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

[[search-aggregations-bucket-geotilegrid-aggregation]]
=== GeoTile Grid Aggregation

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 corresponds to a
https://en.wikipedia.org/wiki/Tiled_web_map[map tile] as used by many online map
sites. Each cell is labeled using a "{zoom}/{x}/{y}" format, where zoom is equal
to the user-specified precision.

* High precision keys have a larger range for x and y, and represent tiles that
cover only a small area.
* Low precision keys have a smaller range for x and y, and represent tiles that
each cover a large area.

See https://wiki.openstreetmap.org/wiki/Zoom_levels[Zoom level documentation]
on how precision (zoom) correlates to size on the ground. Precision for this
aggregation can be between 0 and 29, inclusive.

WARNING: The highest-precision geotile of length 29 produces cells that cover
less than a 10cm by 10cm 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,js]
--------------------------------------------------
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" : {
            "geotile_grid" : {
                "field" : "location",
                "precision" : 8
            }
        }
    }
}
--------------------------------------------------
// CONSOLE

Response:

[source,js]
--------------------------------------------------
{
    ...
    "aggregations": {
        "large-grid": {
            "buckets": [
                {
                  "key" : "8/131/84",
                  "doc_count" : 3
                },
                {
                  "key" : "8/129/88",
                  "doc_count" : 2
                },
                {
                  "key" : "8/131/85",
                  "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,js]
--------------------------------------------------
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":{
                    "geotile_grid" : {
                        "field": "location",
                        "precision": 22
                    }
                }
            }
        }
    }
}
--------------------------------------------------
// CONSOLE
// TEST[continued]

[source,js]
--------------------------------------------------
{
    ...
    "aggregations" : {
        "zoomed-in" : {
            "doc_count" : 3,
            "zoom1" : {
                "buckets" : [
                    {
                      "key" : "22/2154412/1378379",
                      "doc_count" : 1
                    },
                    {
                      "key" : "22/2154385/1378332",
                      "doc_count" : 1
                    },
                    {
                      "key" : "22/2154259/1378425",
                      "doc_count" : 1
                    }
                ]
            }
        }
    }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"_shards": $body._shards,"hits":$body.hits,"timed_out":false,/]


==== Options

[horizontal]
field::         Mandatory. The name of the field indexed with GeoPoints.

precision::     Optional. The integer zoom of the key used to define
                cells/buckets in the results. Defaults to 7.
                Values outside of [0,29] will be 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 `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.