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Geo-Spatial Indexes

ArangoDB features a Google S2 based geospatial index. We support indexing on a subset of the GeoJSON standard (as well as simple latitude longitude pairs).

AQL's geospatial utility functions are described in Geo functions. Helper functions to easily create GeoJSON objects are described in GeoJSON Constructors.

Using a Geo-Spatial Index

The geospatial index supports containment and intersection queries for various geometric 2D shapes. You should be mainly using AQL queries to perform these types of operations. The index can operate in two different modes, depending on if you want to use the GeoJSON data-format or not. The modes are mainly toggled by using the geoJson field when creating the index.

This index assumes coordinates with the latitude between -90 and 90 degrees and the longitude between -180 and 180 degrees. A geo index will ignore all documents which do not fulfill these requirements.

GeoJSON Mode

To create an index in GeoJSON mode execute:

collection.ensureIndex({ type: "geo", fields: [ "geometry" ], geoJson:true })

This creates the index on all documents and uses geometry as the attributed field where the value is either a Geometry Object or a coordinate array. The array must contain at least two numeric values with longitude (first value) and the latitude (second value). This corresponds to the format described in RFC 7946 Position

All documents, which do not have the attribute path or have a non-conform value in it, are excluded from the index.

A geo index is implicitly sparse, and there is no way to control its sparsity. In case that the index was successfully created, an object with the index details, including the index-identifier, is returned.

Non-GeoJSON mode

This index mode exclusively supports indexing on coordinate arrays. Values that contain GeoJSON or other types of data will be ignored. In the non-GeoJSON mode the index can be created on one or two fields.

The following examples will work in the arangosh command shell.

To create a geo-spatial index on all documents using latitude and longitude as separate attribute paths, two paths need to be specified in the fields array:

collection.ensureIndex({ type: "geo", fields: [ "latitude", "longitude" ] })

The first field is always defined to be the latitude and the second is the longitude. The geoJson flag is implicitly false in this mode.

Alternatively you can specify only one field:

collection.ensureIndex({ type: "geo", fields: [ "location" ], geoJson:false })

It creates a geospatial index on all documents using location as the path to the coordinates. The value of the attribute has to be an array with at least two numeric values. The array must contain the latitude (first value) and the longitude (second value).

All documents, which do not have the attribute path(s) or have a non-conforming value in it, are excluded from the index.

In case that the index was successfully created, an object with the index details, including the index-identifier, is returned.

Indexed GeoSpatial Queries

The geospatial index supports a variety of AQL queries, which can be built with the help of the geo utility functions. There are three specific geo functions that can be optimized, provided that they are used correctly: GEO_DISTANCE, GEO_CONTAINS, GEO_INTERSECTS. Additionally, there is a built-in support to optimize the older geo functions DISTANCE, NEAR and WITHIN (the last two only if they are used in their 4 argument version, without distanceName).

When in doubt whether your query is being properly optimized, check the AQL explain output to check for index usage.

Query for Results near Origin (NEAR type query)

A basic example of a query for results near an origin point:

FOR x IN geo_collection
  FILTER GEO_DISTANCE([@lng, @lat], x.geometry) <= 100000
  RETURN x._key

The first parameter can be a GeoJSON object or a coordinate array in [longitude, latitude] ordering. The second parameter is the document field on which the index was created. The function GEO_DISTANCE always returns the distance in meters, so will receive results up until 100km.

Query for Sorted Results near Origin (NEAR type query)

A basic example of a query for the 1000 nearest results to an origin point (ascending sorting):

FOR x IN geo_collection
  SORT GEO_DISTANCE([@lng, @lat], x.geometry) ASC
  LIMIT 1000
  RETURN x._key

The first parameter can be a GeoJSON object or a coordinate array in [longitude, latitude] ordering. The second parameter is the documents field on which the index was created.

You may also get results farthest away (distance sorted in descending order):

FOR x IN geo_collection
  SORT GEO_DISTANCE([@lng, @lat], x.geometry) DESC
  LIMIT 1000
  RETURN x._key

Query for Results within Distance

A query which returns documents at a distance of 1km or farther away, up to 100km from the origin. This will return the documents with a GeoJSON value that is located in the specified search annulus.

FOR x IN geo_collection
  FILTER GEO_DISTANCE([@lng, @lat], x.geometry) <= 100000
  FILTER GEO_DISTANCE([@lng, @lat], x.geometry) >= 1000
  RETURN x

Query for Results contained in Polygon

A query which returns documents whose stored geometry is contained within a GeoJSON Polygon.

