elasticsearch/docs/reference/aggregations/bucket/geohashgrid-aggregation.asciidoc

[[search-aggregations-bucket-geohashgrid-aggregation]]
=== Geohash grid aggregation
++++
<titleabbrev>Geohash grid</titleabbrev>
++++

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 {wikipedia}/Geohash[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.

WARNING: 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.


==== Simple low-precision request

[source,console,id=geohashgrid-aggregation-low-precision-example]
--------------------------------------------------
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:

[source,console-result]
--------------------------------------------------
{
  ...
  "aggregations": {
  "large-grid": {
    "buckets": [
      {
        "key": "u17",
        "doc_count": 3
      },
      {
        "key": "u09",
        "doc_count": 2
      },
      {
        "key": "u15",
        "doc_count": 1
      }
    ]
  }
}
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]

==== 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=geohashgrid-aggregation-high-precision-example]
--------------------------------------------------
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
          }
        }
      }
    }
  }
}
--------------------------------------------------
// TEST[continued]

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:

[source,console]
--------------------------------------------------
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
          }
        }
      }
    }
  }
}
--------------------------------------------------
// TEST[continued]

[source,console-result]
--------------------------------------------------
{
  ...
  "aggregations": {
    "zoomed-in": {
      "doc_count": 3,
      "zoom1": {
        "buckets": [
          {
            "key": "u173zy3j",
            "doc_count": 1
          },
          {
            "key": "u173zvfz",
            "doc_count": 1
          },
          {
            "key": "u173zt90",
            "doc_count": 1
          }
        ]
      }
    }
  }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]

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 https://github.com/sunng87/node-geohash[node-geohash] library
can be used:

[source,js]
--------------------------------------------------
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');
--------------------------------------------------
// NOTCONSOLE

==== Requests with additional bounding box filtering

The `geohash_grid` aggregation supports an optional `bounds` parameter
that restricts the points considered to those that fall within the
bounds provided. The `bounds` parameter accepts the bounding box in
all the same <<query-dsl-geo-bounding-box-query-accepted-formats,accepted formats>> of the
bounds specified in the Geo Bounding Box Query. This bounding box can be used with or
without an additional `geo_bounding_box` query 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=geohashgrid-aggregation-with-bounds]
--------------------------------------------------
POST /museums/_search?size=0
{
  "aggregations": {
    "tiles-in-bounds": {
      "geohash_grid": {
        "field": "location",
        "precision": 8,
        "bounds": {
          "top_left": "53.4375, 4.21875",
          "bottom_right": "52.03125, 5.625"
        }
      }
    }
  }
}
--------------------------------------------------
// TEST[continued]

[source,console-result]
--------------------------------------------------
{
  ...
  "aggregations": {
    "tiles-in-bounds": {
      "buckets": [
        {
          "key": "u173zy3j",
          "doc_count": 1
        },
        {
          "key": "u173zvfz",
          "doc_count": 1
        },
        {
          "key": "u173zt90",
          "doc_count": 1
        }
      ]
    }
  }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]

==== Cell dimensions at the equator
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.

[horizontal]
*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


[discrete]
[role="xpack"]
==== Aggregating `geo_shape` fields

Aggregating on <<geo-shape>> fields works just as it does for points, except that a single
shape can be counted for in multiple tiles. A shape will contribute to the count of matching values
if any part of its shape intersects with that tile. Below is an image that demonstrates this:


image:images/spatial/geoshape_grid.png[]

==== Options

[horizontal]
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.

bounds::        Optional. The bounding box to filter the points in the bucket.

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 `max(10,(size x number-of-shards))` buckets from each
                shard. If this heuristic is undesirable, the number considered
                from each shard can be over-ridden using this parameter.