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Check constraints are a wonderful tool to help you ensure that the data being loaded into your database is what you expect and require Using some custom code, you can force data to conform to almost any pattern your heart desires To check data, you use an expression to look at the data and compare it to some desired result For example, to ensure that all values in a salary column are greater than or equal to the minimum wage ($585, in this example), your expression would be

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When using the parallel construct alone, every thread executes the same block of statements There are times, however, when we need different code to map onto different threads This is called worksharing

10272458 function calls (10272457 primitive calls) in 37718 CPU secs ncalls tottime percall cumtime percall filename:lineno(function) 1 0000 0000 37718 37718 <string>:1(<module>) 1 0719 0719 37717 37717 <string>:12(<module>) 1000 1569 0002 1569 0002 <string>:20(function_a) 1000 0011 0000 22560 0023 <string>:27(function_b) 5128000 7078 0000 7078 0000 <string>:28(<genexpr>) 1000 6510 0007 12825 0013 <string>:35(function_c) 5128000 6316 0000 6316 0000 <string>:36(<genexpr>)

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In cProfile terminology, a primitive call is a nonrecursive function call Using the cProfile module in this way can be useful for identifying areas that are worth investigating further Here, for example, we can clearly see that function_b() takes a long time But how do we drill down into the details We could instrument the program by replacing calls to function_b() with cProfile run("function_b()") Or we could save the complete pro le data and analyze it using the pstats module To save the pro le we must modify our command line slightly: python3 -m cProfile -o profileDataFile programOrModulepy We can then analyze the pro le data, for example, by starting IDLE, importing the pstats module, and giving it the saved profileDataFile, or by using pstats interactively at the console Here s a very short example console session that has been tidied up slightly to t on the page, and with our input shown in bold:

$ python3 -m cProfile -o profiledat MyModulepy $ python3 -m pstats Welcome to the profile statistics browser % read profiledat profiledat% callers function_b Random listing order was used List reduced from 44 to 1 due to restriction <'function_b'> Function was called by ncalls tottime cumtime <string>:27(function_b) <- 1000 0011 22251 <string>:12(<module>) profiledat% callees function_b Random listing order was used List reduced from 44 to 1 due to restriction <'function_b'> Function called ncalls tottime cumtime <string>:27(function_b) -> 1000 0005 0005 built-in method bisect_left 1000 0001 0001 built-in method len 1000 15297 22234 built-in method sorted profiledat% quit

Let s look at an example from the Mountain View Music database As you will recall, we created a table named tbl_customer that holds all the data pertinent Mountain View s customers The table has three columns for phone numbers called homephone, workphone, and mobilephone Each of these columns allows NULL values, and this means that a customer could conceivably choose not to provide a phone number at all The company has decided that it needs to have at least one phone number that it can use to call the customer in the event of a problem with an order To that end, you need to place a check constraint on tbl_customer to ensure that at least one phone number is provided Here is the simple expression to check for this condition

The most commonly used worksharing construct in OpenMP is the construct to split loop iterations between different threads Designing a parallel algorithm around parallel loops is an old tradition in parallel programming [X393] This style is sometimes called loop splitting and is discussed at length in the Loop Parallelism pattern In this approach, the programmer identifies the most time consuming loops in the program Each loop is restructured, if necessary, so the loop iterations are largely independent The program is then parallelized by mapping different groups of loop iterations onto different threads For example, consider the program in Fig A5 In this program, a computationally intensive function big_comp() is called repeatedly to compute results that are then combined into a single global answer For the sake of this example, we assume the following

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