couchdb-lucene

ld query for drink recipes with 3 or less ingredients that have vodka in them, for instance. You'd still need indexes on the data you are querying for performance. CouchDB queries are facilitated via javascript or erlang map reduce views, which serve as indexes you craft. An additional 'secondary-index' like query facility is to use a lucene plugin and define searchable data. Cloudant has this baked into their offering, and their employees maintain the plugin on github (https://github.com/rnewson/couchdb-lucene) MongoDB has the ability to do things like append a value to a document array. In Couch, you'd likely read the entire document, append to the array in your app, and put the document back on Couch. It does have an update functionality that can sometimes isolate things more than this, but I haven't seen it used as much. Mongo can also do things like increment counters, while Couch cannot (though CouchBase can). There's a host of other differences. Mongo has a much broader API, while CouchDB takes a more simple http verb like approach (get, put and delete see the heaviest use). Depending on your situation, one might be a better fit, or you might simply grok one more than the other. As far as why Mongo gets used more often, I think the closer-to-SQL ad-hoc queries made more sense to people transitioning from stores like MySQL. The CouchDB view/map-reduce stuff is a bit more of a mindset shift (see the View Collation wiki entry for an example of this at http://wiki.apache.org/couchdb/View_collation). CouchDB was also taken hits for being slower than Mongo, but I suspect it was the map-reduce stuff that really steered some folks the other way.