Geo-shape datatype
editGeo-shape datatype
editThe geo_shape
datatype facilitates the indexing of and searching
with arbitrary geo shapes such as rectangles and polygons. It should be
used when either the data being indexed or the queries being executed
contain shapes other than just points.
You can query documents using this type using geo_shape Query.
Mapping Options
editThe geo_shape mapping maps geo_json geometry objects to the geo_shape type. To enable it, users must explicitly map fields to the geo_shape type.
Option | Description | Default |
---|---|---|
|
[6.6]
Deprecated in 6.6. PrefixTrees no longer used
Name of the PrefixTree
implementation to be used: |
|
|
[6.6]
Deprecated in 6.6. PrefixTrees no longer used
This parameter may
be used instead of |
|
|
[6.6]
Deprecated in 6.6. PrefixTrees no longer used
Maximum number
of layers to be used by the PrefixTree. This can be used to control the
precision of shape representations andtherefore how many terms are
indexed. Defaults to the default value of the chosen PrefixTree
implementation. Since this parameter requires a certain level of
understanding of the underlying implementation, users may use the
|
various |
|
[6.6]
Deprecated in 6.6. PrefixTrees no longer used
The strategy
parameter defines the approach for how to represent shapes at indexing
and search time. It also influences the capabilities available so it
is recommended to let Elasticsearch set this parameter automatically.
There are two strategies available: |
|
|
[6.6]
Deprecated in 6.6. PrefixTrees no longer used
Used as a
hint to the PrefixTree about how precise it should be. Defaults to 0.025 (2.5%)
with 0.5 as the maximum supported value. PERFORMANCE NOTE: This value will
default to 0 if a |
|
|
Optionally define how to interpret vertex order for
polygons / multipolygons. This parameter defines one of two coordinate
system rules (Right-hand or Left-hand) each of which can be specified in three
different ways. 1. Right-hand rule: |
|
|
[6.6]
Deprecated in 6.6. PrefixTrees no longer used
Setting this option to
|
|
|
If true, malformed GeoJSON or WKT shapes are ignored. If false (default), malformed GeoJSON and WKT shapes throw an exception and reject the entire document. |
|
|
If |
|
Indexing approach
editGeoShape types are indexed by decomposing the shape into a triangular mesh and
indexing each triangle as a 7 dimension point in a BKD tree. This provides
near perfect spatial resolution (down to 1e-7 decimal degree precision) since all
spatial relations are computed using an encoded vector representation of the
original shape instead of a raster-grid representation as used by the
Prefix trees indexing approach. Performance of the tessellator primarily
depends on the number of vertices that define the polygon/multi-polygon. While
this is the default indexing technique prefix trees can still be used by setting
the tree
or strategy
parameters according to the appropriate
Mapping Options. Note that these parameters are now deprecated
and will be removed in a future version.
IMPORTANT NOTES
The following features are not yet supported with the new indexing approach:
-
geo_shape
query withMultiPoint
geometry types - Elasticsearch currently prevents searching geo_shape fields with a MultiPoint geometry type to avoid a brute force linear search over each individual point. For now, if this is absolutely needed, this can be achieved using abool
query with each individual point. -
CONTAINS
relation query - when using the new default vector indexing strategy,geo_shape
queries withrelation
defined ascontains
are not yet supported. If this query relation is an absolute necessity, it is recommended to setstrategy
toquadtree
and use the deprecated PrefixTree strategy indexing approach.
Prefix trees
edit[6.6] Deprecated in 6.6. PrefixTrees no longer used To efficiently represent shapes in an inverted index, Shapes are converted into a series of hashes representing grid squares (commonly referred to as "rasters") using implementations of a PrefixTree. The tree notion comes from the fact that the PrefixTree uses multiple grid layers, each with an increasing level of precision to represent the Earth. This can be thought of as increasing the level of detail of a map or image at higher zoom levels. Since this approach causes precision issues with indexed shape, it has been deprecated in favor of a vector indexing approach that indexes the shapes as a triangular mesh (see Indexing approach).
