注：low-cardinality是指该列或者列的组合具有的不同值的个数较少，即该列有很多重复值。high-cardinality是指该列或者列的组合具有不同的值的个数较多，即该列有很少的重复值。

理解每种索引的适用场合将对性能产生重大影响。

传统观念认为位图索引最适用于拥有很少不同值的列 —- 例如GENDER, MARITAL_STATUS,和RELATION。但是，这种假设是不准确的。实际上，对于大多非频繁更新的并发系统，位图索引也是适用的。事实上，下面将会展示，对以一个具有100%唯一值的列（主键的候选列）来说，位图索引和B树索引一样有效。

本文将提供一些例子以及优化建议，它们对于low-cardinality和high-cardinality上的两种索引都是通用的。这些例子将帮助DBA理解位图索引不是依赖于cardinality而是依赖于程序自身。

索引比较

在唯一列上使用位图索引有一些缺点 — 其中一个是需要足够的空间(Oracle不推荐使用)。但是，位图索引的大小不但与位图索引列的cardinality有关，还与数据的分布有关。因此，GENDER列上的位图索引比B树索引要小，相反，EMPNO上的位图索引比B树索引大的多。但是，相对于OLTP系统来说，决策支持系统只有很少的用户访问，因而对于这些系统，资源不是问题。

为了阐明这个观点，我创建了两个表，test_normal和test_random。用PL/SQL块在test_normal中插入100万条记录，然后在test_random表中随机插入相同的记录。

1. Create table test_normal (empno number(10), ename varchar2(30), sal number(10));
3. Begin
4. For i in 1..1000000
5. Loop
6. Insert into test_normal
7. values(i, dbms_random.string(‘U’,30), dbms_random.value(1000,7000));
8. If mod(i, 10000) = 0 then
9. Commit;
10. End if;
11. End loop;
12. End;
13. /
15. Create table test_random
16. as
17. select /*+ append */ * from test_normal order by dbms_random.random;
19. SQL> select count(*) “Total Rows” from test_normal;
21. Total Rows
22. ———-
23. 1000000
25. Elapsed: 00:00:01.09
27. SQL> select count(distinct empno) “Distinct Values” from test_normal;
29. Distinct Values
30. —————
31. 1000000
33. Elapsed: 00:00:06.09
34. SQL> select count(*) “Total Rows” from test_random;
36. Total Rows
37. ———-
38. 1000000
40. Elapsed: 00:00:03.05
41. SQL> select count(distinct empno) “Distinct Values” from test_random;
43. Distinct Values
44. —————
45. 1000000
47. Elapsed: 00:00:12.07

Create table test_normal (empno number(10), ename varchar2(30), sal number(10)); Begin For i in 1..1000000 Loop Insert into test_normal values(i, dbms_random.string(‘U’,30), dbms_random.value(1000,7000)); If mod(i, 10000) = 0 then Commit; End if; End loop; End; / Create table test_random as select /*+ append */ * from test_normal order by dbms_random.random; SQL> select count(*) “Total Rows” from test_normal; Total Rows ———- 1000000 Elapsed: 00:00:01.09 SQL> select count(distinct empno) “Distinct Values” from test_normal; Distinct Values ————— 1000000 Elapsed: 00:00:06.09 SQL> select count(*) “Total Rows” from test_random; Total Rows ———- 1000000 Elapsed: 00:00:03.05 SQL> select count(distinct empno) “Distinct Values” from test_random; Distinct Values ————— 1000000 Elapsed: 00:00:12.07

注意，test_normal表是组织良好的，test_random表是随机创建的，因此，其中的数据是无组织的。在上面的表中，EMPNO列上的值完全不同，因此可以作为候选主键。如果你把该列定义为主键，oracle将会建立一个B树索引，因为Oracle不支持主键位图索引。

为了分析这些索引的行为，我们执行下面的步骤：

1. 在表test_normal上：
   1. 在EMPNO列上建立一个位图索引，并执行一些相等性查询。
   2. 在EMPNO列上建立一个B树索引，执行一些相等性查询，并且比较获得不同结果集所执行的查询需要的物理I/O和逻辑I/O的次数。
2. 在表test_random表上：
   1. 和1.1相同的步骤
   2. 和1.2相同的步骤
3. 在表test_normal上：
   1. 和1.1相同的步骤，但是执行范围查询。
   2. 和1.2相同的步骤，但是执行范围查询。比较统计结果。
4. 在表test_random表上：
   1. 和3.1相同的步骤。
   2. 和3.2相同的步骤
5. 在表test_normal上：
   1. 在SAL列上建立一个位图索引，并且执行一些相等性查询和范围查询。
   2. 在SAL列上建立一个B树索引，并且执行一些相等性查询和范围查询(和5.1相同的结果集)，比较获取结果执行的I/O次数。
6. 在两个表中添加GENDER列，并且把该列更新为3个可能的值：M(女性), F(男性), null(未知)。根据一些条件更新该列的值。
7. 在该列上建立一个位图索引并且执行一些相等性查询。
8. 在GENDER列上建立一个B树索引并且执行一些相等性查询，和步骤7的结果比较。

步骤1到4涉及一个high-cardinality列（完全不同），步骤5是一个normal-cardinality列，步骤7和8是一个low-cardinality列。

步骤1.1（在表test_normal上）

在该步中，我们在表test_normal上建立一个位图索引，然后检查索引的大小、聚簇因子(clustering factor)和表的大小。然后执行一些相等性查询并且查看使用位图索引时查询需要的I/O次数。

1. SQL> create bitmap index normal_empno_bmx on test_normal(empno);
3. Index created.
5. Elapsed: 00:00:29.06
6. SQL> analyze table test_normal compute statistics for table for all indexes for all indexed columns;
9. Table analyzed.
11. Elapsed: 00:00:19.01
12. SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB”
13. 2 from user_segments
14. 3* where segment_name in (‘TEST_NORMAL’,‘NORMAL_EMPNO_BMX’);
16. SEGMENT_NAME Size in MB
17. ———————————— —————
18. TEST_NORMAL 50
19. NORMAL_EMPNO_BMX 28
21. Elapsed: 00:00:02.00
22. SQL> select index_name, clustering_factor from user_indexes;
24. INDEX_NAME CLUSTERING_FACTOR
25. —————————— ———————————
26. NORMAL_EMPNO_BMX 1000000
28. Elapsed: 00:00:00.00

