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So, your function gets a set of values in through its parameters. Can you change them And what happens if you do Well, the parameters are just variables like all others, so this works as you would expect. Assigning a new value to a parameter inside a function won t change the outside world at all: >>> def try_to_change(n): n = 'Mr. Gumby' >>> name = 'Mrs. Entity' >>> try_to_change(name) >>> name 'Mrs. Entity' Inside try_to_change, the parameter n gets a new value, but as you can see, that doesn t affect the variable name. After all, it s a completely different variable. It s just as if you did something like this: >>> name = 'Mrs. Entity' >>> n = name # This is almost what happens when passing a parameter >>> n = 'Mr. Gumby' # This is done inside the function >>> name 'Mrs. Entity' Here, the result is obvious. While the variable n is changed, the variable name is not. Similarly, when you rebind (assign to) a parameter inside a function, variables outside the function will not be affected.



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Note If you re using FastCGI, you can accomplish this same distribution/load-balancing step by separating your front-end Web servers and back-end FastCGI processes onto different machines. The front-end server essentially becomes the load balancer, and the back-end FastCGI processes replace the Apache/ mod_python/Django servers.

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With the Web servers now clustered, our evolving architecture starts to look more complex, as shown in Figure 20-4.

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Using a function to change a data structure (such as a list or a dictionary) can be a good way of introducing abstraction into your program. Let s say you want to write a program that stores names and that allows you to look up people either by their first, middle, or last names. You might use a data structure like this:





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Notice that in the diagram the Web servers are referred to as a cluster to indicate that the number of servers is basically variable. Once you have a load balancer out front, you can easily add and remove back-end Web servers without a second of downtime.

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At this point, the next few steps are pretty much derivatives of the last one: As you need more database performance, you ll need to add replicated database servers. MySQL includes built-in replication; PostgreSQL users should look into Slony (http://www.djangoproject.com/r/slony/) and pgpool (http://www.djangoproject.com/ r/pgpool/) for replication and connection pooling, respectively. If the single load balancer isn t enough, you can add more load balancer machines out front and distribute among them using round-robin DNS. If a single media server doesn t suffice, you can add more media servers and distribute the load with your load-balancing cluster. If you need more cache storage, you can add dedicated cache servers. At any stage, if a cluster isn t performing well, you can add more servers to the cluster. After a few of these iterations, a large-scale architecture might look like Figure 20-5.

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storage = {} storage['first'] = {} storage['middle'] = {} storage['last'] = {} The data structure storage is a dictionary with three keys: 'first', 'middle', and 'last'. Under each of these keys, you store another dictionary. In these subdictionaries, you ll use names (first, middle, or last) as keys, and insert lists of people as values. For example, to add me to this structure, you could do the following: >>> >>> >>> >>> me = 'Magnus Lie Hetland' storage['first']['Magnus'] = [me] storage['middle']['Lie'] = [me] storage['last']['Hetland'] = [me]

Figure 20-5. An example large-scale Django setup Though we ve shown only two or three servers at each level, there s no fundamental limit to how many you can add. Once you get up to this level, you ve got quite a few options. Appendix A has some information from a few developers responsible for some large-scale Django installations. If you re planning a high-traffic Django site, it s worth a read.

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