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Figure 12-2. Onepass hash join In this diagram, we see more of the components that are actually involved when Oracle performs a hash join. These are also present in the optimal hash join, but I chose to ignore them in my original diagram so that I could keep the picture as simple as possible. The most important omission from Figure 12-1 was the (small amount of) memory reserved for a bitmap representing the hash table at the rate of 1 bit per bucket in the hash table. When a build row hashes to a particular bucket, the corresponding bit is set. The rest of the hash_area_size is broken up into chunks (known as clusters or slots) of a size dictated by the parameter _hash_multiblock_io_count (obsolete as of 10g). This parameter always seemed to take the value 9 in earlier versions of Oracle, but is now much more dynamic, and the value is set individually for each query as it is optimized.

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In C#, you might call Dispose directly, or you might use the using statement to create a scope for your object, and have the Dispose method called automatically at the end of that scope Instead, C++/CLI provides a more familiar (to classic C++ programmers) way to use the RAII model You implement a destructor much as you would in classic C++ Implementing a destructor causes the object to implicitly implement IDisposable The destructor, in fact, becomes the Dispose method and hence implements the interface In C++/CLI, if you define a destructor as usual, you can be assured that your object s destructor will be called when the object goes out of scope as a result of the stack going out of scope or the destruction of the enclosing object, or an explicit call to delete on a handle to the object.

In the diagram, you can see that some of these memory clusters have been used to create the hash table, which has been sliced into four separate partitions (a word that is used to mean far too many different things in Oracle). The number of partitions always seems to be a power of two, and the number of partitions is chosen to leave a few memory clusters spare for juggling the anticipated I/O that will be needed to dump the hash table to disk. (The bottom right hash partition is split to show that it is made up of two chunks, one of which seems to be on its way down to disk.) Bear in mind that the hash table is really just a collection of linked lists the image of partitions as nice tidy squares with the same number of chunks is a visual convenience I am using to try to show the logical identity of hash buckets in the hash table. At any one moment, each partition could be made up of a different number of memory chunks. You can get a lot of detail about how memory has been used by setting event 10104, the hash join trace, before executing a test query. The trace for the query from script hash_one.sql, for example, includes the following information that describes the components I have been describing: Number of partitions: 8 Number of slots: 13 Multiblock IO: 9 Block size(KB): 8 Cluster (slot) size(KB): 72 Bit vector memory allocation(KB): 32 Per partition bit vector length(KB): 4 ------13 clusters at 9 blocks each number of blocks to a cluster size of block size of cluster in KB: 9 * 8KB = 72KB total memory for bitmap: 8 * 4KB = 32KB memory per partition for bitmap

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