Pros of awesome-crawler

• More comprehensive, with a larger number of resources and tools listed
• Better organized into categories like general-purpose crawlers, distributed crawlers, etc.
• Includes resources for multiple programming languages, not just Python

Cons of awesome-crawler

• Less frequently updated compared to awesome-spider
• Some links may be outdated or no longer maintained
• Lacks detailed descriptions for many of the listed resources

Code comparison

While both repositories are curated lists and don't contain much code, here's a small example of how they structure their markdown:

awesome-crawler:

## Python
* [Scrapy](https://github.com/scrapy/scrapy) - A fast high-level screen scraping and web crawling framework.
* [pyspider](https://github.com/binux/pyspider) - A powerful spider system.

awesome-spider:

#### Python
- [Scrapy](https://github.com/scrapy/scrapy) - An open source and collaborative framework for extracting the data you need from websites.
- [PySpider](https://github.com/binux/pyspider) - A powerful spider system in Python.

Both repositories serve as valuable resources for web scraping and crawling tools, with awesome-crawler offering a broader scope and awesome-spider providing a more focused, Python-centric approach. The choice between them depends on the user's specific needs and preferred programming language.

Pros of awesome-streaming

• Broader focus on streaming technologies and frameworks, not limited to web scraping
• More comprehensive coverage of streaming-related topics, including data processing and analytics
• Better organized with clear categorization of resources

Cons of awesome-streaming

• Less specific to web scraping and data extraction tasks
• May not provide as much depth for those specifically interested in web crawling techniques

Code comparison

While both repositories are curated lists and don't contain actual code samples, we can compare their structure:

awesome-streaming:

## Streaming Engine

- [Apache Apex](https://apex.apache.org/) [Java]
- [Apache Flink](https://flink.apache.org/) [Java]
- [Apache Samza](http://samza.apache.org/) [Scala/Java]

awesome-spider:

### Python

* [scrapy](https://github.com/scrapy/scrapy) - A fast high-level screen scraping and web crawling framework.
* [pyspider](https://github.com/binux/pyspider) - A powerful spider system.

Summary

awesome-streaming provides a more comprehensive overview of streaming technologies, while awesome-spider focuses specifically on web scraping tools. The former is better organized and covers a broader range of topics, making it more suitable for those interested in general streaming technologies. However, awesome-spider may be more valuable for developers specifically looking for web scraping resources.

Pros of awesome-web-scraping

• More comprehensive, covering a wider range of tools and resources
• Better organized with clear categories and subcategories
• Includes resources for multiple programming languages

Cons of awesome-web-scraping

• Less focused on specific spider/crawler implementations
• May be overwhelming for beginners due to the large number of resources

Code comparison

While both repositories are primarily curated lists without significant code samples, awesome-web-scraping does include some basic usage examples for certain tools. For instance:

awesome-web-scraping:

import requests
from bs4 import BeautifulSoup

url = 'http://example.com'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

awesome-spider doesn't provide code examples directly in the README.

Summary

awesome-web-scraping offers a more comprehensive and well-organized collection of web scraping resources across multiple languages. It's ideal for developers looking for a wide range of tools and libraries. awesome-spider, on the other hand, is more focused on specific spider implementations and may be easier for beginners to navigate due to its simpler structure. The choice between the two depends on the user's specific needs and level of expertise in web scraping.

Pros of big-list-of-naughty-strings

• Focused on a specific use case: testing input validation and sanitization
• Comprehensive list of edge cases and potentially problematic strings
• Regularly updated with community contributions

Cons of big-list-of-naughty-strings

• Limited scope compared to awesome-spider's broad collection of web scraping resources
• Less practical for general-purpose development tasks
• Requires additional implementation to be useful in testing scenarios

Code Comparison

big-list-of-naughty-strings:

with open('blns.txt', 'r') as file:
    naughty_strings = file.readlines()
for string in naughty_strings:
    test_input_validation(string.strip())

awesome-spider:

import scrapy

class ExampleSpider(scrapy.Spider):
    name = 'example'
    start_urls = ['https://example.com']

    def parse(self, response):
        yield {'title': response.css('h1::text').get()}

The code snippets highlight the different focus areas of the two repositories. big-list-of-naughty-strings is primarily a data resource, while awesome-spider provides a curated list of tools and frameworks for web scraping, such as Scrapy in the example above.

Pros of awesome-mac

• More comprehensive and diverse content, covering a wide range of Mac applications and tools
• Better organized with clear categories and subcategories
• Regularly updated with new entries and maintenance

Cons of awesome-mac

• Focused solely on Mac ecosystem, limiting its audience
• May include some outdated or less relevant applications due to its broad scope

Code comparison

Not applicable for these repositories, as they are curated lists without significant code content.

Additional notes

awesome-spider:

• Specializes in web scraping tools and libraries
• Primarily focused on Python-based solutions
• Smaller, more focused list

awesome-mac:

• Covers a wide range of Mac applications and tools
• Includes both free and paid software
• Provides brief descriptions and links for each entry

Both repositories serve as curated lists of resources in their respective domains. awesome-spider is more specialized and targeted towards developers working on web scraping projects, while awesome-mac caters to a broader audience of Mac users looking for various software solutions across different categories.

Pros of awesome-python

• Much larger and more comprehensive, covering a wide range of Python topics and libraries
• More actively maintained with frequent updates and contributions
• Better organized with clear categories and subcategories

Cons of awesome-python

• Less focused, may be overwhelming for beginners looking for specific tools
• Not specialized in web scraping or data extraction techniques

Code comparison

While both repositories are curated lists and don't contain actual code samples, here's an example of how they might differ in structure:

awesome-python:

## Web Scraping

- [scrapy](https://github.com/scrapy/scrapy) - A fast high-level screen scraping and web crawling framework.
- [pyspider](https://github.com/binux/pyspider) - A powerful spider system.
- [cola](https://github.com/chineking/cola) - A distributed crawling framework.

awesome-spider:

### [Python爬虫框架](https://github.com/facert/awesome-spider#python爬虫框架)

- [Scrapy](https://github.com/scrapy/scrapy) - 最出名的爬虫框架
- [PySpider](https://github.com/binux/pyspider) - 国人编写的强大的网络爬虫系统并带有强大的WebUI
- [Crawley](https://github.com/jmg/crawley) - 基于非阻塞式，可以高效爬取大量网站

awesome-spider focuses specifically on web scraping tools and resources, primarily in Chinese, while awesome-python covers a broader range of Python-related topics and libraries in English.