Geospatial Queries
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MongoDB supports query operations on geospatial data. This section introduces MongoDB's geospatial features.
Compatibility
You can use geospatial queries for deployments hosted in the following environments:
MongoDB Atlas: The fully managed service for MongoDB deployments in the cloud
MongoDB Enterprise: The subscription-based, self-managed version of MongoDB
MongoDB Community: The source-available, free-to-use, and self-managed version of MongoDB
For deployments hosted in MongoDB Atlas, you can run geospatial queries in the UI by using the query Filter bar or aggregation builder. To learn more, see Perform Geospatial Queries in Atlas.
Geospatial Data
In MongoDB, you can store geospatial data as GeoJSON objects or as legacy coordinate pairs.
GeoJSON Objects
To calculate geometry over an Earth-like sphere, store your location data as GeoJSON objects.
To specify GeoJSON data, use an embedded document with:
a field named
type
that specifies the GeoJSON object type, anda field named
coordinates
that specifies the object's coordinates.
<field>: { type: <GeoJSON type> , coordinates: <coordinates> }
Important
If specifying latitude and longitude coordinates, list the longitude first, and then latitude.
Valid longitude values are between
-180
and180
, both inclusive.Valid latitude values are between
-90
and90
, both inclusive.
For example, to specify a GeoJSON Point:
location: { type: "Point", coordinates: [-73.856077, 40.848447] }
For a list of the GeoJSON objects supported in MongoDB as well as examples, see GeoJSON objects.
MongoDB geospatial queries on GeoJSON objects calculate on a sphere; MongoDB uses the WGS84 reference system for geospatial queries on GeoJSON objects.
Legacy Coordinate Pairs
To calculate distances on a Euclidean plane, store your location data
as legacy coordinate pairs and use a 2d
index. MongoDB
supports spherical surface calculations on legacy coordinate pairs by using
a 2dsphere
index if you manually convert the data to
the GeoJSON Point type.
To specify data as legacy coordinate pairs, you can use either an array (preferred) or an embedded document.
- Specify via an array (Preferred):
<field>: [ <x>, <y> ] If specifying latitude and longitude coordinates, list the longitude first and then latitude; i.e.
<field>: [<longitude>, <latitude> ] Valid longitude values are between
-180
and180
, both inclusive.Valid latitude values are between
-90
and90
, both inclusive.
- Specify via an embedded document:
<field>: { <field1>: <x>, <field2>: <y> } If specifying latitude and longitude coordinates, the first field, regardless of the field name, must contain the longitude value and the second field, the latitude value ; i.e.
<field>: { <field1>: <longitude>, <field2>: <latitude> } Valid longitude values are between
-180
and180
, both inclusive.Valid latitude values are between
-90
and90
, both inclusive.
To specify legacy coordinate pairs, arrays are preferred over an embedded document as some languages do not guarantee associative map ordering.
Geospatial Indexes
MongoDB provides the following geospatial index types to support the geospatial queries.
2dsphere
2dsphere indexes support queries that calculate geometries on an earth-like sphere.
To create a 2dsphere
index, use the
db.collection.createIndex()
method and specify the string
literal "2dsphere"
as the index type:
db.collection.createIndex( { <location field> : "2dsphere" } )
where the <location field>
is a field whose value is either a
GeoJSON object or a legacy
coordinates pair.
Note
If you try to create an index on a field that contains an array of geoJSON points, the index build fails and returns the following error:
MongoServerError: Index build failed
For more information on the 2dsphere
index, see
2dsphere
Indexes.
2d
2d indexes support queries that calculate
geometries on a two-dimensional plane.
Although the index can support $nearSphere
queries that
calculate on a sphere, if possible, use the 2dsphere
index
for spherical queries.
To create a 2d
index, use the db.collection.createIndex()
method, specifying the location field as the key and the string literal
"2d"
as the index type:
db.collection.createIndex( { <location field> : "2d" } )
where the <location field>
is a field whose value is a legacy
coordinates pair.
For more information on the 2d
index, see 2d
Indexes.
Geospatial Indexes and Sharded Collections
You cannot use a geospatial index as a shard key when sharding a collection. However, you can create a geospatial index on a sharded collection by using a different field as the shard key.
The following geospatial operations are supported on sharded collections:
$geoNear
aggregation stage$near
and$nearSphere
query operators
You can also query for geospatial data for a sharded cluster using
$geoWithin
and $geoIntersects
.
Covered Queries
Geospatial indexes cannot cover a query.
Geospatial Queries
Note
For spherical queries, use the 2dsphere
index result.
The use of 2d
index for spherical queries may lead to incorrect
results, such as the use of the 2d
index for spherical queries
that wrap around the poles.
Geospatial Query Operators
MongoDB provides the following geospatial query operators:
Name | Description |
---|---|
Selects geometries that intersect with a GeoJSON geometry.
The 2dsphere index supports
| |
Selects geometries within a bounding GeoJSON geometry. The 2dsphere and 2d indexes support
| |
Returns geospatial objects in proximity to a point on a sphere.
Requires a geospatial index. The 2dsphere and 2d indexes support
|
For more details, including examples, see the individual reference page.
