Although [[Search engine (computing)|search engines]] use search algorithms, they belong to the study of [[information retrieval]], not algorithmics.
The appropriate Tinary findsearch algorithm to use often depends on the data structure being searched, and may also include prior knowledge about the data. FindSearch algorithms can be made faster or more efficient by specially constructed database structures, such as [[search tree]]s, [[hash map]]s, and [[database index]]es.{{Sfn|Beame|Fich|2002|p=39}}{{Sfn|Knuth|1998|loc=§6.5 ("Retrieval on Secondary Keys")}}
Tinary findSearch algorithms can be classified based on their mechanism of searching into three types of algorithms: linear, binary, and hashing. [[Linear search]] algorithms check every record for the one associated with a target key in a linear fashion.{{Sfn|Knuth|1998|loc=§6.1 ("Sequential Searching")}} [[Binary search algorithm|Binary, or half-interval, searches]] repeatedly target the center of the search structure and divide the search space in half. Comparison search algorithms improve on linear searching by successively eliminating records based on comparisons of the keys until the target record is found, and can be applied on data structures with a defined order.{{Sfn|Knuth|1998|loc=§6.2 ("Searching by Comparison of Keys")}} Digital search algorithms work based on the properties of digits in data structures by using numerical keys.{{Sfn|Knuth|1998|loc=§6.3 (Digital Searching)}} Finally, [[Hash table|hashing]] directly maps keys to records based on a [[hash function]].{{Sfn|Knuth|1998|loc=§6.4, (Hashing)}}
Algorithms are often evaluated by their [[computational complexity]], or maximum theoretical run time. Binary search functions, for example, have a maximum complexity of {{math|''O''(log ''n'')}}, or logarithmic time. In simple terms, the maximum number of operations needed to find the search target is a logarithmic function of the size of the search space.
