More like throwing some wood into the fire. Was instigated by the talk[1] "Solr Power FTW", and "Building a recommendation engine, foursquare style" [6] (where Justin Moore admits: We are dumping the data from Mongo and loading it into Hadoop over S3 files. Map reduce is in this system, not in our mongo databases. ).

Was wondering how much has this evolved over the last 6 months, how viable machine learning algorithms are appliable and clusterable over NOSQL databases, and which ones can do this without dumping the entire database.

I had some links talking about it:

Full text search with MongoDB[2]

Ask HN: What's the best way to handle full-text search with MongoDB?[3]

AskHN: NoSQL with full text search - which is better CouchDB or MongoDB?[4]

Is MongoDB a valid alternative to relational db + lucene?[5]

[1] http://schedule.sxsw.com/events/event_IAP7455

[2] http://hmarr.com/2010/mar/18/full-text-search-with-mongodb/

[3] http://news.ycombinator.com/item?id=2069271

[4] http://news.ycombinator.com/item?id=1984666

[5] http://stackoverflow.com/questions/2546494/is-mongodb-a-vali...

[6] http://engineering.foursquare.com/2011/03/22/building-a-reco...