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2d Index Internals

This document provides a more in-depth explanation of the internals of MongoDB’s 2d geospatial indexes. This material is not necessary for normal operations or application development but may be useful for troubleshooting and for further understanding.

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:

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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

Note

2dsphere indexes can cover multiple geospatial fields in a document, and can express lists of points using MultiPoint embedded documents.

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:

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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:

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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:

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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:

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

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