In data warehouse environments B-Tree and Bitmap indexes are commonly used.
Bitmap indexes are primarily intended for data warehousing applications where typically have large amounts of data and ad hoc queries, but a low level of concurrent DML transactions. This will not be of much use if there are large number of concurrent transactions.
In a bitmap index, a bitmap for each key value replaces a list of rowids. Each bit in the bitmap corresponds to a possible rowid, and if the bit is set, it means that the row with the corresponding rowid contains the key value. A mapping function converts the bit position to an actual rowid, so that the bitmap index provides the same functionality as a regular index. If the number of different key values is small, bitmap indexes save space.
Bitmap indexes are most effective for queries that contain multiple conditions in the WHERE clause. Rows that satisfy some, but not all, conditions are filtered out before the table itself is accessed. This improves response time, often dramatically.
The advantages of using bitmap indexes are greatest for columns in which the ratio of the number of distinct values to the number of rows in the table is under 1%. We refer to this ratio as the degree of cardinality. A gender column, which has only two distinct values (male and female), is ideal for a bitmap index.
Following are major advantages of using Bitmap Index:
- Reduced response time for large classes of ad hoc queries
- Reduced storage requirements compared to other indexing techniques
- Dramatic performance gains even on hardware with a relatively small number of CPUs or a small amount of memory
- Efficient maintenance during parallel DML and loads
A B-tree index is organized like an upside-down tree. The bottom level of the index holds the actual data values and pointers to the corresponding rows, much as the index in a book has a page number associated with each index entry.
B-tree indexes are most effective for high-cardinality data: that is, for data with many possible values, such as customer_name or phone_number. In a data warehouse, B-tree indexes should be used only for unique columns or other columns with very high cardinalities that is, columns that are almost unique.
Fully indexing a large table with a traditional B-tree index can be prohibitively expensive in terms of space because the indexes can be several times larger than the data in the table.In general, B-tree indexes can be used when a typical query refers to the indexed column and retrieves a few rows. In these queries, it is faster to find the rows by looking at the index.
For more details refer to the following page.
- prev: DWH Facts