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cardinality formula rather than in the join selectivity formula. This means that some queries (multitable joins along the same nullable column, in particular) may produce a higher computed cardinality after the upgrade and therefore may change their execution plans. When you upgrade to 10g, two new strategies are employed for multicolumn joins the multicolumn sanity check, and the concatenated index sanity check. This means that the cardinality of multicolumn joins may again change (increase) on upgrade. There are still cases where you can change the join cardinality by adding extra predicates that are technically redundant. In some cases, it will not always be intuitively obvious that the predicates are redundant. On the other hand, there are some cases of code fixes in 9i and 10g that identify some of the classic cases of redundant predicate and ignore them. This means that some queries may show a dramatic increase in join cardinality when you upgrade from 8i to 9i or 10g. Joins involving badly matched tables, may produce better cardinality calculations, hence better plans, if you create histograms on one, or both, ends of the worst-matching columns.

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3. Insert a range of years in the boxes on the UploadListReport page, and click Execute to view the results. Figure 8-15 shows an example of the output when the years 1980 and 1990 are inserted into the text boxes.

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How It Works When you click the Execute button, you load an XPathDocument constructed with the reader returned by the method you have used so far: ' Set up the document Dim doc As New XPathDocument(GetReader()) You create the navigator and set up the namespace manager as you did before: ' Get the navigator over the document Dim nav As XPathNavigator = doc.CreateNavigator()

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Crosstabs can either be filled from left to right (the default) or from right to left (mainly for reports in right-to-left languages). When a crosstab is filled from right to left, the crosstab contents will start from the right extremity of the crosstab element area and grow toward the left.

Simple case demonstrating the join selectivity and cardinality formulae Demonstration that the formulae still work with nulls in the join columns Demonstration that the formulae still work with nulls in the join columns and filter columns Example of the wrong num_distinct being used in the formula Two join predicates with an AND between them Two join predicates with an OR between them Effects of variation in low/high values of the joined column Effects of transitive closure (predicate generation) on joins Joining three tables Changes in null treatment three-table join Effects of storing all reference data in a single table Is this a bug, a side effect, or three simultaneous special cases A bug in cardinality where descending indexes exist A strange example of cardinality changing Sets a standardized environment for SQL*Plus

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fter working out the cardinality of a join, the optimizer then has to consider the cost of the three ways of performing that join, nested loop, hash, or merge join. As I pointed out in 9, the star join and star transformation are not join mechanisms, they are join strategies, so they don t get a mention from this point onwards. The next three chapters examine each of the three join mechanisms in turn, first examining the operation and then the cost calculations. As we go through the mechanisms, it is important to remember that several contradictory effects may appear. The run-time engine may not do exactly what the execution plan appears to say; the cost calculation may also not tally with what the plan seems to say, and the cost calculation may not tally with what the run-time engine actually does. We start with the nested loop join, as that is the easiest to visualize, and the one whose cost calculation is best known.

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