWW or WWWW-FM until October 2000
Web.com, Inc. (NASDAQ symbol WWWW)
World Wide Web Wanderer, a web crawler used to measure the size of the Web in 1993
World-Wide Web Worm, an early Internet search engineWeb scraping
Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a bot or web crawler. It is a form of copying, in which specific data is gathered and copied from the web, typically into a central local database or spreadsheet, for later retrieval or analysis.
Web scraping a web page involves fetching it and extracting from it. Fetching is the downloading of a page (which a browser does when you view the page). Therefore, web crawling is a main component of web scraping, to fetch pages for later processing. Once fetched, then extraction can take place. The content of a page may be parsed, searched, reformatted, its data copied into a spreadsheet, and so on. Web scrapers typically take something out of a page, to make use of it for another purpose somewhere else. An example would be to find and copy names and phone numbers, or companies and their URLs, to a list (contact scraping).
Web scraping is used for contact scraping, and as a component of applications used for web indexing, web mining and data mining, online price change monitoring and price comparison, product review scraping (to watch the competition), gathering real estate listings, weather data monitoring, website change detection, research, tracking online presence and reputation, web mashup and, web data integration.
Web pages are built using text-based mark-up languages (HTML and XHTML), and frequently contain a wealth of useful data in text form. However, most web pages are designed for human end-users and not for ease of automated use. Because of this, tool kits that scrape web content were created. A web scraper is an Application Programming Interface (API) to extract data from a web site. Companies like Amazon AWS and Google provide web scraping tools, services and public data available free of cost to end users.
Newer forms of web scraping involve listening to data feeds from web servers. For example, JSON is commonly used as a transport storage mechanism between the client and the web server.
There are methods that some websites use to prevent web scraping, such as detecting and disallowing bots from crawling (viewing) their pages. In response, there are web scraping systems that rely on using techniques in DOM parsing, computer vision and natural language processing to simulate human browsing to enable gathering web page content for offline parsing.