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We still have to deal with problems like reverse key indexes, indexes with added columns, and indexes where the column order has been rearranged. Playing around with the sys_op_countchg() function is not going to help in these cases. However, if you consider the examples in this chapter, you will see that the they have a common thread to them. In each case the driving use of the index comes from a subset of the columns. In the reverse key example, (date_ord, seq_no), the critical use of the index depended on only the date_ord column and the presence of the seq_no added no precision to our queries. In the example about adding extra columns, (date_movement, product_id), the critical use of the index was the date_movement; the product_id was a little tweak to enhance performance (for certain queries). In the example of rearranging columns, (scattered, clustered), the argument is weaker, but we can detect that an underlying pattern in the table is strongly dictated by the clustered column, regardless of the fact that the index columns are not ordered in a way that picks this up. In all three cases, you could argue that a more appropriate clustering_factor could be found by creating an index using only the driving columns, calculating the clustering_factor for that index, and transferring the result to the original index. (You might want to do this on a backup copy of the database, of course.) I think the argument for doing this is very good in the first two cases mentioned previously, but a little weak for the third case. In the third case, the validity of the argument depends much more on the actual use of the index, and the nature of the queries. However, when the driving

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column argument fails, you may be able to fall back to the sys_op_countchg() technique. In the example, the data is grouped by the clustered column with a group of 9 or 10 blocks calling the sys_op_countchg() function with the value 9 may be the best way of finding an appropriate clustering_factor for your use of that index. Finally, there is the option of just knowing the right answer. If you know that a typical key value will find all its data in (say) 5 table blocks, but Oracle thinks it will have to visit 100 table blocks, then you can simply divide the clustering_factor by 20 to tell Oracle the truth. To find out how many table blocks Oracle thinks it has to visit, simply look at the column user_indexes. avg_data_blocks_per_key, which is simply a restated form of the clustering_factor, calculated as round (clustering_factor / distinct_keys).

try { d = safe_cast<Derived^>(b); } catch(InvalidCastException^ e) { // Handle the exception. }

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There are many other cases to consider if you want to produce a complete picture of how the clustering_factor can affect the optimizer, and I don t have space to go into them, but here s a thought for the future. Oracle 10g has introduced a mechanism to compact a table online. This only works for a table with row movement enabled that is stored in a tablespace using ASSM. You might use a sequence of commands like the following to compact a table: alter table x enable row movement; alter table x shrink space compact; alter table x shrink space; -- moves rows around -- drops the high water mark

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Before you rush into using this feature, just remember that it allows you to reclaim space by filling holes at the start of the table with data moved from the end of the table. In other words, any natural clustering of data based on arrival time could be lost as data is moved one row at a time from one end of the table to the other. Be careful about the effect this could have on the clustering_factor and desirability of the indexes on such a table.

d = dynamic_cast<Derived^>(b); if (d == nullptr) { /* cast failed*/ }

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Summary

CHAPTER 37: Hotkeys Open Almost any App with One Key!