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class Program // Declare class Program. { static void Main() { Variable name DaysTemp Temp = new DaysTemp(); // Create the object. Variable name and field Temp.High = 85; // Assign to the fields. Temp.Low = 60; Variable name and field Console.WriteLine("High: {0}", Temp.High ); // Read from fields. Console.WriteLine("Low: {0}", Temp.Low ); } } This code produces the following output: High: Low: 85 60

The values stored in Memcached, by the way, can be at most 1MB in length Second, you must always remember that Memcached is a cache; it is ephemeral, it uses RAM for storage, and, if re-started, it remembers nothing that you have ever stored! Your application should always be able to recover if the cache should disappear Third, make sure that your cache does not return data that is too old to be accurately presented to your users Too old depends entirely upon your problem domain; a bank balance probably needs to be absolutely up-to-date, while today s top headline can probably be an hour old There are three approaches to solving this problem: Memcached will let you set an expiration date and time on each item that you place in the cache, and it will take care of dropping these items silently when the time comes.

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The following code creates two instances and stores their references in variables named T1 and T2. Figure 4-8 illustrates T1 and T2 in memory. The code demonstrates the following three actions discussed so far in the use of a class: Declaring a class Creating instances of the class Accessing the class members (i.e., writing to a field and reading from a field) class DaysTemp { public int High, Low; public int Avg() { return (High + Low) / 2; } } class Class1 { static void Main() { DaysTemp T1 = new DaysTemp(); // Create 2 instances of DaysTemp. DaysTemp T2 = new DaysTemp(); // Write to the fields of each instance. T1.High = 76; T1.Low = 57; T2.High = 75; T2.Low = 53; // Read from the fields of each instance and call a method of // each instance. Console.WriteLine("T1: {0}, {1}, {2}", T1.High, T1.Low, T1.Avg ()); Console.WriteLine("T2: {0}, {1}, {2}", T2.High, T2.Low, T2.Avg ()); Field Field Method // Declare the class. // Declare the instance fields. // Declare the instance method.

You can reach in and actively invalidate particular cache entries at the moment they become no longer valid..

A browser window should open, with the test harness running (see Figure 8-18). The URL for my web site is http://localhost:56488/Tester/Default.aspx. So VWD started its internal web server localhost set to port 56488 (remember the settings in Figure 8-15) to the web site Tester and opened the default page set to Default.aspx.

This code produces the following output: T1: 76, 57, 66 T2: 75, 53, 64

You can rewrite and replace entries that are invalid instead of simply removing them, which works well for entries that might be hit dozens of times per second: instead of all of those clients finding the missing entry and all trying to simultaneously recompute it, they find the rewritten entry there instead. For the same reason, pre-populating the cache when an application first comes up can also be a crucial survival skill for large sites.

The Structure of a Method Local Variables Method Invocations Return Values Parameters Value Parameters Reference Parameters Output Parameters Parameter Arrays Summary of Parameter Types Stack Frames Recursion Method Overloading

As you might guess, decorators are a very popular way to add caching in Python since they wrap function calls without changing their names or signatures. If you look at the Python Package Index, you will find several decorator cache libraries that can take advantage of Memcached, and two that target popular web frameworks: django-cache-utils and the plone.memoize extension to the popular CMS. Finally, as always when persisting data structures with Python, you will have to either create a string representation yourself (unless, of course, the data you are trying to store is itself simply a string!), or use a module like pickle or json. Since the point of Memcached is to be fast, and you will be using it at crucial points of performance, I recommend doing some quick tests to choose a data representation that is both rich enough and also among your fastest choices. Something ugly, fast, and Python-specific like cPickle will probably do very well.

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