How to find nearby events or tweets - mongodb

Question

I'm new to NoSQL databases and I'm stuck with a fairly basic query.
I have a collection of tweets in a MongoDB database, which I'm querying through both the Mongo shell and pyMongo. The documents are similar to:
{ loc : { lng : 40, lat : 3 },
timestamp : 124125512,
userid = 55 }
I need to find all pairs of users with events close to each other with less than 4 hours of difference. The most naive way would be:
db.tweets.find().forEach(function(tweet)
{
found = db.tweets.find({ "timestamp": { "$gt" : tweet['timestamp'] - 60*60*4,
"$lt" : tweet['timestamp'] + 60*60*4},
"loc" : {"$near" : [ tweet['loc']['lng'],
tweet['loc']['lat'] ],
"$maxDistance" : 500 }
});
//... extract the users from those tweets...
}
Which of course is extremely slow (it can contain as many as a few million tweets).
I haven't been able to express this query using neither aggregation nor MapReduce. How would you do it? What is the most NoSQL-y, efficient and clear way of making this kind of query?
EDIT: I've kind of given up. I've been convinced by a friend that it is not going to worth it using Mongo for this. I can leverage that time restriction to avoid iterating over the whole collection and do it in a simple, more traditional iterative script. Since it is not such a huge dataset as to not fit in RAM, it's going to be faster.

Answer 1

Use $near in conjuction with $maxDistance is the most recommended way
db.collectionName.find({loc: {$near: [50, 50], $maxDistance: 5}});
For performance issues you can try creating index as mentioned below:
To create a geospatial index for GeoJSON-formatted data, use the ensureIndex() method and set the value of the location field for your collection to 2dsphere.
db.points.ensureIndex( { loc : "2dsphere" } );
For more information:
Index creation
Build a 2dsphere index
Geospatial indexes and queries