SQL> create bitmap index normal_empno_bmx on test_normal(empno); Index created. Elapsed: 00:00:29.06 SQL> analyze table test_normal compute statistics for table for all indexes for all indexed columns; Table analyzed. Elapsed: 00:00:19.01 SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB” 2 from user_segments 3* where segment_name in (‘TEST_NORMAL’,’NORMAL_EMPNO_BMX’); SEGMENT_NAME Size in MB ———————————— ————— TEST_NORMAL 50 NORMAL_EMPNO_BMX 28 Elapsed: 00:00:02.00 SQL> select index_name, clustering_factor from user_indexes; INDEX_NAME CLUSTERING_FACTOR —————————— ——————————— NORMAL_EMPNO_BMX 1000000 Elapsed: 00:00:00.00

可以看到，表上索引的大小是28M并且聚簇因子的大小等于表中的行数。现在我们为不同的结果集执行一些相等性查询：

1. SQL> set autotrace only
2. SQL> select * from test_normal where empno=&empno;
3. Enter value for empno: 1000
4. old 1: select * from test_normal where empno=&empno
5. new 1: select * from test_normal where empno=1000
7. Elapsed: 00:00:00.01
9. Execution Plan
10. ———————————————————-
11. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=4 Card=1 Bytes=34)
12. 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=4 Car
13. d=1 Bytes=34)
14. 2 1 BITMAP CONVERSION (TO ROWIDS)
15. 3 2 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_EMPNO_BMX’
17. Statistics
18. ———————————————————-
19. 0 recursive calls
20. 0 db block gets
21. 5 consistent gets
22. 0 physical reads
23. 0 redo size
24. 515 bytes sent via SQL*Net to client
25. 499 bytes received via SQL*Net from client
26. 2 SQL*Net roundtrips to/from client
27. 0 sorts (memory)
28. 0 sorts (disk)
29. 1 rows processed

SQL> set autotrace only SQL> select * from test_normal where empno=&empno; Enter value for empno: 1000 old 1: select * from test_normal where empno=&empno new 1: select * from test_normal where empno=1000 Elapsed: 00:00:00.01 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=4 Card=1 Bytes=34) 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=4 Car d=1 Bytes=34) 2 1 BITMAP CONVERSION (TO ROWIDS) 3 2 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_EMPNO_BMX’ Statistics ———————————————————- 0 recursive calls 0 db block gets 5 consistent gets 0 physical reads 0 redo size 515 bytes sent via SQL*Net to client 499 bytes received via SQL*Net from client 2 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 1 rows processed

步骤1.2（在表test_normal上）

现在删除表中EMPNO列上的位图索引并创建一个B树索引。像前面一样我们查看索引的大小、聚簇因子的大小并且执行相同的查询，比较I/O的次数。

1. SQL> drop index NORMAL_EMPNO_BMX;
3. Index dropped.
5. SQL> create index normal_empno_idx on test_normal(empno);
7. Index created.
9. SQL> analyze table test_normal compute statistics for table for all indexes for all indexed columns;
11. Table analyzed.
13. SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB”
14. 2 from user_segments
15. 3 where segment_name in (‘TEST_NORMAL’,‘NORMAL_EMPNO_IDX’);
17. SEGMENT_NAME Size in MB
18. ———————————- —————
19. TEST_NORMAL 50
20. NORMAL_EMPNO_IDX 18
22. SQL> select index_name, clustering_factor from user_indexes;
24. INDEX_NAME CLUSTERING_FACTOR
25. ———————————- ———————————-
26. NORMAL_EMPNO_IDX 6210

SQL> drop index NORMAL_EMPNO_BMX; Index dropped. SQL> create index normal_empno_idx on test_normal(empno); Index created. SQL> analyze table test_normal compute statistics for table for all indexes for all indexed columns; Table analyzed. SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB” 2 from user_segments 3 where segment_name in (‘TEST_NORMAL’,’NORMAL_EMPNO_IDX’); SEGMENT_NAME Size in MB ———————————- ————— TEST_NORMAL 50 NORMAL_EMPNO_IDX 18 SQL> select index_name, clustering_factor from user_indexes; INDEX_NAME CLUSTERING_FACTOR ———————————- ———————————- NORMAL_EMPNO_IDX 6210

很明显，在该表的EMPNO列上，B树索引比位图索引要小。B树索引上的聚簇因子接近于表中的数据块数；因此B树索引对于范围查询更有效。

现在，我们使用B树索引执行相同的查询。

1. SQL> set autot trace
2. SQL> select * from test_normal where empno=&empno;
3. Enter value for empno: 1000
4. old 1: select * from test_normal where empno=&empno
5. new 1: select * from test_normal where empno=1000
7. Elapsed: 00:00:00.01
9. Execution Plan
10. ———————————————————-
11. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=4 Card=1 Bytes=34)
12. 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=4 Car
13. d=1 Bytes=34)
14. 2 1 INDEX (RANGE SCAN) OF ‘NORMAL_EMPNO_IDX’ (NON-UNIQUE) (C
15. ost=3 Card=1)
17. Statistics
18. ———————————————————-
19. 29 recursive calls
20. 0 db block gets
21. 5 consistent gets
22. 0 physical reads
23. 0 redo size
24. 515 bytes sent via SQL*Net to client
25. 499 bytes received via SQL*Net from client
26. 2 SQL*Net roundtrips to/from client
27. 0 sorts (memory)
28. 0 sorts (disk)
29. 1 rows processed

SQL> set autot trace SQL> select * from test_normal where empno=&empno; Enter value for empno: 1000 old 1: select * from test_normal where empno=&empno new 1: select * from test_normal where empno=1000 Elapsed: 00:00:00.01 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=4 Card=1 Bytes=34) 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=4 Car d=1 Bytes=34) 2 1 INDEX (RANGE SCAN) OF ‘NORMAL_EMPNO_IDX’ (NON-UNIQUE) (C ost=3 Card=1) Statistics ———————————————————- 29 recursive calls 0 db block gets 5 consistent gets 0 physical reads 0 redo size 515 bytes sent via SQL*Net to client 499 bytes received via SQL*Net from client 2 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 1 rows processed

