Building a crawler with Scrapy

Vishesh Nagpal -

Whether you're a marketer tracking competitor prices, a researcher gathering information, or a developer building the next big data-driven application, the ability to efficiently extract data from the web is invaluable. Enter web crawling – the automated process of navigating through websites and extracting structured data. And when it comes to web crawling in Python, Scrapy stands out as a powerful, flexible, and scalable framework.

In this guide, we'll dive deep into the world of Scrapy, exploring how to build web crawlers. This tutorial will equip you with the basic knowledge to build a web crawling with Scrapy. So let’s scrape quotes from quotes.toscrape.com.

Introduction to Scrapy

Scrapy is an open-source web crawling framework written in Python. It's designed to be fast, simple, and extensible, making it an excellent choice for projects of all sizes – from small scripts to large-scale web crawling systems.

Key features of Scrapy include:

  • Asynchronous networking: Scrapy uses asynchronous networking to send multiple requests concurrently, significantly speeding up the crawling process.
  • Extensible architecture: With a system of middlewares and pipelines, Scrapy allows you to customize every step of the crawling process.
  • Built-in support for extracting data: Scrapy provides powerful selectors based on XPath and CSS to extract data from HTML and XML sources.
  • Robust encoding support: It can handle various character encodings, ensuring you can crawl websites in different languages.
  • Portable output: Scrapy can export scraped data in multiple formats like JSON, CSV, and XML out of the box.

Why Use Scrapy?

Scrapy offers several advantages over other web scraping tools and frameworks:

  • Flexibility: Scrapy allows you to customize your crawler to extract specific data from websites, handling complex scenarios with ease.
  • Efficiency: Scrapy's architecture is designed for performance, making it capable of handling large-scale web scraping tasks with minimal resource usage.
  • Scalability: Scrapy's distributed architecture enables you to scale your crawler to handle massive amounts of data, making it an ideal choice for big data projects.
  • Community Support: Scrapy has an active community of developers and users, ensuring that you can find help and resources when needed.

Setting Up Your Environment

Before we dive into coding, let's set up our development environment:

  1. Ensure you have Python 3.6 or later installed. You can check your version by running:
python --version

2. It's recommended to use a virtual environment to keep your Scrapy project isolated. Create and activate a virtual environment:

python -m venv scrapy_env
source scrapy_env/bin/activate

3. Install Scrapy using pip:

pip install scrapy

With these steps completed, you're ready to start building your first Scrapy project!

Creating Your First Scrapy Project

Let's create a new Scrapy project:

  1. Open your terminal and navigate to your desired directory.
  2. Create a new Scrapy project

Run the following command:

scrapy startproject quotes_scraper

This command generates a new Scrapy project with the following structure:

Let's briefly go over what each of these files does:

  • scrapy.cfg: The project configuration file.
  • items.py: Defines the data structure for your scraped items.
  • middlewares.py: Customizes the request/response processing.
  • pipelines.py: Processes the scraped items.
  • settings.py: Configures your Scrapy project.
  • spiders/: Directory where you'll place your spiders.

3. Define the Spider:
Navigate to the spiders directory inside your project and create a new Python file called quotes_spider.py. Copy the following spider code and paste in this file

import scrapy


class QuotesSpider(scrapy.Spider):
   name = "quotes"
   start_urls = [
       'http://quotes.toscrape.com/page/1/',
   ]


   def parse(self, response):
       # Extracting the content using CSS selectors
       for quote in response.css('div.quote'):
           yield {
               'text': quote.css('span.text::text').get(),
               'author': quote.css('small.author::text').get(),
               'tags': quote.css('div.tags a.tag::text').getall(),
           }


       # Follow pagination links to scrape multiple pages
       next_page = response.css('li.next a::attr(href)').get()
       if next_page is not None:
           next_page = response.urljoin(next_page)
           yield scrapy.Request(next_page, callback=self.parse)

4. Breakdown of the Spider:

  1. Name: The spider is named "quotes", which is used to run the spider.
  2. start_urls: A list of URLs that Scrapy will begin crawling. In this case, it starts with the first page of quotes.
  3. parse method: This is where Scrapy handles the response. It:
  • Loops through each quote block on the page using CSS selectors.
  • Extracts the quote text, author name, and associated tags for each quote.
  • Yields this data as a dictionary, which will later be saved or processed by Scrapy’s pipeline.

