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public ref class MethodSamples { public: void NativeByValArgs(int i, string str, string* pstr) { // caller will not see all these changes i = 10; str = "123"; pstr = new string("123"); // leaking string object! } void NativeByRefArgs(int& i, string& str, string*& pstr) { // caller will see all these changes i = 10; str = "123"; pstr = new string("123"); // caller is responsible for deleting pstr } void ManagedByValArgs(int i, String^ str) { // caller will not see these changes i = 10; str = gcnew String('a', 5); // GC can reclaim memory for String immediately } void ManagedByRefArgs(int% i, String^% str) { // caller will see these changes i = 10; // lifetime of string depends on usage of str outside this function str = gcnew String('a', 5); } }; To compile existing C++ code to managed code, C-style support for functions with a variable number of arguments still exists; however, if you define a new managed function, you should consider using an alternative concept supported by C++/CLI. The syntax for this alternative is also based on an ellipsis (...), but in addition to the ellipsis, a managed array type is used to define the argument. The following code shows how you can use this new syntax: Object^ CallAnyFunction( Object^ obj, String^ function, ... array<Object^>^ args ) { if (!obj)

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During this phase, Oracle transforms the logical query plan into a physical query plan. The Optimizer may be faced with a choice of several algorithms to resolve a query. It needs to choose the most efficient algorithm to answer a query, and it needs to determine the most efficient way to implement the operations. In addition to deciding on the best operational steps, the Optimizer determines the order in which it will perform these steps. For example, the Optimizer may decide that a join between table A and table B is called for. It then needs to decide on the type of join and the order in which it will perform the table join. The physical query or execution plan takes into account the following factors: The various operations (for example, joins) to be performed during the query The order in which the operations are performed The algorithm to be used for performing each operation The best way to retrieve data from disk or memory The best way to pass data from one operation to another during the query The Optimizer may generate several valid physical query plans, all of which are potential execution plans. The Optimizer then chooses among them by estimating the cost of each possible physical plan based on the table and index statistics available to it, and selecting the plan with the lowest estimated cost. This evaluation of the possible physical query plans is called cost-based query optimization. The cost of executing a plan is directly proportional to the amount of resources such as I/O, memory, and CPU necessary to execute the proposed plan. The Optimizer passes this lowcost physical query plan to Oracle s query execution engine. The next section presents a simple example to help you understand the principles of cost-based query optimization.

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Let s say you want to run the following query, which seeks to find all the supervisors who work in Dallas. The query looks like this: SQL> SELECT * FROM employee e, dept d WHERE e.dept_no = d.dept_no AND(e.job = 'SUPERVISOR' AND d.city = 'DALLAS'); SQL> Now, you have several ways to arrive at the list of the supervisors. Let s consider three ways to arrive at this list, and compute the cost of accessing the results in each of the three ways.

Make the following simplifying assumptions for the cost computations: You can only read and write data one row at a time (in the real world, you do I/O at the block level, not the row level). The database writes each intermediate step to disk (again, this may not be the case in the real world). No indexes are on the tables. The employee table has 2,000 rows. The dept table has 40 rows. The number of supervisors is also 40 (one for each department). Ten departments are in the city of Dallas. In the following sections, you ll see three different queries that retrieve the same data, but that use different access methods. For each query, a crude cost is calculated, so you can compare how the three queries stack up in terms of resource cost. The first query uses a Cartesian join.