create a distributed scraper class in python in python

To create a distributed scraper in python, you can use a combination of Redis and Celery. Redis is used as a broker to send messages between different processes, while Celery provides an easy-to-use distributed task queue that enables you to distribute work across multiple workers.

Here's an example scraper class that can be distributed across multiple workers:

main.py
import redis
from celery import Celery

app = Celery('scraper', backend='redis://localhost', broker='redis://localhost')

class Scraper:
    def __init__(self, urls):
        self.urls = urls
        self.results = []

    def run(self):
        for url in self.urls:
            result = self.scrape(url)
            self.results.append(result)
        return self.results

    def scrape(self, url):
        # Use requests library or any other http client to make request
        # and parse using beautifulsoup or any other html parser
        pass

@app.task
def scrape_url(url):
    scraper = Scraper([url])
    return scraper.run()

if __name__ == '__main__':
    urls = [...] #list of urls to scrape
    for url in urls:
        scrape_url.delay(url)
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In this example, the Scrape class represents the scraper logic and Celery is used to create a distributed task queue to distribute the scraping work across multiple workers.

The scrape_url function is a Celery task that creates a Scraper object for each URL and runs the scrape method on a worker. The results are aggregated and returned to the main thread.

You can start multiple workers using the celery command line tool celery -A scraper worker --concurrency={number of workers}. Each worker will pick up tasks from the queue and start scraping URLs.

This allows you to distribute the scraping workload across multiple machines/nodes, providing a scalable solution for scraping large amounts of data.