What is a web crawler?

A website crawler (or web crawler) is a piece of software that does the process of hunting down information from web pages, websites, and the files that websites are built from.

Basically, Crawlers are used to collect information from semi-structured or unstructured data formats and can be converted to any structural data form that can then be used and processed to classify documents or provide insights into the data collected.


It is primarily used in search engines where it crawls/ fetches the web content and indexes them appropriately, which is used to deliver the right pages for search keywords and phrases.

Based on the strategies we can categorize the web crawlers into four:

Focused Web Crawler:  A focused Crawler is the Web crawler that tries to download pages that are related to each other. Because of its way of working, it is also known as a Topic Crawler. It helps in determining how far the given page is relevant to the particular topic and how to proceed forward, i.e. it focuses on current, content-relevant websites when indexing.
For example, writing a crawler to crawl details only about the CVOID-19 case details in India or crawl from a particular domain.

Incremental Crawler:  A traditional crawler, which incrementally refreshes the existing collection of pages by visiting them frequently; based upon the estimate as to how often pages change. A typical example would be crawling any e-commerce domain where there’s a constant data freshness issue.

Distributed Crawler:  Many crawlers are working to distribute in the process of web crawling, to have the most coverage of the web. A central server manages the communication and synchronization of the nodes, as it is geographically distributed. It uses the Page rank algorithm for its increased efficiency and quality search

Parallel Crawler:  A parallel crawler consists of multiple crawling Processes that can run on a network of workstations. The Parallel crawlers depend on Page freshness and Page Selection. A Parallel crawler can be on a local network or be distributed at geographically distant locations. Parallelization of the crawling system is very vital for downloading the documents in a reasonable amount of time.

Scraping vs Crawling

While both web crawling and scraping are essential methods of retrieving the data, the information needed and the processes involved in the respective methods are different.


Crawling a website is landing on a page and following the links you find when you scan the content. A crawler will then move to another page and so on. Whereas scraping on the other hand is scanning a page and collecting specific data from the page: title tag, meta description, h1 tag, or a specific area of your website such as a list of prices.
Crawl bot needs to handle data-duplications. Duplication is not necessarily a part.
Usually done at large scale Can be done at any scale
Needs only a crawler agent. Needs a crawler agent and a parser to extract pieces of information.

Common challenges in Crawling:

Non-uniform structures:  The web is a dynamic space that doesn’t have a set standard for data formats and structures. Collecting data in a format that can be understood by machines can be a challenge due to the lack of uniformity.

The Rise of Anti-Scraping Tools:  Tools like ScrapeDefender, ScrapeShield, ScrapeSentry are capable of differentiating bots from humans, which does Restriction on the web crawlers via e-mail obfuscation, real-time monitoring, and instant alerts, etc.

Complicated and changeable web page structures:  Most web pages are based on HTML and web page structures vary. When you need to scrape multiple websites, you need to build one scraper for each website. Moreover, websites periodically update their content to improve the user experience or add new features, which often causes re-writing the scrapers.

Dynamic contents:  AJAX and interactive web components make websites more user-friendly. But not for crawlers. Content is produced dynamically (and on-the-go) by the browser and therefore not visible to crawlers.

Slow/unstable load speed:  Websites may respond slowly or even fail to load when receiving too many access requests. That is not a problem when humans browse the site as they just need to reload the web page and wait for the website to recover. But scraping may break as the scraper does not know how to deal with or how long to wait for in such cases.

Login requirements:  Some protected information may require you to log in first. After you submit your login credentials, your browser automatically appends the cookie value to multiple requests you make the way most sites, so the website knows you’re the same person who just logged in earlier. So when scraping websites requiring a login, be sure that cookies have been sent with the requests.

Real-time data scraping:  Real-time data scraping is essential when it comes to price comparison, inventory tracking, etc. The data can change in the blink of an eye and may lead to huge capital gains for a business. The scraper needs to monitor the websites all the time and scrape data. Even so, it still has some delay as the requesting and data delivery take time. Furthermore, acquiring a large amount of data in real-time is a big challenge, too.

Now we’ll see about some well-known crawl frameworks and how it handles the challenges of a web-crawling.

