Scrapy optimization tips: How to reduce crawling of duplicate URLs and improve efficiency

Scrapy is a powerful Python crawler framework that can be used to obtain large amounts of data from the Internet. However, when developing Scrapy, we often encounter the problem of crawling duplicate URLs, which wastes a lot of time and resources and affects efficiency. This article will introduce some Scrapy optimization techniques to reduce the crawling of duplicate URLs and improve the efficiency of Scrapy crawlers.

1. Use the start_urls and allowed_domains attributes

In the Scrapy crawler, you can use the start_urls attribute to specify the URLs that need to be crawled. At the same time, you can also use the allowed_domains attribute to specify the domain names that the crawler can crawl. The use of these two attributes can help Scrapy quickly filter out URLs that do not need to be crawled, saving time and resources while improving efficiency.

2. Use Scrapy-Redis to implement distributed crawling

When a large number of URLs need to be crawled, single-machine crawling is inefficient, so you can consider using distributed crawling technology. Scrapy-Redis is a plug-in for Scrapy that uses the Redis database to implement distributed crawling and improve the efficiency of Scrapy crawlers. By setting the REDIS_HOST and REDIS_PORT parameters in the settings.py file, you can specify the address and port number of the Redis database that Scrapy-Redis connects to achieve distributed crawling.

3. Use incremental crawling technology

In Scrapy crawler development, we often encounter the need to crawl the same URL repeatedly, which will cause a lot of waste of time and resources. Therefore, incremental crawling techniques can be used to reduce repeated crawling. The basic idea of ​​incremental crawling technology is: record the crawled URL, and during the next crawl, check whether the same URL has been crawled based on the record. If it has been crawled, skip it. In this way, crawling of duplicate URLs can be reduced and efficiency improved.

4. Use middleware to filter duplicate URLs

In addition to incremental crawling technology, you can also use middleware to filter duplicate URLs. The middleware in Scrapy is a custom processor. During the running of the Scrapy crawler, requests and responses can be processed through the middleware. We can implement URL deduplication by writing custom middleware. Among them, the most commonly used deduplication method is to use the Redis database to record a list of URLs that have been crawled, and query the list to determine whether the URL has been crawled.

5. Use DupeFilter to filter duplicate URLs

In addition to custom middleware, Scrapy also provides a built-in deduplication filter DupeFilter, which can effectively reduce the crawling of duplicate URLs. DupeFilter hashes each URL and saves unique hash values ​​in memory. Therefore, during the crawling process, only URLs with different hash values ​​will be crawled. Using DupeFilter does not require additional Redis server support and is a lightweight duplicate URL filtering method.

Summary:

In Scrapy crawler development, crawling of duplicate URLs is a common problem. Various optimization techniques are needed to reduce the crawling of duplicate URLs and improve the efficiency of Scrapy crawlers. . This article introduces some common Scrapy optimization techniques, including using the start_urls and allowed_domains attributes, using Scrapy-Redis to implement distributed crawling, using incremental crawling technology, using custom middleware to filter duplicate URLs, and using the built-in DupeFilter to filter duplicates URL. Readers can choose appropriate optimization methods according to their own needs to improve the efficiency of Scrapy crawlers.

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