可以看到，对于相同的结果集，在唯一列上的位图索引和B树索引需要相同的物理和逻辑读取次数。

步骤2.1（在表test_random上）

现在，在test_random表上执行相同的操作：

1. SQL> create bitmap index random_empno_bmx on test_random(empno);
3. Index created.
5. SQL> analyze table test_random compute statistics for table for all indexes for all indexed columns;
7. Table analyzed.
9. SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB”
10. 2 from user_segments
11. 3* where segment_name in (‘TEST_RANDOM’,‘RANDOM_EMPNO_BMX’);
13. SEGMENT_NAME Size in MB
14. ———————————— —————
15. TEST_RANDOM 50
16. RANDOM_EMPNO_BMX 28
18. SQL> select index_name, clustering_factor from user_indexes;
20. INDEX_NAME CLUSTERING_FACTOR
21. —————————— ———————————
22. RANDOM_EMPNO_BMX 1000000

SQL> create bitmap index random_empno_bmx on test_random(empno); Index created. SQL> analyze table test_random compute statistics for table for all indexes for all indexed columns; Table analyzed. SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB” 2 from user_segments 3* where segment_name in (‘TEST_RANDOM’,’RANDOM_EMPNO_BMX’); SEGMENT_NAME Size in MB ———————————— ————— TEST_RANDOM 50 RANDOM_EMPNO_BMX 28 SQL> select index_name, clustering_factor from user_indexes; INDEX_NAME CLUSTERING_FACTOR —————————— ——————————— RANDOM_EMPNO_BMX 1000000

再次，索引上的统计结果（大小和聚簇因子）和在表test_normal中是相同的：

1. SQL> select * from test_random where empno=&empno;
2. Enter value for empno: 1000
3. old 1: select * from test_random where empno=&empno
4. new 1: select * from test_random where empno=1000
6. Elapsed: 00:00:00.01
8. Execution Plan
9. ———————————————————-
10. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=4 Card=1 Bytes=34)
11. 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_RANDOM’ (Cost=4 Card=1 Bytes=34)
12. 2 1 BITMAP CONVERSION (TO ROWIDS)
13. 3 2 BITMAP INDEX (SINGLE VALUE) OF ‘RANDOM_EMPNO_BMX’
15. Statistics
16. ———————————————————-
17. 0 recursive calls
18. 0 db block gets
19. 5 consistent gets
20. 0 physical reads
21. 0 redo size
22. 515 bytes sent via SQL*Net to client
23. 499 bytes received via SQL*Net from client
24. 2 SQL*Net roundtrips to/from client
25. 0 sorts (memory)
26. 0 sorts (disk)
27. 1 rows processed

SQL> select * from test_random where empno=&empno; Enter value for empno: 1000 old 1: select * from test_random where empno=&empno new 1: select * from test_random where empno=1000 Elapsed: 00:00:00.01 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=4 Card=1 Bytes=34) 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_RANDOM’ (Cost=4 Card=1 Bytes=34) 2 1 BITMAP CONVERSION (TO ROWIDS) 3 2 BITMAP INDEX (SINGLE VALUE) OF ‘RANDOM_EMPNO_BMX’ Statistics ———————————————————- 0 recursive calls 0 db block gets 5 consistent gets 0 physical reads 0 redo size 515 bytes sent via SQL*Net to client 499 bytes received via SQL*Net from client 2 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 1 rows processed

步骤2.2（在表test_random上）

现在，和步骤1.2一样，我们删除EMPNO列上的位图索引并且创建一个B树索引。

1. SQL> drop index RANDOM_EMPNO_BMX;
3. Index dropped.
5. SQL> create index random_empno_idx on test_random(empno);
7. Index created.
9. SQL> analyze table test_random compute statistics for table for all indexes for all indexed columns;
11. Table analyzed.
13. SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB”
14. 2 from user_segments
15. 3 where segment_name in (‘TEST_RANDOM’,‘RANDOM_EMPNO_IDX’);
17. SEGMENT_NAME Size in MB
18. ———————————- —————
19. TEST_RANDOM 50
20. RANDOM_EMPNO_IDX 18
22. SQL> select index_name, clustering_factor from user_indexes;
24. INDEX_NAME CLUSTERING_FACTOR
25. ———————————- ———————————-
26. RANDOM_EMPNO_IDX 999830

SQL> drop index RANDOM_EMPNO_BMX; Index dropped. SQL> create index random_empno_idx on test_random(empno); Index created. SQL> analyze table test_random compute statistics for table for all indexes for all indexed columns; Table analyzed. SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB” 2 from user_segments 3 where segment_name in (‘TEST_RANDOM’,’RANDOM_EMPNO_IDX’); SEGMENT_NAME Size in MB ———————————- ————— TEST_RANDOM 50 RANDOM_EMPNO_IDX 18 SQL> select index_name, clustering_factor from user_indexes; INDEX_NAME CLUSTERING_FACTOR ———————————- ———————————- RANDOM_EMPNO_IDX 999830

该表的索引大小和表test_normal是一样的，但是聚簇因子更接近于行数，这就使得该索引对于范围查询不再高效。该聚簇因子不影响相等性查询，因为该列的值是唯一的，每个键对应1行记录。

现在，在相同的结果集上执行相等性查询。

1. SQL> select * from test_random where empno=&empno;
2. Enter value for empno: 1000
3. old 1: select * from test_random where empno=&empno
4. new 1: select * from test_random where empno=1000
6. Elapsed: 00:00:00.01
8. Execution Plan
9. ———————————————————-
10. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=4 Card=1 Bytes=34)
11. 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_RANDOM’ (Cost=4 Card=1 Bytes=34)
12. 2 1 INDEX (RANGE SCAN) OF ‘RANDOM_EMPNO_IDX’ (NON-UNIQUE) (Cost=3 Card=1)
14. Statistics
15. ———————————————————-
16. 0 recursive calls
17. 0 db block gets
18. 5 consistent gets
19. 0 physical reads
20. 0 redo size
21. 515 bytes sent via SQL*Net to client
22. 499 bytes received via SQL*Net from client
23. 2 SQL*Net roundtrips to/from client
24. 0 sorts (memory)
25. 0 sorts (disk)
26. 1 rows processed

