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    <p>There are some issues when using memcached for invalidating/updating stale data due to race <a href="http://code.google.com/p/memcached/wiki/FAQ#Race_conditions_and_stale_data" rel="nofollow">conditions</a>, which one of the main <a href="http://packages.python.org/johnny-cache/" rel="nofollow">Johnny Cache</a> backends relies on. In your particular case, say you have 10 updates in total and then two different users in two separate processes create a ticket update. It's quite possible that you end up displaying only 11 updates to both, even though there are 12 in total: one of them is going to end up with a serious case of <em>WTF?</em></p> <p>That's the kind of problem you're going to have with any model/queryset invalidation solution if memcached is your store. Your not joining a lot of rows from several different tables, so this shouldn't be considered as an expensive query--you could probably just rely on the database. </p> <p>But not understanding the full extent of the work your doing, it would be improper for me to suggest such a thing. What I would suggest, though, is that you take a look at the possibilities of using <a href="http://www.mongodb.org/" rel="nofollow">mongoDB</a>, <a href="http://fallabs.com/tokyocabinet/" rel="nofollow">Tokyo Cabinet</a> or <a href="http://redis.io/" rel="nofollow">Redis</a> as a cache replacement for memcached, in situations where you need to have fine-grained control over updating critical user data.</p> <p>Another thing you can do is to have all requests that update such data routed through an instance of your application that runs in a single process exclusively. This would completely alleviate any update contention on memcached and it's viable for applications where reads greatly outperform writes, although I can see how this can quickly become a maintenance nightmare for developers.</p>
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