Geo-grid query
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.
Example
Assume the following the following documents are indexed:
resp = client.indices.create(
index="my_locations",
mappings={
"properties": {
"location": {
"type": "geo_point"
}
}
},
)
print(resp)
resp1 = client.index(
index="my_locations",
id="1",
refresh=True,
document={
"location": "POINT(4.912350 52.374081)",
"city": "Amsterdam",
"name": "NEMO Science Museum"
},
)
print(resp1)
resp2 = client.index(
index="my_locations",
id="2",
refresh=True,
document={
"location": "POINT(4.405200 51.222900)",
"city": "Antwerp",
"name": "Letterenhuis"
},
)
print(resp2)
resp3 = client.index(
index="my_locations",
id="3",
refresh=True,
document={
"location": "POINT(2.336389 48.861111)",
"city": "Paris",
"name": "Musée du Louvre"
},
)
print(resp3) response = client.indices.create(
index: 'my_locations',
body: {
mappings: {
properties: {
location: {
type: 'geo_point'
}
}
}
}
)
puts response
response = client.index(
index: 'my_locations',
id: 1,
refresh: true,
body: {
location: 'POINT(4.912350 52.374081)',
city: 'Amsterdam',
name: 'NEMO Science Museum'
}
)
puts response
response = client.index(
index: 'my_locations',
id: 2,
refresh: true,
body: {
location: 'POINT(4.405200 51.222900)',
city: 'Antwerp',
name: 'Letterenhuis'
}
)
puts response
response = client.index(
index: 'my_locations',
id: 3,
refresh: true,
body: {
location: 'POINT(2.336389 48.861111)',
city: 'Paris',
name: 'Musée du Louvre'
}
)
puts response const response = await client.indices.create({
index: "my_locations",
mappings: {
properties: {
location: {
type: "geo_point",
},
},
},
});
console.log(response);
const response1 = await client.index({
index: "my_locations",
id: 1,
refresh: "true",
document: {
location: "POINT(4.912350 52.374081)",
city: "Amsterdam",
name: "NEMO Science Museum",
},
});
console.log(response1);
const response2 = await client.index({
index: "my_locations",
id: 2,
refresh: "true",
document: {
location: "POINT(4.405200 51.222900)",
city: "Antwerp",
name: "Letterenhuis",
},
});
console.log(response2);
const response3 = await client.index({
index: "my_locations",
id: 3,
refresh: "true",
document: {
location: "POINT(2.336389 48.861111)",
city: "Paris",
name: "Musée du Louvre",
},
});
console.log(response3); 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 grid
Using a geohash_grid aggregation, it is possible to group documents depending on their geohash value:
resp = client.search(
index="my_locations",
size=0,
aggs={
"grouped": {
"geohash_grid": {
"field": "location",
"precision": 2
}
}
},
)
print(resp) response = client.search(
index: 'my_locations',
body: {
size: 0,
aggregations: {
grouped: {
geohash_grid: {
field: 'location',
precision: 2
}
}
}
}
)
puts response const response = await client.search({
index: "my_locations",
size: 0,
aggs: {
grouped: {
geohash_grid: {
field: "location",
precision: 2,
},
},
},
});
console.log(response); 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:
resp = client.search(
index="my_locations",
query={
"geo_grid": {
"location": {
"geohash": "u0"
}
}
},
)
print(resp) const response = await client.search({
index: "my_locations",
query: {
geo_grid: {
location: {
geohash: "u0",
},
},
},
});
console.log(response); 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 grid
Using a geotile_grid aggregation, it is possible to group documents depending on their geotile value:
resp = client.search(
index="my_locations",
size=0,
aggs={
"grouped": {
"geotile_grid": {
"field": "location",
"precision": 6
}
}
},
)
print(resp) response = client.search(
index: 'my_locations',
body: {
size: 0,
aggregations: {
grouped: {
geotile_grid: {
field: 'location',
precision: 6
}
}
}
}
)
puts response const response = await client.search({
index: "my_locations",
size: 0,
aggs: {
grouped: {
geotile_grid: {
field: "location",
precision: 6,
},
},
},
});
console.log(response); 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:
resp = client.search(
index="my_locations",
query={
"geo_grid": {
"location": {
"geotile": "6/32/22"
}
}
},
)
print(resp) const response = await client.search({
index: "my_locations",
query: {
geo_grid: {
location: {
geotile: "6/32/22",
},
},
},
});
console.log(response); 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 grid
Using a geohex_grid aggregation, it is possible to group documents depending on their geohex value:
resp = client.search(
index="my_locations",
size=0,
aggs={
"grouped": {
"geohex_grid": {
"field": "location",
"precision": 1
}
}
},
)
print(resp) const response = await client.search({
index: "my_locations",
size: 0,
aggs: {
grouped: {
geohex_grid: {
field: "location",
precision: 1,
},
},
},
});
console.log(response); 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:
resp = client.search(
index="my_locations",
query={
"geo_grid": {
"location": {
"geohex": "811fbffffffffff"
}
}
},
)
print(resp) const response = await client.search({
index: "my_locations",
query: {
geo_grid: {
location: {
geohex: "811fbffffffffff",
},
},
},
});
console.log(response); 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"
}
}
]
}
}
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https://www.elastic.co/guide/en/elasticsearch/reference/8.17/query-dsl-geo-grid-query.html
