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Python for Google App Engine

You're reading from   Python for Google App Engine Master the full range of development features provided by Google App Engine to build and run scalable web applications in Python

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Product type Paperback
Published in Jan 2015
Publisher Packt
ISBN-13 9781784398194
Length 198 pages
Edition 1st Edition
Languages
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Author (1):
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Massimiliano Pippi Massimiliano Pippi
Author Profile Icon Massimiliano Pippi
Massimiliano Pippi
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Table of Contents (10) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. A More Complex Application 3. Storing and Processing Users' Data 4. Improving Application Performance 5. Storing Data in Google Cloud SQL 6. Using Channels to Implement a Real-time Application 7. Building an Application with Django 8. Exposing a REST API with Google Cloud Endpoints Index

Using Memcache

We already know that Memcache is the distributed in-memory data cache provided by App Engine. A typical use case would be to use it as a cache for rapid data retrieval from persistent storage such as Datastore, but we already know that the NDB API does this for us, so there's no need to explicitly cache entities.

Data stored in Memcache can be evicted at any time, so we should cache only data that we can safely lose without affecting integrity. For example, in our Notes application, we can cache the total number of notes globally stored for every user and display this nice kind of metric on the home page. We can perform a Datastore query counting Note entities every time a user visits the main page but this would be cumbersome, possibly nullifying every optimization we made so far. A better strategy would be to keep a counter in the Memcache and increment that counter every time a note is created within the application; if Memcache data expires, we make the counting query...

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