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A bitmap index is a special kind of database index that uses bitmaps. Bitmap indexes have traditionally been considered to work well for low- cardinality columns , which have a modest number of distinct values, either absolutely, or relative to the number of records that contain the data.
A bitmap index is a special kind of indexing that stores the bulk of its data as bit arrays (bitmaps) and answers most queries by performing bitwise logical operations on these bitmaps. The most commonly used indexes, such as B+ trees, are most efficient if the values they index do not repeat or repeat a small number of times. In contrast, the ...
The Oracle implementation limits itself to using bitmap indexes. A bitmap join index is used for low-cardinality columns (i.e., columns containing fewer than 300 distinct values, according to the Oracle documentation): it combines low-cardinality columns from multiple related tables. The example Oracle uses is that of an inventory system, where ...
Materialized views that store data based on remote tables were also known as snapshots [5] (deprecated Oracle terminology). In any database management system following the relational model, a view is a virtual table representing the result of a database query. Whenever a query or an update addresses an ordinary view's virtual table, the DBMS ...
The compact trie node representation uses a bitmap to mark every valid branch – a bitwise trie with bitmap. The AMT uses eight 32-bit bitmaps per node to represent ...
Note (4): Used for InMemory ColumnStore index, temporary hash index for hash join, Non/Cluster & fill factor. Note (5): InnoDB automatically generates adaptive hash index [125] entries as needed. Note (6): Can be implemented using Function-based Indexes in Oracle 8i and higher, but the function needs to be used in the sql for the index to be used.
For example, a table of 128 rows with a Boolean column requires 128 bytes a row-oriented format (one byte per Boolean) but 128 bits (16 bytes) in a column-oriented format (via a bitmap). Another example is the use of run-length encoding to encode a column.
A large database index would typically use B-tree algorithms. BRIN is not always a substitute for B-tree, it is an improvement on sequential scanning of an index, with particular (and potentially large) advantages when the index meets particular conditions for being ordered and for the search target to be a narrow set of these values.