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Bitmap indexes were added to Oracle in version 7.3 of the database. They are currently available with the Oracle Enterprise and Personal Editions, but not the Standard Edition. Bitmap indexes are designed for data warehousing/ad hoc query environments where the full set of queries that may be asked of the data is not totally known at system implementation time. They are specifically not designed for OLTP systems or systems where data is frequently updated by many concurrent sessions. Bitmap indexes are structures that store pointers to many rows with a single index key entry, as compared to a B*Tree structure where there is parity between the index keys and the rows in a table. In a bitmap index, there will be a very small number of index entries, each of which points to many rows. In a conventional B*Tree, one index entry points to a single row. Let s say we are creating a bitmap index on the JOB column in the EMP table as follows: Ops$tkyte@ORA10G> create BITMAP index job_idx on emp(job); Index created. Oracle will store something like what is shown in Table 11-6 in the index. Table 11-6. Representation of How Oracle Would Store the JOB-IDX Bitmap Index
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Value/Row
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ANALYST CLERK MANAGER PRESIDENT SALESMAN
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0 0 1 0 0
1 0 0 0 0
0 0 0 1 0
1 0 0 0 0
0 1 0 0 0
0 1 0 0 0
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0 1 0 0 0
Table 11-6 shows that rows 8, 10, and 13 have the value ANALYST, whereas rows 4, 6, and 7 have the value MANAGER. It also shows us that no rows are null (bitmap indexes store null entries; the lack of a null entry in the index implies there are no null rows). If we wanted to count the rows that have the value MANAGER, the bitmap index would do this very rapidly. If we wanted to find all the rows such that the JOB was CLERK or MANAGER, we could simply combine their bitmaps from the index, as shown in Table 11-7.
CHAPTER 11 INDEXES
Table 11-7. Representation of a Bitwise OR
Value/Row
CLERK MANAGER CLERK or MANAGER
1 0 1
0 0 0
0 0 0
0 1 1
0 0 0
0 1 1
0 1 1
0 0 0
0 0 0
0 0 0
1 0 1
1 0 1
0 0 0
1 0 1
Table 11-7 rapidly shows us that rows 1, 4, 6, 7, 11, 12, and 14 satisfy our criteria. The bitmap Oracle stores with each key value is set up so that each position represents a rowid in the underlying table, if we need to actually retrieve the row for further processing. Queries such as the following: select count(*) from emp where job = 'CLERK' or job = 'MANAGER' will be answered directly from the bitmap index. A query such as this: select * from emp where job = 'CLERK' or job = 'MANAGER' on the other hand, will need to get to the table. Here, Oracle will apply a function to turn the fact that the i th bit is on in a bitmap, into a rowid that can be used to access the table.
When Should You Use a Bitmap Index
Bitmap indexes are most appropriate on low distinct cardinality data (i.e., data with relatively few discrete values when compared to the cardinality of the entire set). It is not really possible to put a value on this in other words, it is difficult to define what low distinct cardinality truly is. In a set of a couple thousand records, 2 would be low distinct cardinality, but 2 would not be low distinct cardinality in a two-row table. In a table of tens or hundreds of millions records, 100,000 could be low distinct cardinality. So, low distinct cardinality is relative to the size of the resultset. This is data where the number of distinct items in the set of rows divided by the number of rows is a small number (near zero). For example, a GENDER column might take on the values M, F, and NULL. If you have a table with 20,000 employee records in it, then you would find that 3/20000 = 0.00015. Likewise, 100,000 unique values out of 10,000,000 results in a ratio of 0.01 again, very small. These columns would be candidates for bitmap indexes. They probably would not be candidates for a having B*Tree indexes, as each of the values would tend to retrieve an extremely large percentage of the table. B*Tree indexes should be selective in general, as outlined earlier. Bitmap indexes should not be selective on the contrary, they should be very unselective in general. Bitmap indexes are extremely useful in environments where you have lots of ad hoc queries, especially queries that reference many columns in an ad hoc fashion or produce aggregations such as COUNT. For example, suppose you have a large table with three columns: GENDER, LOCATION, and AGE_GROUP. In this table, GENDER has a value of M or F, LOCATION can take on the values 1 through 50, and AGE_GROUP is a code representing 18 and under, 19-25, 26-30, 31-40, and 41 and over. You have to support a large number of ad hoc queries that take the following form:
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