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How a Web Crawler Works: Insights into a Modern Web Crawler | PromptCloud
 

How a Web Crawler Works: Insights into a Modern Web Crawler

In the last few years, internet has become too big and too complex to traverse easily. With the need to be present on the search engine bots listing, each page is in a race to get noticed by optimizing its content and curating data to align with the crawling bots’ algorithms. Similarly,  there are multiple parties who wish to access this data and extract it for their benefit. Hence, to bridge this need gap, web crawlers came into existence.

It is a widely known fact that creating and maintaining a single web crawler across all pages present on the internet is no easy task. It is essential for the crawlers to evolve at the same pace as internet is involving in the current scenario. In order to support this evolution, each crawler should get their basic layout right, so new features and code snippets can be extended upon the same crawler. Following is the basic functionality layout how the crawlers should work.

Extract Data from websites

  1. Seed List: Before a crawler sets out on its journey of traversing a list of sites, it is essential to develop a basic seed list of URLs that would in turn consist of Other URLs on their destination pages, from where we can catapult the web crawlers onto their desired path. This takes care of what URLs and pages a crawler would end up extracting since it would go through all the pages that are connected to the Seed URLs and in turn other URLs that would all be transitively connected and interlinked with this initial set of URLs. The crawler would then follow its inbuilt traversing algorithm via which it shall crawl the connected sets of pages and their nodes horizontally or vertically. Nowadays, most of the websites have a sitemap.xml file with a list of all URLs present on the website, to help the search engine bots discover all pages on visit. You can check out one such sitemap example at: Sitemap.
  1. Fetching the Actual Content: Before starting on its journey, each web crawler has some sort of DB to check against , which maintains a list of all seed URLs. Then it needs to check if various urls from a page need to be crawled, it is often based on update frequency of the site(Sidenote – this is why if you update your site more often, so search engines crawl it more often) . Once the URLs to be crawled have been shortlisted, All the URLs that need to be crawled are pushed into the queue that follows a LIFO/FIFO pattern depending upon the crawler’s algorithm and the URLs are removed as and when they get crawled. In most such setup from technical point of view, queue comes as a handy tool to simplify the architecture of the whole system. After that crawler goes and gets the page and saves it on local machine. Actual fetching of the pages in layman’s terms is similar to going on a webpage and then doing a ‘Right click save’. Bots achieve the similar functionalities in various ways. If the site is more interactive have lot of AJAX interactions, then bots have to be more advanced/custom to get the data.After fetching the data, which is then stored separately for extraction and structuring.
    1. Discovery of new urls and sites: There are over 1 Trillion web pages present on the internet, with more web pages coming up each day. Hence it is not feasible to store all these URLs in the queue manually or even mechanically for that matter. Thus each time a crawler is developed, it is essential to add the Discover function in the web crawler code. This way, you can train a crawler to discover URLs by itself that need to be crawled by hopping from one seed URL to the connecting URL and so forth. Now a days, the way webpages are linked and interlinked, bots go from one page to another. However , if there are standalone pages(silos), which neither link to other pages nor are linked from other pages, they are difficult to discover. So webmasters take extra care to put them in sitemap.xml or add the link for them somewhere on the site so that search engines can reach them easily.
  1. Intelligent crawling: Another important task to perform while Discovery of URLs is to check whether these URLs have been crawled previously in the same crawl or not.
      • Deduplication: While discovering more URLs from each page that is crawled, it is highly possible that the crawler might encounter URLs of web pages it has already crawled earlier, since the internet is nothing but a set of interlinked web pages. In order to avoid recrawling these pages and prevent the crawler from going into a loop, it is essential to perform a deduplication check before crawling a page. If that page has already been crawled, you can push that page to the seed list to make further discovery of pages easier. If not, well then go ahead and crawl the page happily.
  • Frequency Adaptation: Crawlers today have developed a niche set of complex queries that can help prevent the excess load on crawler to crawl the pages multiple times a day and also the pages that get crawled multiple times a day, thereby affecting their load time. Hence, while crawling a page, we run another check to see the last time stamp when that page was updated, per crawl. In case a page is updated more frequently, then it makes sense to crawl that page regularly to identify and report the changes accordingly. If a page is updated less frequently, then it is rude to burden the page’s server with repeated crawl requests and hence it is only polite to ease the burden on the crawlers to crawl the page unnecessarily.
    1. Parsing: Once the pages have been fetched, next task is to get information from it. Search engines use various algorithm and heuristics to find information from the text present on the webpage. So that when we search for particular terms, search engines show up relevant pages based on the information they extracted from these pages. Nowadays there is more focus on semantic markup and semantic results, wherein, search engines try to infer various fields present on the webpage. Various markups suggested by schema.org is another good step which helps site owners, as well as search engines. But core of it is that those micro tags help in parsing and inferring information from the text.
    1. Data Storage: When you are writing a crawler, given the sheer volume, storage of data becomes a big enough problem. Considering the amount of data that is crawled and required on a daily basis, traditional SQL databases are not equipped to handle that sort of volume on routine basis, moreover this data doesn’t have much relational attributes. This is where the Hadoop and other nosql systems come into the picture. It can store and query large amounts of  data  easily and finish the processing it within minutes. Some people also use systems such as Cepth and S3 and other similar services that could be used to store and share data across multiple platforms. At times people have also used flat files with reasonable success.

The above points formulate a jist of what all goes in the making of a modern web crawler. Our crawler does this and much more. Considering all the above mentioned aspects and including them in the web crawlers, has vastly improved our service and has allowed us to get an edge above all other web crawling services functioning in the market. However, this is just a glimpse of how we can help your company get the data log to analyse and build your strategic decisions upon. We believe in continuous improvisations and reiterations to build a crawler that would surpass all.

Looking to extract data from the web? Find out if our DaaS solution is a right fit for your requirements here.

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