I was in a discussion recently about how to estimate the size of a bitmap index before you build it, and why it’s much harder to do this for bitmap indexes than it is for B-tree indexes. Here’s what I wrote in “Practical Oracle 8i”:
An interesting feature of bitmap indexes is that it is rather hard to predict how large the index segment will be. The size of a b-tree index is based very closely on the number of rows and the typical size of the entries in the index column. The size of a bitmap index is dictated by a fairly small number of bit-strings which may have been compressed to some degree depending upon the number of consecutive 1’s and 0’s. To pick an extreme example, imagine a table of one million rows that has one column that may contain one of eight values ‘A’ to ‘H’ say, which has been generated in one of of the two following extreme patterns:
All the rows for a given value appear together, so scanning down the table we get 125,000 rows with ‘A’ followed by 125,000 rows of ‘B’ and so on.
The rows cycle through the values in turn, so scanning down the table we get ‘A’,’B’. . . ‘H’ repeated 125,000 times.
What will the bitmap indexes look like in the two cases?
For the first example, the basic map for the ‘A’ value will be 125,000 one-bits, followed by 875,000 zero bits – which will be trimmed off. Splitting the 125,000 bits into bytes and adding the necessary overhead of about 12% we get an entry for the ‘A’ rows of 18K. A similar argument applies for each of the values ‘B’ to ‘H’, so we get a total index size of around 8 x 18K – giving 156K.
For the second example, the basic map for the ‘A’ value will be a one followed by 7 zeros, repeated 125,000 times. There is no chance of compression here, so the ‘A’ entry will start at 125,000 bytes. Adding the overhead this goes up to 140K, and repeating the argument for the values ‘B’ to ‘H’ we get a total index of 1.12 MB.
This wild variation in size looks like a threat, but to put this into perspective, a standard B-tree index on this column would run to about 12 Mb irrespective of the pattern of the data. It would probably take about ten times as long to build as well.
I wrote up a test case to recreate this model some time ago, so here it is with the results from an instance of 220.127.116.11:
create table t1 as with generator as ( select --+ materialize rownum id from dual connect by level <= 10000 ) select chr(65 + mod(rownum-1,8)) bit_scattered, chr(65+trunc((rownum-1)/125000)) bit_clustered from generator v1, generator v2 where rownum <= 1e6 ; create bitmap index t1_b1_scattered on t1(bit_scattered); create bitmap index t1_b1_clustered on t1(bit_clustered); select index_name, leaf_blocks, 8 * leaf_blocks KB from user_indexes where table_name = 'T1' ; set doc off doc Results from 18.104.22.168 --------------------- INDEX_NAME LEAF_BLOCKS KB -------------------- ----------- ---------- T1_B1_SCATTERED 164 1312 T1_B1_CLUSTERED 24 192 2 rows selected. #
So, no big change there, then.
If you modify the code to create B-tree indexes you’ll find they are 14MB each if you don’t use compression, 12MB if you do.