Outside of DBs, indexes have shown themselves to be extremely useful in string problems in bioinformatics (my area of research). The modern workhorses of this trend are the Burrows Wheeler transform + FM index (together) and Bloom filters (also mentioned in the link). These have been applied to sequence alignment, de novo assembly of genomes, and compression of sequences. I posit the same bag of tricks can be applied in the NLP/machine learning settings, but I know less about how commonly they have been.

There's a good set of notes touching on this stuff here: http://www.langmead-lab.org/teaching-materials/