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After the ADT and its operations have been identified, the objective is to implement a concurrency control protocol to ensure that these operations give the same results as if they were executed serially There are several ways to do this; start with the first technique, which is the simplest, and then try the other more complex techniques if it does not yield acceptable performance These more complex techniques can be combined if more than one is applicable One at a time execution The easiest solution is to ensure that the operations are indeed executed serially In a shared memory environment, the most straightforward way to do this is to treat each operation as part of a single critical section and use a mutual exclusion protocol to ensure that only one UE at a time is executing its critical section This means that all of the operations on the data are mutually exclusive Exactly how this is implemented will depend on the facilities of the target programming environment Typical choices include mutex locks, synchronized blocks, critical sections, and semaphores These mechanisms are described in the Implementation Mechanisms design space If the programming language naturally supports the implementation of abstract data types, it is usually appropriate to implement each operation as a procedure or method, with the mutual exclusion protocol implemented in the method itself In a message passing environment, the most straightforward way to ensure serial execution is to assign the shared data structure to a particular UE Each operation should correspond to a message type;

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To import a module we use the import statement followed by the name of the py le, but omitting the extension Once a module has been imported, we can access any functions, classes, or variables that it contains For example:

print(sysargv)

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The sys module provides the argv variable a list whose rst item is the name under which the program was invoked and whose second and subsequent items are the program s command-line arguments The two previously quoted lines constitute the entire echoargspy program If the program is invoked with the command line echoargspy -v, it will print ['echoargspy', '-v'] on the console (On Unix the rst entry may be '/echoargspy')

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In general, the syntax for using a function from a module is moduleNamefunctionName(arguments) It makes use of the dot ( access attribute ) operator we introduced in Piece #3 The standard library contains lots of modules, and we will make use of many of them throughout the book The standard modules all have lowercase names, so some programmers use title-case names (eg, MyModule) for their own modules to keep them distinct Let us look at just one example, the random module (in the standard library s randompy le), which provides many useful functions:

elements simply describe the groupings of data as they might exist in the real world; in contrast, physical elements actually store the data in a database A single entity might be stored in only one table or in multiple tables In fact, sometimes more than one entity wind up being stored in one table The various physical elements and the ways they are used are the topics of 3, Physical Elements of Data Models

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