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The numbers make the impact of the clustering_factor very obvious. Although the first set of figures shows that the range-based predicate on the first column has reduced the effectiveness of the t1_i1_good index, the reduction is minor compared with the impact caused by the enormous increase in the clustering_factor in the second set of figures. In this example, the extra resources used because we have picked the wrong index will be minimal we still visit exactly the same number of rows in the table so no extra I/O there and we happen to examine two leaf blocks rather than one when we use the wrong index. The guaranteed penalty of using the wrong index would be a little extra CPU spent scanning an unnecessary number of entries in the index. There are 500 entries in either index where clustered between 1 and 5, and we will examine about 400 of them (from (1,50) to (5,50)) if we use index t1_i1_good for our query. There are 100 entries in the index where scattered = 50, and we will examine about five of them (from (50,1) to (50,5)) if we use index t1_i2_bad. In real systems, the choice is more subtle than picking one of two indexes with the same columns in a slightly different order; the scope of the error becomes much larger with changes in complex execution plans not just a little waste of CPU.

It s not just a change in column order that could introduce a problem. It s a fairly common (and often effective) practice to add a column or two to an existing index. By now I m sure you won t be surprised to discover that this, too, can make a dramatic difference to the clustering_factor, hence to the desirability of the index. Imagine a system that includes a table for tracking product movements. It has a fairly obvious index on the movement_date, but after some time, it might become apparent to the DBA that a number of commonly used queries would benefit from the addition of the product_id to this index (see script extra_col.sql in the online code suite). create table t1 as select sysdate + trunc((rownum-1) / 500) movement_date, -- 500 rows per day trunc(dbms_random.value(1,60.999)) product_id, trunc(dbms_random.value(1,10.000)) qty, lpad(rownum,10) small_vc, rpad('x',100) padding from all_objects where rownum <= 10000 -- 20 days * 500 rows per day. ; rem rem create index t1_i1 on t1(movement_date); -- original index create index t1_i1 on t1(movement_date, product_id); -- modified index

* Inline asm, most compiler intrinsics ** Including downcasts with static_cast and all uses of reinterpret cast Not detected by Visual C++ 2005 compiler Not in the same function as managed or C++ exception handling

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INDEX_COLUMNS BLEVEL LEAF_BLOCKS CLUSTERING_FACTOR -------------------- ---------- ----------- ----------------movement_date 1 27 182 movement_date, product_id 1 31 6645

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Although the index size (as indicated by the leaf block count) has grown somewhat, the significant change is yet again the clustering_factor When the index is just (movement_date), we expect to see lots of rows for the same date entering the database at the same time, and the 500 rows we have created for each date will be packed in a clump of 9 or 10 adjacent blocks in the table at about 50 rows per block An index based on just the movement_date will have a very good clustering_factor When we change the index to (movement_date, product_id), the data is still clustered by date, but any two entries for the same product_id on the same date are likely to be in two different table blocks in that little group of nine or ten.

In these examples, assume R is a reference type (ref class) and V is a value type (value class), I is an interface (interface class), and P is a property (property int P). Also assume r is a handle to R and v is an instance of V. Assume i, j, and k are integer fields or local variables, s is a handle to String, and ai is a one-dimensional managed array of integers. Assume Base and Derived are reference classes in an inheritance relationship. Assume d is typed as a handle to Derived and b has type handle to Base, but could be an actual instance of a Base or Derived object, or nullptr. The order of the examples is the order in which they are covered in the text.