.NET/ASP.NET/C#/VB.NET PDF Document SDK

Managed-unmanaged transitions are based on metadata and thunks. The compiler produces the necessary metadata and the CLR produces the thunks. For each native function that is called from managed code, P/Invoke metadata is automatically generated. Whenever a managed function is called from native code, the compiler generates an interoperability vtable. If an address of a managed function is stored in a function pointer with a native calling convention, native code can use this function pointer to call the managed function. Therefore, an interoperability vtable is produced for such a managed function, too. Since virtual functions are called internally via function pointers, interoperability vtables are produced for virtual functions, too. There are two major strategies for optimizing managed-unmanaged transitions. You can either reduce the number of transitions or you can optimize the performance of the generated thunks. To reduce the number of transitions as well as the amount of generated interop metadata, the __clrcall calling convention can be used. By defining custom P/Invoke functions or using a few linker switches, you can optimize the performance of the generated thunks. Managed-unmanaged transitions often require a deep understanding of the interoperability features provided by the CLR, as well as the C++/CLI language features that allow you to use these features. Many developers appreciate it when managed-unmanaged transitions are hidden behind a simpler fa ade. The next chapter describes how to hide managedunmanaged transitions in managed libraries that wrap native APIs.

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Only cities that fall in this list will be included in the partition A ticket with the information: 9999, 2004, 06, 01, DFW, HOU will be stored in the southwest_sales partition..

Sometimes, merely partitioning on range, hash, or list schemes may not be enough. You can further break down a large table into subpartitions for more control over data placement and performance. Oracle offers two types of composite partitioning: Composite range-hash partitioning: You first partition the table using range partitioning and then subpartition each of those partitions using a hash scheme. Composite range-list partitioning: You first partition the table using range partitioning and then subpartition those partitions using list partitioning.

Sometimes you may partition a table range-wise, but the distribution may not be very equal. You can make this a better partitioning scheme by hash partitioning after the range partitioning is done. This will allow you to store the data more efficiently, although it becomes more complex to manage. Composite range-hash partitioning combines the best of the range and hash partitioning schemes. Range partitioning, as you ve already seen, is easy to implement, and hash partitioning provides you benefits such as striping and parallelism. Listing 5-10 shows a simple example showing how to create a range-hash-partitioned table. Listing 5-10. Creating a Range-Hash-Partitioned Table SQL> CREATE TABLE scout_gear (equipno NUMBER,equipname VARCHAR(32),price NUMBER) 2 PARTITION BY RANGE (equipno) SUBPARTITION BY HASH(equipname) 3 SUBPARTITIONS 8 STORE IN (ts1, ts2, ts3, ts4) 4 (PARTITION p1 VALUES LESS THAN (1000), 5 PARTITION p2 VALUES LESS THAN (2000), 6 PARTITION p3 VALUES LESS THAN (3000), 7* PARTITION p4 VALUES LESS THAN (MAXVALUE)); Table created. SQL> In this example, the scout_gear table is first partitioned by range on the equipno column four range-based partitions are created. These four partitions are then subpartitioned on the equipname column using a hash-partitioning scheme, resulting in 32 subpartitions altogether. Note the SUBPARTITIONS clause in line 3.

ps -ef > /tmp/testfile while read firstvar do echo firstvar within the loop: $firstvar secondvar=$firstvar echo secondvar within the loop: $secondvar done < /tmp/testfile echo firstvar outside the loop: $firstvar echo secondvar outside the loop: $secondvar