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Nonclustered Index on a Heap
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A nonclustered index is also structured as a balanced tree, and in many respects is similar to a clustered index. The only difference is that a leaf row in a nonclustered index contains only the index
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key columns and a row locator value pointing to a particular data row. The content of the row locator depends on whether the table is a heap or a clustered table. This section describes nonclustered indexes on a heap, and the following section will describe nonclustered indexes on a clustered table. Figure 3-19 illustrates the nonclustered index created by our primary key constraint (PK_Orders ) defining the orderid column as the key column.
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Figure 3-19. Nonclustered index on a heap
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The row locator used by a nonclustered index leaf row to point to a data row is an 8-byte physical pointer called RID . It consists of the file number in the database, the target page number in the file, and the row number in the target page (zero-based). When looking for a particular data row through the index, SQL Server will have to follow the seek operation with an RID lookup operation, which translates to reading the page that contains the data row. Therefore, the cost of an RID lookup is one page read. For a single lookup or a very small number of lookups the cost is not high, but for a large number of lookups the cost can be very high because SQL Server ends up reading one whole page per sought row. For range queries that use a nonclustered index, and a series of lookupsone per qualifying keythe cumulative cost of the lookup operations typically makes up the bulk of the cost of the query. I'll demonstrate this point in the "Index Access Methods " section. As for the cost of a seek operation, remember that the formulas I provided earlier are just as relevant to nonclustered indexes. It's just that the leaf_row_size is smaller, and therefore the rows_per_leaf_page will be higher. But the formulas are the same.
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Nonclustered Index on a Clustered Table
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As of SQL Server 7.0, nonclustered indexes created on a clustered table are architected differently than on a heap. The only difference is that the row locator in a nonclustered index created on a clustered table is a value called a clustering key , as opposed to being an RID. The clustering key
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consists of the values of the clustered index keys from the pointed row, and the uniquifier (if present). The idea is to point to a row "logically" as opposed to "physically." This architecture was designed mainly for OLTP systems, where clustered indexes often suffer from many page splits upon data insertions. In a page split, half the rows from the split page are physically moved to a newly allocated page. If nonclustered indexes kept physical pointers to rows, all pointers to the data rows that moved would have to be changed to reflect their new physical locationsand that's true for all relevant pointers in all nonclustered indexes. Instead, SQL Server maintains logical pointers that don't change when data rows physically move. Figure 3-20 illustrates what the PK_Orders nonclustered index might look like; the index is defined with the orderid as the key column, and the Orders table has a clustered index defined with the orderdate as the key column.
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Figure 3-20. Nonclustered index on a clustered table
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A seek operation looking for a particular key in the nonclustered index (some orderid value) will end up reaching the relevant leaf row and have access to the row locator. The row locator in this case is the clustering key of the pointed row. To actually grab the pointed row, a lookup operation will need to perform a whole seek within the clustered index based on the acquired clustering key. I will demonstrate this access method later in the chapter. The cost of each lookup operation here (in terms of the number of page reads) is as high as the number of levels in the clustered index (3 in our case). That's compared to a single page read for an RID lookup when the table is a heap. Of course, with range queries that use a nonclustered index and a series of lookups, the ratio between the number of logical reads in a heap case and a clustered table case will be close to 1:L , where L is the number of levels in the clustered index.
Before you worry too much about this point and remove all clustered indexes from your tables, keep in mind that with all lookups going through the clustered index, the non-leaf levels of the clustered index will typically reside in cache. Typically, most of the physical reads in the clustered index will be against the leaf level. Therefore, the additional cost of lookups against a clustered table compared to a heap is usually a small portion of the total query cost. Now that the background information about table and index structures has been covered, the next section will describe index access methods.
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