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bitmap on the fact1 table We scan each dimension table in turn to pick up the primary key values the primary key values (when sorted) allow us to walk along the corresponding bitmap index of the fact table, picking up the relevant bitmap sections and merging them into a single bitstream After we have done this for all three dimensions, we have three bitstreams that we can apply the bitmap and operation to, converting any resulting bits to their corresponding rowid The line marked with the +++ is particularly important In this line, the optimizer tells us how many relevant rows it thinks there are in the fact table: card = 53 (which happens to be correct thanks to the rather contrived way that I constructed the data) Compare this with the cardinality reported from the brutal hash join: card = 423404 The difference is quite significant.

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In fact, the divergence gets worse, because the final cardinality reported for the complete query is only 11 according to the star transformation plan The discrepancy arises from two distinct factors First, there seems to be a bug in the code that handles the bitmap and portion of the plan The reason for the strange names of the dimensions is that I had 23, 31, and 53 distinct values of each column appearing in the fact1 table (plus a lot of other values in the dimension tables to pad them up a bit) The predicates I had on the dimension tables selected 20 rows each, so in theory they should have selected 20 sections from each of the bitmaps on the fact table.

Listing 5-18. Initializing Arrays // arrays_initializing.cpp int main() { // Declare, create, and initialize a 1D native array. int native_array[2] = { 10, 20 }; // Declare, create, and initialize a 1D managed array. array<int>^ managed_array = gcnew array<int>(2) { 10, 20 }; // Declare, create, and initialize a 2D native array. int native_array_2D[2][2] = { { 1, 0 }, { 0, 1 } }; // Declare, create, and initialize a 2D managed array. array<int, 2>^ managed_array_2D = gcnew array<int, 2>(2, 2) { { 1, 0 }, { 0, 1 } }; } If an initializer is present, the size of the dimensions may be omitted. In this case, the number of elements in the initializer determines the size of the array. You can also use an initializer by itself on the right side of an assignment operator, without gcnew, to create a new array. You can use variables in an initializer, as for array_int3 in Listing 5-19. Listing 5-19. Initializing an Array Without gcnew // arrays_initializing2.cpp int main() { // initialization without gcnew array<int>^ array_int1 = { 0, 1, 2 }; // Initialization with gcnew (no equal sign is used). // Here, the size is omitted and determined by the three // elements in the initializer list. array<int>^ array_int2 = gcnew array<int> { 0, 1, 2 }; // You can use variables in the initializer list. int i = 1, j = 2, k = 3; array<int>^ array_int3 = { i, j, k }; } As in the C++ new expression, the default constructor (the constructor with no arguments) is called to initialize the elements of the array. You can use gcnew in the array initializer to call a specific constructor, as in Listing 5-20.

However, the optimizer s arithmetic is based on the absolute selectivity of the bitmap indexes, and doesn t allow for the actual number of bitmap sections identified by each of the dimension tables I had 2,000,000 rows in the fact table, and the selectivities of my three bitmap indexes were 23, 31, and 53, respectively, so the optimizer calculated the cardinality of the bitmap access step as 2,000,000 / (23 * 31 * 53) = 52925 This happens to be the correct answer in my example, but only because I had rigged my dimension tables so that 19 of the 20 values that I had chosen would not appear in the fact table The second factor that comes into play arises from the fact that star transformations obey normal join arithmetic.

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