4. Pagination: The spider identifies the link to the next page using CSS selectors, then recursively follows it until there are no more pages left.

5. Running the Spider:

Once your spider is ready, you can run it from the terminal. Navigate to the project folder (the one containing scrapy.cfg) and run:

scrapy crawl quotes

The output

Understanding Scrapy's Architecture

Scrapy's architecture is modular and event-driven, which allows for great flexibility and performance. The main components are:

  • Spiders: These are classes you define to crawl specific sites and extract data.
  • Engine: The core of Scrapy, coordinating all the components.
  • Scheduler: Receives requests from the engine and queues them for the downloader.
  • Downloader: Fetches web pages and feeds them to the engine.
  • Item Pipeline: Processes the items after they've been extracted by the spiders.
  • Downloader Middlewares: Process requests before they're sent to the downloader and responses before they reach the spider.
  • Spider Middlewares: Process a spider's input (responses) and output (items and requests).

Understanding this architecture helps you know where to make changes when you want to customize Scrapy's behavior.

Scrapy Best Practices and Optimization

To get the most out of Scrapy, consider these best practices and optimization techniques:

  1. Use response.follow instead of scrapy.Request for relative URLs.
  2. Implement concurrent requests using ConcurrentRequest for better performance.
  3. Use ItemLoader to separate extraction logic from spiders.
  4. Implement proper error handling to make your spiders more robust.
  5. Use caching mechanisms to avoid re-downloading unchanged pages.
  6. Implement rate limiting to be respectful to the websites you're crawling.
  7. Use CONCURRENT_REQUESTS and DOWNLOAD_DELAY settings to control crawling speed.
  8. Implement retry middleware for handling temporary errors.

Read the official documentation and the official tutorial for more details. - https://docs.scrapy.org/en/latest/intro/tutorial.html

One of the best things about Scrapy is its community. Join the Scrapy Discord,and connect with web scraping developers. Make the best out of scrapy.

Ethical Considerations in Web Crawling

When crawling websites, it's crucial to be respectful and ethical:

  1. Always check and respect robots.txt files.
  2. Implement proper rate limiting to avoid overwhelming servers.
  3. Identify your bot in the user agent string.
  4. Be mindful of copyright and terms of service of the websites you're crawling.
  5. Don't scrape personal information without explicit permission.
  6. Consider the impact of your crawler on the website's resources.

Challenges with Web Scraping Using Scrapy

While Scrapy is a powerful and flexible framework, it comes with a few challenges. Websites often employ anti-scraping measures such as CAPTCHAs, IP blocking, or rate-limiting, which Scrapy does not handle natively. Additionally, dynamic content generated by JavaScript can be difficult for Scrapy to scrape. To tackle such challenges, developers usually rely on additional tools like rotating proxies, user-agent rotation, or CAPTCHAs-solving services.

However, for more advanced scraping needs such as handling JavaScript rendering, IP rate limitations, and ban management, pairing Scrapy with specialized tools like Ujeebu API can be a game-changer. Ujeebu provides solutions that seamlessly integrate with Scrapy to manage large-scale scraping tasks, ensuring high success rates even on websites with complex anti-scraping mechanisms.

Conclusion

As we've journeyed through the intricacies of Scrapy, from setting up your first project to navigating the challenges of modern web scraping, it's clear that this powerful framework opens up a world of possibilities for data extraction and analysis.

Scrapy's robust architecture, coupled with its flexibility and scalability, makes it an invaluable tool in the data scientist's toolkit. While Scrapy provides a solid foundation for web crawling, the ever-evolving landscape of the web presents ongoing challenges. From JavaScript-heavy sites to sophisticated anti-bot measures, the world of web scraping is constantly shifting. This is where the true beauty of Scrapy shines – its extensibility allows you to integrate with specialized various tools and Web Scraping APIs like Ujeebu API, enabling you to tackle even the most complex scraping scenarios with confidence.

So, fire up your terminal, start your spiders, and begin your adventure in web crawling with Scrapy.