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To control the color of each series in a chart displaying multiple series, you can use the <seriesColor> tag available at the chart-plot level. If only one <seriesColor> tag is specified, it becomes the color of the first series. If more than one <seriesColor> tag is specified, the chart will cycle through the supplied colors. Pie charts do not have multiple series, but they do need different colors for each slice, so the specified colors will be used. Meter and Thermometer charts do not have series and will ignore any <seriesColor> settings. When used in a chart that is part of a Multi-axis chart, the series colors are treated a bit differently. The default color series to cycle through is defined in the plot of the Multi-axis chart, and the color series for the nested charts define series colors for that chart only. This is useful when a Multi-axis chart contains several line charts, each with one series. By default every line will be the first in its plot and will have the first color defined in the Multi-axis plot, so every line will be the same color. To solve this, you can set a <seriesColor> for each nested chart to override the default colors. All series colors are sorted by the value of the seriesOrder attribute and appear in that order when coloring the series.

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In general, if you are going to write SQL with subqueries, you might as well start by writing code that reads like the natural language version of a problem, as this may be the easiest for other people to understand Generally, the optimizer can transform such queries into more efficient representations Sometimes, though, you will find that an obvious transformation will not take place, and you will have to do a manual transformation of your code If you do, make sure that it is logically the same as the original Star transformations are a popular join strategy for data warehouses But it is possible that when you enable the feature, some of your execution plans may show dramatic changes in cardinality This may be due to a collision between your design strategy for dimension tables and the arithmetic the optimizer does for the driving bitmap and.

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Note For a complete list of built-in simple types and their definition, see the W3C XML Schema Part 0:

Every time you upgrade, even on a point release, there may be a couple of new transformations that have been enabled Usually this means that the optimizer can find a better way of operating a query efficiently occasionally a new transformation may be a bad idea for your specific data set If the performance of a critical query changes dramatically (for better or worse) on an upgrade, make sure you can check the old execution plan against the new execution plan there may be a new transformation option that you need to know about..

Primer document at http://www.w3.org/TR/xmlschema-0.

filter_cost_01.sql push_subq.sql ord_pred.sql filter_cost_02.sql filter_cost_01a.sql scalar_sub_01.sql scalar_sub_02.sql scalar_sub_03.sql with_subq_01.sql with_subq_02.sql view_merge_01.sql push_pred.sql unnest_cost_01.sql unnest_cost_02.sql unnest_cost_01a.sql semi_01.sql anti_01.sql book_subq.sql notin.sql ordered.sql star_trans.sql star_join.sql intersect_join.sql setenv.sql

This type of plot is used for rendering Pie charts. There is no special setting for this type of plot other than the common settings just presented. Listing 13-13 provides the JRXML syntax. Listing 13-13. JRXML Syntax <!ELEMENT piePlot (plot)>

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Example of forcing a filter subquery path Example of pushing subqueries (8.1 only) Demonstration of the ordered_predicates hint Subquery filter going faster with sorted driving table How bad luck can affect the performance of a filtered subquery Uses an inline scalar subquery instead of a traditional correlated subquery Uses a scalar subquery to fake determinism in function calls Uses a scalar subquery to investigate the hash table Simple example of scalar subqueries used to generate large data sets More complex example of using scalar subqueries to simplify problems. Demonstration of changes due to complex_view_merging Demonstration of pushing join predicates The first filter example, with the SQL written with a manual unnest A filter that looks as if it should take place (in 10g) but doesn t As unnest_cost_01.sql, with modifications in which averages are required Examples leading up to the semi-join Examples leading up to the anti-join Example of an anomaly where not in does something that not exists cannot Highlights the problem of nulls and not in subqueries Example of how the ordered hint can cause problems on upgrade Example of a star transformation Simple demonstration of a star join (not star transformation) Demonstration of how 10g can convert a set operation to a join (or anti-join) Sets a standardized environment for SQL*Plus

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