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Step-by-Step Guide to Build a Web Crawler

In the intricate tapestry of the internet, where information is scattered across countless websites, web crawlers emerge as the unsung heroes, diligently working to organize, index, and make this wealth of data accessible. This article embarks on an exploration of web crawlers, shedding light on their fundamental workings, distinguishing between web crawling and web scraping, and providing practical insights such as a step-by-step guide to crafting a simple Python-based web crawler. As we delve deeper, we’ll uncover the capabilities of advanced tools like Scrapy and discover how PromptCloud elevates web crawling to an industrial scale. 

What is a Web Crawler 

A web crawler, also known as a spider or bot, is a specialized program designed to systematically and autonomously navigate the vast expanse of the World Wide Web. Its primary function is to traverse websites, collect data, and index information for various purposes, such as search engine optimization, content indexing, or data extraction.

At its core, a web crawler mimics the actions of a human user, but at a much faster and more efficient pace. It starts its journey from a designated starting point, often referred to as a seed URL, and then follows hyperlinks from one web page to another. This process of following links is recursive, allowing the crawler to explore a significant portion of the internet.

As the crawler visits web pages, it systematically extracts and stores relevant data, which can include text, images, metadata, and more. The extracted data is then organized and indexed, making it easier for search engines to retrieve and present relevant information to users when queried.

Web crawlers play a pivotal role in the functionality of search engines like Google, Bing, and Yahoo. By continuously and systematically crawling the web, they ensure that search engine indexes are up-to-date, providing users with accurate and relevant search results. Additionally, web crawlers are utilized in various other applications, including content aggregation, website monitoring, and data mining.

The effectiveness of a web crawler relies on its ability to navigate diverse website structures, handle dynamic content, and respect rules set by websites through the robots.txt file, which outlines what portions of a site can be crawled. Understanding how web crawlers operate is fundamental to appreciating their importance in making the vast web of information accessible and organized.

How Web Crawlers Work

Web crawlers, also known as spiders or bots, operate through a systematic process of navigating the World Wide Web to gather information from websites. Here is an overview of how web crawlers work:

Seed URL Selection:

The web crawling process typically starts with a seed URL. This is the initial web page or website that the crawler begins its journey from.

HTTP Request:

The crawler sends an HTTP request to the seed URL to retrieve the HTML content of the web page. This request is similar to the requests made by web browsers when accessing a website.

HTML Parsing:

Once the HTML content is fetched, the crawler parses it to extract relevant information. This involves breaking down the HTML code into a structured format that the crawler can navigate and analyze.

URL Extraction:

The crawler identifies and extracts hyperlinks (URLs) present in the HTML content. These URLs represent links to other pages that the crawler will visit subsequently.

Queue and Scheduler:

The extracted URLs are added to a queue or scheduler. The queue ensures that the crawler visits URLs in a specific order, often prioritizing new or unvisited URLs first.


The crawler follows the links in the queue, repeating the process of sending HTTP requests, parsing HTML content, and extracting new URLs. This recursive process allows the crawler to navigate through multiple layers of web pages.

Data Extraction:

As the crawler traverses the web, it extracts relevant data from each visited page. The type of data extracted depends on the purpose of the crawler and may include text, images, metadata, or other specific content.

Content Indexing:

The collected data is organized and indexed. Indexing involves creating a structured database that makes it easy to search, retrieve, and present information when users submit queries.

Respecting Robots.txt:

Web crawlers typically adhere to the rules specified in the robots.txt file of a website. This file provides guidelines on which areas of the site can be crawled and which should be excluded.

Crawl Delays and Politeness:

To avoid overloading servers and causing disruptions, crawlers often incorporate mechanisms for crawl delays and politeness. These measures ensure that the crawler interacts with websites in a respectful and non-disruptive manner.

Web crawlers systematically navigate the web, following links, extracting data, and building an organized index. This process enables search engines to deliver accurate and relevant results to users based on their queries, making web crawlers a fundamental component of the modern internet ecosystem.

Web Crawling vs. Web Scraping

While web crawling and web scraping are often used interchangeably, they serve distinct purposes. Web crawling involves systematically navigating the web to index and collect information, while web scraping focuses on extracting specific data from web pages. In essence, web crawling is about exploring and mapping the web, whereas web scraping is about harvesting targeted information.