SQL> select * from test_random where empno=&empno; Enter value for empno: 1000 old 1: select * from test_random where empno=&empno new 1: select * from test_random where empno=1000 Elapsed: 00:00:00.01 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=4 Card=1 Bytes=34) 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_RANDOM’ (Cost=4 Card=1 Bytes=34) 2 1 INDEX (RANGE SCAN) OF ‘RANDOM_EMPNO_IDX’ (NON-UNIQUE) (Cost=3 Card=1) Statistics ———————————————————- 0 recursive calls 0 db block gets 5 consistent gets 0 physical reads 0 redo size 515 bytes sent via SQL*Net to client 499 bytes received via SQL*Net from client 2 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 1 rows processed

再次表明，结果和步骤1.1和1.2几乎相同。对于唯一列来说，数据分布不影响逻辑和物理I/O。

步骤3.1（在表test_normal上）

在该步中，我们将创建一个位图索引。我们知道索引的聚簇因子大小和表中的行数相同。现在我们执行一些范围查询。

1. SQL> select * from test_normal where empno between &range1 and &range2;
2. Enter value for range1: 1
3. Enter value for range2: 2300
4. old 1: select * from test_normal where empno between &range1 and &range2
5. new 1: select * from test_normal where empno between 1 and 2300
7. 2300 rows selected.
9. Elapsed: 00:00:00.03
11. Execution Plan
12. ———————————————————-
13. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=451 Card=2299 Bytes=78166)
14. 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=451 Card=2299 Bytes=78166)
15. 2 1 BITMAP CONVERSION (TO ROWIDS)
16. 3 2 BITMAP INDEX (RANGE SCAN) OF ‘NORMAL_EMPNO_BMX’
18. Statistics
19. ———————————————————-
20. 0 recursive calls
21. 0 db block gets
22. 331 consistent gets
23. 0 physical reads
24. 0 redo size
25. 111416 bytes sent via SQL*Net to client
26. 2182 bytes received via SQL*Net from client
27. 155 SQL*Net roundtrips to/from client
28. 0 sorts (memory)
29. 0 sorts (disk)
30. 2300 rows processed

SQL> select * from test_normal where empno between &range1 and &range2; Enter value for range1: 1 Enter value for range2: 2300 old 1: select * from test_normal where empno between &range1 and &range2 new 1: select * from test_normal where empno between 1 and 2300 2300 rows selected. Elapsed: 00:00:00.03 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=451 Card=2299 Bytes=78166) 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=451 Card=2299 Bytes=78166) 2 1 BITMAP CONVERSION (TO ROWIDS) 3 2 BITMAP INDEX (RANGE SCAN) OF ‘NORMAL_EMPNO_BMX’ Statistics ———————————————————- 0 recursive calls 0 db block gets 331 consistent gets 0 physical reads 0 redo size 111416 bytes sent via SQL*Net to client 2182 bytes received via SQL*Net from client 155 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 2300 rows processed

步骤3.2（在表test_normal上）

该步中，我们在test_normal的B树索引上执行查询。

1. SQL> select * from test_normal where empno between &range1 and &range2;
2. Enter value for range1: 1
3. Enter value for range2: 2300
4. old 1: select * from test_normal where empno between &range1 and &range2
5. new 1: select * from test_normal where empno between 1 and 2300
7. 2300 rows selected.
9. Elapsed: 00:00:00.02
11. Execution Plan
12. ———————————————————-
13. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=23 Card=2299 Bytes=78166)
14. 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=23 Card=2299 Bytes=78166)
15. 2 1 INDEX (RANGE SCAN) OF ‘NORMAL_EMPNO_IDX’ (NON-UNIQUE) (Cost=8 Card=2299)
17. Statistics
18. ———————————————————-
19. 0 recursive calls
20. 0 db block gets
21. 329 consistent gets
22. 15 physical reads
23. 0 redo size
24. 111416 bytes sent via SQL*Net to client
25. 2182 bytes received via SQL*Net from client
26. 155 SQL*Net roundtrips to/from client
27. 0 sorts (memory)
28. 0 sorts (disk)
29. 2300 rows processed

SQL> select * from test_normal where empno between &range1 and &range2; Enter value for range1: 1 Enter value for range2: 2300 old 1: select * from test_normal where empno between &range1 and &range2 new 1: select * from test_normal where empno between 1 and 2300 2300 rows selected. Elapsed: 00:00:00.02 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=23 Card=2299 Bytes=78166) 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=23 Card=2299 Bytes=78166) 2 1 INDEX (RANGE SCAN) OF ‘NORMAL_EMPNO_IDX’ (NON-UNIQUE) (Cost=8 Card=2299) Statistics ———————————————————- 0 recursive calls 0 db block gets 329 consistent gets 15 physical reads 0 redo size 111416 bytes sent via SQL*Net to client 2182 bytes received via SQL*Net from client 155 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 2300 rows processed

在不同的范围上执行的查询结果如下：

可以看到，在两种索引上需要的逻辑和物理IO基本上是相同的。最后一个范围(984888-1000000)差不多返回了15,000行，是所有范围查询中最大的。当我们执行全表扫描时（通过/*+ full(test_normal) */ )，物理和逻辑IO的次数是7239和5663.

