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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.
The purpose of an inverted index is to allow fast full-text searches, at a cost of increased processing when a document is added to the database. [2] The inverted file may be the database file itself, rather than its index. It is the most popular data structure used in document retrieval systems, [3] used on a large scale for example in search ...
Index is a full index so data file does not have to be ordered; Pros and cons versatile data structure – sequential as well as random access; access is fast; supports exact, range, part key and pattern matches efficiently. volatile files are handled efficiently because index is dynamic – expands and contracts as table grows and shrinks
[citation needed] This space requirement may be even larger for a fault-tolerant distributed storage architecture. Depending on the compression technique chosen, the index can be reduced to a fraction of this size. The tradeoff is the time and processing power required to perform compression and decompression. [citation needed]
The inverted file data model can put indexes in a set of files next to existing flat database files, in order to efficiently directly access needed records in these files. Notable for using this data model is the ADABAS DBMS of Software AG , introduced in 1970.
Indexes of key fields are maintained to achieve fast retrieval of required file records in indexed files. IBM originally developed ISAM for mainframe computers, but implementations are available for most computer systems. The term ISAM is used for several related concepts: The IBM ISAM product and the algorithm it employs. [1]
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.
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