Multiple PrefixTree implementations are provided:
- GeohashPrefixTree - Uses geohashes for grid squares. Geohashes are base32 encoded strings of the bits of the latitude and longitude interleaved. So the longer the hash, the more precise it is. Each character added to the geohash represents another tree level and adds 5 bits of precision to the geohash. A geohash represents a rectangular area and has 32 sub rectangles. The maximum number of levels in Elasticsearch is 24; the default is 9.
- QuadPrefixTree - Uses a quadtree for grid squares. Similar to geohash, quad trees interleave the bits of the latitude and longitude the resulting hash is a bit set. A tree level in a quad tree represents 2 bits in this bit set, one for each coordinate. The maximum number of levels for the quad trees in Elasticsearch is 29; the default is 21.
Spatial strategies
edit[6.6] Deprecated in 6.6. PrefixTrees no longer used The indexing implementation selected relies on a SpatialStrategy for choosing how to decompose the shapes (either as grid squares or a tessellated triangular mesh). Each strategy answers the following:
- What type of Shapes can be indexed?
- What types of Query Operations and Shapes can be used?
- Does it support more than one Shape per field?
The following Strategy implementations (with corresponding capabilities) are provided:
Strategy | Supported Shapes | Supported Queries | Multiple Shapes |
---|---|---|---|
|
|
Yes |
|
|
|
Yes |
Accuracy
editRecursive
and Term
strategies do not provide 100% accuracy and depending on
how they are configured it may return some false positives for INTERSECTS
,
WITHIN
and CONTAINS
queries, and some false negatives for DISJOINT
queries.
To mitigate this, it is important to select an appropriate value for the tree_levels
parameter and to adjust expectations accordingly. For example, a point may be near
the border of a particular grid cell and may thus not match a query that only matches
the cell right next to it — even though the shape is very close to the point.
Example
editPUT /example { "mappings": { "properties": { "location": { "type": "geo_shape" } } } }
This mapping definition maps the location field to the geo_shape type using the default vector implementation. It provides approximately 1e-7 decimal degree precision.
Performance considerations with Prefix Trees
edit[6.6] Deprecated in 6.6. PrefixTrees no longer used With prefix trees, Elasticsearch uses the paths in the tree as terms in the inverted index and in queries. The higher the level (and thus the precision), the more terms are generated. Of course, calculating the terms, keeping them in memory, and storing them on disk all have a price. Especially with higher tree levels, indices can become extremely large even with a modest amount of data. Additionally, the size of the features also matters. Big, complex polygons can take up a lot of space at higher tree levels. Which setting is right depends on the use case. Generally one trades off accuracy against index size and query performance.
The defaults in Elasticsearch for both implementations are a compromise between index size and a reasonable level of precision of 50m at the equator. This allows for indexing tens of millions of shapes without overly bloating the resulting index too much relative to the input size.
Input Structure
editShapes can be represented using either the GeoJSON or Well-Known Text (WKT) format. The following table provides a mapping of GeoJSON and WKT to Elasticsearch types:
GeoJSON Type | WKT Type | Elasticsearch Type | Description |
---|---|---|---|
|
|
|
A single geographic coordinate. Note: Elasticsearch uses WGS-84 coordinates only. |
|
|
|
An arbitrary line given two or more points. |
|
|
|
A closed polygon whose first and last point
must match, thus requiring |
|
|
|
An array of unconnected, but likely related points. |
|
|
|
An array of separate linestrings. |
|
|
|
An array of separate polygons. |
|
|
|
A GeoJSON shape similar to the
|
|
|
|
A bounding rectangle, or envelope, specified by specifying only the top left and bottom right points. |
|
|
|
A circle specified by a center point and radius with
units, which default to |
For all types, both the inner type
and coordinates
fields are
required.
In GeoJSON and WKT, and therefore Elasticsearch, the correct coordinate order is longitude, latitude (X, Y) within coordinate arrays. This differs from many Geospatial APIs (e.g., Google Maps) that generally use the colloquial latitude, longitude (Y, X).