步骤4.1（在表test_random上）

在该步中，我们将在表test_random的位图索引上执行范围查询，在这儿，你将看到聚簇因子的影响。

1. SQL>select * from test_random where empno between &range1 and &range2;
2. Enter value for range1: 1
3. Enter value for range2: 2300
4. old 1: select * from test_random where empno between &range1 and &range2
5. new 1: select * from test_random where empno between 1 and 2300
7. 2300 rows selected.
9. Elapsed: 00:00:08.01
11. Execution Plan
12. ———————————————————-
13. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=453 Card=2299 Bytes=78166)
14. 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_RANDOM’ (Cost=453 Card=2299 Bytes=78166)
15. 2 1 BITMAP CONVERSION (TO ROWIDS)
16. 3 2 BITMAP INDEX (RANGE SCAN) OF ‘RANDOM_EMPNO_BMX’
18. Statistics
19. ———————————————————-
20. 0 recursive calls
21. 0 db block gets
22. 2463 consistent gets
23. 1200 physical reads
24. 0 redo size
25. 111416 bytes sent via SQL*Net to client
26. 2182 bytes received via SQL*Net from client
27. 155 SQL*Net roundtrips to/from client
28. 0 sorts (memory)
29. 0 sorts (disk)
30. 2300 rows processed

SQL>select * from test_random where empno between &range1 and &range2; Enter value for range1: 1 Enter value for range2: 2300 old 1: select * from test_random where empno between &range1 and &range2 new 1: select * from test_random where empno between 1 and 2300 2300 rows selected. Elapsed: 00:00:08.01 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=453 Card=2299 Bytes=78166) 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_RANDOM’ (Cost=453 Card=2299 Bytes=78166) 2 1 BITMAP CONVERSION (TO ROWIDS) 3 2 BITMAP INDEX (RANGE SCAN) OF ‘RANDOM_EMPNO_BMX’ Statistics ———————————————————- 0 recursive calls 0 db block gets 2463 consistent gets 1200 physical reads 0 redo size 111416 bytes sent via SQL*Net to client 2182 bytes received via SQL*Net from client 155 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 2300 rows processed

步骤4.2（在表test_random上）

在该步中，我们将在test_random的B树索引上执行范围查询。回想一下，该索引上的聚簇因子接近于表中记录的行数。下面是优化器的输出：

1. SQL> select * from test_random where empno between &range1 and &range2;
2. Enter value for range1: 1
3. Enter value for range2: 2300
4. old 1: select * from test_random where empno between &range1 and &range2
5. new 1: select * from test_random where empno between 1 and 2300
7. 2300 rows selected.
9. Elapsed: 00:00:03.04
11. Execution Plan
12. ———————————————————-
13. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=613 Card=2299 Bytes=78166)
14. 1 0 TABLE ACCESS (FULL) OF ‘TEST_RANDOM’ (Cost=613 Card=2299 Bytes=78166)
16. Statistics
17. ———————————————————-
18. 0 recursive calls
19. 0 db block gets
20. 6415 consistent gets
21. 4910 physical reads
22. 0 redo size
23. 111416 bytes sent via SQL*Net to client
24. 2182 bytes received via SQL*Net from client
25. 155 SQL*Net roundtrips to/from client
26. 0 sorts (memory)
27. 0 sorts (disk)
28. 2300 rows processed

SQL> select * from test_random where empno between &range1 and &range2; Enter value for range1: 1 Enter value for range2: 2300 old 1: select * from test_random where empno between &range1 and &range2 new 1: select * from test_random where empno between 1 and 2300 2300 rows selected. Elapsed: 00:00:03.04 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=613 Card=2299 Bytes=78166) 1 0 TABLE ACCESS (FULL) OF ‘TEST_RANDOM’ (Cost=613 Card=2299 Bytes=78166) Statistics ———————————————————- 0 recursive calls 0 db block gets 6415 consistent gets 4910 physical reads 0 redo size 111416 bytes sent via SQL*Net to client 2182 bytes received via SQL*Net from client 155 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 2300 rows processed

因为聚簇因子的缘故，优化器选择了全表扫描而不是使用索引：

仅对于最后一个范围（984888-1000000），对于位图索引优化器选择了全表扫描。然而，对于B树索引，全部使用全表扫描。引起这种差异的原因是聚簇因子：优化器在产生执行计划时不考虑位图索引的聚簇因子，但是对于B树索引来说，则需要 考虑聚簇因子。在上面的情况中，位图索引比B树索引更有效。

下面的步骤揭示了这些索引更有趣的方面。

步骤5.1（在表test_normal上）

在表test_normal的SAL列上建立一个位图索引，该列拥有普通的cardinality。

1. SQL> create bitmap index normal_sal_bmx on test_normal(sal);
3. Index created.
5. SQL> analyze table test_normal compute statistics for table for all indexes for all indexed columns;
7. Table analyzed.

SQL> create bitmap index normal_sal_bmx on test_normal(sal); Index created. SQL> analyze table test_normal compute statistics for table for all indexes for all indexed columns; Table analyzed.

得到索引的大小和聚簇因子：

1. SQL>select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB”
2. 2* from user_segments
3. 3* where segment_name in (‘TEST_NORMAL’,‘NORMAL_SAL_BMX’);
5. SEGMENT_NAME Size in MB
6. —————————— ————–
7. TEST_NORMAL 50
8. NORMAL_SAL_BMX 4
10. SQL> select index_name, clustering_factor from user_indexes;
12. INDEX_NAME CLUSTERING_FACTOR
13. —————————— ———————————-
14. NORMAL_SAL_BMX 6001

SQL>select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB” 2* from user_segments 3* where segment_name in (‘TEST_NORMAL’,’NORMAL_SAL_BMX’); SEGMENT_NAME Size in MB —————————— ————– TEST_NORMAL 50 NORMAL_SAL_BMX 4 SQL> select index_name, clustering_factor from user_indexes; INDEX_NAME CLUSTERING_FACTOR —————————— ———————————- NORMAL_SAL_BMX 6001

下面执行查询，首先执行相等性查询：

1. SQL> set autot trace
2. SQL> select * from test_normal where sal=&sal;
3. Enter value for sal: 1869
4. old 1: select * from test_normal where sal=&sal
5. new 1: select * from test_normal where sal=1869
7. 164 rows selected.
9. Elapsed: 00:00:00.08
11. Execution Plan
12. ———————————————————-
13. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=39 Card=168 Bytes=4032)
14. 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=39 Card=168 Bytes=4032)
15. 2 1 BITMAP CONVERSION (TO ROWIDS)
16. 3 2 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’
18. Statistics
19. ———————————————————-
20. 0 recursive calls
21. 0 db block gets
22. 165 consistent gets
23. 0 physical reads
24. 0 redo size
25. 8461 bytes sent via SQL*Net to client
26. 609 bytes received via SQL*Net from client
27. 12 SQL*Net roundtrips to/from client
28. 0 sorts (memory)
29. 0 sorts (disk)
30. 164 rows processed

