Bitmap indexing benefits data warehousing applications which have large amounts of
data and ad hoc queries but a low level of concurrent transactions. For such
applications, bitmap indexing provides:
■ Reduced response time for large classes of ad hoc queries
■ A substantial reduction of space use compared to other indexing techniques
■ Dramatic performance gains even on very low end hardware
■ Very efficient parallel DML and loads
Fully indexing a large table with a traditional B-tree index can be prohibitively
expensive in terms of space, because the index can be several times larger than the
data in the table. Bitmap indexes are typically only a fraction of the size of the indexed
data in the table.

Bitmap indexes are not suitable for OLTP applications with large numbers of
concurrent transactions modifying the data. These indexes are primarily intended for
decision support in data warehousing applications where users typically query the
data rather than update it.

Bitmap indexes are also not suitable for columns that are primarily queried with less
than or greater than comparisons. For example, a salary column that usually appears
in WHERE clauses in a comparison to a certain value is better served with a B-tree
index. Bitmapped indexes are only useful with equality queries, especially in
combination with AND, OR, and NOT operators.

Bitmap indexes are integrated with the Oracle optimizer and execution engine. They
can be used seamlessly in combination with other Oracle execution methods. For
example, the optimizer can decide to perform a hash join between two tables using a
bitmap index on one table and a regular B-tree index on the other. The optimizer
considers bitmap indexes and other available access methods, such as regular B-tree
indexes and full table scan, and chooses the most efficient method, taking parallelism
into account where appropriate.

Parallel query and parallel DML work with bitmap indexes as with traditional
indexes. Bitmap indexes on partitioned tables must be local indexes. Parallel create
index and concatenated indexes are also supported.

数据仓库应用中位图索引的获益
1. 能够减少大数据量特别查询的响应时间
能够节省大量存储空间
显著提高性能
有利于并行 DML 和并行加载
2. 位图索引所占的空间比被索引数据小得多
3. 位图索引主要用于数据仓库系统中的决策支持功能，在这种环境下用户对数据的操作主要是查询而非修改
4. 位图索引适用于等值查询，尤其是存在 AND，OR，和 NOT 等逻辑操作符的组合时
5. 位图索引是集成在 Oracle 的优化器和执行引擎之中的。位图索引也能够和 Oracle 中的其他执行方法无缝地组合。
例如，优化器可以在利用一个表的位图索引和另一个表的平衡树索引对这两张表进行哈希连接。
优化器能够在位图索引及其他可用的访问方法中选择效率最高的方式，同时考虑是否适合使用并行执行。
6. 位图索引如同常规索引一样，可以结合并行查询和并行 DML。建立在分区表上的位图索引必须为本地索引

ad hoc ：adv. 特别