A point is a single geographic coordinate, such as the location of a building or the current position given by a smartphone’s Geolocation API. The following is an example of a point in GeoJSON.
POST /example/_doc { "location" : { "type" : "point", "coordinates" : [-77.03653, 38.897676] } }
The following is an example of a point in WKT:
POST /example/_doc { "location" : "POINT (-77.03653 38.897676)" }
A linestring
defined by an array of two or more positions. By
specifying only two points, the linestring
will represent a straight
line. Specifying more than two points creates an arbitrary path. The
following is an example of a LineString in GeoJSON.
POST /example/_doc { "location" : { "type" : "linestring", "coordinates" : [[-77.03653, 38.897676], [-77.009051, 38.889939]] } }
The following is an example of a LineString in WKT:
POST /example/_doc { "location" : "LINESTRING (-77.03653 38.897676, -77.009051 38.889939)" }
The above linestring
would draw a straight line starting at the White
House to the US Capitol Building.
A polygon is defined by a list of a list of points. The first and last points in each (outer) list must be the same (the polygon must be closed). The following is an example of a Polygon in GeoJSON.
POST /example/_doc { "location" : { "type" : "polygon", "coordinates" : [ [ [100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0], [100.0, 0.0] ] ] } }
The following is an example of a Polygon in WKT:
POST /example/_doc { "location" : "POLYGON ((100.0 0.0, 101.0 0.0, 101.0 1.0, 100.0 1.0, 100.0 0.0))" }
The first array represents the outer boundary of the polygon, the other arrays represent the interior shapes ("holes"). The following is a GeoJSON example of a polygon with a hole:
POST /example/_doc { "location" : { "type" : "polygon", "coordinates" : [ [ [100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0], [100.0, 0.0] ], [ [100.2, 0.2], [100.8, 0.2], [100.8, 0.8], [100.2, 0.8], [100.2, 0.2] ] ] } }
The following is an example of a Polygon with a hole in WKT:
POST /example/_doc { "location" : "POLYGON ((100.0 0.0, 101.0 0.0, 101.0 1.0, 100.0 1.0, 100.0 0.0), (100.2 0.2, 100.8 0.2, 100.8 0.8, 100.2 0.8, 100.2 0.2))" }
IMPORTANT NOTE: WKT does not enforce a specific order for vertices thus ambiguous polygons around the dateline and poles are possible. GeoJSON mandates that the outer polygon must be counterclockwise and interior shapes must be clockwise, which agrees with the Open Geospatial Consortium (OGC) Simple Feature Access specification for vertex ordering.
Elasticsearch accepts both clockwise and counterclockwise polygons if they appear not to cross the dateline (i.e. they cross less than 180° of longitude), but for polygons that do cross the dateline (or for other polygons wider than 180°) Elasticsearch requires the vertex ordering to comply with the OGC and GeoJSON specifications. Otherwise, an unintended polygon may be created and unexpected query/filter results will be returned.
The following provides an example of an ambiguous polygon. Elasticsearch will apply the GeoJSON standard to eliminate ambiguity resulting in a polygon that crosses the dateline.
