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You will recall from 9 that it is always possible for two different input values to a hashing function to collide on the same output value Since it is possible for rows with different values in the join column(s) to be in the same hash bucket, we have to do the exact check Any rows that survive the exact check can immediately be reported (or passed on to the next step in the execution plan)..

h> using namespace System; using namespace msclr; // managed class R ref class R { public: void f() { Console::WriteLine("managed member function"); }.

In the Replace field type any combination of letters we would recommend putting your initials. If you plan to have several different signatures, possibly one for work and one for personal use, then you might want to use a number or extra letter after your initials like (initials)w (for work) and (initials)p for personal. In the With field, type in your full e-mail signature exactly as you would like it to appear in your e-mails. Choose SmartCase if you want the BlackBerry to capitalize the letters according to the correct context in the sentence when they are replaced. Select Specify Case to replace these letters with the capitalization exactly as you have entered them in the AutoText entry. For example, if you entered DeSoto with Specified Case then it would always replace the words as DeSoto never Desoto .

There is an important difference between Oracle s approach to acquiring memory for hashing compared to its approach for sorting. As you might guess from the comments about hash collisions in step 3, the hash join works most efficiently (as far as CPU is concerned) if there is at most one row in any hash bucket, so Oracle demands a huge fraction of the hash_area_size as soon as the hash join starts so that it can create a large number of buckets, as this helps to minimize hash collisions.

5. 6.

If you are familiar with single-table hash clusters, you will realize that an optimal hash join is really just a backto-front nested loop into a dynamically created single-table hash cluster. We build a single-table hash cluster in local memory from the rows selected from build table, and then, for each row selected from the probe table, check that hash cluster by hash key. The main benefit of an optimal hash join is that the build table is transferred into local memory, rather than being a real single-table hash cluster in the buffer cache. This means that the latch, buffer, and read consistency costs that normally occur on a table access simply don t appear as we probe the hash table.

~R() { Console::WriteLine("destructor"); } }; // native class N class N { gcroot<R^> r_gcroot; auto_gcroot<R^> r_auto_gcroot; public: N() { r_gcroot = gcnew R(); r_gcroot->f(); r_auto_gcroot = gcnew R(); r_auto_gcroot->f(); } }; int main() { N n; // When n goes out of scope, the destructor for the auto_gcroot object // will be executed, but not the gcroot object. } The output of Listing 6-14 is as follows: managed member function managed member function destructor You see only one call to the destructor the destructor for the auto_gcroot object. 12 will present more examples of interoperability between managed and native types and functions.

In this example we can put in Gary s full name, title and E-mail just by setting up an AutoText for gam.

Given that we have managed to produce an optimal (i.e., in-memory) hash join in this case, it seems reasonable that the final cost of the query (106) that we see in the execution plan would be as follows: The cost of acquiring data from the build table (42), plus The cost of acquiring data from the probe table (60), plus A little extra cost to represent the CPU cost of performing the hashing and matching. Nevertheless, an increment of four in the cost for such a small amount of CPU work might be considered a little suspect (especially since we have not enabled CPU costing anyway). And in fact this example is a little deceptive, because the hash_area_size (1MB) is very much larger than the size of the build table data set (15KB).