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javautilconcurrent package Here we develop implementations from scratch to illustrate the concepts Implementing shared queues can be tricky Appropriate synchronization must be utilized to avoid race conditions, and performance considerations especially for problems where large numbers of UEs access the queue can require sophisticated synchronization In some cases, a noncentralized queue might be needed to eliminate performance bottlenecks However, if it is necessary to implement a shared queue, it can be done as an instance of the Shared Data pattern: First, we design an ADT for the queue by defining the values the queue can hold and the set of operations on the queue Next, we consider the concurrency control protocols, starting with the simplest "one at a time execution" solution and then applying a series of refinements To make this discussion more concrete, we will consider the queue in terms of a specific problem: a queue to hold tasks in a master/worker algorithm The solutions presented here, however, are general and can be easily extended to cover other applications of a shared queue

When Python outputs a tuple it encloses it in parentheses Many programmers emulate this and always enclose the tuple literals they write in parentheses If we have a one-item tuple and want to use parentheses, we must still use the comma for example, (1,) An empty tuple is created by using empty parentheses, () The comma is also used to separate arguments in function calls, so if we want to pass a tuple literal as an argument we must enclose it in parentheses to avoid confusion Here are some example lists:

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[1, 4, 9, 16, 25, 36, 49] ['alpha', 'bravo', 'charlie', 'delta', 'echo'] ['zebra', 49, -879, 'aardvark', 200] []

The abstract data type (ADT)

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One way to create a list is to use square brackets ([]) as we have done here; later on we will see other ways The fourth list shown is an empty list Under the hood, lists and tuples don t store data items at all, but rather object references When lists and tuples are created (and when items are inserted in the case of lists), they take copies of the object references they are given In the case of literal items such as integers or strings, an object of the appropriate data type is created in memory and suitably initialized, and then an object reference referring to the object is created, and it is this object reference that is put in the list or tuple Like everything else in Python, collection data types are objects, so we can nest collection data types inside other collection data types, for example, to create lists of lists, without formality In some situations the fact that lists, tuples, and most of Python s other collection data types hold object references rather than objects makes a difference this is covered in 3 In procedural programming we call functions and often pass in data items as arguments For example, we have already seen the print() function Another frequently used Python function is len(), which takes a single data item as its argument and returns the length of the item as an int Here are a few calls to len():

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FIGURE 911 The physical Orders submodel complete with the foreign key columns modeling software, this can be done right inside the model; otherwise, you will need to make the changes to your scripts before you implement your tables in SQL Server Again, go though each table and set the name of the primary key and the foreign keys

>>> len(("one",)) 1 >>> len([3, 5, 1, 2, "pause", 5]) 6 >>> len("automatically") 13

def test_list_succeed(self): items = selforiginal_list[:] with AtomicAtomic(items) as atomic: atomicappend(1999) atomicinsert(2, -915) del atomic[5] atomic[4] = -782 atomicinsert(0, -9) selfassertEqual(items, [-9, 0, 1, -915, 2, -782, 5, 6, 7, 8, 9, 1999])

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