LET polygon = GEO_POLYGON([[[60,35],[50,5],[75,10],[70,35]]])
FOR x IN geo_collection
  FILTER GEO_CONTAINS(polygon, x.geometry)
  RETURN x

The first parameter of GEO_CONTAINS must be a polygon. Other types are not valid. The second parameter must contain the document field on which the index was created.

Query for Results Intersecting a Polygon

A query which returns documents with an intersection of their stored geometry and a GeoJSON Polygon.

LET polygon = GEO_POLYGON([[[60,35],[50,5],[75,10],[70,35]]])
FOR x IN geo_collection
  FILTER GEO_INTERSECTS(polygon, x.geometry)
  RETURN x

The first parameter of GEO_CONTAINS must be a polygon. Other types are not valid. The second parameter must contain the document field on which the index was created.

GeoJSON

GeoJSON is a geospatial data format based on JSON. It defines several different types of JSON objects and the way in which they can be combined to represent data about geographic shapes on the earth surface. GeoJSON uses a geographic coordinate reference system, World Geodetic System 1984 (WGS 84), and units of decimal degrees.

Internally ArangoDB maps all coordinates onto a unit sphere. Distances are projected onto a sphere with the Earth's Volumetric mean radius of 6371 km. ArangoDB implements a useful subset of the GeoJSON format (RFC 7946). We do not support Feature Objects or the GeometryCollection type. Supported geometry object types are:

Point
MultiPoint
LineString
MultiLineString
Polygon

Point

The following section of the RFC specifies a GeoJSON Point:

{
  "type": "Point",
  "coordinates": [100.0, 0.0]
}

MultiPoint

The following section of the RFC specifies a GeoJSON MultiPoint:

{
  "type": "MultiPoint",
  "coordinates": [
    [100.0, 0.0],
    [101.0, 1.0]
  ]
}

LineString

The following section of the RFC specifies a GeoJSON LineString:

{
  "type": "LineString",
  "coordinates": [
    [100.0, 0.0],
    [101.0, 1.0]
  ]
}

MultiLineString

The following section of the RFC specifies a GeoJSON MultiLineString. The "coordinates" member is an array of LineString coordinate arrays:

{
  "type": "MultiLineString",
  "coordinates": [
    [
      [100.0, 0.0],
      [101.0, 1.0]
    ],
    [
      [102.0, 2.0],
      [103.0, 3.0]
    ]
  ]
}

Polygon

GeoJSON polygons consists of a series of closed LineString objects (ring-like). These LineRing objects consist of four or more vertices with the first and last coordinate pairs being equal. Coordinates of a Polygon are an array of linear ring coordinate arrays. The first element in the array represents the exterior ring. Any subsequent elements represent interior rings (or holes).

No Holes:

{
  "type": "Polygon",
    "coordinates": [
    [
      [100.0, 0.0],
      [101.0, 0.0],
      [101.0, 1.0],
      [100.0, 1.0],
      [100.0, 0.0]
    ]
  ]
}

With Holes:

The exterior ring should not self-intersect.
The interior rings must be contained in the outer ring
Interior rings cannot overlap (or touch) with each other

{
  "type": "Polygon",
  "coordinates": [
    [
      [100.0, 0.0],
      [101.0, 0.0],
      [101.0, 1.0],
      [100.0, 1.0],
      [100.0, 0.0]
    ],
    [
      [100.8, 0.8],
      [100.8, 0.2],
      [100.2, 0.2],
      [100.2, 0.8],
      [100.8, 0.8]
    ]
  ]
}

Arangosh Examples

ensures that a geo index exists collection.ensureIndex({ type: "geo", fields: [ "location" ] })

Creates a geospatial index on all documents using location as the path to the coordinates. The value of the attribute has to be an array with at least two numeric values. The array must contain the latitude (first value) and the longitude (second value).

All documents, which do not have the attribute path or have a non-conforming value in it, are excluded from the index.

A geo index is implicitly sparse, and there is no way to control its sparsity.

In case that the index was successfully created, an object with the index details, including the index-identifier, is returned.

To create a geo index on an array attribute that contains longitude first, set the geoJson attribute to true. This corresponds to the format described in RFC 7946 Position

collection.ensureIndex({ type: "geo", fields: [ "location" ], geoJson: true })

To create a geo-spatial index on all documents using latitude and longitude as separate attribute paths, two paths need to be specified in the fields array:

collection.ensureIndex({ type: "geo", fields: [ "latitude", "longitude" ] })

In case that the index was successfully created, an object with the index details, including the index-identifier, is returned.

