WARNING: Version 1.4 of Elasticsearch has passed its EOL date.
This documentation is no longer being maintained and may be removed. If you are running this version, we strongly advise you to upgrade. For the latest information, see the current release documentation.
Geo Point Type
editGeo Point Type
editMapper type called geo_point
to support geo based points. The
declaration looks as follows:
{ "pin" : { "properties" : { "location" : { "type" : "geo_point" } } } }
Indexed Fields
editThe geo_point
mapping will index a single field with the format of
lat,lon
. The lat_lon
option can be set to also index the .lat
and
.lon
as numeric fields, and geohash
can be set to true
to also
index .geohash
value.
A good practice is to enable indexing lat_lon
as well, since both the
geo distance and bounding box filters can either be executed using in
memory checks, or using the indexed lat lon values, and it really
depends on the data set which one performs better. Note though, that
indexed lat lon only make sense when there is a single geo point value
for the field, and not multi values.
Geohashes
editGeohashes are a form of lat/lon encoding which divides the earth up into a grid. Each cell in this grid is represented by a geohash string. Each cell in turn can be further subdivided into smaller cells which are represented by a longer string. So the longer the geohash, the smaller (and thus more accurate) the cell is.
Because geohashes are just strings, they can be stored in an inverted index like any other string, which makes querying them very efficient.
If you enable the geohash
option, a geohash
“sub-field” will be
indexed as, eg pin.geohash
. The length of the geohash is controlled by
the geohash_precision
parameter, which can either be set to an absolute
length (eg 12
, the default) or to a distance (eg 1km
).
More usefully, set the geohash_prefix
option to true
to not only index
the geohash value, but all the enclosing cells as well. For instance, a
geohash of u30
will be indexed as [u,u3,u30]
. This option can be used
by the Geohash Cell Filter to find geopoints within a
particular cell very efficiently.
Input Structure
editThe above mapping defines a geo_point
, which accepts different
formats. The following formats are supported:
Lat Lon as Properties
edit{ "pin" : { "location" : { "lat" : 41.12, "lon" : -71.34 } } }
Lat Lon as String
editFormat in lat,lon
.
{ "pin" : { "location" : "41.12,-71.34" } }
Geohash
edit{ "pin" : { "location" : "drm3btev3e86" } }
Lat Lon as Array
editFormat in [lon, lat]
, note, the order of lon/lat here in order to
conform with GeoJSON.
{ "pin" : { "location" : [-71.34, 41.12] } }
Mapping Options
editOption | Description |
---|---|
|
Set to |
|
Set to |
|
Sets the geohash precision. It can be set to an absolute geohash length or a distance value (eg 1km, 1m, 1ml) defining the size of the smallest cell. Defaults to an absolute length of 12. |
|
If this option is set to |
|
Set to |
|
Set to |
|
Set to |
|
Set to |
|
Set to |
|
Set to |
|
The precision step (influences the number of terms
generated for each number value) for |
Field data
editBy default, geo points use the array
format which loads geo points into two
parallel double arrays, making sure there is no precision loss. However, this
can require a non-negligible amount of memory (16 bytes per document) which is
why Elasticsearch also provides a field data implementation with lossy
compression called compressed
:
{ "pin" : { "properties" : { "location" : { "type" : "geo_point", "fielddata" : { "format" : "compressed", "precision" : "1cm" } } } } }
This field data format comes with a precision
option which allows to
configure how much precision can be traded for memory. The default value is
1cm
. The following table presents values of the memory savings given various
precisions:
Precision |
Bytes per point |
Size reduction |
1km |
4 |
75% |
3m |
6 |
62.5% |
1cm |
8 |
50% |
1mm |
10 |
37.5% |
Precision can be changed on a live index by using the update mapping API.
Usage in Scripts
editWhen using doc[geo_field_name]
(in the above mapping,
doc['location']
), the doc[...].value
returns a GeoPoint
, which
then allows access to lat
and lon
(for example,
doc[...].value.lat
). For performance, it is better to access the lat
and lon
directly using doc[...].lat
and doc[...].lon
.