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perform only one task at a time (and we can t split them into pieces), so we re looking for a set of jobs that we can actually do, and that gives us as large a total profit as possible. However, to keep things simple, this time all tasks take the same amount of time one time step. If d is the latest deadline, as measured in time steps from the starting point, we can then start with an empty schedule of d empty slots, and then fill those slots with tasks. The solution to this problem is, in a way, doubly greedy. First, we consider the tasks by decreasing profit (that is, we start with the most profitable task); that s the first greedy part. Then comes the second part: we place each task in the latest possible free slot that it can occupy, based on its deadline. If there is no free, valid slot, we discard the task. Once we re done, if we haven t filled out all the slots, we re certainly free to perform tasks earlier, so as to remove the gaps it won t affect the profit (or allow us to perform any more tasks). To get a feel for this solution, you might want to actually implement it (Exercise 7-20). The solution sounds intuitively appealing; we give the profitable tasks precedence, and we make sure they use a minimum of our precious early time, by pushing them as far toward their deadline as possible. But, once again, we won t rely on intuition. We ll use a bit of induction, showing that as we add tasks in this greedy fashion, our schedule stays promising.

EXEC #2

Selecting the TodoController link in Figure 4-10 brings you to the Todo List page shown in Figure 4-11. The Todo List page is a paginated list of domain objects.

Caution Although the following proof does not involve any deep math or rocket science, it is a bit involved and might hurt your brain. If you re not up for it, feel free to skip ahead to the chapter summary.

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According to Millsap and Holt ([MiHo 2003]), the response time R represented by a SQL trace file is defined as the sum of the elapsed time spent in database calls (e values) at recursive call depth 0 (dep=0) plus the sum of all ela values from inter database call wait events. The wait time (ela values) accumulated while processing a database call is rolled up into the parameter e of the database call that engendered the wait. The categorization of wait events discussed in the previous section is applied in the calculation of R. Time spent waiting for intra database call wait events must not be added to R, since this would result in double counting. The e values of a database call already contain the wait time of all intra database call wait events. Database calls are emitted to trace files upon completion. This is why WAIT entries for intra database call wait events appear before the PARSE, EXEC, and FETCH entries that engendered them. Runtime statistics, such as consistent reads, physical writes, and db block gets at recursive call depths other than zero are rolled up into PARSE, EXEC, and FETCH calls at recursive call depth 0. Just like ela values of intra database call wait events, these must not be double counted. To promote a thorough understanding of how an extended SQL trace profiler calculates a resource

From the list page shown in Figure 4-11, you have the ability to create new domain objects (as shown in Figure 4-12) by clicking the New button on the navigation bar. You can also show domain objects (as shown in Figure 4-14) by clicking on the ID. The save page displays the list of properties in the order in which the constraints are ordered as shown in the Create Todo page in Figure 4-12. Note that the Grails scaffolding displays the edit fields in the appropriate date type format. A String is displayed as an HTML text input field, while createdDate is displayed as a series of drop-downs for the day of month, month, year, hour, and seconds. In addition, the note property is displayed as an HTML text-area input due to the maxSize constraint. That s not all: the Grails scaffolding is also smart enough to use the constraints for doing form validation, as shown in Figure 4-13.

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