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This chapter is not intended to be a manual for any of these technologies, nor will code examples be plentiful. Ample documentation for each of the libraries mentioned exists online, and for the more popular ones, you can even find entire books that have been written about them. Instead, this chapter s purpose is to introduce you to the problem that each tool solves; explain how to use the service to address that issue; and give a few hints about using the tool from Python. After all, the greatest challenge that a programmer often faces aside from the basic, lifelong process of learning to program itself is knowing that a solution exists. We are inveterate inventors of wheels that already exist, had we only known it. Think of this chapter as offering you a few wheels in the hopes that you can avoid hewing them yourself.

you can see the Tester project properties. Notice the first property called Developer Web Server. VWD has a preconfigured internal web server there s no need to install a separate web server to do your testing. Leave the default setting for port number and dynamic ports. Set the virtual path property to a slash character (/) for the test harness to work properly.

The figures in this text represent classes as labeled boxes, as shown in Figure 4-5. The class members are represented as smaller labeled boxes inside the class boxes. Private members are represented enclosed entirely within their class box. Public members are represented sticking partially outside their class box.

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Memcached is the memory cache daemon. Its impact on many large Internet services has been, by all accounts, revolutionary. After glancing at how to use it from Python, we will discuss its implementation, which will teach us about a very important modern network concept called sharding. The actual procedures for using Memcached are designed to be very simple: You run a Memcached daemon on every server with some spare memory. You make a list of the IP address and port numbers of your new Memcached daemons, and distribute this list to all of the clients that will be using the cache. Your client programs now have access to an organization-wide blazing-fast keyvalue cache that acts something like a big Python dictionary that all of your servers can share. The cache operates on an LRU (least-recently-used) basis, dropping old items that have not been accessed for a while so that it has room to both accept new entries and keep records that are being frequently accessed.

Class C1 declares both public and private fields and methods. Figure 4-6 illustrates the visibility of the members of class C1. class C1 { int F1; private int F2; public int F3; void DoCalc() { ... } public int GetVal() { ... } }

Enough Python clients are currently listed for Memcached that I had better just send you to the page that lists them, rather than try to review them here: http://code.google.com/p/memcached/wiki/Clients. The client that they list first is written in pure Python, and therefore will not need to compile against any libraries. It should install quite cleanly into a virtual environment (see 1), thanks to being available on the Python Package Index: $ pip install python-memcached The interface is straightforward. Though you might have expected an interface that more strongly resembles a Python dictionary with native methods like __getitem__, the author of python-memcached chose instead to use the same method names as are used in other languages supported by Memcached which I think was a good decision, since it makes it easier to translate Memcached examples into Python: >>> import memcache >>> mc = memcache.Client(['127.0.0.1:11211']) >>> mc.set('user:19', '{name: "Lancelot", quest: "Grail"}') True >>> mc.get('user:19') '{name: "Lancelot", quest: "Grail"}' The basic pattern by which Memcached is used from Python is shown in Listing 8 1. Before embarking on an (artificially) expensive operation, it checks Memcached to see whether the answer is already present. If so, then the answer can be returned immediately; if not, then it is computed and stored in the cache before being returned. Listing 8 1. Constants and Functions for the Lancelot Protocol #!/usr/bin/env python # Foundations of Python Network Programming - 8 - squares.py # Using memcached to cache expensive results. import memcache, random, time, timeit mc = memcache.Client(['127.0.0.1:11211']) def compute_square(n): value = mc.get('sq:%d' % n) if value is None:

Now that we have the project set up, let s start the block test harness. First, make sure the test harness s default page starts up in the web browser by right-clicking Default.aspx (see Figure 8-16) and selecting Set As Start Page. Similar to index.htm pages, default.aspx pages are .NET pages that are started up by web sites when you navigate to a URL. Since you are using a development tool, though, you have complete control over what is started first by the internal web server.

time.sleep(0.001) # pretend that computing a square is expensive value = n * n mc.set('sq:%d' % n, value) return value

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