Late to the party. As a search practioner, a formal proof would not convince me so I don't think sales would become a breeze with one. I am interested in perfo

Late to the party. As a search practioner, a formal proof would not convince me so I don't think sales would become a breeze with one. I am interested in performance but I am more interested in relevance. In my experience, sub-second performance is good enough for most use cases, less than 300ms is ideal, and faster is great but I stop caring. What is really hard with Solr, ES, and Lucene is relevance. Solr has the best out the box experience, but I find ES the easiest to customize though still not easy. What I personally would love is something that has great defaults and easy customization. I would also love to see integration with machine learning algorithms, such as word2vec, as a feature rather than something you have to do yourself. My advice would be to build on top of Lucene/Solr/ES rather than start from scratch because performance for all three is already good enough. Instead do something that makes using those technologies easier/better. For example, Algolia built autocomplete on steroids which is a big value add if you need that and it is something Lucene doesn't do well. Algolia did write their own search engine from scratch but you can duplicate their functionality in ES (contrary to their marketing claims). So if I could give Resin any arbitrary dataset and it would automatically compute word vectors and add that as a relevance signal along with BM25 and custom ranking (e.g. popularity), that would get me very excited.