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In 3, I described the selectivity as the fraction of the rows that the optimizer expected to find based on the predicates supplied. But now I have raised the stakes to two selectivities, the effective index selectivity and the effective table selectivity. The principle is exactly the same, though. Forget about the table for a moment; what fraction of the row entries in the index will we return if we use the three predicates from the query All three predicates are on columns in the index, so we investigate all three of them, starting with their density or num_distinct:

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code that calls into native code frequently in a tight loop, you need to be particularly careful to minimize transitions. Consider the code in Listing 12-16, which demonstrates the performance effect of native to managed transitions. It also demonstrates the use of #pragma to include both native and managed code in the same file. In this case, everything after #pragma unmanaged and before #pragma managed is interpreted as native code. No managed constructs are allowed there. By moving these pragmas around in code, you can see what the effects are of having various portions of the code native or managed. Listing 12-16. Using #pragma managed and #pragma unmanaged // context_switch.cpp #include <stdio.h> #include <time.h> #include <stdlib.h> #include <math.h> #include <string.h> #pragma unmanaged int native_function(wchar_t* str1, wchar_t* str2 ) { int i = 0; while (*str1++ = *str2++) i++; return i; } #pragma managed wchar_t* random_string(wchar_t* wcs, int n) { for (int i = 0; i < n - 1; i++) { wcs[i] = (wchar_t) floor(((double) rand() / (double) RAND_MAX * 26)) + L'A'; } return wcs; } // Try commenting out the pragma above random_string and uncomment this: // #pragma managed. int main() { wchar_t wcs1[100]; wchar_t* wcs2 = new wchar_t[100]; memset(wcs1, 0, 100 * sizeof(wchar_t)); clock_t t = clock(); const int num_iter = 100000;

n1 = {constant} ind_pad = {constant} n2 = {constant} (Target: 1 row in 25, or 4% of the rows, or 0.04 * number of rows) (Target: 100% of the rows) (Target: 1 row in 20, or 5% of the rows, or 0.05 * number of rows)

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Apply the formula from 3 for combining selectivities: selectivity (P and Q) = selectivity(P) * selectivity(Q) But we have three events so we have to extend the formula a little: selectivity (X and Y and Z) = selectivity((X and Y) and Z) = selectivity(X and Y) * selectivity(Z) = selectivity(X) * selectivity(Y) * selectivity(Z) We simply multiply together all three selectivities. In this case, the effective index selectivity is 0.04 * 1 * 0.05 = 0.002 (remember this figure for later). Our predicates will require us to visit 0.2% of the entries in the index. The thing about indexes, though, is that their leaf blocks are packed in sorted order. If we work out that we are going to examine X% of the index rows, then we are going to walk along X% of the leaf blocks to do so. This is why one component of the cost is leaf_blocks * effective index selectivity.

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