mirror of
https://github.com/elastic/elasticsearch.git
synced 2025-06-29 01:44:36 -04:00
* Soft-deprecation of point/geo_point formats Since GeoJSON and WKT are now common formats for all three types: geo_shape, geo_point and point We decided to soft-deprecate the other point formats by ordering: * GeoJSON (object with keys `type` and `coordinates`) * WKT `POINT(x y)` * Object with keys `lat` and `lon` (or `x` and `y` for point) * Array [lon,lat] * String `"lat,lon"` (or `"x,y"` in point) * String with geohash (only in `geo_point`) The geohash is last because it is only in one field type. The string version is second last because it is the most controversial being the only version to reverse the coordinate order from all other formats (for geo_point only, since the coordinates are not reversed in point). In addition we replaced many examples in both documentation and tests to prioritize WKT over the plain string format. Many remaining examples of array format or object with keys still exist and could be replaced by, for example, GeoJSON, if we feel the need. * Incorrect quote position
249 lines
7 KiB
Text
249 lines
7 KiB
Text
[role="xpack"]
|
|
[[search-aggregations-bucket-geohexgrid-aggregation]]
|
|
=== Geohex grid aggregation
|
|
++++
|
|
<titleabbrev>Geohex grid</titleabbrev>
|
|
++++
|
|
|
|
A multi-bucket aggregation that groups <<geo-point,`geo_point`>>
|
|
values into buckets that represent a grid.
|
|
The resulting grid can be sparse and only
|
|
contains cells that have matching data. Each cell corresponds to a
|
|
https://h3geo.org/docs/core-library/h3Indexing#h3-cell-indexp[H3 cell index] and is
|
|
labeled using the https://h3geo.org/docs/core-library/h3Indexing#h3index-representation[H3Index representation].
|
|
|
|
See https://h3geo.org/docs/core-library/restable[the table of cell areas for H3
|
|
resolutions] on how precision (zoom) correlates to size on the ground.
|
|
Precision for this aggregation can be between 0 and 15, inclusive.
|
|
|
|
WARNING: High-precision requests can be very expensive in terms of RAM and
|
|
result sizes. For example, the highest-precision geohex with a precision of 15
|
|
produces cells that cover less than 10cm by 10cm. We recommend you use a
|
|
filter to limit high-precision requests to a smaller geographic area. For an example,
|
|
refer to <<geohexgrid-high-precision>>.
|
|
|
|
[[geohexgrid-low-precision]]
|
|
==== Simple low-precision request
|
|
|
|
[source,console,id=geohexgrid-aggregation-example]
|
|
--------------------------------------------------
|
|
PUT /museums
|
|
{
|
|
"mappings": {
|
|
"properties": {
|
|
"location": {
|
|
"type": "geo_point"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
POST /museums/_bulk?refresh
|
|
{"index":{"_id":1}}
|
|
{"location": "POINT (4.912350 52.374081)", "name": "NEMO Science Museum"}
|
|
{"index":{"_id":2}}
|
|
{"location": "POINT (4.901618 52.369219)", "name": "Museum Het Rembrandthuis"}
|
|
{"index":{"_id":3}}
|
|
{"location": "POINT (4.914722 52.371667)", "name": "Nederlands Scheepvaartmuseum"}
|
|
{"index":{"_id":4}}
|
|
{"location": "POINT (4.405200 51.222900)", "name": "Letterenhuis"}
|
|
{"index":{"_id":5}}
|
|
{"location": "POINT (2.336389 48.861111)", "name": "Musée du Louvre"}
|
|
{"index":{"_id":6}}
|
|
{"location": "POINT (2.327000 48.860000)", "name": "Musée d'Orsay"}
|
|
|
|
POST /museums/_search?size=0
|
|
{
|
|
"aggregations": {
|
|
"large-grid": {
|
|
"geohex_grid": {
|
|
"field": "location",
|
|
"precision": 4
|
|
}
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
|
|
Response:
|
|
|
|
[source,console-result]
|
|
--------------------------------------------------
|
|
{
|
|
...
|
|
"aggregations": {
|
|
"large-grid": {
|
|
"buckets": [
|
|
{
|
|
"key": "841969dffffffff",
|
|
"doc_count": 3
|
|
},
|
|
{
|
|
"key": "841fb47ffffffff",
|
|
"doc_count": 2
|
|
},
|
|
{
|
|
"key": "841fa4dffffffff",
|
|
"doc_count": 1
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]
|
|
|
|
[[geohexgrid-high-precision]]
|
|
==== High-precision requests
|
|
|
|
When requesting detailed buckets (typically for displaying a "zoomed in" map),
|
|
a filter like <<query-dsl-geo-bounding-box-query,geo_bounding_box>> should be
|
|
applied to narrow the subject area. Otherwise, potentially millions of buckets
|
|
will be created and returned.
