2d Index Internals

Calculation of Geohash Values for 2d Indexes

When you create a geospatial index on legacy coordinate pairs, MongoDB computes geohash values for the coordinate pairs within the specified location range and then indexes the geohash values.

To calculate a geohash value, recursively divide a two-dimensional map into quadrants. Then assign each quadrant a two-bit value. For example, a two-bit representation of four quadrants would be:

01  11
00  10

These two-bit values (00, 01, 10, and 11) represent each of the quadrants and all points within each quadrant. For a geohash with two bits of resolution, all points in the bottom left quadrant would have a geohash of 00. The top left quadrant would have the geohash of 01. The bottom right and top right would have a geohash of 10 and 11, respectively.

To provide additional precision, continue dividing each quadrant into sub-quadrants. Each sub-quadrant would have the geohash value of the containing quadrant concatenated with the value of the sub-quadrant. The geohash for the upper-right quadrant is 11, and the geohash for the sub-quadrants would be (clockwise from the top left): 1101, 1111, 1110, and 1100, respectively.

Multi-location Documents for 2d Indexes

While 2d geospatial indexes do not support more than one geospatial field in a document, you can use a multi-key index to index multiple coordinate pairs in a single document. In the simplest example you may have a field (e.g. locs) that holds an array of coordinates, as in the following example:

db.places.save( {
  locs : [ [ 55.5 , 42.3 ] ,
           [ -74 , 44.74 ] ,
           { lng : 55.5 , lat : 42.3 } ]
} )

The values of the array may be either arrays, as in [ 55.5, 42.3 ], or embedded documents, as in { lng : 55.5 , lat : 42.3 }.

You could then create a geospatial index on the locs field, as in the following:

db.places.createIndex( { "locs": "2d" } )

You may also model the location data as a field inside of an embedded document. In this case, the document would contain a field (e.g. addresses) that holds an array of documents where each document has a field (e.g. loc:) that holds location coordinates. For example:

db.records.save( {
  name : "John Smith",
  addresses : [ {
                 context : "home" ,
                 loc : [ 55.5, 42.3 ]
                } ,
                {
                 context : "work",
                 loc : [ -74 , 44.74 ]
                }
              ]
} )

You could then create the geospatial index on the addresses.loc field as in the following example:

db.records.createIndex( { "addresses.loc": "2d" } )