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*************************************** STAR TRANSFORMATION PLANS *********************** After this, you get the first of the join orders (again starting with join order[1]) for the join-back. The strange thing about it, though, is that there are no clues in the trace file about the arithmetic that has been used to determine the cardinality of the target data set in the fact table. All you get is a sudden appearance of an assumed cardinality (the 53 rows in my example) with no justification. Just as a closing thought on star transformations, you might like to wonder why I used such an odd data pattern to demonstrate the effects it can have. First, of course, I wanted to make the divergence in the calculations, and its cause, very obvious. Second, the construction mimics a fairly common design error many people put lots of little dimensions into a single table with a type column. If you adopt this strategy, then a single column in the fact table corresponds to a subset of the available values in the dimension table, and the peculiar selectivity of the type column results in the optimizer exaggerating the difference between the cardinality of the ordinary join and the star transformation join. My predicate rep_23 = 10 might be your predicate ref_type = 'COUNTRY CODE'.

End. Listing 5-23 shows the syntax for creation and element access of a managed one-dimensional array alongside the classic C++ equivalent. Listing 5-23. Creating One-Dimensional Arrays // arrays_managed_native_comparison.cpp #include <iostream> using namespace std;

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For the sake of completeness, I will mention star joins very briefly. They don t really belong in this chapter, as there is no rewrite or transformation involved in a star join. However, people sometimes confuse star transformations and star joins, so it s worth mentioning them at the same time to eliminate the confusion. A star join is simply a special case of evaluating a join order that takes advantage of a multicolumn index on the fact table to drive a query through a Cartesian join of its dimension tables. There are probably some special circumstances where this could be useful, but I have yet to see a production example where it was appropriate to take advantage of the feature. (Of course, you have to remember that the star join was made available in Oracle 7, before the implementation of the star transformation so it may simply be an example of outdated technology.) The script star_join.sql in the online code suite has a simple example (with no data) to show this in action. We have a fact table with the following definition: create table fact_tab ( id1 number not null, id2 number not null, id3 number not null, small_vc varchar2(10), padding varchar2(100), constraint f_pk primary key (id1,id2,id3) ); The three IDn columns correspond to the unique ID columns of three separate dimension tables. When we run a query such as the following:

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select /*+ star */ d1.p1, d2.p2, d3.p3, f.small_vc from dim1 dim2 dim3 fact_tab where and and and and and ; d1.v1 d2.v2 d3.v3 f.id1 f.id2 f.id3 = = = = = = 'abc' 'def' 'ghi' d1.id d2.id d3.id d1, d2, d3, f

int main() { // native 1D array int native_array_1D[10]; // managed 1D array array<int>^ managed_array_1D = gcnew array<int>(10); for (int i = 0; i < 10; i++) { native_array_1D[i] = i*i; cout << native_array_1D[i] << " "; managed_array_1D[i] = native_array_1D[i]; cout << managed_array_1D[i] << " "; } cout << endl; } Here is the output of Listing 5-23: