Because of the sheer volume of data and websites that exist online, it would be impossible to manually search through all the information. Imagine the largest library you know as a disorganized mess; the internet would be worse than that many times over. This is why search engines exist.

However, how do search engines make searching on the internet easier? Through the process of indexing. Ironically, it would be similarly impossible to manually index all the websites, webpages, and information on the internet. Cue web crawlers. They are the tools that give search engines a comprehensive understanding and index of online information. In this article, we will explain what web crawlers are, how they work, and how to build a basic one using Python.  

What Is a Web Crawler?

A web crawler is an automated software tool that systematically browses the internet, indexing pages and websites to identify the information on them. As stated earlier, search engines use them to index the internet, and bring relevant results to users of the engines. Web scrapers, however, use these crawlers to find relevant pages they intend to scrape. Other uses of a web crawler include content aggregation, website monitoring, etc. 

Also known as a web spider or web robot, starts from a seed list of URLs and works, its way downward, adding hyperlinks obtained from those pages to another list of URLs to crawl. The process can go indefinitely until all the URLs are exhausted or until the crawlers reach a preset limit. Despite this, the relative inexhaustibility of the internet means that a sizeable portion of the indexable internet may not have been indexed, depending on who you’re listening to.

That said, the key to sidestepping the bulky internet is to understand a web crawler. Here is a summarized process explaining the functioning of a web crawler:

1. Seed URLs. As stated, the crawler can start from a seed URL or a list of them. Usually, the individual using the crawler develops the seed.
2. Fetching Web Pages. The web crawler sends HTTP requests to the servers hosting the URLs on the seed list. However, where a browser sends requests to help a user visit the site, crawlers do so to fetch web pages and retrieve their HTML content for parsing. 
3. Parsing HTML Content. On getting the HTML content, the crawler parses it and extracts internal and external links to other web pages (hyperlinks). The collected links are added to a list of future links to visit (a crawl URL frontier). URLs on the frontier are set aside for later processing after the seed list, based on a predetermined configuration, algorithm, or set priority. 
4. Recursion. The crawler recursively visits the URLs on the frontier list, thus visiting web pages using the links discovered in previous pages and so on. While this could lead to an infinite loop of endless crawling, these tools may employ link-tracking, depth limits, and other strategies to prevent this from happening.
5. Data Extraction and Indexing. While parsing and retrieving hyperlinks from the HTML content of the page, the crawler also collects required data like metadata, text and image content, page titles, etc. for indexing.  Indexed data may be employed for analytical purposes, archiving, and search engines, among others.

 

Web crawling strategies

There are many ways to classify web crawling strategies. Here are a few of them, and the web crawling strategies under the classifications.

Implementation-based Classification of Web Crawling Strategies.

• Using Only Standard Libraries

Here, a basic web crawler uses standard libraries available in a programming language (such as Python) for making HTTP requests, and `html.parser` for parsing HTML content. The crawler starts with a list of seed URLs and recursively follows hyperlinks in the seed URLs to discover and crawl additional pages. 

When it crawls through the seed URLs, It retrieves the HTML content of the web pages using the standard HTTP library. From this HTML content, it extracts relevant information for indexing, alongside the hyperlinks. Extracted hyperlinks are added to the crawl frontier of URLs to be crawled later.  

This strategy is lightweight, possesses minimal dependencies, and is ideal for building familiarity with crawling concepts. However, it lacks flexibility, and additional features, such as URL normalization and concurrency management, require manual implementation. 

• Using Third-Party Libraries for HTTP Requests and HTML Parsing

In this strategy, the web crawler employs third-party libraries for the process of making HTTP requests and parsing HTML content. Specific examples of third-party libraries commonly used are ‘requests’ for HTTP requests and ‘Beautiful Soup’ for HTML parsing. 

Specifically, the crawler would use the tools afforded by the libraries to simplify tasks. Some such tasks involved in handling HTTP requests include the management of cookies, redirects, and other network-related tasks. On the HTML parsing side, these tools simplify the navigation and manipulation of web documents and the extraction of data. 

The major advantage of this strategy is the improved functionality it presents users with. Then, there’s the added boon of simplified tasks, and advanced features if users want them implemented. All of these, though, create a dependency not seen when standard libraries are employed. 

• Using a Web Crawling Framework 

A web crawling framework is a library designed for building and managing crawlers. These frameworks are also usually attained in certain programming languages. As a result of their international design, they offer a comprehensive set of tools and APIs to help users build crawlers of varying complexities. They also provide advanced features and advanced customization options for more flexibility. 

On the flip side, these additional features and options require a higher level of technical know-how to understand and utilize. Furthermore, the frameworks necessitate the developers of web crawlers to possess in-depth familiarity with the programming language and the associated ecosystem. 

Traversal-based or Path-based Web Crawling Strategies

• Breadth-First Search (BFS)

In this strategy,  the web crawler seeks to explore all the web pages at a certain depth level before moving on to pages at the next depth level. In this context, a depth level is the distance between the seed URL of a site and another page (B) of the site, using the number of links taken to reach (B) as a unit of measurement. As such, the seed URL is at depth level 0, pages directly linked to the seed URL are at depth level 1, and so on. 

