Scrapy is a great tool for web scraping but it's not designed for distributed computing out of the box. If you want to use Scrapy in a distributed system, you'll need to use a tool called ScrapyD in conjunction with a tool like Scrapyd-Client or ScrapingHub.
Here's a step-by-step guide on how to set it up:
Step 1: Install ScrapyD and Scrapyd-Client
ScrapyD is a service for running Scrapy spiders, while Scrapyd-Client is a client for managing the spiders.
You can install both of these tools with the following pip commands:
pip install scrapy scrapyD scrapyd-client
Step 2: Setup ScrapyD
ScrapyD needs to be running on every machine you want to use for your distributed system. You can start the service with the following command:
scrapyd
Step 3: Deploy Your Spiders
With ScrapyD running, you can now deploy your spiders to all the machines. This step assumes you have a Scrapy project with spiders ready to go.
scrapyd-deploy target -p project
Replace target with the alias you've set up for your server in the ScrapyD configuration file and project with the name of your Scrapy project.
Step 4: Schedule Your Spiders
Once your spiders are deployed, you can schedule them to run with the following command:
curl http://localhost:6800/schedule.json -d project=project -d spider=spider
Replace project and spider with the name of your project and the spider you want to run.
Using ScrapingHub
If you want a more out-of-the-box solution, you can use a service like ScrapingHub. ScrapingHub can handle the distributed computing aspect of your Scrapy projects, allowing you to focus on writing your spiders.
Here's a basic example of how to use ScrapingHub:
from scrapinghub import ScrapinghubClient
client = ScrapinghubClient('YOUR_API_KEY')
project = client.get_project('PROJECT_ID')
job = project.jobs.run('spider_name')
print("Job ID: %s" % job.key)
Replace 'YOUR_API_KEY' and 'PROJECT_ID' with your ScrapingHub API key and your project's ID.
That's a basic overview of how you can use Scrapy in a distributed system. The specific details will depend on the nature of your project and the spiders you're working with. By using ScrapyD and Scrapyd-Client, or a service like ScrapingHub, you can take full advantage of distributed computing to make your Scrapy projects more efficient.
