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Dictionaries provide associative array functionality based on key-value pairs. An associative array is an array in which one type, the key, is used to access a particular stored value, often of a different type. All the keys are usually the same type, and all the values are normally the same type as well. The name, Dictionary, suggests an analogy. Using a real dictionary, you look up a definition (value) using the word as the key. The generic Dictionary<TKey,TValue> class may be used directly and is probably the best bet for the associative array-like functionality. We ll also use the IDictionary interface in Listing 11-22. Listing 11-22. Using a Dictionary // dictionary.cpp using namespace System; using namespace System::Collections::Generic; int main() { IDictionary<String^, String^>^ dict; dict = gcnew Dictionary<String^, String^>(); // The add method takes the key and the value. dict->Add("hat", "head adornment"); dict->Add("hot", "at a high temperature"); dict->Add("hit", "to strike"); // Use the KeyValuePair generic class when using the // for each statement. for each (KeyValuePair<String^, String^>^ pair in dict) { Console::WriteLine(" {0}: {1}", pair->Key, pair->Value); }

create table t1 as select trunc(dbms_random.value(0, 100 )) trunc(dbms_random.value(0, 30 )) lpad(rownum,10) rpad('x',100) from all_objects where rownum <= 1000 ; create table t2 as select trunc(dbms_random.value(0, 100 )) trunc(dbms_random.value(0, 40 )) lpad(rownum,10) rpad('x',100) from all_objects where rownum <= 1000 ; -select t1.v1, t2.v1 from t1, t2 where -- and and ; t2.join1 = t1.join1 t1.filter = 1 t2.filter = 1

In this test, I have 10,000 rows in each table, and a filter column with 100 distinct values. The query shown filters only on table t2 and has the filter on table t1 commented out, but the complete script for the test case has a second query that filters only t1, commenting out the filter on t2. There are 30 distinct values in the join column for t1 and 40 for t2. If you check the selectivity formula, it shouldn t really matter which table has the filter condition on it; the selectivity is only looking at the num_distinct on the join columns: Join Selectivity = ((num_rows(t1) - num_nulls(t1.c1)) / num_rows(t1)) * ((num_rows(t2) - num_nulls(t2.c2)) / num_rows(t2)) / greater(num_distinct(t1.c1), num_distinct(t2.c2)) in this case:

In addition to using the Sounds app, you can use the Contacts app to set custom ring tones or alerts for people. 1. Start the Contacts icon and type a few letters to find the contact you wish to edit, e.g., Ma Tr to find Martin Trautschold. Click the Menu key and select Edit.

Join Selectivity = (1000 / 1000) * (1000 / 1000) / greater(30, 40) = 1/40 = 0025 Since the filter condition is the same (one value in 100) on each of the tables, the join cardinality is going to come out the same, whichever table has the filter applied: Join Cardinality = Join Selectivity * filtered cardinality(t1) * filtered cardinality(t2) = 0025 * 10 * 1000 = 250 So here are the two execution plans: Execution Plan (9206 Filter on just T1) ---------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=11 Card=250 Bytes=7750) 1 0 HASH JOIN (Cost=11 Card=250 Bytes=7750) 2 1 TABLE ACCESS (FULL) OF 'T1' (Cost=5 Card=10 Bytes=170) 3 1 TABLE ACCESS (FULL) OF 'T2' (Cost=5 Card=1000 Bytes=14000) Execution Plan (920.

// The remove method takes the key as an argument. dict->Remove("hat"); // Use the KeyValuePair generic class when using the // for each statement. for each (KeyValuePair<String^, String^>^ pair in dict) { Console::WriteLine(" {0}: {1}", pair->Key, pair->Value); } } The output of Listing 11-22 is as follows: hat: hot: hit: hot: hit: head adornment at a high temperature to strike at a high temperature to strike

2. 3.

6 Filter on just T2) ---------------------------------------------------------0 SELECT STATEMENT Optimizer=ALL_ROWS (Cost=11 Card=333 Bytes=10323) 1 0 HASH JOIN (Cost=11 Card=333 Bytes=10323) 2 1 TABLE ACCESS (FULL) OF 'T2' (Cost=5 Card=10 Bytes=170) 3 1 TABLE ACCESS (FULL) OF 'T1' (Cost=5 Card=1000 Bytes=14000) Change the filter table, and you change the cardinality of the result Filtering on table t1, we get a cardinality of 250, as we inferred from the formula Filter on table t2, and the cardinality changes to 333 Can you guess where that came from Is it a coincidence that 1,000 / 30 = 333 One simple variation on the join cost algorithm appears when there is a filter predicate at just one end of the join we may use the num_distinct values from the table at the other end of the join, not the greater of the two num_distinct.

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(And I suspect that this statement is probably just a very special case of a more general rule about how filter predicates may affect the way that the optimizer decides which table to use as the source of num_distinct) When we have a filter only on t2, we use the num_distinct of 30 from table t1, rather than the larger num_distinct of 40 required by the standard formula..