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Indexes: Internals and Management
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Intermediate non-leaf level (Level 2) = 62,500 rows = 7,813 pages (8 rows per page) Intermediate non-leaf level (Level 1) = 500,000 rows = 62,500 pages (8 rows per page) Leaf level (Level 0) = 1,000,000 rows = 500,000 pages (2 rows per page)
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An index with a smaller key size would scale even faster. Imagine the same leaf-level pages as shown previously (1,000,000 rows at 2 rows per page) but with a smaller index key and therefore a smaller row size in the non-leaf levels (including some space for overhead) of only 20 bytes, you can t 404 rows per non-leaf-level page:
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Root page of non-leaf level (Level 3) = 4 rows = 1 page (404 rows per page) Intermediate non-leaf level (Level 2) = 1,238 rows = 4 pages (404 rows per page) Intermediate non-leaf level (Level 1) = 500,000 rows = 1,238 pages (404 rows per page) Leaf level (Level 0) = 1,000,000 rows = 500,000 pages (2 rows per page)
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In this second example, not only is the initial index only four levels, but it can have an additional 130,878,528 rows added (the maximum possible number of rows is 404*404*404*2 or 131,878,528 minus the number of rows that already exist 1,000,000) before it would require another level. Think of it like this the root page currently allows 404 entries; however, we re only storing 4 (and the existing non-leaf levels are not entirely 100 percent full). This is only a theoretical maximum, but without any other factors such as fragmentation a four-level tree would be able to seek into a table with over 131 million rows (again, with this small index key size). This means that a lookup into this index which uses the tree to navigate down to the corresponding row requires only four I/Os. And because the trees are balanced, nding any record requires the same amount of resources. Retrieval speed is consistent because the index has the same depth throughout. An index can become fragmented and pages can become less dense but these trees do not become unbalanced. This is something we look at later in this chapter when we cover index maintenance. It s not critical to memorize all the math that was used to show these examples, but understanding the true scalability of indexes especially with reasonably created keys means you are likely to create more effective indexes (that is, more ef cient, with narrower keys). In addition, there are tools inside SQL Server to help you see the actual structures (no math required). Most importantly, the size of an index (and the number of levels) depends on three things the index de nition, whether or not the base table has a clustered index, and the number of pages in the leaf level of the indexes. The number of leaf-level pages is directly tied to both row size and the number of rows in the table. This does not mean that the goal when de ning indexes is to have only very narrow indexes in fact, extremely narrow indexes usually have fewer uses than slightly wider indexes. It just means that you should understand the implications of different indexing choices and decisions. In addition, features such as INCLUDE and ltered indexes can profoundly affect the index in both size and usefulness. However, knowing how SQL Server works and the internal structures of indexes are a large part of nding the right balance between having too many and too few indexes, but most importantly, of having the right indexes.
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To expose and understand index structures fully, there are a few tools that we re going to use. To make the scenarios easier to understand, we need to get a feel for which tool is the most appropriate to use and when. In addition, this section focuses on an overview of the options for execution, as well as some tips and tricks. However, details on analyzing various aspects of the output can be found throughout this chapter.
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