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CHAPTER 4 SIMPLE B-TREE ACCESS
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INDEXES AND THE CLUSTERING FACTOR
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Historically it has been common practice to say that a good index has a low clustering_factor, and a bad index has a high clustering_factor. There is obviously a degree of truth in this comment, especially in the light of what the clustering_factor represents. However, I have always had an aversion to words like low, high, small, large, and expressions like close to zero, when talking about Oracle. After all, is 10,000 a low clustering_factor or a high clustering_ factor It s low if you have 10,000 blocks in your table, and high if you have 100 blocks in your table. So you might want to write a couple of little scripts that join user_tables to user_indexes (and other scripts for partitioned tables, etc.) so that you can compare the critical figures. In fact, for reasons I describe in 5, I often use the column avg_data_blocks_per_key to get an idea of how good Oracle thinks the index is.
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So why does the clustering_factor feature in the formula for costing When you acquire more than one row from a table through an index (in other words, when you are going to do an index range scan), you walk a section of the index call it X% of the index. As you walk the index, you will be hopping around the table from row to row. If the clustering_factor is truly representative of the way that the data is scattered around the table, then as you walk X% of the index, the number of times you change table block will be X% of clustering_factor. The optimizer behaves as if every change of block takes you to a block that you have not previously visited, which will therefore require an I/O request. This may be a fairly reasonable assumption in some cases, and explains why clustering_factor * effective table selectivity (rounded up) appears as the final component of the cost. There are flaws in the argument, of course, and we shall examine them in more detail in 5. Consider, for example, the first table block in Figure 4-1. If we execute a query that uses the index to visit all five rows in that block, the optimizer will have allowed for three physical I/O requests to that specific block in its cost calculation (there are three visit numbers in the block). But the block will probably be buffered (and possibly pinned) for all visits after the first one. It is quite common for this part of the formula to produce a value that does not reflect reality. In fact, thanks to a table pre-fetch mechanism that appeared in 9i (see 11), there is likely to be an ever-increasing difference between the calculated costs and actual block visits in increasing numbers of cases.
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Putting It Together
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We have the formula for the cost of an index-driven access path as cost = blevel + ceiling(leaf_blocks * effective index selectivity) + ceiling(clustering_factor * effective table selectivity)
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CHAPTER 4 SIMPLE B-TREE ACCESS
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We know what each of the terms represents, so we can check our simple example to see whether the formula gives us the right answer in other words, whether it provides the numbers we see in the autotrace output. Let s just slot in the numbers from our first test:
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blevel = 2 Effective index selectivity = 0.002 leaf_blocks = 1,111 Effective table selectivity = 0.002 clustering_factor = 9,745 Table rows = 10,000 (As calculated previously) (As calculated previously)
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So, according to the formula, the cost should be 2 + 2 + 2 + 5 + 25 ceiling(1,111 * 0.002) + ceiling(9,745 * 0.002) = ceiling(2.222) + ceiling(19.49) = 3 + 20 = 20 =
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Compare this with the execution plan, where the cost on the index line was 5 (which we hypothesized was 2 for descending the branches and 3 for walking the leaf blocks) and the total cost was 25. The correspondence is perfect. So the formula seems to be sound for this simple case; the results of the arithmetic match the figures produced by the execution plan listing. So what does it tell us specifically about the optimizer s perceived cost of using B-tree indexes Practical experience tells us that the blevel is typically 3 or less, often 2, occasionally 4; that indexes tend to be densely packed; that index entries tend to be smaller than table rows, so leaf blocks usually hold far more entries than the corresponding table blocks; and that the number of leaf blocks in an index is often small compared to the number of blocks in the table. So, for any set of predicates targeting more than three or four rows in the table, the most significant component in the cost calculation is quite likely to depend on the clustering_factor i.e., how randomly scattered the target data appears to be. If the optimizer thinks the data in your table is well clustered, then the cost will be low, favoring the use of indexes. If the optimizer thinks the data in the table is very scattered, then the cost will be higher, favoring the use of alternative execution plans (such as tablescans). Counterintuitively, the state of your table can have a much bigger impression on the optimizer s calculations than the state of the index itself. Of course, if your data really is distributed the way the optimizer thinks it is, then the cost calculation will often come close to a realistic run-time use of resources, and you will think the optimizer has done a good job. If the clustering_factor doesn t represent the real distribution, then the cost calculation will be wrong, and the optimizer is likely to choose an unsuitable execution plan.
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