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In this chapter we learned a lot more about Python s support for procedural and object-oriented programming, and got a taste of Python s support for functional-style programming In the rst section we learned how to create generator expressions, and covered generator functions in more depth We also learned how to dynamically import modules and how to access functionality from such modules, as well as how to dynamically execute code In this section we saw examples of how to create and use recursive functions and nonlocal variables We also learned how to create custom function and method decorators, and how to write and make use of function annotations In the chapter s second section we studied a variety of different and more advanced aspects of object-oriented programming First we learned more about attribute access, for example, using the __getattr__() special method Then we learned about functors and saw how we could use them to provide functions with state something that can also be achieved by adding properties to functions or using closures, both covered in this chapter We learned how to use the with statement with context managers and how to create custom context managers Since Python s le objects are also context managers, from now on we will do our le handling using try with except structures that ensure that opened les are closed without the need for finally blocks The second section continued with coverage of more advanced object-oriented features, starting with descriptors These can be used in a wide variety of ways and are the technology that underlies many of Python s standard decorators such as @property and @classmethod We learned how to create custom descriptors and saw three very different examples of their use Next we studied class decorators and saw how we could modify a class in much the same way that a function decorator can modify a function In the last three subsections of the second section we learned about Python s support for ABCs (abstract base classes), multiple inheritance, and metaclasses We learned how to make our own classes t in with Python s standard ABCs and how to create our own ABCs We also saw how to use multiple inheritance to unify the features of different classes together in a single class And from the coverage of metaclasses we learned how to intervene when a class (as opposed to an instance of a class) is created and initialized The penultimate section introduced some of the functions and modules that Python provides to support functional-style programming We learned how to use the common functional idioms of mapping, ltering, and reducing We also learned how to create partial functions and how to create and use coroutines.

straction layer In the example of splitting one table (TableA) into two tables (TableA and TableB), any code that selects, inserts, updates, or deletes from TableA must be changed to perform its action on both tables If we have an abstraction layer, we need only modify a few stored procedures and views to accommodate the new structure You are probably thinking, Code will need to be modified either way, so what s the difference Although it is true that in either case you need to make a change, the difference is huge For starters, unlike code in a NET application, code on SQL Server is not compiled; therefore, if the procedure takes the same inputs and returns the same outputs, you can alter that procedure in the middle of the day while users are online and no one will know the difference Granted, there are other considerations based on what else you have to change in the database, but we are talking about the transparency of the stored procedure change The second, and probably the more important, difference in modifying stored procedure code over application code is that there is only one database If you change application code, you often need to recompile and then have all your users update or reinstall the client application Abstraction layers give you the flexibility to make changes for performance reasons, for normalization, and for reasons of extensibility Future growth and changes are always similar with an abstraction layer

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Summary

This example uses the FJTask framework included as part of the public domain package EDUoswegocsdlutilconcurrent [Lea00b][4] Instead of creating a new thread to execute each task, an instance of a (subclass of) FJTask is created The package then dynamically maps the FJTask objects to a static set of threads for execution Although less general than a Thread, an FJTask is a much lighter weight object than a thread and is thus much cheaper to create and destroy In Fig 530 and Fig 531, we show how to modify the mergesort example to use FJTasks instead of Java threads The needed classes are imported from package EDUoswegocsdlutilconcurrent Before starting any FJTasks, a FJTaskRunnerGroup must be instantiated, as shown in Fig 530 This creates the threads that will constitute the thread pool and takes the number of threads (group size) as a parameter Once instantiated, the master task is invoked using the invoke method on the FJTaskRunnerGroup

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