In other words, the crawler would explore all the pages at depth level 1 before moving on to depth level 2. This strategy can aid a comprehensive discovery of all sections of a website. However, large websites might be incredibly slow to crawl through, given the amount of pages that could exist at every depth level. 

• Depth-First Search (DFS)

In a depth-first search, the web crawler traverses as far down a branch as it can go or until it hits a depth limit, before backtracking. As such, it does a deep dive into sections before moving on to the next. As such, it is ideal for crawling into deep-nested sites. However, in the absence of proper monitoring or control policies, it is prone to crawling in loops or delving into relatively irrelevant web pages.

Focus-Based Strategies

• Focused Crawling

In focused crawling, the web crawler retrieves web pages that meet particular content criteria. The focus may be keywords, subject matter, etc. This crawling strategy saves bandwidth, time, and resources and facilitates precise activity. Unfortunately, it is much more complex to implement because of the need for very well-defined relevance criteria. 

• Incremental Crawling

How do search engines ensure that the index they keep on websites is up-to-date? They recrawl the websites. At certain intervals, a web crawler revisits previously crawled web pages to assess updates to them and ensure the freshness of collected data. This process is an example of incremental crawling because it builds upon the knowledge of a web page instead of working outward by hyperlinks. 

Other examples of web crawling strategies include parallel crawling (which involves running multiple scrawlers simultaneously as a scaling strategy), policy crawling, API-based crawling, document-based crawling, etc.

 

How to build your Web Crawler in Python

Have you ever wondered how search engines crawl and index billions of web pages? Well, the secret is web crawling. Python has a simple way for you to do it, too. Web crawling, also known as web scraping. It is the process of automatically downloading and extracting data from websites.

Building a web crawler in Python is not only a technical feat. It is also a strategic advantage in today’s ever-expanding online world. With the vast amount of data on the internet, harnessing its potential needs a powerful tool. Python’s versatility and simplicity make it an ideal choice for creating a web crawler. It allows developers to move through networks and gain a competitive edge.

Web crawling is pretty straightforward. Here’s the basic process:

• You start with a list of seed URLs to crawl.
• You visit the first URL and download the page content.
• You parse the page content to look for new URLs to add to your crawl queue.
• You add those new URLs to your crawl queue.
• You visit the next URL in the row and repeat steps 2-4.
• You continue this process until you’ve crawled all the URLs you want.

So, are you ready to start building your web crawler? The web awaits! With some basic Python skills, you’ll be well on your way. Here is a Python web crawler tutorial for you to do as best as possible:

Gather Tools and Resources

To build a Python web crawler, you’ll need a few things:

• Python that you install on your computer. You need to install this programming language to create a web crawler in Python. Download and install the latest version of Python from the official website.
• A code editor like VS Code, Atom, or Sublime Text. Choose a convenient text editor or an integrated development environment.
• Some packages like Beautiful Soup, Requests, and Scrapy. To develop a web crawler, you will need packages. These can be Beautiful Soup, Requests, and Scrapy. They will help you with website interaction and page processing.

Choose a Website to Scrape

Choosing a website to collect data is key to developing a web spider. Let’s look at how to crawl a website using Python:

• Target Website. Determine which site is of interest. Is it news, a shop, a forum, or something else?
• Legal Issues. Make sure that the process of data scraping from the site is legal. It should not violate the rules of the site.
• Data Scope and Structure. Consider what information you want to collect and how it is organized on the site.

Inspect the Website

Before collecting data, it is important to inspect the site carefully. It is to understand its structure:

• Analysis of HTML code. Use your browser’s developer tools to examine the site’s HTML code.
• Definition of Selectors. Look for CSS selectors or XPath expressions. It is to detect the items you are interested in.

Install Packages 

It is the next step of the Python web crawler tutorial. Before you can start working on the web spider, you need to install the required packages:

• Beautiful Soup and Requests. Install these packages. The first will help you parse HTML code. And the second will help you interact with websites.
• Scrappy. If you use Scrapy, also install it for structured data parsing.

Making a Request and Parse the HTML

What is the next step for web crawler Python code? Now it’s time to make a request to the website and parse the resulting HTML code:

• Request to the Site. Your crawler needs to query the target website. You can do it using Requests.
• Getting the HTML code. Get HTML content from a website after a request.

Extract the Data

Extracting data is a key stage in the work of a web spider. And proxies play a significant role in this. So, the reasons to buy proxies in this are vital. After all, their use allows you to hide your IP address. It provides anonymity and helps in avoiding blocking by websites. Here is a web crawler Python example of why proxy is useful:

• Speed Increase. A proxy can improve your online speed. It allows you to make more requests to the server simultaneously.
• Protection from Blocking. Using different IP addresses increases resistance to website blocking.

