我如何优化此Google App Engine代码？ [英] How can I optimize this Google App Engine code?

问题描述

Google向我大声说这个代码需要优化：

I'm relatively new to the python world, but this seems very straight forward.

我对python世界相对陌生，但这看起来非常直截了当。 p>

Google is yelling at me that this code needs to be optimized:

class AddLinks(webapp.RequestHandler):
     def post(self):
          # Hash the textarea input to generate pseudo-unique value
          hash = md5.new(self.request.get('links')).hexdigest()

          # Seperate the input by line
          allLinks = self.request.get('links').splitlines()

          # For each line in the input, add to the database
          for x in allLinks:
               newGroup = LinkGrouping()
               newGroup.reference = hash
               newGroup.link = x
               newGroup.put()

          # testing vs live
          #baseURL = 'http://localhost:8080'
          baseURL = 'http://linkabyss.appspot.com'

          # Build template parameters
          template_values = {
               'all_links': allLinks,
               'base_url': baseURL,
               'reference': hash,
          }

          # Output the template
          path = os.path.join(os.path.dirname(__file__), 'addLinks.html')
          self.response.out.write(template.render(path, template_values))   

仪表板告诉我这是使用大量的CPU。

The dashboard is telling me that this is using a ton of CPU.

我应该在哪里寻找改进？

Where should I look for improvements?

推荐答案

这里的主要开销是多个单独放入数据存储。如果可以的话，按照Andre的说法，将链接存储为一个实体。您可以将链接始终分割成一个数组并将其存储在ListProperty中。

The main overhead here is the multiple individual puts to the datastore. If you can, store the links as a single entity, as Andre suggests. You can always split the links into an array and store it in a ListProperty.

如果您确实需要每个链接的实体，请尝试以下操作：

If you do need an entity for each link, try this:

# For each line in the input, add to the database
groups = []
for x in allLinks:
     newGroup = LinkGrouping()
     newGroup.reference = hash
     newGroup.link = x
     groups.append(newGroup)
db.put(groups)

它会将数据存储区往返次数减少到一次，这是真正杀死您的高CPU上限的往返次数。

It will reduce the datastore roundtrips to one, and it's the roundtrips that are really killing your high CPU cap.

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