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Only the Bar 3D and the Stacked Bar 3D charts make use of the Bar 3D plot. Like the aforementioned Bar plot, the Bar 3D plot allows customization of the labels for both of its axes and the display of the item labels. Listing 13-16 gives the JRXML syntax for the Bar 3D plot. Listing 13-16. JRXML Syntax <!ELEMENT bar3DPlot (plot, categoryAxisLabelExpression , categoryAxisFormat , valueAxisLabelExpression , valueAxisFormat )> <!ATTLIST bar3DPlot isShowLabels (true | false ) "false" xOffset CDATA #IMPLIED yOffset CDATA #IMPLIED >

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XML in general, and XML Schemas in particular, are quite advanced topics, and there are many aspects that we have left untouched For more in-depth information, try the following books: Definite XML Schema, by Priscilla Walmsley (Prentice Hall; ISBN: 0-13065-567-8) Pro NET XML, by Kent Tegels (Apress; ISBN: 1-59059-366-9).

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With this change to the data (and after collecting statistics), we just have one more detail to include in the join selectivity: Join Selectivity = (10,000 - 500) / 10,000) * (10,000 - 333) / 10,000) / greater(30, 40) = 0.022959125 Join Cardinality = 400 * 200 * 0.022959125 = 1,836.73 And again, we see that the cardinality predicted by the formula matches the cardinality of 1,837 reported by autotrace. Execution Plan (9.2.0.6 autotrace) ---------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=57 Card=1837 Bytes=62458) 1 0 HASH JOIN (Cost=57 Card=1837 Bytes=62458) 2 1 TABLE ACCESS (FULL) OF 'T2' (Cost=28 Card=200 Bytes=3400) 3 1 TABLE ACCESS (FULL) OF 'T1' (Cost=28 Card=400 Bytes=6800) As a final complication, we can introduce some null values to the filter columns (see join_card_03.sql in the online code suite) as well as the nulls we put into the join columns. The following code introduces 200 nulls to the filter column in table t1, and 100 nulls to table t2: update t1 set filter = null where mod(to_number(v1),50) = 0; -- 200 rows updated update t2 set filter = null where mod(to_number(v1),100) = 0; -- 100 rows updated We already have a join selectivity of 0.22959125 from our previous example, so all we have to do is work out the filtered cardinality of each of the tables when the columns with the filter predicates have some nulls. But I showed you how to do that in 3. The formula is

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Sometimes, you need to keep some data globally available to all users, who can share it. Of course, you can think of a database record as application-level state: all users can query it as needed. The performance impact would be unacceptable though, especially if it s used very often. Additionally, it involves several steps, such as opening the connection, issuing a query,

Adjusted (computed) Cardinality = Base Selectivity * (num_rows - num_nulls) For table t1 we have 1/25 * (10,000 200) = 392. For table t2 we have 1/50 * (10,000 100) = 198. Putting these values into the formula for join cardinality, we get Join Cardinality = 392 * 198 * 0.022959125 = 1,781.995 Compare this with the autotrace output, noting also the cardinality for each of the separate tablescans: Execution Plan (9.2.0.6 autotrace) ---------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=57 Card=1782 Bytes=60588) 1 0 HASH JOIN (Cost=57 Card=1782 Bytes=60588) 2 1 TABLE ACCESS (FULL) OF 'T2' (Cost=28 Card=198 Bytes=3366) 3 1 TABLE ACCESS (FULL) OF 'T1' (Cost=28 Card=392 Bytes=6664) So the formulae seem to work at least, for very simple cases. (Strangely, the 8i calculations go slightly wrong, with a cardinality of 199 rather than 198 on table t2, leading to a join cardinality of 1791. This is probably just an example of simple computational error dividing by 5 is a common cause of errors in the Nth decimal place.) Unfortunately there are problems that still need addressing lots of them. Let s try to find a few questions about the limitations of the formulae. What are you supposed to do if you have two or more join columns What do you do about a join condition that includes a range scan How do you join a third table Why doesn t the formula seem to allow for cases where the ranges of the join columns only have a partial overlap Do histograms have any effect With a little patience and experimentation, we can find the answers to some of these questions. As usual, it turns out that the code contains many rational strategies, some decisions that don t seem entirely reasonable, some special cases, and some things that look like bugs.

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