132 lines
4.6 KiB
Plaintext
132 lines
4.6 KiB
Plaintext
[[search-aggregations-bucket-geohashgrid-aggregation]]
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=== GeoHash grid Aggregation
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A multi-bucket aggregation that works on `geo_point` fields and groups points into buckets that represent cells in a grid.
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The resulting grid can be sparse and only contains cells that have matching data. Each cell is labeled using a http://en.wikipedia.org/wiki/Geohash[geohash] which is of user-definable precision.
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* High precision geohashes have a long string length and represent cells that cover only a small area.
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* Low precision geohashes have a short string length and represent cells that each cover a large area.
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Geohashes used in this aggregation can have a choice of precision between 1 and 12.
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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.
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Please see the example below on how to first filter the aggregation to a smaller geographic area before requesting high-levels of detail.
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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.
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==== Simple low-precision request
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[source,js]
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--------------------------------------------------
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{
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"aggregations" : {
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"myLarge-GrainGeoHashGrid" : {
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"geohash_grid" : {
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"field" : "location",
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"precision" : 3
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}
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}
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}
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}
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--------------------------------------------------
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Response:
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[source,js]
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--------------------------------------------------
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{
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"aggregations": {
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"myLarge-GrainGeoHashGrid": {
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"buckets": [
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{
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"key": "svz",
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"doc_count": 10964
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},
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{
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"key": "sv8",
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"doc_count": 3198
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}
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]
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}
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}
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}
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--------------------------------------------------
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==== High-precision requests
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When requesting detailed buckets (typically for displaying a "zoomed in" map) a filter like <<query-dsl-geo-bounding-box-filter,geo_bounding_box>> should be applied to narrow the subject area otherwise potentially millions of buckets will be created and returned.
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[source,js]
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--------------------------------------------------
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{
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"aggregations" : {
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"zoomedInView" : {
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"filter" : {
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"geo_bounding_box" : {
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"location" : {
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"top_left" : "51.73, 0.9",
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"bottom_right" : "51.55, 1.1"
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}
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}
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},
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"aggregations":{
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"zoom1":{
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"geohash_grid" : {
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"field":"location",
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"precision":8,
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}
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}
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}
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}
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}
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}
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--------------------------------------------------
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==== Cell dimensions at the equator
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The table below shows the metric dimensions for cells covered by various string lengths of geohash.
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Cell dimensions vary with latitude and so the table is for the worst-case scenario at the equator.
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[horizontal]
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*GeoHash length*:: *Area width x height*
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1:: 5,009.4km x 4,992.6km
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2:: 1,252.3km x 624.1km
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3:: 156.5km x 156km
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4:: 39.1km x 19.5km
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5:: 4.9km x 4.9km
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6:: 1.2km x 609.4m
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7:: 152.9m x 152.4m
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8:: 38.2m x 19m
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9:: 4.8m x 4.8m
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10:: 1.2m x 59.5cm
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11:: 14.9cm x 14.9cm
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12:: 3.7cm x 1.9cm
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==== Options
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[horizontal]
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field:: Mandatory. The name of the field indexed with GeoPoints.
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precision:: Optional. The string length of the geohashes used to define
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cells/buckets in the results. Defaults to 5.
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size:: Optional. The maximum number of geohash buckets to return
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(defaults to 10,000). When results are trimmed, buckets are
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prioritised based on the volumes of documents they contain.
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added[1.1.0] A value of `0` will return all buckets that
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contain a hit, use with caution as this could use a lot of CPU
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and network bandwith if there are many buckets.
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shard_size:: Optional. To allow for more accurate counting of the top cells
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returned in the final result the aggregation defaults to
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returning `max(10,(size x number-of-shards))` buckets from each
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shard. If this heuristic is undesirable, the number considered
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from each shard can be over-ridden using this parameter.
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added[1.1.0] A value of `0` makes the shard size unlimited.
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