POST /example/_doc { "location" : { "type" : "polygon", "coordinates" : [ [ [-177.0, 10.0], [176.0, 15.0], [172.0, 0.0], [176.0, -15.0], [-177.0, -10.0], [-177.0, 10.0] ], [ [178.2, 8.2], [-178.8, 8.2], [-180.8, -8.8], [178.2, 8.8] ] ] } }
An orientation
parameter can be defined when setting the geo_shape mapping (see Mapping Options). This will define vertex
order for the coordinate list on the mapped geo_shape field. It can also be overridden on each document. The following is an example for
overriding the orientation on a document:
POST /example/_doc { "location" : { "type" : "polygon", "orientation" : "clockwise", "coordinates" : [ [ [100.0, 0.0], [100.0, 1.0], [101.0, 1.0], [101.0, 0.0], [100.0, 0.0] ] ] } }
The following is an example of a list of geojson points:
POST /example/_doc { "location" : { "type" : "multipoint", "coordinates" : [ [102.0, 2.0], [103.0, 2.0] ] } }
The following is an example of a list of WKT points:
POST /example/_doc { "location" : "MULTIPOINT (102.0 2.0, 103.0 2.0)" }
The following is an example of a list of geojson linestrings:
POST /example/_doc { "location" : { "type" : "multilinestring", "coordinates" : [ [ [102.0, 2.0], [103.0, 2.0], [103.0, 3.0], [102.0, 3.0] ], [ [100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0] ], [ [100.2, 0.2], [100.8, 0.2], [100.8, 0.8], [100.2, 0.8] ] ] } }
The following is an example of a list of WKT linestrings:
POST /example/_doc { "location" : "MULTILINESTRING ((102.0 2.0, 103.0 2.0, 103.0 3.0, 102.0 3.0), (100.0 0.0, 101.0 0.0, 101.0 1.0, 100.0 1.0), (100.2 0.2, 100.8 0.2, 100.8 0.8, 100.2 0.8))" }
The following is an example of a list of geojson polygons (second polygon contains a hole):
POST /example/_doc { "location" : { "type" : "multipolygon", "coordinates" : [ [ [[102.0, 2.0], [103.0, 2.0], [103.0, 3.0], [102.0, 3.0], [102.0, 2.0]] ], [ [[100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0], [100.0, 0.0]], [[100.2, 0.2], [100.8, 0.2], [100.8, 0.8], [100.2, 0.8], [100.2, 0.2]] ] ] } }
The following is an example of a list of WKT polygons (second polygon contains a hole):
POST /example/_doc { "location" : "MULTIPOLYGON (((102.0 2.0, 103.0 2.0, 103.0 3.0, 102.0 3.0, 102.0 2.0)), ((100.0 0.0, 101.0 0.0, 101.0 1.0, 100.0 1.0, 100.0 0.0), (100.2 0.2, 100.8 0.2, 100.8 0.8, 100.2 0.8, 100.2 0.2)))" }
The following is an example of a collection of geojson geometry objects:
POST /example/_doc { "location" : { "type": "geometrycollection", "geometries": [ { "type": "point", "coordinates": [100.0, 0.0] }, { "type": "linestring", "coordinates": [ [101.0, 0.0], [102.0, 1.0] ] } ] } }
The following is an example of a collection of WKT geometry objects:
POST /example/_doc { "location" : "GEOMETRYCOLLECTION (POINT (100.0 0.0), LINESTRING (101.0 0.0, 102.0 1.0))" }
Envelope
editElasticsearch supports an envelope
type, which consists of coordinates
for upper left and lower right points of the shape to represent a
bounding rectangle in the format [[minLon, maxLat], [maxLon, minLat]]
:
POST /example/_doc { "location" : { "type" : "envelope", "coordinates" : [ [100.0, 1.0], [101.0, 0.0] ] } }
The following is an example of an envelope using the WKT BBOX format:
NOTE: WKT specification expects the following order: minLon, maxLon, maxLat, minLat.
POST /example/_doc { "location" : "BBOX (100.0, 102.0, 2.0, 0.0)" }
Circle
editElasticsearch supports a circle
type, which consists of a center
point with a radius. Note that this circle representation can only
be indexed when using the recursive
Prefix Tree strategy. For
the default Indexing approach circles should be approximated using
a POLYGON
.
POST /example/_doc { "location" : { "type" : "circle", "coordinates" : [101.0, 1.0], "radius" : "100m" } }
Note: The inner radius
field is required. If not specified, then
the units of the radius
will default to METERS
.
NOTE: Neither GeoJSON or WKT support a point-radius circle type.
Sorting and Retrieving index Shapes
editDue to the complex input structure and index representation of shapes,
it is not currently possible to sort shapes or retrieve their fields
directly. The geo_shape value is only retrievable through the _source
field.