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As you have seen in the earlier chapters, the cost of a query is a measure of time to completion, and one of the most significant factors in the time required is the number of real, physical I/O requests that have to be satisfied. This shows up very clearly in the basic formula for the singletable indexed access path. Before quoting the formula, though, it is worth creating a mental image of what you would do to walk through a typical index access path. You have predicates on some of the columns that define the index. You locate the root block of the index. You descend through the branch levels of the index to the leaf block that is the only place for the first possible entry (the start key) that could match your predicates. You walk along a chain of leaf blocks until you overshoot the entry that is the last possible entry (the stop key) that could match your predicates. For each index entry, you decide whether or not to visit a table block. So the formula for the cost of accessing a table by way of an index ought to cover three block-related components: the number of branch levels you descend through, the number of leaf blocks you traverse, and the number of table block visits you make. And as we know, the optimizer assumes that a single block visit equates to a real I/O request so the number of block visits is the cost (if you have not enabled CPU costing).

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Once they are enabled, you can now create new roles. As mentioned earlier, you will create two new roles for this example one for administrators and one for users. Creating a role is as easy as typing in the desired name for the role and clicking Add Role (see Figure 13-9).

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' Create command Dim cmd As SqlCommand = conn.CreateCommand() ' Specify stored procedure to execute cmd.CommandType = CommandType.StoredProcedure cmd.CommandText = "sp_select_all_employees"

System.out.println(e.getMessage()); e.printStackTrace(); } } } else if (DISPLAY_PARENT.equals(cmd)) { String path = request.getParameter(PATH); if (path != null) { System.out.println("path: " + path); try { helper.setPath(helper.getParentPath(path)); } catch (WebdavException e) { System.out.println(e.getMessage()); e.printStackTrace(); } } } } }

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GNU Make, NMake, and OPus Make were all extensions of Make but use the same style used by the original BSD Make. For example, they all relied on the makefile and added features (such as macros). Jam and Cook are variants of Make that don t rely on the makefile but use another variation of a text file to define what needs to be built. The interesting feature of these two products is that they support parallel builds while avoiding recursion.

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From our single selectivity formula, (required range / total range), we calculate the selectivity for predicate1, month_no > 8, as (12 8) / (12 1) = 4 / 11 = 0363636 Similarly, the selectivity for predicate2, month_no <= 8, is (8 1) / (12 1) + 1/12 = 7/11 + 1/12 = 0719696 Our formula for selectivity(P1 OR P2) is selectivity(P1) + selectivity(P2) selectivity(P1 AND P2), so the combined selectivity is 0363636 + 0719696 (0363636 * 0719696) = 08216 Multiply this selectivity by the 1,200 rows in the table, round up, and you get 986, exactly as we saw in Table 3-6 It s clearly the wrong answer for reasons that are intuitively obvious to the human mind But the machine is not human it follows the code, it doesn t understand the situation.

In fact, this error is just a special case of a more general problem, which I will introduce by going back one last time to my audience of 1,200 people Assume everyone in the audience knows which star sign they were born under (I know that astrology is not a topic that should appear in a book that s trying to be scientific, but it does provide a convenient demonstration) If I ask all the people born under Aries to raise their hands, I expect to see 100 hands there are 12 star signs, and we assume uniform distribution of data selectivity is 1/12, cardinality is 1,200/12 = 100 If I ask all the people born in December to raise their hands, I expect to see 100 hands there are 12 months, and we assume uniform distribution of data selectivity is 1/12, cardinality is 1,200/12 = 100.

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