How to Build a Web Crawler

You may want to know how to build a web crawler. Building a simple web crawler in Python involves several steps, from setting up the development environment to coding the crawler logic. Below is a detailed guide to help you on how to build a web crawler using Python, utilizing the requests library for making HTTP requests and BeautifulSoup for HTML parsing.

Step 1: Set Up the Environment

On how to build a web crawler, the first step is to ensure you have Python installed on your system. You can download it from Additionally, you’ll need to install the required libraries:

pip install requests beautifulsoup4 

Step 2: Import Libraries

On how to build a web crawler, the next step is to create a new Python file (e.g., and import the necessary libraries:

import requests from bs4 import BeautifulSoup 

Step 3: Define the Crawler Function

Create a function that takes a URL as input, sends an HTTP request, and extracts relevant information from the HTML content:

def simple_crawler(url): 

# Send HTTP request to the URL 

response = requests.get(url) 

# Check if the request was successful (status code 200) 

if response.status_code == 200: 

# Parse HTML content with BeautifulSoup 

soup = BeautifulSoup(response.text, 'html.parser') 

# Extract and print relevant information (modify as needed) 

title = soup.title.text 

print(f'Title: {title}') 

# Additional data extraction and processing can be added here 


print(f'Error: Failed to fetch {url}') 

Step 4: Test the Crawler

Provide a sample URL and call the simple_crawler function to test the crawler:

if __name__ == "__main__": sample_url = '' simple_crawler(sample_url) 

Step 5: Run the Crawler

Execute the Python script in your terminal or command prompt:


The crawler will fetch the HTML content of the provided URL, parse it, and print the title. You can expand the crawler by adding more functionality for extracting different types of data.

Web Crawling with Scrapy

Web crawling with Scrapy opens the door to a powerful and flexible framework designed specifically for efficient and scalable web scraping. Scrapy simplifies the complexities of building web crawlers, offering a structured environment for crafting spiders that can navigate websites, extract data, and store it in a systematic manner. Here’s a closer look at web crawling with Scrapy:


Before you start, make sure you have Scrapy installed. You can install it using:

pip install scrapy 

Creating a Scrapy Project:

Initiate a Scrapy Project:

Open a terminal and navigate to the directory where you want to create your Scrapy project. Run the following command:

scrapy startproject your_project_name 

This creates a basic project structure with the necessary files.

Define the Spider:

Inside the project directory, navigate to the spiders folder and create a Python file for your spider. Define a spider class by subclassing scrapy.Spider and providing essential details like name, allowed domains, and start URLs.

import scrapy 

class YourSpider(scrapy.Spider): 

name = 'your_spider' 

allowed_domains = [''] 

start_urls = [''] 

def parse(self, response): 

# Define parsing logic here 


Extracting Data:

Using Selectors:

Scrapy utilizes powerful selectors for extracting data from HTML. You can define selectors in the spider’s parse method to capture specific elements.

def parse(self, response): 

title = response.css('title::text').get() 

yield {'title': title} 

This example extracts the text content of the <title> tag.

Following Links:

Scrapy simplifies the process of following links. Use the follow method to navigate to other pages.

def parse(self, response):

for next_page in response.css('a::attr(href)').getall(): 

yield response.follow(next_page, self.parse) 

Running the Spider:

Execute your spider using the following command from the project directory:

scrapy crawl your_spider 

Scrapy will initiate the spider, follow links, and execute the parsing logic defined in the parse method.

Web crawling with Scrapy offers a robust and extensible framework for handling complex scraping tasks. Its modular architecture and built-in features make it a preferred choice for developers engaging in sophisticated web data extraction projects.

Web Crawling at Scale

Web crawling at scale presents unique challenges, especially when dealing with a vast amount of data spread across numerous websites. PromptCloud is a specialized platform designed to streamline and optimize the web crawling process at scale. Here’s how PromptCloud can assist in handling large-scale web crawling initiatives:

  1. Scalability
  2. Data Extraction and Enrichment
  3. Data Quality and Accuracy
  4. Infrastructure Management
  5. Ease of Use
  6. Compliance and Ethics
  7. Real-Time Monitoring and Reporting
  8. Support and Maintenance

PromptCloud is a robust solution for organizations and individuals seeking to conduct web crawling at scale. By addressing key challenges associated with large-scale data extraction, the platform enhances the efficiency, reliability, and manageability of web crawling initiatives.

