Web crawling with python & proxies

Have you ever wondered how search engines like Google manage to index billions of web pages and serve up relevant results in a fraction of a second? The answer lies in web crawling – the process of systematically browsing and indexing websites in an automated way. Web crawling enables search engines to discover new pages, update their indexes, and provide fresh, comprehensive results to searchers.

But search engines aren‘t the only ones who can benefit from web crawling. Businesses and individuals can also utilize this powerful technique to gather data and extract insights from websites at scale. Whether you want to monitor competitors, generate leads, conduct market research, or build extensive datasets, knowing how to perform web crawling can unlock a wealth of opportunities.

In this in-depth guide, we‘ll walk you through everything you need to know to get started with web crawling using Python and Scrapy. You‘ll learn the fundamentals of web crawling, discover the best tools and frameworks, and gain hands-on experience by building your own web crawler step-by-step. We‘ll also cover important topics like using proxies, adhering to legal guidelines, and optimizing your crawlers for maximum efficiency.

By the end of this guide, you‘ll have the knowledge and skills to tackle your own web crawling projects with confidence. Let‘s dive in!

What is Web Crawling?

At its core, web crawling is the process of programmatically visiting web pages and extracting data from them. A web crawler, also known as a spider bot, systematically browses websites by following links from page to page. As the crawler visits each page, it can read the HTML content, extract specific data points, and store them for later analysis.

Web crawling is often confused with web scraping, but there are important distinctions between the two. While web scraping focuses on extracting data from a specific page or set of pages, web crawling is more about discovering and navigating an extensive network of pages. In practice, most real-world data extraction projects involve a combination of crawling and scraping techniques.

Some common use cases for web crawling include:

  • Search engine indexing: Search engines use web crawlers to discover new pages, understand their content, and rank them in search results. By continuously crawling the web, search engines can provide up-to-date and comprehensive results to users.

  • Price monitoring: Businesses can use web crawling to keep track of competitor prices, monitor their own product listings across multiple websites, and adjust pricing strategies accordingly.

  • Lead generation: Marketers can leverage web crawling to find potential customers, gather contact information, and build targeted prospect lists for outreach campaigns.

  • Market research: Researchers can use web crawling to collect data on consumer trends, sentiment analysis, product reviews, and more to gain insights and inform business decisions.

  • Archiving and preservation: Organizations can employ web crawling to create historical archives of web content, preserving important information for future reference and analysis.

Now that we have a high-level understanding of what web crawling is and why it‘s useful, let‘s take a closer look at the tools and techniques involved.

Introducing Scrapy: A Powerful Web Crawling Framework

When it comes to web crawling with Python, Scrapy is one of the most popular and feature-rich frameworks available. Scrapy is an open-source library that provides a complete ecosystem for extracting data from websites efficiently and scalably. It offers built-in support for tasks like making HTTP requests, handling cookies and sessions, parsing HTML and XML, and dealing with pagination and redirects.

Some of the key features and benefits of using Scrapy for web crawling include:

  • Asynchronous requests: Scrapy uses asynchronous programming to make multiple requests in parallel, maximizing throughput and minimizing latency. This allows you to crawl large websites much faster than with traditional synchronous approaches.

  • Extensible architecture: Scrapy follows a modular and extensible design, making it easy to customize and extend its functionality. You can create your own middleware, pipelines, and extensions to tailor Scrapy to your specific crawling needs.

  • Built-in selectors: Scrapy provides a powerful selection mechanism based on XPath and CSS expressions, enabling you to extract data from HTML and XML documents easily. It also integrates with libraries like BeautifulSoup and lxml for even more flexible parsing options.

  • Automatic request throttling: To avoid overwhelming servers with too many requests, Scrapy automatically throttles requests based on pre-defined rules. You can fine-tune the throttling settings to strike a balance between crawling speed and server load.

  • Robust error handling: Scrapy includes built-in error handling and retry mechanisms to deal with common issues like network failures, timeouts, and rate limits. It can automatically retry failed requests and adjust crawling speed based on server responses.

  • Broad ecosystem: Scrapy has a large and active community, which means you can find plenty of extensions, middlewares, and utilities contributed by other developers. This ecosystem makes it easier to tackle common crawling challenges and integrate with other tools and services.

Now that we have a sense of why Scrapy is a great choice for web crawling, let‘s walk through a practical example of building a web crawler with Scrapy.

Building a Wikipedia Crawler with Scrapy

In this section, we‘ll create a Scrapy spider that can crawl a subset of Wikipedia and extract information from the pages it visits. Specifically, our crawler will start from a seed page and follow links to find all the articles that are two clicks away from the starting point. Along the way, it will extract the title and URL of each page and store them in a structured format.

Before we start coding, make sure you have Python and Scrapy installed on your system. You can install Scrapy using pip:

pip install scrapy

Step 1: Create a new Scrapy project
First, let‘s create a new Scrapy project using the startproject command:

scrapy startproject wikipedia

This will create a new directory called wikipedia with the basic structure of a Scrapy project.

Step 2: Define the spider
Navigate to the spiders directory inside the project and create a new file called wiki_spider.py. This is where we‘ll define our spider.

