A multi-bucket aggregation that works on geo_point fields and groups points into buckets that represent cells in a grid.
The resulting grid can be sparse and only contains cells that have matching data. Each cell is labeled using a geohash which is of user-definable precision.
- High precision geohashes have a long string length and represent cells that cover only a small area.
- Low precision geohashes have a short string length and represent cells that each cover a large area.
Geohashes used in this aggregation can have a choice of precision between 1 and 12.
The highest-precision geohash of length 12 produces cells that cover less than a square metre of land and so high-precision requests can be very costly in terms of RAM and result sizes. Please see the example below on how to first filter the aggregation to a smaller geographic area before requesting high-levels of detail.
The specified field must be of type geo_point (which can only be set explicitly in the mappings) and it can also hold an array of geo_point fields, in which case all points will be taken into account during aggregation.
PUT /museums
{
"mappings": {
"properties": {
"location": {
"type": "geo_point"
}
}
}
}
POST /museums/_bulk?refresh
{"index":{"_id":1}}
{"location": "52.374081,4.912350", "name": "NEMO Science Museum"}
{"index":{"_id":2}}
{"location": "52.369219,4.901618", "name": "Museum Het Rembrandthuis"}
{"index":{"_id":3}}
{"location": "52.371667,4.914722", "name": "Nederlands Scheepvaartmuseum"}
{"index":{"_id":4}}
{"location": "51.222900,4.405200", "name": "Letterenhuis"}
{"index":{"_id":5}}
{"location": "48.861111,2.336389", "name": "Musée du Louvre"}
{"index":{"_id":6}}
{"location": "48.860000,2.327000", "name": "Musée d'Orsay"}
POST /museums/_search?size=0
{
"aggregations" : {
"large-grid" : {
"geohash_grid" : {
"field" : "location",
"precision" : 3
}
}
}
}Response:
{
...
"aggregations": {
"large-grid": {
"buckets": [
{
"key": "u17",
"doc_count": 3
},
{
"key": "u09",
"doc_count": 2
},
{
"key": "u15",
"doc_count": 1
}
]
}
}
}When requesting detailed buckets (typically for displaying a "zoomed in" map) a filter like geo_bounding_box should be applied to narrow the subject area otherwise potentially millions of buckets will be created and returned.
POST /museums/_search?size=0
{
"aggregations" : {
"zoomed-in" : {
"filter" : {
"geo_bounding_box" : {
"location" : {
"top_left" : "52.4, 4.9",
"bottom_right" : "52.3, 5.0"
}
}
},
"aggregations":{
"zoom1":{
"geohash_grid" : {
"field": "location",
"precision": 8
}
}
}
}
}
}The geohashes returned by the geohash_grid aggregation can be also used for zooming in. To zoom into the
first geohash u17 returned in the previous example, it should be specified as both top_left and bottom_right corner:
POST /museums/_search?size=0
{
"aggregations" : {
"zoomed-in" : {
"filter" : {
"geo_bounding_box" : {
"location" : {
"top_left" : "u17",
"bottom_right" : "u17"
}
}
},
"aggregations":{
"zoom1":{
"geohash_grid" : {
"field": "location",
"precision": 8
}
}
}
}
}
}{
...
"aggregations" : {
"zoomed-in" : {
"doc_count" : 3,
"zoom1" : {
"buckets" : [
{
"key" : "u173zy3j",
"doc_count" : 1
},
{
"key" : "u173zvfz",
"doc_count" : 1
},
{
"key" : "u173zt90",
"doc_count" : 1
}
]
}
}
}
}For "zooming in" on the system that don’t support geohashes, the bucket keys should be translated into bounding boxes using one of available geohash libraries. For example, for javascript the node-geohash library can be used:
var geohash = require('ngeohash');
// bbox will contain [ 52.03125, 4.21875, 53.4375, 5.625 ]
// [ minlat, minlon, maxlat, maxlon]
var bbox = geohash.decode_bbox('u17');The table below shows the metric dimensions for cells covered by various string lengths of geohash. Cell dimensions vary with latitude and so the table is for the worst-case scenario at the equator.
|
GeoHash length |
Area width x height |
|
1 |
5,009.4km x 4,992.6km |
|
2 |
1,252.3km x 624.1km |
|
3 |
156.5km x 156km |
|
4 |
39.1km x 19.5km |
|
5 |
4.9km x 4.9km |
|
6 |
1.2km x 609.4m |
|
7 |
152.9m x 152.4m |
|
8 |
38.2m x 19m |
|
9 |
4.8m x 4.8m |
|
10 |
1.2m x 59.5cm |
|
11 |
14.9cm x 14.9cm |
|
12 |
3.7cm x 1.9cm |
|
field |
Mandatory. The name of the field indexed with GeoPoints. |
|
precision |
Optional. The string length of the geohashes used to define cells/buckets in the results. Defaults to 5. The precision can either be defined in terms of the integer precision levels mentioned above. Values outside of [1,12] will be rejected. Alternatively, the precision level can be approximated from a distance measure like "1km", "10m". The precision level is calculate such that cells will not exceed the specified size (diagonal) of the required precision. When this would lead to precision levels higher than the supported 12 levels, (e.g. for distances <5.6cm) the value is rejected. |
|
size |
Optional. The maximum number of geohash buckets to return (defaults to 10,000). When results are trimmed, buckets are prioritised based on the volumes of documents they contain. |
|
shard_size |
Optional. To allow for more accurate counting of the top cells
returned in the final result the aggregation defaults to
returning |