In Summary

Web crawlers stand as the unsung heroes in the vast digital landscape, diligently navigating the web to index, gather, and organize information. As the scale of web crawling projects expands, PromptCloud steps in as a solution, offering scalability, data enrichment, and ethical compliance to streamline large-scale initiatives. Get in touch with us at 

Frequently Asked Questions

What is WebCrawler used for?

A WebCrawler, also known as a web spider or web robot, is a program or automated script that browses the World Wide Web in a methodical, automated manner. WebCrawlers are primarily used for:

Indexing the Web for Search Engines

The most common use of WebCrawlers is by search engines like Google, Bing, and Yahoo. They crawl web pages to collect information and index it, so that the search engine can quickly return relevant web pages in response to user queries. This indexing involves analyzing the content of web pages, understanding their topics, and storing this information in a way that makes it efficiently retrievable.

Web Archiving

WebCrawlers are used to archive the web, creating copies of web pages that can be preserved for future reference. This is important for historical records and for accessing content that may later be changed or removed from its original location online.

Data Mining

Many businesses and researchers use WebCrawlers to extract useful information from the web for data analysis purposes. This can include gathering data on market trends, competitor analysis, social media sentiment analysis, and more. Data mining with WebCrawlers can provide valuable insights for decision-making and strategy development.

SEO Monitoring

WebCrawlers are employed to monitor and evaluate the search engine optimization (SEO) health of websites. These crawlers can identify issues like broken links, poor mobile optimization, slow loading times, and problems with metadata that could affect a website’s search engine ranking.

Website Change Detection

Crawlers can monitor websites for changes, alerting users or systems when updates are made. This is useful for keeping track of competitors, monitoring websites for compliance, or tracking updates on sites of personal interest.

Content Aggregation

WebCrawlers are used to aggregate content from multiple sources for websites that compile news, articles, or other information from across the web. This allows for the creation of content feeds and databases that provide users with a centralized source of information on specific topics.

Link Verification

WebCrawlers can check the validity of hyperlinks on websites, identifying broken links that need to be fixed. This is important for website maintenance and user experience.

In essence, WebCrawlers are fundamental tools for navigating and processing the vast amounts of information available on the internet, enabling a variety of applications from search engine operation and web archiving to data mining and SEO monitoring.

What is the first web crawler?

The first web crawler is widely recognized as the “World Wide Web Wanderer,” created in 1993 by Matthew Gray. Initially developed to measure the size of the World Wide Web, the Wanderer was a pioneering tool in the early days of the internet. It began as a simple program designed to access the web, count web servers, and collect URLs. Over time, it evolved to perform more complex tasks, including building a database of the web pages it visited, which inadvertently became one of the earliest forms of a web index.

The World Wide Web Wanderer’s operation marked the beginning of web crawling technology, laying the groundwork for the development of search engines and the systematic indexing of the internet. Its creation was a significant milestone in the history of the web, demonstrating the feasibility and utility of automating the exploration and mapping of web content.

What is the difference between Google and web crawler?

The difference between Google and a web crawler lies in their nature and functionalities. Google is a comprehensive search engine, while a web crawler (also known as a spider or bot) is a tool or a part of a search engine that performs a specific task. Here’s a breakdown of their key differences:


  • Comprehensive Search Engine: Google is a search engine that uses a complex set of algorithms to index, rank, and retrieve web pages in response to user queries. It provides a user interface where users can enter search terms to find relevant information on the web.
  • Multiple Components: It consists of several components,What does a web crawler do? including web crawlers, indexing algorithms, and search algorithms, all working together to deliver search results. Googlebot, Google’s web crawler, is just one part of this larger system.
  • User-Oriented Service: Google is designed to serve end-users, offering various services such as web search, image search, Google Maps, YouTube, and more, all integrated into its platform.

