Scrapy is a powerful web scraping framework in Python that allows you to extract data from websites easily. It provides a way to define structures known as “spiders”, which scrape and process web pages. However, as your scraping project grows in complexity, managing dependencies between spiders becomes crucial.

Why Manage Dependencies?

When working on a large scraping project with multiple spiders, it’s common to find dependencies between them. For example, one spider might need to scrape data from a specific page before another spider can start its job. Or a spider might rely on data fetched by another spider.

Without proper dependency management, running spiders concurrently or in a specific order can be challenging. This is where Scrapy’s CrawlerRunner class comes in handy.

Using CrawlerRunner

CrawlerRunner is a class in Scrapy that helps manage multiple spiders and their dependencies. It allows you to run multiple spiders concurrently and define which spiders should run before others.

Let’s have a look at an example to understand how to use CrawlerRunner.

import scrapy
from scrapy.crawler import CrawlerRunner
from scrapy.utils.log import configure_logging
from twisted.internet import reactor
from myproject.spiders import Spider1, Spider2, Spider3

configure_logging()
runner = CrawlerRunner()

runner.crawl(Spider1)

@runner.after_spider(Spider1)
def crawl_spider2():
    runner.crawl(Spider2)

@runner.after_spider(Spider2)
def crawl_spider3():
    runner.crawl(Spider3)

d = runner.join()
d.addBoth(lambda _: reactor.stop())

reactor.run()

In this example, we import the necessary modules and define the spiders we want to run. We create an instance of CrawlerRunner and configure_logging for better log output. We then register each spider to be crawled using the runner.crawl() method.

Using the @runner.after_spider() decorator, we can define which spiders should run after a specific spider is finished. In this case, Spider2 will run after Spider1 has finished, and Spider3 will run after Spider2 has finished.

Finally, we join all the spiders together using runner.join() and stop the reactor when all spiders have finished running.

Conclusion

Managing dependencies between spiders in a Scrapy project is essential for efficient web scraping. With Scrapy’s CrawlerRunner, you can easily define the order in which spiders should run and run them concurrently. This helps in developing complex scraping projects and ensures smooth execution.