full index scan

Solution:Index Rebuild: Magic or Voodoo?

Of course my readers have solved the problem. I would like to thank Jure Bratina, Andrew Sayer and Martin Berger for their contributions.
In the following you will see the entire test case in a commented form. IMHO most of it is self-explanatory.
The following little scripts represents the initial set up. The schema is, as I said, the SH example scheme.

alter table sales add  (sparse varchar2(300)); 
update sales set sparse = rpad('sometext',300, '*');
create index sparse_idx on sales (sparse);
select blocks from user_segments where segment_name ='SPARSE_IDX';

Let us now examine the size of the index segment:

select blocks from user_segments where segment_name ='SPARSE_IDX';


select leaf_blocks from user_indexes where index_name ='SPARSE_IDX';


Here is an update similar to the one I found in the original database:

update sales set sparse=NULL;

918843 rows updated.

exec dbms_stats.gather_table_stats(user,'SALES');

And how does it affect the statistics and the segments?

select leaf_blocks from user_indexes where index_name ='SPARSE_IDX';

select blocks from user_segments where segment_name ='SPARSE_IDX';


The optimizer only checks the index statistics. It does not check the segment statistics. The optimizer therefore believes that the index tiny.
All the non-null values of the column sparse must be in the index. Therefore it is optimal, from the optimizer’s perspective, to scan the supposedly small index.
However, the index segment still has it’s full size. An index rebuld is required to resolve the issue.

Index Rebuild: Magic or Voodoo?

I actually don’t like to write about todays topic. However, I hope you will agree this story is worth to be told.

Via Freelist somebody asked if an Index rebuild could be occaisionally usefull. There was a case at hand, where index rebuild seemed to be usefull after a big delete. I sometimes got annoyed by the popular myth, that regular index rebuild would be helpfull. This is one of the more popular database myths as branded by Mr. Index, Richard Foote.

Thus, I could not resist the opportunity to trash the myth I don’t like and answered that there is no such case. I ignored the warning wisper in my head telling me that things are never that easy.
Of course, the unavoidable happened Jonathan Lewis corrected me stating that there are corner cases were index rebuild can be useful ( e.g. after a big delete).

As if this would not be enough shortly thereafter I came accross this little statement below. (I presenting a test case that makes use of the SH schema.)

select time_id from sales where sparse is not null;

The Exection Plan with runtime statistics looks like that:

| Id  | Operation                                  | Name       | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
|   0 | SELECT STATEMENT                           |            |      1 |        |      0 |00:00:00.04 |   41770 |
|   1 |  TABLE ACCESS BY GLOBAL INDEX ROWID BATCHED| SALES      |      1 |      1 |      0 |00:00:00.04 |   41770 |
|*  2 |   INDEX FULL SCAN                          | SPARSE_IDX |      1 |      1 |      0 |00:00:00.04 |   41770 |

Predicate Information (identified by operation id):

   2 - filter("SPARSE" IS NOT NULL)

Why did the optimizer choose a full index scan? Actually a full table scan would be much better. The estimate (E-Rows) is correct. The statistcs are current.

Can you guess what is missing from my test case? Hint: I researched the DML on this table and I found an update statement.