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Geospatial indexes

Geospatial indexing in MongoDB allows you to perform queries on geometric data types like points, lines, and polygons. These indexes are particularly useful for location-based services such as mapping, routing, and geofencing. MongoDB supports two main types of geospatial indexes: 2d indexes and 2dsphere indexes.

2d Index​

The 2d index is suitable for data that lies in a two-dimensional plane. It's generally used for legacy coordinate pairs.

// Create a 2d index on the "loc" field
db.places.createIndex({ "loc": "2d" })

2dsphere Index​

The 2dsphere index is used for data that is based on a sphere (like Earth). It supports more complex geometries like points, lines, and polygons.

// Create a 2dsphere index on the "location" field
db.places.createIndex({ "location": "2dsphere" })

Features of Geospatial Indexes​

  1. Near Queries: Find documents near a specific point.

    // Find places within 1000 meters of a point
    db.places.find({
      "location": {
        "$near": {
          "$geometry": { "type": "Point", "coordinates": [40, -70] },
          "$maxDistance": 1000
        }
      }
    })

  2. Within Queries: Find documents within a specific geometry (e.g., polygon).

    // Find places within a polygon
    db.places.find({
      "location": {
        "$geoWithin": {
          "$geometry": {
            "type": "Polygon",
            "coordinates": [[[0, 0], [3, 6], [6, 1], [0, 0]]]
          }
        }
      }
    })

  3. Intersect Queries: Find geometries that intersect with a given geometry.

    // Find lines intersecting a given point
    db.lines.find({
      "path": {
        "$geoIntersects": {
          "$geometry": { "type": "Point", "coordinates": [2, 2] }
        }
      }
    })

  4. Bounding Box Queries: Find documents within a bounding box.

    // Find places within a bounding box
    db.places.find({
      "location": {
        "$geoWithin": {
          "$box": [[0, 0], [5, 5]]
        }
      }
    })

  5. Geohaystack Index: This is a special type of index optimized for very fast bounding box queries. However, it's less flexible than the other types.

Considerations and Limitations​

  1. Complexity: Geospatial queries can be computationally intensive, especially for complex geometries.

  2. Spherical and Planar Geometry: The 2dsphere index assumes a spherical model of the Earth, while the 2d index assumes a flat, two-dimensional plane. Choose the index type based on your specific use-case.

  3. Multiple Fields: You can have multiple geospatial indexes on a collection, but a single query can only use one of them.

  4. Index Size: Geospatial indexes can consume a significant amount of memory, especially for large datasets with complex geometries.