In this example, accessing the /scrape/ URL will trigger the ScrapeView and start the scraping process. Path('scrape/', ScrapeView.as_view(), name='scrape'), You can then add a URL pattern for the ScrapeView in your Django app's urls.py file: from django.urls import path The run_lay() line calls the Celery task to start the scraping process asynchronously. In this example, we define a Django view called ScrapeView that starts the scraping process when the view is accessed with a GET request. # Return a response indicating that the scraping has started

# Call the Celery task to start the scraping process Next, Create the Viewset in scrapy_app/views.py : from django.shortcuts import render Process = CrawlerProcess(get_project_settings()) Yield response.follow(next_page, self.parse)įinally, create a Celery task in scrapy_app/tasks.py: from celery import shared_taskįrom scrapy.crawler import CrawlerProcessįrom import get_project_settingsįrom scrapy_spider import run_spider(): Next_page = response.css('li.next a::attr(href)').get() # Follow pagination links to continue scraping on subsequent pages 'tags': quote.css('div.tags a.tag::text').getall(), 'author': quote.css('span small::text').get(), # Extract quotes and author information from the response Next, create a Scrapy spider in scrapy_app/spiders/my_spider.py: import scrapy SCRAPY_SETTINGS_MODULE = 'scrapy_project.scrapy_settings'Ĭreate a scrapy_project/scrapy_settings.py file with the following settings: BOT_NAME = 'scrapy_app' In your scrapy_project/settings.py file, add the following settings: # Celery settingsĬELERY_BROKER_URL = 'redis://localhost:6379/0'ĬELERY_RESULT_BACKEND = 'redis://localhost:6379/0' Next, you’ll need to create a Django project and app: django-admin startproject scrapy_project

Here’s an example of how to use Scrapy with Django and Celery:įirst, you’ll need to install the necessary libraries: pip install scrapy django celery With Celery, they can distribute the scraping tasks across multiple worker nodes, allowing for faster processing times and more efficient resource utilization.īut before we deep down into the tutorial, you need this requirement: With Django, developers can easily build a web application to manage and display scraped data. Integrating Scrapy with Django and Celery can be a powerful combination for creating robust web scraping applications. To address these issues, developers often turn to Celery, a distributed task queue that allows for the asynchronous processing of tasks across multiple worker nodes. However, as the amount of data being scraped grows, the process can become quite slow and resource-intensive. Scrapy is a Python-based web scraping framework that allows developers to easily extract data from websites.

Web scraping using django