|
|
|
|
[source,console,id=geohexgrid-high-precision-ex]
|
|
--------------------------------------------------
|
|
POST /museums/_search?size=0
|
|
{
|
|
"aggregations": {
|
|
"zoomed-in": {
|
|
"filter": {
|
|
"geo_bounding_box": {
|
|
"location": {
|
|
"top_left": "POINT (4.9 52.4)",
|
|
"bottom_right": "POINT (5.0 52.3)"
|
|
}
|
|
}
|
|
},
|
|
"aggregations": {
|
|
"zoom1": {
|
|
"geohex_grid": {
|
|
"field": "location",
|
|
"precision": 12
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// TEST[continued]
|
|
|
|
Response:
|
|
|
|
[source,console-result]
|
|
--------------------------------------------------
|
|
{
|
|
...
|
|
"aggregations": {
|
|
"zoomed-in": {
|
|
"doc_count": 3,
|
|
"zoom1": {
|
|
"buckets": [
|
|
{
|
|
"key": "8c1969c9b2617ff",
|
|
"doc_count": 1
|
|
},
|
|
{
|
|
"key": "8c1969526d753ff",
|
|
"doc_count": 1
|
|
},
|
|
{
|
|
"key": "8c1969526d26dff",
|
|
"doc_count": 1
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]
|
|
|
|
[[geohexgrid-addtl-bounding-box-filtering]]
|
|
==== Requests with additional bounding box filtering
|
|
|
|
The `geohex_grid` aggregation supports an optional `bounds` parameter
|
|
that restricts the cells considered to those that intersect the
|
|
provided bounds. The `bounds` parameter accepts the same
|
|
<<query-dsl-geo-bounding-box-query-accepted-formats,bounding box formats>>
|
|
as the geo-bounding box query. This bounding box can be used with or
|
|
without an additional `geo_bounding_box` query for filtering the points prior to aggregating.
|
|
It is an independent bounding box that can intersect with, be equal to, or be disjoint
|
|
to any additional `geo_bounding_box` queries defined in the context of the aggregation.
|
|
|
|
[source,console,id=geohexgrid-aggregation-with-bounds]
|
|
--------------------------------------------------
|
|
POST /museums/_search?size=0
|
|
{
|
|
"aggregations": {
|
|
"tiles-in-bounds": {
|
|
"geohex_grid": {
|
|
"field": "location",
|
|
"precision": 12,
|
|
"bounds": {
|
|
"top_left": "POINT (4.9 52.4)",
|
|
"bottom_right": "POINT (5.0 52.3)"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// TEST[continued]
|
|
|
|
Response:
|
|
|
|
[source,console-result]
|
|
--------------------------------------------------
|
|
{
|
|
...
|
|
"aggregations": {
|
|
"tiles-in-bounds": {
|
|
"buckets": [
|
|
{
|
|
"key": "8c1969c9b2617ff",
|
|
"doc_count": 1
|
|
},
|
|
{
|
|
"key": "8c1969526d753ff",
|
|
"doc_count": 1
|
|
},
|
|
{
|
|
"key": "8c1969526d26dff",
|
|
"doc_count": 1
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]
|
|
|
|
[[geohexgrid-options]]
|
|
==== Options
|
|
|
|
[horizontal]
|
|
field::
|
|
(Required, string) Field containing indexed geo-point values. Must be explicitly
|
|
mapped as a <<geo-point,`geo_point`>> field. If the field contains an array,
|
|
`geohex_grid` aggregates all array values.
|
|
|
|
precision::
|
|
(Optional, integer) Integer zoom of the key used to define cells/buckets in
|
|
the results. Defaults to `6`. Values outside of [`0`,`15`] will be rejected.
|
|
|
|
bounds::
|
|
(Optional, object) Bounding box used to filter the geo-points in each bucket.
|
|
Accepts the same bounding box formats as the
|
|
<<query-dsl-geo-bounding-box-query-accepted-formats,geo-bounding box query>>.
|
|
|
|
size::
|
|
(Optional, integer) Maximum number of buckets to return. Defaults to 10,000.
|
|
When results are trimmed, buckets are prioritized based on the volume of
|
|
documents they contain.
|
|
|
|
shard_size::
|
|
(Optional, integer) Number of buckets returned from each shard. Defaults to
|
|
`max(10,(size x number-of-shards))` to allow for more a accurate count of the
|
|
top cells in the final result.
|