Web Crawler

  • Specific Tool: A web crawler is a software program or script designed to visit websites systematically to index their content. It is a tool used by search engines and other services to update their web content or indices.
  • Single Functionality: Unlike Google, which performs numerous tasks, a web crawler has a singular purpose: to crawl the web and collect data. This data is then processed and indexed by search engines or used for other purposes like data analysis or web archiving.
  • Part of a Larger System: Web crawlers are typically components of search engines or other data-gathering applications. They do not provide search results directly to users but play a crucial role in the operation of search engines by gathering the raw data needed to compile search indexes.

In summary, Google is a search engine that utilizes web crawlers as part of its infrastructure to gather data from the internet, which it then processes and organizes to respond to user queries. A web crawler, on the other hand, is a specific tool focused on the task of navigating the web to collect information, without the additional functionalities related to processing or responding to search queries.

What is meant by web crawling?

Web crawling, also known as web spidering or web scraping, is an automated process used to browse the World Wide Web in a methodical and automated manner. It involves the use of software known as a “crawler” or a “spider,” which systematically browses the internet to collect information from webpages. This process is fundamental to the operation of search engines, which rely on web crawlers to compile a vast index of online content to improve search results.
The primary purpose of web crawling is to index the content of websites so that users can query this information through search engines. Crawlers visit webpages, read the information contained therein, and follow links to other pages on the site as well as to other sites. As they move from link to link, crawlers collect data on each webpage, including text, images, and video content, among other types of data. This collected data is then processed and indexed by search engines, making it searchable for users.

Web crawling is not limited to search engines. Many businesses and researchers use web crawlers to gather specific data from the web for a variety of purposes, such as market research, price monitoring, lead generation, and academic research. These activities often require customized crawling solutions tailored to specific data collection needs.

It’s important to note that responsible web crawling practices involve adhering to the rules specified in the robots.txt file of websites, which outlines which parts of the site can or cannot be crawled, and ensuring that the crawling activities do not negatively impact the performance of the websites being visited.
In summary, web crawling is a crucial technology that powers search engines and enables the automated collection of web data for various analytical and business purposes. It serves as the backbone for indexing the vast amount of information available on the internet, making it accessible and useful for end-users and organizations alike.

What is difference between web scraping and web crawling?

Web scraping and web crawling are related but distinct processes used for gathering data from the internet. While both involve the automated collection of information from websites, they serve different purposes and operate in slightly different ways.

Web crawling, primarily associated with search engines, is the process of systematically browsing the web to index and retrieve web page content. Crawlers, also known as spiders or bots, are used to visit websites and read their pages to create entries for a search engine index. The primary goal of web crawling is to understand the content of a webpage and its relationship to other pages across the web. This process helps search engines deliver relevant search results to users. Web crawling focuses on the exploration of web pages and the discovery of links, acting as the foundation for creating a comprehensive map of the internet.

Key characteristics of web crawling include:

  • Broad Scope: Crawlers aim to visit as many web pages as possible to create a large index for search engines.
  • Link Exploration: Crawlers follow links from one page to another, which helps in discovering new pages and updating information on previously visited pages.
  • Indexing: The main purpose of crawling is to index web content, enabling search engines to provide relevant search results.

Web scraping, on the other hand, is a more targeted process designed to extract specific information from websites. It involves pulling concrete data from web pages, such as product prices, stock quotes, or any other information that needs to be monitored or collected for research, analysis, or data-driven decision-making. Web scraping is often performed by businesses, researchers, and individuals who require detailed data extraction for various applications.

Key characteristics of web scraping include:

  • Targeted Extraction: Scraping is focused on gathering specific data points from web pages, rather than indexing the content of these pages.
  • Data Processing: The extracted data is usually processed, transformed, and stored in a structured format for easy analysis or integration into databases or applications.
  • Automation of Data Collection: Scraping can automate the collection of data from websites that are frequently updated, ensuring timely access to the latest information.

While web crawling is about mapping the web and understanding the relationship between different web pages for indexing purposes, web scraping is about extracting specific pieces of data from websites for use in various applications. Crawling is a prerequisite for search engines to function, allowing them to provide relevant search results based on the content available on the web. Scraping, however, is used by individuals and organizations to capture specific information from the web for analysis, monitoring, or integration into projects or workflows. Both processes are crucial for navigating and utilizing the vast resources of the internet, but they cater to different needs and objectives.

