Supporting faced search and other functionality requiring access to per document field-values is just an extension over the core IR functionality.

Tracking (document, field) values can be used for query by range or by geolocation primitives (that's what Lucene does, where it will index that data into a special tree-like structure, and for each query, it will build a custom 'iterator' and use it along with other iterators to match documents), and for static ranking of matched documents.

BTW, Lucene and Algolia are vastly different in terms of the underlying architecture.

What is Algolia's underlying architecture like? Are there any papers or code?

See links about Algolia arch (and other related material) here: https://github.com/phaistos-networks/Trinity/wiki/IR-Search-...

Thanks for the links, Mark! (Disclaimer, I work for Algolia.)

As Mark mentions on his summary page, the best place for that kind of information is our CTO's "Inside the Engine" series (8 parts).

https://blog.algolia.com/inside-the-algolia-engine-part-1-in...

That's why I mentioned them separately. :)

Could you please include a bibliography in your project? That way, others can more easily find which techniques you are using, and of course it can help others (or even your future self) in figuring out how the code works.

Last time I checked, bleeve was very slow compared to Lucene, I really hope it gets better over time.

I was planning to write one too, wanna team up? :)