Web crawler use cases

1. Search engine indexing using a web crawling framework and breadth-first search strategy
2. E-commerce price monitoring and comparison, using third-party libraries.
3. Content aggregation using a web crawling framework and depth-first searching
4. Academic and research data collection using standard libraries and depth-first searching
5. SEO analysis and monitoring using third-party libraries and breadth-first searching
6. Social media monitoring using a web crawling framework and depth-first searching
7. Job listings scraping using third-party libraries and breadth-first searching    

Web Crawler in Python Example

The uniqueness of the Internet is that it is endless. It has an incredible number of possibilities that anyone can count on. After all, the online space is wide enough for this. And one of the ways to use it effectively is to crawl a website. Here is a web crawler in Python example for you to better understand it.

By following this unique web crawler in Python example, you will not only gain hands-on experience in web crawling but also be equipped with the skills to build robust and efficient web crawlers for various purposes. So, what are you waiting for? Get your coding gears ready and start exploring the fascinating world of web crawling now.

The applications of web crawling are vast and diverse. Whether it is gathering market data for business intelligence, monitoring website changes, or creating tailored APIs, the possibilities are endless. With your newly acquired web crawling skills, you have the power to unlock hidden insights from the ever-expanding digital universe.

By following this step-by-step Python example, you can embark on an exciting journey into the world of web crawling. Armed with the right tools and knowledge, you’ll be able to harness the immense potential of the internet. You will also unlock new opportunities for research, analysis, and innovation. So, why wait? Dive into the world of open doors to a web realm. Here is an example of web crawling for you:

• Step 1. The example starts by sending an HTTP request to the desired website. It is the first thing that you need to do. To ensure efficiency and avoid indefinitely crawling the same pages, implement a crawling frontier. It is a queue that keeps track of unvisited URLs. This way, your crawler will explore the web in a systematic and organized manner.
• Step 2. Then, you should continue with the extra tool. Use the Requests library to retrieve the HTML content of the page. By leveraging diverse libraries, you can store the info in structured formats. It can be CSV or JSON. It makes it readily available for further analysis or integration with other tools.
• Step 3. Then, use the BeautifulSoup library to parse the HTML and extract specific data elements. Some of them include headlines using their class or HTML tags. 
• Step 4. You can proceed with extracted data elements further and store them in the database.
• Step 5. Armed with programming knowledge, you can incorporate extra features to enhance your web crawler. For example, you can instruct it to extract specific elements from web pages. It involves headings, paragraphs, or images, using advanced techniques like regular expressions.

Web Crawler in Python: Code

How to crawl a website using Python is a question that interests many. After all, it allows you to expand your perspectives in the digital dimension. In Python, you can create a web crawler using various libraries. To start, you need to install the necessary options. Open your command prompt or terminal and type the following commands:

• pip install beautifulsoup4
• pip install requests

Once the libraries are installed, you can start writing the code. First, you should import the required libraries:

• import requests
• from bs4 import BeautifulSoup

Next, define a function that will handle the crawling process:

• “`python
• def crawl(url):
• response = requests.get(url)
• html_content = response.content
• return html_content

Now, we can use BeautifulSoup to parse the HTML content. For example, let’s say you want to extract all the links from a webpage:

• “`python
• def extract_links(html_content):
• soup = BeautifulSoup(html_content, ‘html.parser’)
• links = soup.find_all(‘a’)
• return links

It is only a small part of web crawler Python code. They are enough for both beginners and advanced. The main thing is to use them correctly according to the instructions. Then, you will succeed faster.

Frequently asked questions

Can Python Be Used for a Web Crawler?

Yes, Python can be used for a web crawler. In fact, Python is one of the favored programming languages for building web crawlers. This is because it is simple, versatile, and has a rich ecosystem of libraries that provide sophisticated tools for managing HTTP requests, HTML parsing, web page fetching, data extraction, etc. The documentation and available community support also make it a friendly language for beginners looking to learn. 

How Do You Crawl Data from a Website in Python?

You can crawl data from a website in Python by using standard libraries, third-party libraries, or frameworks in the language. These follow the typical web crawling process of making HTTP requests, parsing HTML content, locating data elements of interest, extracting these elements and adding hyperlinks to crawl frontiers, and processing and storing extracted data. 

Can I use the web crawler for scraping dynamic content?

Yes, you can use a web crawler to aid the scraping of dynamic content. However, additional considerations, features, and tools need to come into play. Some of these are the use of headless browsers to render such pages, using APIs where available, and employing event-based triggers to facilitate wait-for-page loading.

How do I ensure the legality of web crawling?

You can ensure the legality of your web crawling activities by adhering to the terms of service and use of websites, respecting robots.txt files, implementing rate limits and politeness policies, using API where available, refusing to circumvent access-control mechanisms, and obtaining permission to recognize intellectual property rights.

Are proxies necessary for web crawling?

No, proxies are not necessary for web crawling. However, their advantages in providing anonymity can help avoid rate limits and expand access to content. 

What if the website’s structure changes?

Changes to a website’s structure could affect the effectiveness and functioning of a web crawler. As such, it is important to regularly monitor your crawlers, design them to be more adaptable, and regularly maintain them in reaction to structural changes.