SQL> set autot trace SQL> select * from test_normal where sal=&sal; Enter value for sal: 1869 old 1: select * from test_normal where sal=&sal new 1: select * from test_normal where sal=1869 164 rows selected. Elapsed: 00:00:00.08 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=39 Card=168 Bytes=4032) 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=39 Card=168 Bytes=4032) 2 1 BITMAP CONVERSION (TO ROWIDS) 3 2 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’ Statistics ———————————————————- 0 recursive calls 0 db block gets 165 consistent gets 0 physical reads 0 redo size 8461 bytes sent via SQL*Net to client 609 bytes received via SQL*Net from client 12 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 164 rows processed

接下来是范围查询：

1. SQL> select * from test_normal where sal between &sal1 and &sal2;
2. Enter value for sal1: 1500
3. Enter value for sal2: 2000
4. old 1: select * from test_normal where sal between &sal1 and &sal2
5. new 1: select * from test_normal where sal between 1500 and 2000
7. 83743 rows selected.
9. Elapsed: 00:00:05.00
11. Execution Plan
12. ———————————————————-
13. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=601 Card=83376 Bytes
14. =2001024)
15. 1 0 TABLE ACCESS (FULL) OF ‘TEST_NORMAL’ (Cost=601 Card=83376
16. Bytes=2001024)
18. Statistics
19. ———————————————————-
20. 0 recursive calls
21. 0 db block gets
22. 11778 consistent gets
23. 5850 physical reads
24. 0 redo size
25. 4123553 bytes sent via SQL*Net to client
26. 61901 bytes received via SQL*Net from client
27. 5584 SQL*Net roundtrips to/from client
28. 0 sorts (memory)
29. 0 sorts (disk)
30. 83743 rows processed

SQL> select * from test_normal where sal between &sal1 and &sal2; Enter value for sal1: 1500 Enter value for sal2: 2000 old 1: select * from test_normal where sal between &sal1 and &sal2 new 1: select * from test_normal where sal between 1500 and 2000 83743 rows selected. Elapsed: 00:00:05.00 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=601 Card=83376 Bytes =2001024) 1 0 TABLE ACCESS (FULL) OF ‘TEST_NORMAL’ (Cost=601 Card=83376 Bytes=2001024) Statistics ———————————————————- 0 recursive calls 0 db block gets 11778 consistent gets 5850 physical reads 0 redo size 4123553 bytes sent via SQL*Net to client 61901 bytes received via SQL*Net from client 5584 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 83743 rows processed

现在，删除test_normal上的位图索引并且建立一个B树索引。

1. SQL> create index normal_sal_idx on test_normal(sal);
3. Index created.
5. SQL> analyze table test_normal compute statistics for table for all indexes for all indexed columns;
7. Table analyzed.

SQL> create index normal_sal_idx on test_normal(sal); Index created. SQL> analyze table test_normal compute statistics for table for all indexes for all indexed columns; Table analyzed.

查看索引大小和聚簇因子：

1. SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB”
2. 2 from user_segments
3. 3 where segment_name in (‘TEST_NORMAL’,‘NORMAL_SAL_IDX’);
5. SEGMENT_NAME Size in MB
6. —————————— —————
7. TEST_NORMAL 50
8. NORMAL_SAL_IDX 17
10. SQL> select index_name, clustering_factor from user_indexes;
12. INDEX_NAME CLUSTERING_FACTOR
13. —————————— ———————————-
14. NORMAL_SAL_IDX 986778

SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB” 2 from user_segments 3 where segment_name in (‘TEST_NORMAL’,’NORMAL_SAL_IDX’); SEGMENT_NAME Size in MB —————————— ————— TEST_NORMAL 50 NORMAL_SAL_IDX 17 SQL> select index_name, clustering_factor from user_indexes; INDEX_NAME CLUSTERING_FACTOR —————————— ———————————- NORMAL_SAL_IDX 986778

从上表可以看出，B树索引大于相同列上的位图索引，它的聚簇因子接近于表中的行数。

现在，先执行相等性查询：

1. SQL> set autot trace
2. SQL> select * from test_normal where sal=&sal;
3. Enter value for sal: 1869
4. old 1: select * from test_normal where sal=&sal
5. new 1: select * from test_normal where sal=1869
7. 164 rows selected.
9. Elapsed: 00:00:00.01
11. Execution Plan
12. ———————————————————-
13. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=169 Card=168 Bytes=4032)
14. 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=169 Card=168 Bytes=4032)
15. 2 1 INDEX (RANGE SCAN) OF ‘NORMAL_SAL_IDX’ (NON-UNIQUE) (Cost=3 Card=168)
17. Statistics
18. ———————————————————-
19. 0 recursive calls
20. 0 db block gets
21. 177 consistent gets
22. 0 physical reads
23. 0 redo size
24. 8461 bytes sent via SQL*Net to client
25. 609 bytes received via SQL*Net from client
26. 12 SQL*Net roundtrips to/from client
27. 0 sorts (memory)
28. 0 sorts (disk)
29. 164 rows processed

SQL> set autot trace SQL> select * from test_normal where sal=&sal; Enter value for sal: 1869 old 1: select * from test_normal where sal=&sal new 1: select * from test_normal where sal=1869 164 rows selected. Elapsed: 00:00:00.01 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=169 Card=168 Bytes=4032) 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=169 Card=168 Bytes=4032) 2 1 INDEX (RANGE SCAN) OF ‘NORMAL_SAL_IDX’ (NON-UNIQUE) (Cost=3 Card=168) Statistics ———————————————————- 0 recursive calls 0 db block gets 177 consistent gets 0 physical reads 0 redo size 8461 bytes sent via SQL*Net to client 609 bytes received via SQL*Net from client 12 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 164 rows processed

