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==Permanent storage== {{Main|Key–value store}} Many programs using associative arrays will need to store that data in a more permanent form, such as a [[computer file]]. A common solution to this problem is a generalized concept known as ''archiving'' or ''[[serialization]]'', which produces a text or binary representation of the original objects that can be written directly to a file. This is most commonly implemented in the underlying object model, like .Net or Cocoa, which includes standard functions that convert the internal data into text. The program can create a complete text representation of any group of objects by calling these methods, which are almost always already implemented in the base associative array class.<ref>[https://developer.apple.com/library/prerelease/ios/documentation/Cocoa/Conceptual/Archiving/Archiving.html#//apple_ref/doc/uid/10000047i "Archives and Serializations Programming Guide"], Apple Inc., 2012</ref> For programs that use very large data sets, this sort of individual file storage is not appropriate, and a [[database management system]] (DB) is required. Some DB systems natively store associative arrays by serializing the data and then storing that serialized data and the key. Individual arrays can then be loaded or saved from the database using the key to refer to them. These [[key–value database|key–value store]]s have been used for many years and have a history as long as that of the more common [[relational database]] (RDBs), but a lack of standardization, among other reasons, limited their use to certain niche roles. RDBs were used for these roles in most cases, although saving objects to a RDB can be complicated, a problem known as [[object-relational impedance mismatch]]. After approximately 2010, the need for high-performance databases suitable for [[cloud computing]] and more closely matching the internal structure of the programs using them led to a renaissance in the key–value store market. These systems can store and retrieve associative arrays in a native fashion, which can greatly improve performance in common web-related workflows.
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