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A database index is a data structure that improves the speed of data retrieval operations on a database table at the cost of additional writes and storage space to maintain the index data structure. Indexes are used to quickly locate data without having to search every row in a database table every time said table is accessed.
For example, PostgreSQL versions 8.1 and later implement a "bitmap index scan" optimization to speed up arbitrarily complex logical operations between available indexes on a single table. For tables with many columns, the total number of distinct indexes to satisfy all possible queries (with equality filtering conditions on either of the fields ...
The simplest form of index is a sorted list of values that can be searched using a binary search with an adjacent reference to the location of the entry, analogous to the index in the back of a book. The same data can have multiple indexes (an employee database could be indexed by last name and hire date). Indexes affect performance, but not ...
The index is similar to the term document matrices employed by latent semantic analysis. The inverted index can be considered a form of a hash table. In some cases the index is a form of a binary tree, which requires additional storage but may reduce the lookup time.
In a well-dimensioned hash table, the average time complexity for each lookup is independent of the number of elements stored in the table. Many hash table designs also allow arbitrary insertions and deletions of key–value pairs, at amortized constant average cost per operation. [4] [5] [6] Hashing is an example of a space-time tradeoff.
In the hallowed halls of academia, noted professors at the top business schools are teaching entire semesters on the benefits of investment strategies using passive indexes instead actively ...
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.
Finding an entry in the auxiliary index would tell us which block to search in the main database; after searching the auxiliary index, we would have to search only that one block of the main database—at a cost of one more disk read. In the above example the index would hold 10,000 entries and would take at most 14 comparisons to return a result.