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As mentioned earlier, this can either be done by refactoring code so that multiple transitions are replaced by one, or by using the __clrcall calling convention to prevent double thunking In contrast to unmanaged-to-managed thunks, the performance of managed-to-unmanaged thunks can be significantly optimized As the output of the test application shows, 10 million calls to the imported function fNativeFromDLL take about 197 seconds, whereas the same number of calls to the function fNativeLocal, which is in the same assembly as the caller, execute in approximately 063 seconds It is possible to optimize the thunk for fNativeFromDLL so that it performs equally fast To give you a solid understanding of thunk optimizations, I ll explain how thunks are invoked and how they work.

Range partitioning is a popular way to partition Oracle tables, and it was the first type of partitioning introduced by Oracle. Range partitioning is used for data that can be separated into ranges based on some criterion. You get the best results from range partitioning if the data falls evenly into the different ranges that you create. Your ranges can be based on a sequence number or a part number, but the range-partitioning technique is usually based on time (monthly or quarterly data, for example). Let s say you need to create a table to hold three years of quarterly sales data for a major airline. This could easily add up to several hundreds of million transactions. If you partition the sales table by a range of quarters and decide to hold no more than three years worth of data at any given time, you could have 12 partitions in the table, partitioned by quarters. Each time you enter a new quarter, you can archive the oldest quarter s data, thus keeping the number of partitions constant. By

partitioning the huge table, which might have a total of 480 million rows, for example, any queries you run would only have to deal with one-twelfth of the table that is, about 40 million rows which makes a big difference Partitioning thus provides you with a divide-and-conquer technique for dealing efficiently with massive amounts of table data Listing 5-8 shows the DDL for creating a range-partitioned table, with each year s worth of data divided into four partitions With each new quarter, you can add another partition Thus, you ll end up with 12 partitions over a three-year period Listing 5-8.

Creating a Range-Partitioned Table SQL> CREATE TABLE sales_data 2 (ticket_no NUMBER, 3 sale_year INT NOT NULL, 4 sale_month INT NOT NULL, 5 sale_day INT NOT NULL) 6 PARTITION BY RANGE (sale_year, sale_month, sale_day) 7 (PARTITION sales_q1 VALUES LESS THAN (2004, 04, 01) 8 TABLESPACE ts1, 9 PARTITION sales_q2 VALUES LESS THAN (2004, 07, 01) 10 TABLESPACE ts2, 11 PARTITION sales_q3 VALUES LESS THAN (2004, 10, 01) 12 TABLESPACE ts3, 13 PARTITION sales_q4 VALUES LESS THAN (2005, 01, 01) 14* TABLESPACE ts4); Table created SQL> The table creation statement in Listing 5-8 will create four partitions, each stored in a separate tablespace Notice how the partitions are based on date ranges The first partition, sales_q1, will include all transactions that took place in the first three months (one quarter) of the year 2004.

The second quarter, sales_q2, will include transactions that occurred between April and June of 2004 (months 4, 5, and 6 of the year), and so on It is common in range-partitioned tables to use a catchall partition as the very last one When this is the case, the last partition will contain values less than a value called maxvalue, which is simply any value higher than the values in the second-to-last partition Note that each partition has a specific name and is stored in a separate tablespace In the partitioned sales_data table, the sales data for June 10, 2004 (sale_year=2004, sale_month=6, and sale_day=10) has a partitioning key of (2004, 6, 10) and would be stored in partition sales_q2 When a query requests data for June 10, 2004, the Oracle query zooms in on partition sales_q2 and completely ignores the rest of the table data..