Examples

Create a geo index for an array attribute:

@startDocuBlockInline geoIndexCreateForArrayAttribute1
@EXAMPLE_ARANGOSH_OUTPUT{geoIndexCreateForArrayAttribute1}
~db._create("geo")
 db.geo.ensureIndex({ type: "geo", fields: [ "loc" ] });
| for (i = -90;  i <= 90;  i += 10) {
|     for (j = -180; j <= 180; j += 10) {
|         db.geo.save({ name : "Name/" + i + "/" + j, loc: [ i, j ] });
|     }
  }
db.geo.count();
db.geo.near(0, 0).limit(3).toArray();
db.geo.near(0, 0).count();
~db._drop("geo")
@END_EXAMPLE_ARANGOSH_OUTPUT
@endDocuBlock geoIndexCreateForArrayAttribute1

Create a geo index for a hash array attribute:

@startDocuBlockInline geoIndexCreateForArrayAttribute2
@EXAMPLE_ARANGOSH_OUTPUT{geoIndexCreateForArrayAttribute2}
~db._drop("geo2")
~db._create("geo2")
db.geo2.ensureIndex({ type: "geo", fields: [ "location.latitude", "location.longitude" ] });
| for (i = -90;  i <= 90;  i += 10) {
|     for (j = -180; j <= 180; j += 10) {
|         db.geo2.save({ name : "Name/" + i + "/" + j, location: { latitude : i, longitude : j } });
|     }
  }
db.geo2.near(0, 0).limit(3).toArray();
~db._drop("geo2")
@END_EXAMPLE_ARANGOSH_OUTPUT
@endDocuBlock geoIndexCreateForArrayAttribute2

Use GeoIndex with AQL SORT statement:

@startDocuBlockInline geoIndexSortOptimization
@EXAMPLE_ARANGOSH_OUTPUT{geoIndexSortOptimization}
~db._create("geoSort")
db.geoSort.ensureIndex({ type: "geo", fields: [ "latitude", "longitude" ] });
| for (i = -90;  i <= 90;  i += 10) {
|     for (j = -180; j <= 180; j += 10) {
|         db.geoSort.save({ name : "Name/" + i + "/" + j, latitude : i, longitude : j });
|     }
  }
var query = "FOR doc in geoSort SORT DISTANCE(doc.latitude, doc.longitude, 0, 0) LIMIT 5 RETURN doc"
db._explain(query, {}, {colors: false});
db._query(query);
~db._drop("geoSort")
@END_EXAMPLE_ARANGOSH_OUTPUT
@endDocuBlock geoIndexSortOptimization

Use GeoIndex with AQL FILTER statement:

@startDocuBlockInline geoIndexFilterOptimization
@EXAMPLE_ARANGOSH_OUTPUT{geoIndexFilterOptimization}
~db._create("geoFilter")
db.geoFilter.ensureIndex({ type: "geo", fields: [ "latitude", "longitude" ] });
| for (i = -90;  i <= 90;  i += 10) {
|     for (j = -180; j <= 180; j += 10) {
|         db.geoFilter.save({ name : "Name/" + i + "/" + j, latitude : i, longitude : j });
|     }
  }
var query = "FOR doc in geoFilter FILTER DISTANCE(doc.latitude, doc.longitude, 0, 0) < 2000 RETURN doc"
db._explain(query, {}, {colors: false});
db._query(query);
~db._drop("geoFilter")
@END_EXAMPLE_ARANGOSH_OUTPUT
@endDocuBlock geoIndexFilterOptimization

@startDocuBlock collectionGeo

@startDocuBlock collectionNear

@startDocuBlock collectionWithin

ensures that a geo index exists collection.ensureIndex({ type: "geo", fields: [ "location" ] })

Since ArangoDB 2.5, this method is an alias for ensureGeoIndex since geo indexes are always sparse, meaning that documents that do not contain the index attributes or has non-numeric values in the index attributes will not be indexed. ensureGeoConstraint is deprecated and ensureGeoIndex should be used instead.

The index does not provide a unique option because of its limited usability. It would prevent identical coordinates from being inserted only, but even a slightly different location (like 1 inch or 1 cm off) would be unique again and not considered a duplicate, although it probably should. The desired threshold for detecting duplicates may vary for every project (including how to calculate the distance even) and needs to be implemented on the application layer as needed. You can write a Foxx service for this purpose and make use of the AQL geo functions to find nearby coordinates supported by a geo index.

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Geo-Spatial Indexes

Using a Geo-Spatial Index

GeoJSON Mode

Non-GeoJSON mode

Indexed GeoSpatial Queries

Query for Results near Origin (NEAR type query)

Query for Sorted Results near Origin (NEAR type query)

Query for Results within Distance

Query for Results contained in Polygon

Query for Results Intersecting a Polygon

GeoJSON

Point

MultiPoint

LineString

MultiLineString

Polygon

Arangosh Examples

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