Because of the linear cost of relevance ranking in the size of the result set, the actual cost of relevance ranking depends heavily on the set of ranking modules used.
In general, modules that do not perform text evaluation introduce significantly lower computational costs than text-matching-oriented modules.
Although the relative cost of the various ranking modules is dependent on the nature of your data and the number of records, the modules can be roughly grouped into four tiers:
Proximity, Phrase with Subphrase or Query Expansion options specified, and First are all high-cost modules, presented in the order of decreasing cost.
The remaining modules (Static, Phrase with no options specified, Freq, Spell, Glom, Nterms, Interp, Numfields, Maxfields and Field) are generally relatively cheap.
In order to maximize the performance of your relevance ranking strategy, consider a less expensive way to get similar results. For example, replacing Exact with Phrase may improve performance in some cases with relatively little impact on results.
Note
Choose the set of modules used for relevance ranking most carefully when the data set is large or contains large/offline file content that is used for search operations.
