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| [[Table (database)|Database tables]] and [[Database index|indexes]] may be stored on disk in one of a number of forms, including ordered/unordered [[Flat file database|flat files]], [[ISAM]], heap files, [[Hash table|hash buckets]], or [[B+ tree]]s. Each form has its own particular advantages and disadvantages. The most commonly used forms are B+ trees and ISAM. Such forms or structures are one aspect of the overall schema used by a [[database engine]] to store information.
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| ==Unordered==
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| {{Expand section|date=June 2008}}
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| '''Unordered''' storage typically stores the records in the order they are inserted. Such storage offers good insertion [[Algorithmic efficiency|efficiency]] (<math>O\left(1\right)</math>), but inefficient retrieval times (<math>O\left(n\right)</math>). Typically these retrieval times are better, however, as most databases use indexes on the [[primary key]]s, resulting in retrieval times of <math>O\left(\log n\right)</math> or <math>O\left(1\right)</math> for keys that are the same as the database row offsets within the storage system.
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| ==Ordered==
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| {{Expand section|date=June 2008}}
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| '''Ordered''' storage typically stores the records in order and may have to rearrange or increase the file size when a new record is inserted, resulting in lower insertion efficiency. However, ordered storage provides more efficient retrieval as the records are pre-sorted, resulting in a complexity of <math>O\left(\log n\right)</math>.
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| ==Structured files==
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| ===Heap files===
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| * Simplest and most basic method
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| ** insert efficient, with new records added at the end of the file, providing chronological order
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| ** retrieval inefficient as searching has to be linear
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| ** deletion is accomplished by marking selected records as "deleted"
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| ** requires periodic reorganization if file is very {{clarify span|volatile|date=September 2012}}
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| * Advantages
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| ** efficient for bulk loading data
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| ** efficient for relatively small relations as indexing overheads are avoided
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| ** efficient when retrievals involve large proportion of stored records
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| * Disadvantages
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| ** not efficient for selective retrieval using key values, especially if large
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| ** sorting may be time-consuming
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| ** not suitable for {{clarify span|volatile|date=September 2012}} tables
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| Heap files are lists of unordered records of variable size. Although sharing a similar name, heap files are widely different from in-memory [[Heap (data structure)|heaps]].{{Elucidate|date=September 2012}}
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| ===Hash buckets===
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| {{main|Hash table}}
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| * Hash functions calculate the address of the page in which the record is to be stored based on one or more fields in the record
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| ** hashing functions chosen to ensure that addresses are spread evenly across the address space
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| ** ‘occupancy’ is generally 40% to 60% of the total file size
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| ** unique address not guaranteed so collision detection and collision resolution mechanisms are required
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| * Open addressing
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| * Chained/unchained overflow
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| * Pros and cons
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| ** efficient for exact matches on key field
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| ** not suitable for range retrieval, which requires sequential storage
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| ** calculates where the record is stored based on fields in the record
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| ** hash functions ensure even spread of data
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| ** collisions are possible, so collision detection and restoration is required
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| ===B+ trees===
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| {{main|B+ tree}}
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| These are the most commonly used in practice.
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| * Time taken to access any record is the same because the same number of nodes is searched
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| * Index is a full index so data file does not have to be ordered
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| * Pros and cons
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| ** versatile data structure – sequential as well as random access
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| ** access is fast
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| ** supports exact, range, part key and pattern matches efficiently
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| ** volatile files are handled efficiently because index is dynamic – expands and contracts as table grows and shrinks
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| ** less well suited to relatively stable files – in this case, ISAM is more efficient
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| ===ISAM===
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| {{main|ISAM}}
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| {{Expand section|date=June 2008}}
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| == Data orientation ==
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| Most conventional [[relational database]]s use "row-oriented" storage, meaning that all data associated with a given row is stored together. By contrast, [[column-oriented DBMS]] store all data from a given column together in order to more quickly serve [[data warehouse]]-style queries. [[Correlation database]]s are similar to row-based databases, but apply a layer of indirection to map multiple instances of the same value to the same numerical identifier.
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| {{Databases}}
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| {{DEFAULTSORT:Database Storage Structures}}
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| [[Category:Databases]]
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| [[Category:Database management systems]]
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| [[Category:Database theory]]
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