Geo-grid queryedit
Matches geo_point and geo_shape values that
intersect a grid cell from a GeoGrid aggregation.
The query is designed to match the documents that fall inside a bucket of a geogrid aggregation by providing the key of the bucket. For geohash and geotile grids, the query can be used for geo_point and geo_shape fields. For geo_hex grid, it can only be used for geo_point fields.
Exampleedit
Assume the following the following documents are indexed:
PUT /my_locations
{
"mappings": {
"properties": {
"location": {
"type": "geo_point"
}
}
}
}
PUT /my_locations/_doc/1?refresh
{
"location" : "POINT(4.912350 52.374081)",
"city": "Amsterdam",
"name": "NEMO Science Museum"
}
PUT /my_locations/_doc/2?refresh
{
"location" : "POINT(4.405200 51.222900)",
"city": "Antwerp",
"name": "Letterenhuis"
}
PUT /my_locations/_doc/3?refresh
{
"location" : "POINT(2.336389 48.861111)",
"city": "Paris",
"name": "Musée du Louvre"
}
geohash gridedit
Using a geohash_grid aggregation, it is possible to group documents depending on their geohash value:
GET /my_locations/_search
{
"size" : 0,
"aggs" : {
"grouped" : {
"geohash_grid" : {
"field" : "location",
"precision" : 2
}
}
}
}
{
"took" : 10,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"grouped" : {
"buckets" : [
{
"key" : "u1",
"doc_count" : 2
},
{
"key" : "u0",
"doc_count" : 1
}
]
}
}
}
We can extract the documents on one of those buckets by executing a geo_grid query using the bucket key with the following syntax:
GET /my_locations/_search
{
"query": {
"geo_grid" :{
"location" : {
"geohash" : "u0"
}
}
}
}
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "my_locations",
"_id" : "3",
"_score" : 1.0,
"_source" : {
"location" : "POINT(2.336389 48.861111)",
"city" : "Paris",
"name" : "Musée du Louvre"
}
}
]
}
}
geotile gridedit
Using a geotile_grid aggregation, it is possible to group documents depending on their geotile value:
GET /my_locations/_search
{
"size" : 0,
"aggs" : {
"grouped" : {
"geotile_grid" : {
"field" : "location",
"precision" : 6
}
}
}
}
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"grouped" : {
"buckets" : [
{
"key" : "6/32/21",
"doc_count" : 2
},
{
"key" : "6/32/22",
"doc_count" : 1
}
]
}
}
}
We can extract the documents on one of those buckets by executing a geo_grid query using the bucket key with the following syntax:
GET /my_locations/_search
{
"query": {
"geo_grid" :{
"location" : {
"geotile" : "6/32/22"
}
}
}
}
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "my_locations",
"_id" : "3",
"_score" : 1.0,
"_source" : {
"location" : "POINT(2.336389 48.861111)",
"city" : "Paris",
"name" : "Musée du Louvre"
}
}
]
}
}
geohex gridedit
Using a geohex_grid aggregation, it is possible to group documents depending on their geohex value:
GET /my_locations/_search
{
"size" : 0,
"aggs" : {
"grouped" : {
"geohex_grid" : {
"field" : "location",
"precision" : 1
}
}
}
}
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"grouped" : {
"buckets" : [
{
"key" : "81197ffffffffff",
"doc_count" : 2
},
{
"key" : "811fbffffffffff",
"doc_count" : 1
}
]
}
}
}
We can extract the documents on one of those buckets by executing a geo_grid query using the bucket key with the following syntax:
GET /my_locations/_search
{
"query": {
"geo_grid" :{
"location" : {
"geohex" : "811fbffffffffff"
}
}
}
}
{
"took" : 26,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "my_locations",
"_id" : "3",
"_score" : 1.0,
"_source" : {
"location" : "POINT(2.336389 48.861111)",
"city" : "Paris",
"name" : "Musée du Louvre"
}
}
]
}
}