Shared State and Customer Confusion

Let’s go back to the good old days of writing web applications in PHP for a paragraph or two. When running PHP under Apache or nginx, every HTTP request resulted in a clean interpreter with completely new state. Developers had to explicitly ask for state to be shared - through the $_SESSION global, by persisting state on disk, or by saving state to some backing data store. This made developing applications amazingly simple. A PHP page was something like a pure function, producing consistent, predictable output based on the state of the underlying data store.

Now, consider this little bit of Python code:

class PatternRemixer(Remixer):
    _samplecache = {}

    def remix(song):
        # do some stuff
        for key in song:
            if key not in self._samplecache:
                self._samplecache[key] = self.render_audio()
            self.output(self._samplecache[key])

Any experienced Pythonista should notice the grievous error in this class - on the second line, no less. Here we have a _samplecache variable being initialized to an empty dictionary. While this itself is fine, what’s not fine is the fact that this variable is being used as a mutable cache. That’s because the variable is declared in the class’ scope, making it common to all instances of that class.

Consider the following bit of code:

def remix_songs(songs):    
    song0       = PatternRemixer().remix(songs[0])

    #    Let's reset the cache here
    PatternRemixer._samplecache = {}

    song1       = PatternRemixer().remix(songs[1])
    song0_again = PatternRemixer().remix(songs[0])
    assert song0 == song0_again

This function will throw an AssertionError - song0 is not equal to song0_again! Even though in the previous snippet we refer to self._samplecache, we’re really accessing PatternRemixer._samplecache instead - the global variable used by all instances. Since this cache only gets cleared after using it once, our song0_again variable actually contains data from songs[1], when it really shouldn’t.

This can be quite a difficult bug to track down, as the problem only manifests itself when one Python interpreter accepts multiple requests. In a distributed system, each request might go to a different box - and possibly a different Python interpreter running on that box, making it very difficult to figure out where the stale data is coming from. Worse yet - if each interpreter is restarted after a certain number of requests, the stale data will not always show itself.

This results in confused emails from customers at all hours of the night, and is generally a Very Bad Thing™.

 
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