笛卡尔边界聚合
编辑笛卡尔边界聚合
编辑一种度量聚合，用于计算包含 点 或 形状 字段所有值的的空间边界框。
示例
resp = client.indices.create(
index="museums",
mappings={
"properties": {
"location": {
"type": "point"
}
}
},
)
print(resp)
resp1 = client.bulk(
index="museums",
refresh=True,
operations=[
{
"index": {
"_id": 1
}
},
{
"location": "POINT (491.2350 5237.4081)",
"city": "Amsterdam",
"name": "NEMO Science Museum"
},
{
"index": {
"_id": 2
}
},
{
"location": "POINT (490.1618 5236.9219)",
"city": "Amsterdam",
"name": "Museum Het Rembrandthuis"
},
{
"index": {
"_id": 3
}
},
{
"location": "POINT (491.4722 5237.1667)",
"city": "Amsterdam",
"name": "Nederlands Scheepvaartmuseum"
},
{
"index": {
"_id": 4
}
},
{
"location": "POINT (440.5200 5122.2900)",
"city": "Antwerp",
"name": "Letterenhuis"
},
{
"index": {
"_id": 5
}
},
{
"location": "POINT (233.6389 4886.1111)",
"city": "Paris",
"name": "Musée du Louvre"
},
{
"index": {
"_id": 6
}
},
{
"location": "POINT (232.7000 4886.0000)",
"city": "Paris",
"name": "Musée d'Orsay"
}
],
)
print(resp1)
resp2 = client.search(
index="museums",
size="0",
query={
"match": {
"name": "musée"
}
},
aggs={
"viewport": {
"cartesian_bounds": {
"field": "location"
}
}
},
)
print(resp2)
response = client.indices.create(
index: 'museums',
body: {
mappings: {
properties: {
location: {
type: 'point'
}
}
}
}
)
puts response
response = client.bulk(
index: 'museums',
refresh: true,
body: [
{
index: {
_id: 1
}
},
{
location: 'POINT (491.2350 5237.4081)',
city: 'Amsterdam',
name: 'NEMO Science Museum'
},
{
index: {
_id: 2
}
},
{
location: 'POINT (490.1618 5236.9219)',
city: 'Amsterdam',
name: 'Museum Het Rembrandthuis'
},
{
index: {
_id: 3
}
},
{
location: 'POINT (491.4722 5237.1667)',
city: 'Amsterdam',
name: 'Nederlands Scheepvaartmuseum'
},
{
index: {
_id: 4
}
},
{
location: 'POINT (440.5200 5122.2900)',
city: 'Antwerp',
name: 'Letterenhuis'
},
{
index: {
_id: 5
}
},
{
location: 'POINT (233.6389 4886.1111)',
city: 'Paris',
name: 'Musée du Louvre'
},
{
index: {
_id: 6
}
},
{
location: 'POINT (232.7000 4886.0000)',
city: 'Paris',
name: "Musée d'Orsay"
}
]
)
puts response
response = client.search(
index: 'museums',
size: 0,
body: {
query: {
match: {
name: 'musée'
}
},
aggregations: {
viewport: {
cartesian_bounds: {
field: 'location'
}
}
}
}
)
puts response
const response = await client.indices.create({
index: "museums",
mappings: {
properties: {
location: {
type: "point",
},
},
},
});
console.log(response);
const response1 = await client.bulk({
index: "museums",
refresh: "true",
operations: [
{
index: {
_id: 1,
},
},
{
location: "POINT (491.2350 5237.4081)",
city: "Amsterdam",
name: "NEMO Science Museum",
},
{
index: {
_id: 2,
},
},
{
location: "POINT (490.1618 5236.9219)",
city: "Amsterdam",
name: "Museum Het Rembrandthuis",
},
{
index: {
_id: 3,
},
},
{
location: "POINT (491.4722 5237.1667)",
city: "Amsterdam",
name: "Nederlands Scheepvaartmuseum",
},
{
index: {
_id: 4,
},
},
{
location: "POINT (440.5200 5122.2900)",
city: "Antwerp",
name: "Letterenhuis",
},
{
index: {
_id: 5,
},
},
{
location: "POINT (233.6389 4886.1111)",
city: "Paris",
name: "Musée du Louvre",
},
{
index: {
_id: 6,
},
},
{
location: "POINT (232.7000 4886.0000)",
city: "Paris",
name: "Musée d'Orsay",
},
],
});
console.log(response1);
const response2 = await client.search({
index: "museums",
size: 0,
query: {
match: {
name: "musée",
},
},
aggs: {
viewport: {
cartesian_bounds: {
field: "location",
},
},
},
});
console.log(response2);
PUT /museums
{
"mappings": {
"properties": {
"location": {
"type": "point"
}
}
}
}
POST /museums/_bulk?refresh
{"index":{"_id":1}}
{"location": "POINT (491.2350 5237.4081)", "city": "Amsterdam", "name": "NEMO Science Museum"}
{"index":{"_id":2}}
{"location": "POINT (490.1618 5236.9219)", "city": "Amsterdam", "name": "Museum Het Rembrandthuis"}
{"index":{"_id":3}}
{"location": "POINT (491.4722 5237.1667)", "city": "Amsterdam", "name": "Nederlands Scheepvaartmuseum"}
{"index":{"_id":4}}
{"location": "POINT (440.5200 5122.2900)", "city": "Antwerp", "name": "Letterenhuis"}
{"index":{"_id":5}}
{"location": "POINT (233.6389 4886.1111)", "city": "Paris", "name": "Musée du Louvre"}
{"index":{"_id":6}}
{"location": "POINT (232.7000 4886.0000)", "city": "Paris", "name": "Musée d'Orsay"}
POST /museums/_search?size=0
{
"query": {
"match": { "name": "musée" }
},
"aggs": {
"viewport": {
"cartesian_bounds": {
"field": "location"
}
}
}
}
与 geo_bounds 聚合不同，没有选项可以设置 wrap_longitude。这是因为笛卡尔空间是欧几里得空间，不会自身环绕。因此，边界始终具有小于或等于最大 x 值的最小 x 值。
上述聚合演示了如何计算名称匹配“musée”的所有文档的 location 字段的边界框。
上述聚合的响应
{
...