Is Google a web crawler?

Yes, Google operates a web crawler known as Googlebot. Googlebot is the search bot software used by Google, which collects documents from the web to build a searchable index for the Google Search engine. This process is fundamental to how Google Search works, as it allows the search engine to retrieve and serve relevant web pages to users based on their search queries.

Googlebot systematically crawls the web, visiting websites to discover and record information about new and updated pages. This information is then processed and indexed by Google, enabling it to quickly deliver search results that are relevant, comprehensive, and up-to-date. The crawler respects rules set out in robots.txt files on websites, which tell search engines which pages should or should not be crawled, to ensure that it operates ethically and does not access restricted areas of websites.

In essence, Googlebot is a critical component of Google’s search infrastructure, enabling the search engine to function effectively by continuously updating its vast database of web pages, making the information accessible and searchable to users worldwide.

What is web crawling vs indexing?

Web crawling and indexing are two critical processes used by search engines to gather and organize information from the internet, making it searchable and accessible to users. While they are part of the same workflow, they serve distinct purposes and operate in different stages of the search engine operation.

Web crawling is the process by which search engines use automated software, known as crawlers or spiders, to visit and read web pages across the internet. The primary purpose of web crawling is to discover new web pages and to update the content of previously visited pages. Crawlers navigate the web by following links from one page to another. This allows search engines to find new content and keep their indexes updated with the latest information available on the web.

Key aspects of web crawling include:

  • Discovery: Finding new web pages or websites that have not been indexed yet.
  • Updates: Identifying changes to existing web pages so that the search engine can refresh its index with the most current information.
  • Link Following: Using hyperlinks to navigate from one page to another, which helps in discovering new content.

Indexing is the process that follows crawling; it involves analyzing and organizing the content found by crawlers into a searchable database, known as an index. During indexing, the search engine processes the content of a web page, extracting information like text, images, and video, and then organizes this information in a way that makes it efficiently retrievable.

Key aspects of indexing include:

  • Content Analysis: Understanding the subject matter and context of web pages. This can involve processing text, recognizing images, and more.
  • Data Structuring: Organizing the extracted information into a structured format that allows for efficient storage and retrieval. This includes cataloging the content by keywords, topics, and other metadata.
  • Searchability: Ensuring that the content can be quickly found by users through the search engine. This involves creating associations between keywords, topics, and the content of web pages.

While web crawling and indexing are distinct processes, they are closely interconnected. Crawling is the first step, involving the discovery and collection of web page data. Indexing comes next, where the collected data is analyzed, organized, and made ready for search queries.

In essence, web crawling provides the raw materials (web pages) that are necessary for indexing. Without crawling, there would be no data to index. Conversely, without indexing, the data collected by crawling would not be searchable or useful to end-users. Together, these processes enable search engines like Google to function, allowing users to find the information they need on the internet quickly and efficiently.

What does a web crawler do?

A web crawler, also known as a spider or bot, is an internet bot that systematically browses the World Wide Web, primarily for the purpose of web indexing. Web crawlers are used by search engines to update their content and index the vast amount of information available on the web. Here’s a breakdown of what a web crawler does:

Visits Web Pages

Starting from a list of URLs to visit, known as seeds, the crawler begins its task by loading the web page associated with each URL.

Analyzes Page Content

Once a page is accessed, the crawler analyzes the content of the page. This can involve reading the text, the metadata in the HTML header, and other relevant information that can be indexed or used to understand the page’s content.

Extracts Links

While analyzing the page content, the crawler also looks for links to other pages (a tags in HTML). These links are then added to the list of URLs to visit next.

Follows Links

The crawler follows the extracted links to visit other pages, repeating the process of analyzing content and extracting links. This allows the crawler to navigate through the web in a methodical way.

Avoids Duplicate Crawling

To prevent the same page from being indexed multiple times, crawlers typically keep track of the pages they’ve visited. This is often achieved through a combination of URL normalization and tracking mechanisms.

Respects robots.txt

Well-behaved crawlers check a website’s robots.txt file to determine which parts of the site should not be crawled and indexed. This helps in avoiding the collection of sensitive data or overloading the website’s servers.