Geospatial Aggregation Stage
MongoDB provides the following geospatial aggregation pipeline stage:
Stage | Description |
---|---|
Returns an ordered stream of documents based on the proximity to a
geospatial point. Incorporates the functionality of
|
For more details, including examples, see $geoNear
reference page.
Geospatial Models
MongoDB geospatial queries can interpret geometry on a flat surface or a sphere.
2dsphere
indexes support only spherical queries (i.e. queries that
interpret geometries on a spherical surface).
2d
indexes support flat queries (i.e. queries that interpret
geometries on a flat surface) and some spherical queries. While 2d
indexes support some spherical queries, the use of 2d
indexes for
these spherical queries can result in error. If possible, use
2dsphere
indexes for spherical queries.
The following table lists the geospatial query operators, supported query, used by each geospatial operations:
Operation | Spherical/Flat Query | Notes |
---|---|---|
Spherical | See also the | |
| Flat | |
| Spherical | Provides the same functionality as For spherical queries, it may be preferable to use
|
| Spherical | Use GeoJSON points instead. |
| Spherical | |
| Flat | |
| Flat | |
| Flat | |
| Spherical | |
Spherical | ||
Spherical | ||
Flat |
Perform Geospatial Queries in Atlas
You can use the MongoDB Atlas UI to perform geospatial queries in Atlas.
Create an index
If your geospatial collection does not already have a geospatial index, you must create one.
Select the database for the collection.
The main panel and Namespaces on the left side list the collections in the database.
Select the collection.
Select the collection that contains your geospatial data on the left-hand side or in the main panel. The main panel displays the Find, Indexes, and Aggregation views.
Select the Index view.
When you open the Index view, Atlas displays any indexes that exist on the collection.
Define the Index for the geo Type
Press the Create Index button.
Define a geo Type index. Refer to How to Index GeoJSON Objects.
Query the geospatial data
Select the Find view.
From the collection that contains your geospatial data, select the Find tab to view your geospatial collection.
Enter a query.
Enter a query in the Filter text box. Use any of the geospatial query operators to perform the relevant query on your geospatial data. A geospatial query might resemble:
{ "coordinates": { $geoWithin: { $geometry: { type: "Polygon", coordinates: [ [ [-80.0, 10.00], [ -80.0, 9.00], [ -79.0, 9.0], [ -79.0, 10.00 ], [ -80.0, 10.0 ] ] ] } } } } Press the Apply button.
Press the Apply button to apply your query. Atlas filters the geospatial data to show only documents that match your geospatial query.
You can create and execute aggregation pipelines to perform geospatial queries in the MongoDB Atlas UI.
Access the aggregation pipeline builder
Select the database for the collection.
The main panel and Namespaces on the left side list the collections in the database.
Select the collection.
Select the collection that contains your geospatial data on the left-hand side or in the main panel. The main panel displays the Find, Indexes, and Aggregation views.
Select the Aggregation view.
When you first open the Aggregation view, Atlas displays an empty aggregation pipeline.
Create your geospatial query aggregation pipeline
Select an aggregation stage.
Select an aggregation stage from the Select dropdown in the bottom-left panel.
The toggle to the right of the dropdown dictates whether the stage is enabled.
Use the
$geoNear
stage to perform geospatial queries in your aggregation pipeline.Fill in your aggregation stage.
Fill in your stage with the appropriate values. If Comment Mode is enabled, the pipeline builder provides syntactic guidelines for your selected stage.
As you modify your stage, Atlas updates the preview documents on the right based on the results of the current stage.
Your
$geoNear
stage may resemble:{ near: { type: "Point", coordinates: [ -73.9667, 40.78 ] }, spherical: true, query: { category: "Parks" }, distanceField: "calcDistance" } Run other pipeline stages as needed.
Add stages as needed to complete your aggregation pipeline. You might add
$out
or$merge
to write the results to a view or the current collection.
Examples
Create a collection places
with the following documents:
db.places.insertMany( [ { name: "Central Park", location: { type: "Point", coordinates: [ -73.97, 40.77 ] }, category: "Parks" }, { name: "Sara D. Roosevelt Park", location: { type: "Point", coordinates: [ -73.9928, 40.7193 ] }, category: "Parks" }, { name: "Polo Grounds", location: { type: "Point", coordinates: [ -73.9375, 40.8303 ] }, category: "Stadiums" } ] )
The following operation creates a 2dsphere
index on the
location
field:
db.places.createIndex( { location: "2dsphere" } )
The places
collection above has a 2dsphere
index.
The following query uses the $near
operator to return
documents that are at least 1000 meters from and at most 5000 meters
from the specified GeoJSON point, sorted in order from nearest to
farthest:
db.places.find( { location: { $near: { $geometry: { type: "Point", coordinates: [ -73.9667, 40.78 ] }, $minDistance: 1000, $maxDistance: 5000 } } } )
The following operation uses the $geoNear
aggregation
operation to return documents that match the query filter { category:
"Parks" }
, sorted in order of nearest to farthest to the specified
GeoJSON point:
db.places.aggregate( [ { $geoNear: { near: { type: "Point", coordinates: [ -73.9667, 40.78 ] }, spherical: true, query: { category: "Parks" }, distanceField: "calcDistance" } } ] )