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Let s start with a very simple class, Point, that holds an (x, y) coordinate The class is in le Shapepy, and its complete implementation (excluding docstrings) is show here:

Last, but certainly not least, what if the table already exists and you want to add a primary key First, you must make sure that any data already in the column conforms to the rules of a primary key It cannot contain NULLs, and each row must be unique After that, another simple script will do the trick

class Point:

Forces The main forces influencing the design at this point are flexibility, efficiency, and simplicity

def __init__(self, x=0, y=0): selfx = x selfy = y def distance_from_origin(self): return mathhypot(selfx, selfy) def __eq__(self, other): return selfx == otherx and selfy == othery def __repr__(self): return "Point({0x!r}, {0y!r})"format(self) def __str__(self): return "({0x!r}, {0y!r})"format(self)

Since no base classes are speci ed, Point is a direct subclass of object, just as though we had written class Point(object) Before we discuss each of the methods, let s see some examples of their use:

import Shape a = ShapePoint() repr(a) b = ShapePoint(3, 4) str(b) bdistance_from_origin() bx = -19 str(b) a == b, a != b

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Flexibility Flexibility in the design will allow it to be adapted to different implementation requirements For example, it is usually not a good idea to narrow the options to a single computer system or style of programming at this stage of the design Efficiency A parallel program is only useful if it scales efficiently with the size of the parallel computer (in terms of reduced runtime and/or memory utilization) For a task decomposition, this means we need enough tasks to keep all the PEs busy, with enough work per task to compensate for overhead incurred to manage dependencies However, the drive for efficiency can lead to complex decompositions that lack flexibility Simplicity The task decomposition needs to be complex enough to get the job done, but simple enough to let the program be debugged and maintained with reasonable effort

ALTER TABLE Products ADD CONSTRAINT pk_product_sku PRIMARY KEY (sku)

# returns: 'Point(0, 0)' # returns: '(3, 4)' # returns: 50 # returns: '(-19, 4)' # returns: (False, True)

The Point class has two data attributes, selfx and selfy, and ve methods (not counting inherited methods), four of which are special methods; they are illustrated in Figure 62 Once the Shape module is imported, the Point class can be used like any other The data attributes can be accessed directly (eg, y = ay), and the class integrates nicely with all of Python s other classes by providing support for the equality operator (==) and for producing strings in representational and string forms And Python is smart enough to supply the inequality operator (!=) based on the equality operator (It is also possible to specify each operator individually if we want total control, for example, if they are not exact opposites of each other) Python automatically supplies the rst argument in method calls it is an object reference to the object itself (called this in C++ and Java) We must include this argument in the parameter list, and by convention the parameter is called self All object attributes (data and method attributes) must be quali ed by self This requires a little bit more typing compared with some other languages, but has the advantage of providing absolute clarity: we always know that we are accessing an object attribute if we qualify with self

Solution The key to an effective task decomposition is to ensure that the tasks are sufficiently independent so that managing dependencies takes only a small fraction of the program's overall execution time It is also important to ensure that the execution of the tasks can be evenly distributed among the ensemble of PEs (the load balancing problem) In an ideal world, the compiler would find the tasks for the programmer Unfortunately, this almost never happens Instead, it must usually be done by hand based on knowledge of the problem and the code required to solve it In some cases, it might be necessary to completely recast the problem into a form that exposes relatively independent tasks In a task based decomposition, we look at the problem as a collection of distinct tasks, paying particular attention to

Point x y __new__() __init__() distance_from_origin() __eq__() __repr__() __str__()

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But wait there s more Using the sku column as we have done here is fine, but there are other PK options we need to discuss If you were to go through your entire database and define PKs as we have done on the Products table, you would likely end up with a different column name in

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