Stores Indexed Information

The information collected by the crawler is processed and indexed. This index is then used by search engines to quickly provide relevant web pages in response to user queries.

Updates Indexed Content

Crawlers periodically revisit websites to check for new or updated content, ensuring that the search engine’s index is current and includes the latest information available on the web.

Are web crawlers legal?

The legality of web crawlers depends on how they are used, the data they collect, and the jurisdictions involved. Generally, using web crawlers for indexing public information is considered legal, especially when done by search engines like Google, Bing, or Yahoo to provide search services. However, there are several legal and ethical considerations that must be taken into account:

Terms of Service (ToS)

Many websites have Terms of Service (ToS) that specify conditions under which the site may be accessed and used. Some ToS may explicitly restrict or prohibit the use of automated crawlers. Disregarding these terms could potentially lead to legal actions or being banned from the site.


Websites use the robots.txt file to communicate with web crawlers, indicating which parts of the site should not be crawled or indexed. While not legally binding, ignoring the directives in a robots.txt file is generally considered bad practice and, in some contexts, could be viewed as accessing the site without authorization.

Copyright Laws

The data collected by crawlers might be subject to copyright. While crawling for indexing purposes and showing snippets in search results is often covered under fair use in many jurisdictions, using copyrighted material without permission for other purposes might infringe on copyright laws.

Privacy and Data Protection Laws

Laws such as the General Data Protection Regulation (GDPR) in the European Union impose strict rules on how personal data can be collected, processed, and stored. Crawlers that collect personal information without proper consent or legal basis could be violating these laws.

Computer Fraud and Abuse Act (CFAA)

In the United States, the CFAA has been interpreted to address unauthorized access to computer systems, which could include disregarding a site’s ToS or robots.txt directives under certain circumstances. However, the application of the CFAA to web crawling activities has been a contentious legal issue.

Do web crawlers still exist?

Yes, web crawlers still exist and are an essential component of the internet’s infrastructure. They are actively used by search engines, research organizations, marketing companies, and various online services for a wide range of purposes, including:

  • Indexing Content for Search Engines: Web crawlers like Googlebot (Google), Bingbot (Bing), and others are crucial for search engines to discover new and updated content on the internet. They crawl the web to index pages so that they can be retrieved and ranked in response to user queries.
  • Web Archiving: Organizations like the Internet Archive use crawlers to collect and preserve digital content. This ensures that historical versions of websites and online content are saved for future generations.
  • Data Analysis and Market Research: Many businesses use web crawlers to gather data from the internet for analysis, competitive research, consumer behavior studies, and to inform business strategies.
  • SEO Monitoring: Webmasters and SEO professionals use specialized crawlers to audit websites, checking for SEO best practices, identifying broken links, and assessing site structure to optimize for search engine rankings.
  • Content Aggregation: News outlets, content aggregators, and social media platforms use crawlers to gather and compile content from various sources across the web.

The technology and methodologies behind web crawling have evolved significantly since the early days of the internet, becoming more sophisticated to navigate the complexities of modern web architectures and to respect web standards such as robots.txt (which tells crawlers which parts of a site can be crawled) and meta tags (which provide crawlers with page-specific instructions).

Moreover, the ethical and legal considerations surrounding web crawling have become more prominent, leading to discussions about privacy, data protection, and respectful use of crawled data. Despite these challenges, web crawlers continue to be a fundamental tool for organizing, analyzing, and accessing the vast amount of information available online.

How to create an own web crawler?

Creating your own web crawler involves several steps, from setting up the environment to writing the code and handling the data. Here’s a medium-length guide to help you get started:

1. Define Your Goals:

  • Scope: Determine what you want to crawl (e.g., specific websites, types of content).
  • Frequency: Decide how often you need to crawl the data (e.g., real-time, daily, weekly).

2. Set Up Your Environment:

  • Programming Language: Choose a language suited for web scraping, such as Python.
  • Libraries and Tools: Install necessary libraries such as BeautifulSoup, Scrapy, or Selenium for Python.

3. Write the Crawler:

  • URL Fetching: Start by writing code to fetch URLs. You can use libraries like requests to download web pages.
  • HTML Parsing: Use BeautifulSoup or lxml to parse HTML content and extract the required data.
  • Navigation: Implement logic to follow links and navigate through pages, collecting URLs to visit next.