接下来是范围查询：

1. SQL> select * from test_normal where sal between &sal1 and &sal2;
2. Enter value for sal1: 1500
3. Enter value for sal2: 2000
4. old 1: select * from test_normal where sal between &sal1 and &sal2
5. new 1: select * from test_normal where sal between 1500 and 2000
7. 83743 rows selected.
9. Elapsed: 00:00:04.03
11. Execution Plan
12. ———————————————————-
13. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=601 Card=83376 Bytes
14. =2001024)
15. 1 0 TABLE ACCESS (FULL) OF ‘TEST_NORMAL’ (Cost=601 Card=83376
16. Bytes=2001024)
19. Statistics
20. ———————————————————-
21. 0 recursive calls
22. 0 db block gets
23. 11778 consistent gets
24. 3891 physical reads
25. 0 redo size
26. 4123553 bytes sent via SQL*Net to client
27. 61901 bytes received via SQL*Net from client
28. 5584 SQL*Net roundtrips to/from client
29. 0 sorts (memory)
30. 0 sorts (disk)
31. 83743 rows processed

SQL> select * from test_normal where sal between &sal1 and &sal2; Enter value for sal1: 1500 Enter value for sal2: 2000 old 1: select * from test_normal where sal between &sal1 and &sal2 new 1: select * from test_normal where sal between 1500 and 2000 83743 rows selected. Elapsed: 00:00:04.03 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=601 Card=83376 Bytes =2001024) 1 0 TABLE ACCESS (FULL) OF ‘TEST_NORMAL’ (Cost=601 Card=83376 Bytes=2001024) Statistics ———————————————————- 0 recursive calls 0 db block gets 11778 consistent gets 3891 physical reads 0 redo size 4123553 bytes sent via SQL*Net to client 61901 bytes received via SQL*Net from client 5584 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 83743 rows processed

在不同的数据集上执行查询的结果如下，可以看出逻辑和物理I/O的次数基本上是相同的。

对于范围查询，优化器选择了全表扫描，根本没有使用索引。但是对于相等性查询，优化器使用了索引。再次，逻辑和物理I/O是相同的。

因此，可以得出结论，对于一个具有normal-cardinality的列来说，优化器对于两种类型的索引的选择是相同的，并且没有明显的I/O差异。

步骤6（增加GENDER列）

在测试low-cardinality列之前，我们先增加一个GENDER列并且把它的值更新成M,F或者null。

1. SQL> alter table test_normal add GENDER varchar2(1);
3. Table altered.
5. SQL> select GENDER, count(*) from test_normal group by GENDER;
7. S COUNT(*)
8. – ———-
9. F 333769
10. M 499921
11. 166310
13. 3 rows selected.

SQL> alter table test_normal add GENDER varchar2(1); Table altered. SQL> select GENDER, count(*) from test_normal group by GENDER; S COUNT(*) – ———- F 333769 M 499921 166310 3 rows selected.

该列上位图索引的大小大约为570KB，如下表所示：

1. SQL> create bitmap index normal_GENDER_bmx on test_normal(GENDER);
3. Index created.
5. Elapsed: 00:00:02.08
7. SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB”
8. 2 from user_segments
9. 3 where segment_name in (‘TEST_NORMAL’,‘NORMAL_GENDER_BMX’);
11. SEGMENT_NAME Size in MB
12. —————————— —————
13. TEST_NORMAL 50
14. NORMAL_GENDER_BMX .5625
16. 2 rows selected.

SQL> create bitmap index normal_GENDER_bmx on test_normal(GENDER); Index created. Elapsed: 00:00:02.08 SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB” 2 from user_segments 3 where segment_name in (‘TEST_NORMAL’,’NORMAL_GENDER_BMX’); SEGMENT_NAME Size in MB —————————— ————— TEST_NORMAL 50 NORMAL_GENDER_BMX .5625 2 rows selected.

相对而言，改列上的B树索引的大小为13M，比位图索引大的多。

1. SQL> create index normal_GENDER_idx on test_normal(GENDER);
3. Index created.
5. SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB”
6. 2 from user_segments
7. 3 where segment_name in (‘TEST_NORMAL’,‘NORMAL_GENDER_IDX’);
9. SEGMENT_NAME Size in MB
10. —————————— —————
11. TEST_NORMAL 50
12. NORMAL_GENDER_IDX 13
14. 2 rows selected.

SQL> create index normal_GENDER_idx on test_normal(GENDER); Index created. SQL> select substr(segment_name,1,30) segment_name, bytes/1024/1024 “Size in MB” 2 from user_segments 3 where segment_name in (‘TEST_NORMAL’,’NORMAL_GENDER_IDX’); SEGMENT_NAME Size in MB —————————— ————— TEST_NORMAL 50 NORMAL_GENDER_IDX 13 2 rows selected.

现在，执行相等性查询，优化器将不使用该索引，不论是位图索引还是B树索引，它将使用全部扫描。

1. SQL> select * from test_normal where GENDER is null;
3. 166310 rows selected.
5. Elapsed: 00:00:06.08
7. Execution Plan
8. ———————————————————-
9. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=601 Card=166310 Bytes=4157750)
10. 1 0 TABLE ACCESS (FULL) OF ‘TEST_NORMAL’ (Cost=601 Card=166310 Bytes=4157750)
12. SQL> select * from test_normal where GENDER=‘M’;
14. 499921 rows selected.
16. Elapsed: 00:00:16.07
18. Execution Plan
19. ———————————————————-
20. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=601 Card=499921 Bytes=12498025)
21. 1 0 TABLE ACCESS (FULL) OF ‘TEST_NORMAL’ (Cost=601 Card=499921Bytes=12498025)
23. SQL>select * from test_normal where GENDER=‘F’
24. /
26. 333769 rows selected.
28. Elapsed: 00:00:12.02
30. Execution Plan
31. ———————————————————-
32. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=601 Card=333769 Byte
33. s=8344225)
34. 1 0 TABLE ACCESS (FULL) OF ‘TEST_NORMAL’ (Cost=601 Card=333769
35. Bytes=8344225)

SQL> select * from test_normal where GENDER is null; 166310 rows selected. Elapsed: 00:00:06.08 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=601 Card=166310 Bytes=4157750) 1 0 TABLE ACCESS (FULL) OF ‘TEST_NORMAL’ (Cost=601 Card=166310 Bytes=4157750) SQL> select * from test_normal where GENDER=’M’; 499921 rows selected. Elapsed: 00:00:16.07 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=601 Card=499921 Bytes=12498025) 1 0 TABLE ACCESS (FULL) OF ‘TEST_NORMAL’ (Cost=601 Card=499921Bytes=12498025) SQL>select * from test_normal where GENDER=’F’ / 333769 rows selected. Elapsed: 00:00:12.02 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=601 Card=333769 Byte s=8344225) 1 0 TABLE ACCESS (FULL) OF ‘TEST_NORMAL’ (Cost=601 Card=333769 Bytes=8344225)