Open wiki_spider.py in your code editor and add the following code:

import scrapy
from scrapy.spiders import CrawlSpider, Rule
from scrapy.linkextractors import LinkExtractor

class WikipediaSpider(CrawlSpider): name = ‘wikipedia‘ allowed_domains = [‘en.wikipedia.org‘] starturls = [‘https://en.wikipedia.org/wiki/Python(programming_language)‘]

rules = (
    Rule(LinkExtractor(allow=r‘wiki/((?!:).)*$‘), callback=‘parse_item‘, follow=True),

def parse_item(self, response):
    yield {
        ‘title‘: response.css(‘h1::text‘).get(),
        ‘url‘: response.url,

Let‘s break down what‘s happening in this code:

  • We define a new class called WikipediaSpider that inherits from Scrapy‘s CrawlSpider class. This gives us access to powerful crawling features like link extraction and depth-first search.

  • We set some basic attributes for our spider, including its name, the allowed domains it can crawl, and the starting URL.

  • We define a tuple of rules that tell the spider how to behave. In this case, we have a single rule that uses LinkExtractor to find all links that match the regular expression wiki/((?!:).)*$. This will match URLs like /wiki/Article_Name but exclude special pages like /wiki/Talk: or /wiki/User:.

  • The parse_item method is called for each response that matches our rules. It extracts the title and URL of the page using CSS selectors and yields them as a dictionary.

Step 3: Set the crawling depth
To limit our crawler to only pages that are two clicks away from the starting page, we need to set the DEPTH_LIMIT setting. Open the settings.py file in the project directory and add the following line:


Step 4: Run the spider
We‘re now ready to run our spider and see it in action. Open a terminal, navigate to the project directory, and run the following command:

scrapy crawl wikipedia -o output.json

This will start the wikipedia spider, follow the rules we defined, and save the extracted data to a file called output.json.

Step 5: Examine the results
After the crawl finishes, you can open output.json to see the extracted data. It should contain a list of dictionaries, each representing a crawled page with its title and URL.

Congratulations! You‘ve just built your first web crawler with Scrapy. This is just the beginning, and there are many ways you can extend and customize your spider to handle more complex scenarios.

Using Proxies for Web Crawling

When you‘re crawling websites at scale, it‘s important to be mindful of your impact on the servers you‘re targeting. Sending too many requests too quickly can put a strain on servers and potentially get your IP address blocked or banned. To mitigate these risks and improve your crawling performance, it‘s a good idea to use proxies.

A proxy server acts as an intermediary between your crawler and the target website. Instead of sending requests directly from your own IP address, you route them through the proxy server, which then forwards them to the destination. This helps mask your true identity and location, making it harder for websites to detect and block your crawling activity.

There are several types of proxies you can use for web crawling, each with its own advantages and trade-offs:

  • Rotating proxies: These proxies automatically rotate the IP address used for each request, distributing the load across multiple servers. This helps avoid rate limits and bans by making your traffic appear to come from different sources.

  • Residential proxies: These proxies use IP addresses assigned by Internet Service Providers (ISPs) to residential users. They are less likely to be detected as proxies and offer better anonymity compared to datacenter proxies. However, they tend to be more expensive and have lower performance.

  • Datacenter proxies: These proxies use IP addresses assigned to servers in data centers. They offer high performance and are generally cheaper than residential proxies. However, they are more easily detectable as proxies and may be more prone to blocking.

When choosing a proxy provider for web crawling, it‘s important to consider factors like proxy pool size, location coverage, speed, reliability, and pricing. Some of the top proxy providers for web crawling include:

  1. Bright Data (formerly Luminati): Offers a large pool of residential and datacenter proxies with global coverage.
  2. IPRoyal: Provides high-quality residential and datacenter proxies with flexible pricing options.
  3. Proxy-Seller: Offers affordable and reliable proxy solutions for various use cases.
  4. SOAX: Provides residential and mobile proxies with advanced rotation and targeting options.
  5. Smartproxy: Offers a mix of residential and datacenter proxies with user-friendly interfaces.
  6. Proxy-Cheap: Provides budget-friendly proxy solutions for small to medium-scale projects.
  7. HydraProxy: Offers fast and secure residential and datacenter proxies with global coverage.

To integrate proxies into your Scrapy crawler, you can use the built-in HttpProxyMiddleware or third-party extensions like scrapy-rotating-proxies or scrapy-proxy-pool. These tools allow you to specify a list of proxy servers and configure rotation and failover settings.

Here‘s an example of how to use proxies with Scrapy‘s HttpProxyMiddleware:

# settings.py
    ‘scrapy.downloadermiddlewares.httpproxy.HttpProxyMiddleware‘: 543,

HTTP_PROXY_LIST = [ ‘http://proxy1.example.com:8080‘, ‘http://proxy2.example.com:8080‘,


In this example, we enable the HttpProxyMiddleware and provide a list of proxy servers in the HTTP_PROXY_LIST setting. Scrapy will automatically rotate through these proxies for each request.

Using proxies can significantly improve the performance and reliability of your web crawlers, but it‘s important to use them responsibly and in compliance with legal and ethical guidelines. Always respect website terms of service, robots.txt rules, and crawling best practices to ensure a positive crawling experience for everyone involved.


Web crawling is a powerful technique for discovering and extracting data from websites at scale. By leveraging tools like Scrapy and proxies, you can build efficient and robust crawlers that can navigate the web and gather valuable insights for various applications.

In this guide, we‘ve covered the fundamentals of web crawling, explored the Scrapy framework, and walked through a step-by-step example of building a Wikipedia crawler. We‘ve also discussed the importance of using proxies for web crawling and compared some of the top proxy providers in the market.

As you embark on your own web crawling projects, remember to always prioritize responsible and ethical crawling practices. Respect website terms of service, honor robots.txt rules, and be mindful of your crawling frequency and impact on servers. With the right tools and approaches, you can unlock the vast potential of web data while maintaining a positive relationship with the websites you crawl.