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CHAPTER 11 INDEXES
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Bitmap join indexes: These provide a means of denormalizing data in an index structure, instead of in a table. For example, consider the simple EMP and DEPT tables. Someone might ask the question, How many people work in departments located in the city of Boston EMP has a foreign key to DEPT, and in order to count the employees in departments with a LOC value of Boston, we would normally have to join the tables to get the LOC column joined to the EMP records to answer this question. Using a bitmap join index, we can instead index the LOC column against the EMP table. The same caveat in regard to OLTP systems applies to a bitmap join index as a regular bitmap index. Function-based indexes: These are B*Tree or bitmap indexes that store the computed result of a function on a row s column(s), not the column data itself. You can consider them an index on a virtual (or derived) column in other words, a column that is not physically stored in the table. These may be used to speed up queries of the form SELECT * FROM T WHERE FUNCTION(DATABASE_COLUMN) = SOME_VALUE, since the value FUNCTION(DATABASE_COLUMN) has already been computed and stored in the index. Application domain indexes: These are indexes you build and store yourself, either in Oracle or perhaps even outside of Oracle. You tell the optimizer how selective your index is and how costly it is to execute, and the optimizer will decide whether or not to use your index based on that information. The Oracle text index is an example of an application domain index; it is built using the same tools you may use to build your own index. It should be noted that the index created here need not use a traditional index structure. The Oracle text index, for example, uses a set of tables to implement its concept of an index.
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As you can see, there are many index types to choose from. In the following sections, I ll present some technical details on how each one works and when it should be used. I would like to stress again that we will not cover certain DBA-related topics. For example, we will not discuss the mechanics of an online rebuild; rather, we will concentrate on practical application-related details.
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B*Tree Indexes
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B*Tree or what I call conventional indexes are the most commonly used type of indexing structure in the database. They are similar in implementation to a binary search tree. Their goal is to minimize the amount of time Oracle spends searching for data. Loosely speaking, if you have an index on a number column, then the structure might conceptually look like Figure 11-1.
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CHAPTER 11 INDEXES
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Figure 11-1. Typical B*Tree index layout
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Note There are block-level optimizations and compression of data that take place that make the real block
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structure look different from Figure 11-1.
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The lowest level blocks in the tree, called leaf nodes or leaf blocks, contain every indexed key and a rowid that points to the row it is indexing. The interior blocks, above the leaf nodes, are known as branch blocks. They are used to navigate through the structure. For example, if we wanted to find the value 42 in the index, we would start at the top of the tree and go to the left. We would inspect that block and discover we needed to go to the block in the range 42..50 . This block would be the leaf block and point us to the rows that contained the number 42. It is interesting to note that the leaf nodes of the index are actually a doubly linked list. Once we find out where to start in the leaf nodes (i.e., once we have found that first value), doing an ordered scan of values (also known as an index range scan) is very easy. We don t have to navigate the structure anymore; we just go forward or backward through the leaf nodes as needed. That makes satisfying a predicate, such as the following, pretty simple: where x between 20 and 30 Oracle finds the first index leaf block that contains the lowest key value that is 20 or greater, and then it just walks horizontally through the linked list of leaf nodes until it finally hits a value that is greater than 30.
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