GeoHash grid Aggregationedit

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.

Simple low-precision requestedit

PUT /museums
    "mappings": {
        "doc": {
            "properties": {
                "location": {
                    "type": "geo_point"

POST /museums/doc/_bulk?refresh
{"location": "52.374081,4.912350", "name": "NEMO Science Museum"}
{"location": "52.369219,4.901618", "name": "Museum Het Rembrandthuis"}
{"location": "52.371667,4.914722", "name": "Nederlands Scheepvaartmuseum"}
{"location": "51.222900,4.405200", "name": "Letterenhuis"}
{"location": "48.861111,2.336389", "name": "Musée du Louvre"}
{"location": "48.860000,2.327000", "name": "Musée d'Orsay"}

POST /museums/_search?size=0
    "aggregations" : {
        "large-grid" : {
            "geohash_grid" : {
                "field" : "location",
                "precision" : 3


    "aggregations": {
        "large-grid": {
            "buckets": [
                    "key": "u17",
                    "doc_count": 3
                    "key": "u09",
                    "doc_count": 2
                    "key": "u15",
                    "doc_count": 1

High-precision requestsedit

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"
                    "geohash_grid" : {
                        "field": "location",
                        "precision": 8

Cell dimensions at the equatoredit

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


5,009.4km x 4,992.6km


1,252.3km x 624.1km


156.5km x 156km


39.1km x 19.5km


4.9km x 4.9km


1.2km x 609.4m


152.9m x 152.4m


38.2m x 19m


4.8m x 4.8m


1.2m x 59.5cm


14.9cm x 14.9cm


3.7cm x 1.9cm



Mandatory. The name of the field indexed with GeoPoints.


Optional. The string length of the geohashes used to define cells/buckets in the results. Defaults to 5.


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.


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.