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ref class R { public: V m_v; }; R^ GetRHandle() { return gcnew R(); } int main() { // i is an lvalue, 12 is an rvalue. int i = 12; // An lvalue i2; i is used here as an rvalue. // Lvalues can always be used as rvalues. int i2 = i; V v; // value type on the stack R r; // reference type (on the managed heap but with stack semantics) int& int% int& int& i4 = R^ // r1 // r2 i3 = i4 = i5 = i6 = v.i; i; // native reference: an lvalue i; // Tracking reference: lvalues can be assigned gc-rvalues. v.i; // OK: v.i is a stack-based object. r.m_v.i; // Illegal: r is a managed heap-based object. // OK: i4 is a gc-lvalue.

6 - select count(*) from t1 where skew = 75) -----------------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=1 Card=1 Bytes=3) 1 0 SORT (AGGREGATE) 2 1 INDEX (RANGE SCAN) OF 'T1_I1' (NON-UNIQUE) (Cost=1 Card=45 Bytes=135) Of course, you might ask at this point how Oracle works out a density it is a subject I don t really want to spend much time on, as it needs a fairly lengthy example to help me explain it and the added value is negligible So I ll give you the short answer now, and save the long answer for Volume 2.

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In purely descriptive terms, the density is as follows: sum of the square of the frequency of the nonpopular values / (number of nonnull rows * number of nonpopular nonnull rows) By virtue of the way the columns have been constructed, this can be calculated from the big histogram query at the end of the Getting Started section as follows: ( sum(sumrepsq) - sum(maxrep(i) * maxrep(i) ) / ( sum(sumrep) * ( sum(sumrep) - sum(maxrep(i) ) ).

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r1, ^r2; // r1 and r2 are gc-lvalues. gcnew R() is a gc-rvalue. = gcnew R(); GetRHandle() is a gc-rvalue, too. = GetRHandle();

The subscripts (i) are there to indicate that only certain rows are to be selected from the query results to contribute to their maxrep value These rows are the one meeting the following condition: maxbkt > min(minbkt) + 1 or min(val) = max(val) In the special case where there are no popular values (or at least, none that have been detected by the query), this formula degenerates to the simpler form sum(sumrepsq) / (sum(sumrep) * (sum(sumrep)) This could be verbalized as: the sum of the squared frequencies over the square of the summed frequencies If you analyze the formula carefully, you will realize that there is a potential trap that could give you problems if you happen to be unlucky with your data set The idea of this density calculation is to factor out the impact of popular values.

For example, if you have 2,000 rows of which 1,000 hold the same value, then a suitably adjusted density would be 1/2,000 rather than 1/1,001 because Oracle should be able to notice queries against the popular value and act accordingly In principle, then, we want the formula for the adjusted density to reduce the density that would appear in the absence of a histogram In practice, this works well for extreme cases like the one I have just described However, if you have popular values in your data and Oracle does not notice them when building the histogram, the formula will produce an increased density Yet again, you have to know your data, and know what you are expecting in order to be sure that the optimizer will be able to do the right job.

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