Apress.Expert.Oracle.Database.Architecture.9i.and.10g.Programming.Techniques.and.Solutions.Sep.2005

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CHAPTER 11 ■ INDEXES
Select count(*)
from T
where gender = 'M'
and location in ( 1, 10, 30 )
and age_group = '41 and over';
select *
from t
where ( ( gender = 'M' and location = 20 )
or ( gender = 'F' and location = 22 ))
and age_group = '18 and under';
select count(*) from t where location in (11,20,30);
select count(*) from t where age_group = '41 and over' and gender = 'F';
You would find that a conventional B*Tree indexing scheme would fail you. If you wanted
to use an index to get the answer, you would need at least three and up to six combinations
of possible B*Tree indexes to access the data via the index. Since any of the three columns or
any subset of the three columns may appear, you would need large concatenated B*Tree
indexes on
• GENDER, LOCATION, AGE_GROUP: For queries that used all three, or GENDER with LOCATION,
or GENDER alone
• LOCATION, AGE_GROUP: For queries that used LOCATION and AGE_GROUP or LOCATION alone
• AGE_GROUP, GENDER: For queries that used AGE_GROUP with GENDER or AGE_GROUP alone
To reduce the amount of data being searched, other permutations might be reasonable as
well, to decrease the size of the index structure being scanned. This is ignoring the fact that a
B*Tree index on such low cardinality data is not a good idea.
Here the bitmap index comes into play. With three small bitmap indexes, one on each of
the individual columns, you will be able to satisfy all of the previous predicates efficiently.
Oracle will simply use the functions AND, OR, and NOT, with the bitmaps of the three indexes
together, to find the solution set for any predicate that references any set of these three
columns. It will take the resulting merged bitmap, convert the 1s into rowids if necessary,
and access the data (if you are just counting rows that match the criteria, Oracle will just
count the 1 bits). Let’s take a look at an example. First, we’ll generate test data that matches
our specified distinct cardinalities—index it and gather statistics. We’ll make use of the
DBMS_RANDOM package to generate random data fitting our distribution:
ops$tkyte@ORA10G> create table t
2 ( gender not null,
3 location not null,
4 age_group not null,
5 data
6 )
7 as
8 select decode( ceil(dbms_random.value(1,2)),