To cap facet counts by 1 specify facet.exists=true. It can be used with facet.method=enum or when it's omitted. It can be used only on non-trie fields i.e. strings. It may speed up facet counting on large indices and/or high-cardinality facet values..
This improves performance for facet.method=enum when it's enough to know that facet count>0, for example when you it's when you dynamically populate filters on search form. New method checks if two bitsets intersect instead of counting intersection size.
We have a dataset containing hundreds of millions of records, we facet by dozens of fields with many of facet-excludes and have relatively small number of unique values in fields, around thousands.
Before executing search, users work with "advanced search" form, our goal is to populate dozens of filters with values which are applicable with other selected values, so basically this is a use case for facets with mincount=1, but without need in actual counts.
Our performance tests showed that facet.method=enum works much better than fc\fcs, probably due to a specific ratio of "docset"\"unique terms count". For example average execution of query time with method fc=1500ms, fcs=2600ms and with enum=280ms. Profiling indicated the majority time for enum was spent on intersecting docsets.
Hers's a patch that introduces an extension to facet calculation for method=enum. Basically it uses docSetA.intersects(docSetB) instead of docSetA. intersectionSize (docSetB).
As a result we were able to reduce our average query time from 280ms to 60ms.