geoHaystack indexes are useful when we need to search for geographical-based results in a small area. Like searching for a needle in a haystack, with a geoHaystack index, we can define buckets of geolocation points and get back all of the results that belong in this area.

We will create a geoHaystack index, as follows:

> db.books.createIndex( { "location" : "geoHaystack" ,
                      "name": 1 } ,
                    { bucketSize: 2 } )

This will create buckets of documents within 2 units of latitude or longitude from each document.

Here, with the preceding example location:

location : { type: "Point", coordinates: [ 51.5876, 0.1643 ] }

Based on the bucketSize: 2, every document with the location [49.5876..53.5876, -2.1643..2.1643] will belong in the same bucket as our location.

A document can appear in multiple buckets. If we want to use spherical geometry, 2dsphere is a better solution. geoHaystack indexes are sparse, by default.

If we need to calculate the nearest document to our location and it is outside of our bucketSize (that is, greater than 2 units of latitude/longitude, in our example), queries will be inefficient, and possibly inaccurate. Use a 2dsphere index for such queries.