Sharing of crawler optimization tips in Scrapy
Jun 23, 2023 am 09:03 AM
Scrapy is a very useful Python crawler framework that can help us easily obtain data from different websites. At the same time, more and more users of Scrapy are using it to crawl data. Therefore, in the process of using Scrapy, we need to consider how to optimize our crawlers so that we can crawl the required data more efficiently. This article will share some tips for crawler optimization in Scrapy.
- Avoid repeated requests
When we use Scrapy to crawl web page data, we may encounter repeated requests. If left unhandled, situations like this waste network resources and time. Therefore, when using Scrapy, we need to pay attention to avoid duplicate requests.
In Scrapy, we can avoid repeated requests by setting the DUPEFILTER_CLASS parameter. We can use Redis or memory deduplication module to avoid repeated requests. The settings are as follows:
DUPEFILTER_CLASS = "scrapy_redis.dupefilter.RFPDupeFilter"
- Increase delay
When crawling web page data, we may encounter the website anti-crawling mechanism, and may be blocked by the website due to too frequent requests. shield. Therefore, we need to consider increasing the delay so that the frequency of crawler requests becomes more stable.
In Scrapy, we can increase the delay of the request by setting the DOWNLOAD_DELAY parameter.
DOWNLOAD_DELAY=3 # 設(shè)置下載延遲為3秒
- Use the appropriate User Agent
In order to prevent being recognized as a crawler by the website, we need to simulate the browser's User Agent. In Scrapy, we can achieve this function by setting the USER_AGENT parameter in the settings.py file. Here is an example:
USER_AGENT = 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36'
- Deduplication Network IO Operation
In Scrapy, by default, each request will be retried when the maximum number of retries is reached. Perform a deduplication operation. Therefore, if you have a lot of requests, this operation will cause a lot of network IO operations, resulting in a slower program. In order to optimize this situation, we can save the URL hash value of the request data and the requested method in memory so that we can quickly determine whether the URL has been requested. You can use the following code to achieve this:
from scrapy.utils.request import request_fingerprint
seen = set()
fp = request_fingerprint(request)
if fp in seen:
return
seen.add(fp)- Use CSS selectors whenever possible
In Scrapy, we can use XPath or CSS selectors to locate elements. XPath can do more than CSS selectors, but CSS selectors are faster than XPath. Therefore, we should use CSS selectors whenever possible to optimize our crawlers.
- Using asynchronous I/O
Scrapy uses blocking I/O operations by default, but asynchronous I/O operations can provide better performance. We can use the asynchronous I/O operations of the Twisted package to turn Scrapy into an asynchronous framework.
- Using multi-threading
When crawling data, we can use multi-threading to speed up our crawler. In Scrapy, we can set the number of threads by setting the CONCURRENT_REQUESTS_PER_IP parameter. The following is a sample code:
CONCURRENT_REQUESTS_PER_IP=16
Summary
Scrapy is an excellent Python crawler framework, but during use we need to pay attention to optimizing our crawler in order to crawl what we need more efficiently. The data. This article shares some tips for crawler optimization in Scrapy, I hope it will be helpful to you.
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