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Web Scraping vs Web Crawling

Web Scraping Is about extracting the data from one or more web sites

Web Crawling Is abut finding or discovering  URLs or links on the web




Usually data extraction project we need to use both combined crawling and scraping. For Example: We might crawl or discover urls , download the HTML files and then scrape the data from those html files. Which means we are extracting data and we know we do something with it like store it in a database or further process it in. 

In web scraping we want to extract from the websites. In scraping we usually know the target websites already. 

In crawling we probably don't know the specific URLs and we probably don't know the domains either and this is the reason we crawl and we want to find the URLs. 


Tools: 
Beautiful Soup: Beautiful Soup is a library that makes it easy to scrape information from web pages. It sits atop an HTML or XML parser, providing Pythonic idioms for iterating, searching, and modifying the parse tree.

Scrapy: Scrapy is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.


1. Strengths and weaknesses each of these library: BeautifulSoup, Scrapy


BeautifulSoup: 

Strengths : 

1. Simplicity: BeautifulSoup provides a simple and intuitive interface for parsing HTML and XML documents. It's easy to learn and use, especially for beginners.

2. Flexibility: It allows for quick parsing and manipulation of HTML content, making it suitable for small to medium-sized scraping projects.

3. Integration: BeautifulSoup can be easily integrated with other libraries like Requests for fetching web pages, providing a robust scraping solution.

Weaknesses:

1. Speed: BeautifulSoup can be slower compared to other libraries like Scrapy because it lacks built-in support for asynchronous requests and parallel processing.

2. Limited Scalability: It may not be the best choice for large-scale scraping projects due to its performance limitations.

3. Requires External Libraries: BeautifulSoup requires additional libraries like Requests for fetching web pages, which adds dependencies to your project.

Scrapy

Strengths:

1. Scalability: Scrapy is designed for large-scale web scraping projects, offering features like asynchronous requests and built-in support for parallel processing, making it highly efficient.

2. Performance: Due to its asynchronous nature and built-in support for parallelism, Scrapy is faster than BeautifulSoup for scraping large volumes of data.

3. Robustness: It provides a comprehensive framework for building web crawlers, including features like middleware support, item pipelines, and built-in error handling.


2. How to  handle dynamic content or JavaScript-heavy websites during web scraping?

1. Headless Selenium 

2. Using Scrapy-Splash:

# To use scrapy-splash we need to have docker installed in our computer because it comes as part of Docker image. Dynamic content and JavaScript can complicate web scraping efforts. The candidate's response should include techniques such as using headless browsers like Selenium or employing APIs when available.

# using lua script:

 


install scrapy-splash and use this lua script in SplashRequest as below 


Pagination: 






3. How to handle anti-scraping measures like CAPTCHAs or rate limiting?

Dealing with anti-scraping measures is a common challenge in web scraping. Candidates should be familiar with strategies like rotating user agents, using proxies, or implementing delays to bypass these measures.


Bypass Restrictions using proxies:

Useing Scrapy-proxy-pool use can bypass restrictions. 

enable proxy pool in settings.py 

PROXY_POOL_ENABLED = True

and follow other instructions in the github repo



4. What are the ethical considerations when scraping data from websites? How do you ensure your scraping activities are ethical and legal?

Answer: website terms of service, avoiding overloading servers, and obtaining data with permission.

5. How would you structure a web scraping project to ensure scalability, maintainability, and robustness?

Modularization: 

1. Break down your scraping logic into reusable modules and functions.

2. Create separate modules for handling HTTP requests, parsing HTML, data processing, and interacting with databases or APIs.\

Error Handling:

1. handle exceptions

2. ensure that your spiders can recover from failures without crashing

3. Implement retry mechanisms for failed requests or intermittent errors.

Rate Limiting and Politeness: 

1. Implement rate limiting to avoid overloading the target website's servers and getting blocked

2. Respect robots.txt rules and use random user-agents to mimic human behavior

3. Add delays between requests to avoid triggering anti-scraping measures


6. Scrapy Unit Testing

A unit test makes sure that a piece of code does what the programmer claims. 

6. What are the advantages and disadvantages of using XPath selectors compared to CSS selectors in web scraping?

7. How would you handle cases where websites require authentication for access?


8. How do you ensure the quality and reliability of scraped data?


9. Can you discuss any performance optimization strategies you've used in web scraping projects?

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