  • It’s a web scraping API
  • It gives 1000 free API calls for 14 -day trial period
  • It provides a great set of premium proxy settings and proxy rotations, where every call using premium proxies will cost 100 API credits.
  • Cheaper than buying proxies, even for a large number of requests per month.
  • Great Documentation and Easy to integrate.
  • Diffbot uses a set of APIs that is powered by Artificial Intelligence. With the help of these APIs, the solution automatically extracts clean, structured, normalized, and accurate data from various sources online like articles, discussions, images, videos, and products.
  • Therefore it doesn’t need any rules and training and is quite easy to set up and implement
  • Article API That Has Text And Sentiment Analysis Capability: There is also an API designed for extracting information from news articles and blog posts, which also includes a sentiment analysis on the article or the blog.
  • Extracts Comments And Reviews From Discussions.
  • API For Image and Videos Metadata Extraction
  • Complete and detailed product information about products published on various shopping or eCommerce sites and pages.
  • The main con in DiffBot is that it’s quite expensive.
  • Is a website crawler application for Windows, macOS, and Ubuntu.
  • It helps you to analyze and audit technical and onsite SEO.
  • You can use this tool to crawl up to 500 URLs for free.
  • It instantly finds broken links and server errors.
  • This tool helps you to analyze page titles and metadata.
  • Screaming Frog helps you to find duplicate content.
  • You can generate XML Sitemaps (a list of your website’s URLs).
  • It allows you to integrate with Google Analytics, GSC (Google Search Console) & PSI (PageSpeed Insights).
  • Pros: Low cost.
  • Cons: It is Slow for large scale scraping.
  • Apify crawls lists of URLs and automates workflows.
  • It enables you to crawl arbitrary websites using the chrome browser and extract data using JavaScript.
  • It can simplify a web crawling job using SDK (System Development Kit).
  • This tool automatically maintains queues of URLs to crawl.
  • Apify can store crawling results into the cloud or local file system.
  • We can also schedule the crawlers for periodic error monitoring.
  • Scrapy is a free and open-source web-crawling framework written in Python.
  • Scrapy provides a powerful framework for extracting the data, processing it, and then store it.
  • Scrapy can get big jobs done very easily. It can crawl a group of URLs in no more than a minute depending on the size of the group and does it very smoothly as it uses Twister which works asynchronously (non-blocking) for concurrency.
  • It provides us a framework for:
    1. Handling request scheduling.
    2. Pre and post-process the downloaded html contents from the crawl.
    3. Allows you to write functions in your spider that can process your data such as validating data, removing data, and saving data to a database.
    4. Provides tools for writing tests for the spiders to monitor breakages.
    5. Gracefully handles the errors and even has the inbuilt ability for resuming a scrape from the last page is encountered.
    6. Scrapy has built-in form handling which you can set up to login to the websites before beginning your scrape.
    7. Scrapy allows you to be one by enabling you to easily throttle the rate at which you are scraping.
    8. It allows you to manage a lot of variables such as retries, redirection, and so on.
  • Scrapy is a powerhouse for web scraping and offers a lot of ways to scrape a web page, hence it requires more time to learn and understand how Scrapy works but once learned, eases the process of making web crawlers and running them from just one line of command
  • Scrapy has the limitations in scraping dynamic web-contents, but we can plug some javascript rendering services with it like Splash, Selenium, or Puppeteer
  • Parsehub is a web scraping desktop application.
  • The scraping itself happens on Parsehub servers, you only have to create the instruction within the app.
  • Lots of visual web scraping tools are very limited when it comes to scraping dynamic websites, not Parsehub. For example, you can:
    1. Scroll
    2. Wait for an element to be displayed on the page
    3. Fill inputs and submit forms
    4. Scrape data behind a login form
    5. Download file and image
  • We can export the scraped content to JSON / CSV file
  • It also has a scheduler (you can choose to execute your scraping task hourly/daily/weekly)
  • Cons: Steep learning curve and quite Expensive
  • They have a lot of products around web scraping, both open-source and commercial.
  • This is the company behind the Scrapy framework and Portia.
  • Best hosting for Scrapy projects, meaning you can easily deploy your scrapy spiders to their cloud.
  • Scrapinghub also provides a cloud-based data extraction tool, which is an open-source visual scraping tool that allows users to scrape websites without any programming knowledge.
  • Cons: Pricing is tricky and can quickly become expensive compared to other options.
  • Dexi.io is a visual web scraping platform.
  • One of the most interesting features is that they offer built-in data flows, Meaning not only you can scrape data from external websites, but you can also transform the data [ like if number floor or ceil or text transformations, etc..), use external APIs (like Clearbit, Google Sheets…)
  • Doesn’t need any technical person to write a script or pages to execute but the knowledge on xpaths or selectors might be required.
  • It also offers Data pipelines i.e we can pipeline our crawl flows like [crawl – > transform -> export].
  • The main disadvantage is that it’s pricey
  • Portia is another great open source project from ScrapingHub.
  • It’s a visual abstraction layer on top of the great Scrapy framework, meaning it allows us to create scrapy spiders without a single line of code, with a visual tool.
  • Portia itself is a web application written in Python, You can run it easily using the docker image they provided.
  • Lots of things can be automated with Portia, but when things get too complicated and custom code/logic needs to be implemented, you can use this tool https://github.com/scrapinghub/portia2code to convert a Portia project to a Scrapy project, in order to add custom logic.
  • One of the biggest problems of Portia is that it uses the Splash engine to render Javascript heavy websites. It works great in many cases but has severe limitations compared to Headless Chrome for example, websites using React.js aren’t supported.
  • Pros: Great “low-code” tool for teams already using Scrapy and it’s Open-source
  • Cons: Limitations regarding Javascript rendering support
  • Import.io is an enterprise web scraping platform. Historically they had a self-serve visual web scraping tool.
  • Users can form their own datasets by simply importing the data from a particular web page and exporting the data to CSV.
  • We can easily scrape thousands of web pages in minutes without writing a single line of code and build 1000+ APIs based on your requirements. Public APIs have provided powerful and flexible capabilities to control Import.io programmatically and gain automated access to the data, Import.io has made crawling easier by integrating web data into your app or web site with just a few clicks.
  • Pros: One of the best UI and Very easy to use
  • Cons: The tool is self-serve, meaning you won’t get much help if you have problems with it, and Like lots of other visual web scraping tools, it is expensive.