结论

现在我们了解了优化器对于这些技术做出的反应，现在我们来看对于位图索引和B树索引最适合的程序。

保持GENDER列上的位图索引，在SAL列上再建立一个位图索引然后执行一些查询。对这些列上的B树索引执行同样的查询。

在表test_normal中，你需要所有工资等于下列值的所有女性雇员的雇员号码：

1000
1500
2000
2500
3000
3500
4000
4500

因此：

1. SQL>select * from test_normal
2. where sal in (1000,1500,2000,2500,3000,3500,4000,4500,5000) and GENDER=‘M’;

SQL>select * from test_normal where sal in (1000,1500,2000,2500,3000,3500,4000,4500,5000) and GENDER=’M’;

这是一个典型的数据仓库查询，绝对不要在OLTP系统中执行该查询。下面是两列上具有位图索引时的结果：

1. SQL>select * from test_normal
2. where sal in (1000,1500,2000,2500,3000,3500,4000,4500,5000) and GENDER=‘M’;
4. 1453 rows selected.
6. Elapsed: 00:00:02.03
8. Execution Plan
9. ———————————————————-
10. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=198 Card=754 Bytes=18850)
11. 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=198 Card=754 Bytes=18850)
12. 2 1 BITMAP CONVERSION (TO ROWIDS)
13. 3 2 BITMAP AND
14. 4 3 BITMAP OR
15. 5 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’
16. 6 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’
17. 7 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’
18. 8 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’
19. 9 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’
20. 10 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’
21. 11 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’
22. 12 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’
23. 13 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’
24. 14 3 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_GENDER_BMX’
26. Statistics
27. ———————————————————-
28. 0 recursive calls
29. 0 db block gets
30. 1353 consistent gets
31. 920 physical reads
32. 0 redo size
33. 75604 bytes sent via SQL*Net to client
34. 1555 bytes received via SQL*Net from client
35. 98 SQL*Net roundtrips to/from client
36. 0 sorts (memory)
37. 0 sorts (disk)
38. 1453 rows processed

SQL>select * from test_normal where sal in (1000,1500,2000,2500,3000,3500,4000,4500,5000) and GENDER=’M’; 1453 rows selected. Elapsed: 00:00:02.03 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=198 Card=754 Bytes=18850) 1 0 TABLE ACCESS (BY INDEX ROWID) OF ‘TEST_NORMAL’ (Cost=198 Card=754 Bytes=18850) 2 1 BITMAP CONVERSION (TO ROWIDS) 3 2 BITMAP AND 4 3 BITMAP OR 5 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’ 6 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’ 7 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’ 8 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’ 9 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’ 10 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’ 11 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’ 12 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’ 13 4 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_SAL_BMX’ 14 3 BITMAP INDEX (SINGLE VALUE) OF ‘NORMAL_GENDER_BMX’ Statistics ———————————————————- 0 recursive calls 0 db block gets 1353 consistent gets 920 physical reads 0 redo size 75604 bytes sent via SQL*Net to client 1555 bytes received via SQL*Net from client 98 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 1453 rows processed

下面是B树索引时的结果：

1. SQL>select * from test_normal
2. where sal in (1000,1500,2000,2500,3000,3500,4000,4500,5000) and GENDER=‘M’;
4. 1453 rows selected.
6. Elapsed: 00:00:03.01
8. Execution Plan
9. ———————————————————-
10. 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=601 Card=754 Bytes=18850)
11. 1 0 TABLE ACCESS (FULL) OF ‘TEST_NORMAL’ (Cost=601 Card=754 Bytes=18850)
13. Statistics
14. ———————————————————-
15. 0 recursive calls
16. 0 db block gets
17. 6333 consistent gets
18. 4412 physical reads
19. 0 redo size
20. 75604 bytes sent via SQL*Net to client
21. 1555 bytes received via SQL*Net from client
22. 98 SQL*Net roundtrips to/from client
23. 0 sorts (memory)
24. 0 sorts (disk)
25. 1453 rows processed

SQL>select * from test_normal where sal in (1000,1500,2000,2500,3000,3500,4000,4500,5000) and GENDER=’M’; 1453 rows selected. Elapsed: 00:00:03.01 Execution Plan ———————————————————- 0 SELECT STATEMENT Optimizer=CHOOSE (Cost=601 Card=754 Bytes=18850) 1 0 TABLE ACCESS (FULL) OF ‘TEST_NORMAL’ (Cost=601 Card=754 Bytes=18850) Statistics ———————————————————- 0 recursive calls 0 db block gets 6333 consistent gets 4412 physical reads 0 redo size 75604 bytes sent via SQL*Net to client 1555 bytes received via SQL*Net from client 98 SQL*Net roundtrips to/from client 0 sorts (memory) 0 sorts (disk) 1453 rows processed

可以看出，如果使用B树索引，优化器使用全表扫描；而对于位图索引，则使用索引。从获取结果需要的I/O次数可以推断出性能。

总之，基于如下的原因，位图索引适用于决策支持系统，而不管cardinality的高低：

• 使用位图索引，优化器可以高效地执行包含AND，OR或者XOR的查询。
• 使用位图索引，优化器可以回答对null的查询和计数。null值在位图索引时同样被加上索引（不像B树索引）。
• 最重要的是，在决策支持系统中，位图索引支持特殊的查询，但B树索引则不能。具体来说，如果你有一个包含50列的表，用户经常查询其中的10列 —- 10列的组合或者有时是其中一列，创建B树索引会比价困难。如果你在这些列上建立10个位图索引，这些查询都可以通过索引回答，不论你查询的是全部10列，还是10列中的4列或者6列，或者其中的一列。

相反，B树索引非常适合于OLTP系统，其用户执行的都是常规的查询。因为在OLTP系统中，数据会频繁地更新和删除，如果使用位图索引将会引起严重的锁定性能问题。

两种索引都有一个共同的目的：尽快地得到结果。但是你应该依据程序的类型来选择其中之一，而不是根据cardinality水平。