"aggregations": {
"viewport": {
"bounds": {
"top_left": {
"x": 232.6999969482422,
"y": 4886.111328125
},
"bottom_right": {
"x": 233.63890075683594,
"y": 4886.0
}
}
}
}
}
shape 字段上的笛卡尔边界聚合
编辑笛卡尔边界聚合也支持 cartesian_shape 字段。
示例
resp = client.indices.create(
index="places",
mappings={
"properties": {
"geometry": {
"type": "shape"
}
}
},
)
print(resp)
resp1 = client.bulk(
index="places",
refresh=True,
operations=[
{
"index": {
"_id": 1
}
},
{
"name": "NEMO Science Museum",
"geometry": "POINT(491.2350 5237.4081)"
},
{
"index": {
"_id": 2
}
},
{
"name": "Sportpark De Weeren",
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
496.5305328369141,
5239.347642069457
],
[
496.6979026794433,
5239.172175893484
],
[
496.9425201416015,
5239.238958618537
],
[
496.7944622039794,
5239.420969150824
],
[
496.5305328369141,
5239.347642069457
]
]
]
}
}
],
)
print(resp1)
resp2 = client.search(
index="places",
size="0",
aggs={
"viewport": {
"cartesian_bounds": {
"field": "geometry"
}
}
},
)
print(resp2)
response = client.indices.create(
index: 'places',
body: {
mappings: {
properties: {
geometry: {
type: 'shape'
}
}
}
}
)
puts response
response = client.bulk(
index: 'places',
refresh: true,
body: [
{
index: {
_id: 1
}
},
{
name: 'NEMO Science Museum',
geometry: 'POINT(491.2350 5237.4081)'
},
{
index: {
_id: 2
}
},
{
name: 'Sportpark De Weeren',
geometry: {
type: 'Polygon',
coordinates: [
[
[
496.5305328369141,
5239.347642069457
],
[
496.6979026794433,
5239.172175893484
],
[
496.9425201416015,
5239.238958618537
],
[
496.7944622039794,
5239.420969150824
],
[
496.5305328369141,
5239.347642069457
]
]
]
}
}
]
)
puts response
response = client.search(
index: 'places',
size: 0,
body: {
aggregations: {
viewport: {
cartesian_bounds: {
field: 'geometry'
}
}
}
}
)
puts response
const response = await client.indices.create({
index: "places",
mappings: {
properties: {
geometry: {
type: "shape",
},
},
},
});
console.log(response);
const response1 = await client.bulk({
index: "places",
refresh: "true",
operations: [
{
index: {
_id: 1,
},
},
{
name: "NEMO Science Museum",
geometry: "POINT(491.2350 5237.4081)",
},
{
index: {
_id: 2,
},
},
{
name: "Sportpark De Weeren",
geometry: {
type: "Polygon",
coordinates: [
[
[496.5305328369141, 5239.347642069457],
[496.6979026794433, 5239.172175893484],
[496.9425201416015, 5239.238958618537],
[496.7944622039794, 5239.420969150824],
[496.5305328369141, 5239.347642069457],
],
],
},
},
],
});
console.log(response1);
const response2 = await client.search({
index: "places",
size: 0,
aggs: {
viewport: {
cartesian_bounds: {
field: "geometry",
},
},
},
});
console.log(response2);
PUT /places
{
"mappings": {
"properties": {
"geometry": {
"type": "shape"
}
}
}
}
POST /places/_bulk?refresh
{"index":{"_id":1}}
{"name": "NEMO Science Museum", "geometry": "POINT(491.2350 5237.4081)" }
{"index":{"_id":2}}
{"name": "Sportpark De Weeren", "geometry": { "type": "Polygon", "coordinates": [ [ [ 496.5305328369141, 5239.347642069457 ], [ 496.6979026794433, 5239.1721758934835 ], [ 496.9425201416015, 5239.238958618537 ], [ 496.7944622039794, 5239.420969150824 ], [ 496.5305328369141, 5239.347642069457 ] ] ] } }
POST /places/_search?size=0
{
"aggs": {
"viewport": {
"cartesian_bounds": {
"field": "geometry"
}
}
}
}
{
...
"aggregations": {
"viewport": {
"bounds": {
"top_left": {
"x": 491.2349853515625,
"y": 5239.4208984375
},
"bottom_right": {
"x": 496.9425048828125,
"y": 5237.408203125
}
}
}
}
}