Some honestly aren't much more... they all have at least a "key" or id field you can lookup a record by, and most have a map/reduce implementation. Some require you to do your own indexed records and others (like MongoDB and RethinkDB) have the ability to add additional indexes (similar to SQL).

I find that MongoDB tends to perform at least as well as an SQL based solution in general use. Some use cases are better suited to SQL (anything with multiple records in a single transaction as a hard requirement for example). It really depends on your needs.

it'w worse , it is basically a bin where you throw paper. You can still search for a specific one ,but as you throw more paper in the trashcan , it gets more complicated to find the paper you want. but hey ,it is easier and faster than to organize them on a rack they say ...

It really depends on the non-sql solution... Most of which do have indexed keys for retrieving data at the very least (key-value storage), and use Map-Reduce in order to perform basic queries, or you setup your own indexed records.

MongoDB specifically allows for additional indexes to be used as part of general queries, or the aggregation methods available. MongoDB even has some nice built in features for querying against geolocation data. They do have some limitations, but in general it works quite well.

Your analogy comparing non-sql to a trashcan is a bad comparison... a better comparison would be to a set of file cabinets.

So it's a "circular file" system.