4. Handle Data Storage:

  • Database: Decide how to store the crawled data. Options include SQL databases (like MySQL or PostgreSQL) or NoSQL databases (like MongoDB).
  • File Storage: For simpler projects, storing data in CSV or JSON files might suffice.

5. Respect Robots.txt and Rate Limits:

  • Robots.txt: Check each website’s robots.txt file to respect the site’s crawling policies.
  • Rate Limiting: Implement delays between requests to avoid overwhelming servers and getting banned.

6. Manage Crawling Sessions:

  • State Management: Keep track of visited URLs to avoid duplicating work and looping indefinitely.
  • Error Handling: Implement robust error handling to manage network issues, timeouts, and unexpected HTML structures.

7. Scale and Optimize:

  • Concurrency: Use libraries like Scrapy, which support concurrent requests, to speed up the crawling process.
  • Proxy Servers: Use proxy servers to distribute requests and avoid IP bans.

8. Test and Maintain:

  • Testing: Regularly test your crawler to ensure it handles different website structures and changes in HTML.
  • Maintenance: Update your crawler to adapt to website changes and new data requirements.

Is it illegal to web crawler?

1. Legality:

  • Depends on Context: The legality of using a web crawler depends on how and where it is used. Crawling publicly accessible websites is generally legal, but there are important considerations and restrictions.

2. Respecting Website Terms of Service:

  • Terms of Service (ToS): Many websites have terms of service that outline acceptable uses of their data. Violating these terms, such as by scraping data without permission, can lead to legal action.
  • Robots.txt: Websites use robots.txt files to specify which parts of the site can be crawled. Respecting robots.txt directives is important to avoid unauthorized access.

3. Intellectual Property and Copyright:

  • Content Ownership: Crawling and using copyrighted content without permission can infringe on intellectual property rights.
  • Data Usage: Even if the data is publicly available, using it for purposes not allowed by the website owner can lead to legal issues.

4. Privacy Concerns:

  • Personal Data: Collecting personal data without consent can violate privacy laws, such as the General Data Protection Regulation (GDPR) in Europe or the California Consumer Privacy Act (CCPA) in the United States.
  • Sensitive Information: Special care must be taken not to collect sensitive personal information unless explicitly allowed.

5. Rate Limiting and Server Load:

  • Overloading Servers: Aggressive crawling can overload a website’s servers, potentially leading to denial of service (DoS) attacks. This can be illegal and unethical.
  • Rate Limiting: Implementing rate limits on your crawler helps prevent overwhelming the website and reduces the risk of getting banned.

6. Ethical Considerations:

  • Respect and Fair Use: Ethical web crawling involves respecting the website’s terms, data ownership, and privacy concerns. It also involves ensuring that your crawling activities do not harm the website’s functionality.

Using a web crawler is not inherently illegal, but it must be done in a manner that respects legal boundaries and ethical guidelines. Always check the terms of service of the websites you intend to crawl, respect robots.txt directives, avoid infringing on intellectual property rights, and handle personal data with care to comply with privacy laws.

How to make a Python web crawler?

1. Set Up Your Environment:

  • Install Python: Ensure Python is installed from
  • Install Libraries: Use pip to install necessary libraries.

pip install requests beautifulsoup4

2. Write the Crawler:

  • Import Libraries:

import requests
from bs4 import BeautifulSoup
from urllib.parse import urljoin
import time

Define the Crawler Function:

visited = set()

def crawl(url, base_url):
if url not in visited:
response = requests.get(url)
if response.status_code == 200:
soup = BeautifulSoup(response.content, ‘html.parser’)
for link in soup.find_all(‘a’):
href = link.get(‘href’)
if href:
full_url = urljoin(base_url, href)
time.sleep(1) # Delay to avoid server overload
crawl(full_url, base_url)

Starting URL:

start_url = ‘’
crawl(start_url, start_url)

This simple web crawler visits a starting URL, extracts links, and recursively crawls them, printing each URL.

Here are some of our best resources if you want to deepen your web crawling knowledge:

Web crawling solutions

Web Scraping vs Web Crawling – Unveiling the Differences

Web Crawling An Unconventional Guide

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