Geotile 网格聚合
编辑Geotile 网格聚合
编辑一种多桶聚合，将 geo_point 和 geo_shape 值分组到表示网格的桶中。生成的网格可以是稀疏的，并且仅包含具有匹配数据的单元格。每个单元格对应于许多在线地图网站使用的地图切片。每个单元格都使用“{zoom}/{x}/{y}”格式标记，其中 zoom 等于用户指定的精度。
- 高精度键具有较大的 x 和 y 范围，表示仅覆盖小区域的切片。
- 低精度键具有较小的 x 和 y 范围，表示每个切片都覆盖较大区域。
请参阅 缩放级别文档，了解精度（缩放）如何与地面大小相关。此聚合的精度可以在 0 到 29 之间（包括 0 和 29）。
长度为 29 的最高精度 geotile 生成的单元格覆盖面积小于 10 厘米 x 10 厘米的陆地，因此高精度请求在 RAM 和结果大小方面可能非常昂贵。请参阅下面的示例，了解如何在请求高细节级别之前先将聚合过滤到较小的地理区域。
您只能使用 geotile_grid 来聚合显式映射的 geo_point 或 geo_shape 字段。如果 geo_point 字段包含数组，则 geotile_grid 会聚合所有数组值。
简单的低精度请求
编辑resp = client.indices.create(
index="museums",
mappings={
"properties": {
"location": {
"type": "geo_point"
}
}
},
)
print(resp)
resp1 = client.bulk(
index="museums",
refresh=True,
operations=[
{
"index": {
"_id": 1
}
},
{
"location": "POINT (4.912350 52.374081)",
"name": "NEMO Science Museum"
},
{
"index": {
"_id": 2
}
},
{
"location": "POINT (4.901618 52.369219)",
"name": "Museum Het Rembrandthuis"
},
{
"index": {
"_id": 3
}
},
{
"location": "POINT (4.914722 52.371667)",
"name": "Nederlands Scheepvaartmuseum"
},
{
"index": {
"_id": 4
}
},
{
"location": "POINT (4.405200 51.222900)",
"name": "Letterenhuis"
},
{
"index": {
"_id": 5
}
},
{
"location": "POINT (2.336389 48.861111)",
"name": "Musée du Louvre"
},
{
"index": {
"_id": 6
}
},
{
"location": "POINT (2.327000 48.860000)",
"name": "Musée d'Orsay"
}
],
)
print(resp1)
resp2 = client.search(
index="museums",
size="0",
aggregations={
"large-grid": {
"geotile_grid": {
"field": "location",
"precision": 8
}
}
},
)
print(resp2)
response = client.indices.create(
index: 'museums',
body: {
mappings: {
properties: {
location: {
type: 'geo_point'
}
}
}
}
)
puts response
response = client.bulk(
index: 'museums',
refresh: true,
body: [
{
index: {
_id: 1
}
},
{
location: 'POINT (4.912350 52.374081)',
name: 'NEMO Science Museum'
},
{
index: {
_id: 2
}
},
{
location: 'POINT (4.901618 52.369219)',
name: 'Museum Het Rembrandthuis'
},
{
index: {
_id: 3
}
},
{
location: 'POINT (4.914722 52.371667)',
name: 'Nederlands Scheepvaartmuseum'
},
{
index: {
_id: 4
}
},
{
location: 'POINT (4.405200 51.222900)',
name: 'Letterenhuis'
},
{
index: {
_id: 5
}
},
{
location: 'POINT (2.336389 48.861111)',
name: 'Musée du Louvre'
},
{
index: {
_id: 6
}
},
{
location: 'POINT (2.327000 48.860000)',
name: "Musée d'Orsay"
}
]
)
puts response
response = client.search(
index: 'museums',
size: 0,
body: {
aggregations: {
"large-grid": {
geotile_grid: {
field: 'location',
precision: 8
}
}
}
}
)
puts response
const response = await client.indices.create({
index: "museums",
mappings: {
properties: {
location: {
type: "geo_point",
},
},
},
});
console.log(response);
const response1 = await client.bulk({
index: "museums",
refresh: "true",
operations: [
{
index: {
_id: 1,
},
},
{
location: "POINT (4.912350 52.374081)",
name: "NEMO Science Museum",
},
{
index: {
_id: 2,
},
},
{
location: "POINT (4.901618 52.369219)",
name: "Museum Het Rembrandthuis",
},
{
index: {
_id: 3,
},
},
{
location: "POINT (4.914722 52.371667)",
name: "Nederlands Scheepvaartmuseum",
},
{
index: {
_id: 4,
},
},
{
location: "POINT (4.405200 51.222900)",
name: "Letterenhuis",
},
{
index: {
_id: 5,
},
},
{
location: "POINT (2.336389 48.861111)",
name: "Musée du Louvre",
},
{
index: {
_id: 6,
},
},
{
location: "POINT (2.327000 48.860000)",
name: "Musée d'Orsay",
},
],
});
console.log(response1);
const response2 = await client.search({
index: "museums",
size: 0,
aggregations: {
"large-grid": {
geotile_grid: {
field: "location",
precision: 8,
},
},
},
});
console.log(response2);
PUT /museums
{
"mappings": {
"properties": {
"location": {
"type": "geo_point"
}
}
}
}
POST /museums/_bulk?refresh
{"index":{"_id":1}}
{"location": "POINT (4.912350 52.374081)", "name": "NEMO Science Museum"}
{"index":{"_id":2}}
{"location": "POINT (4.901618 52.369219)", "name": "Museum Het Rembrandthuis"}
{"index":{"_id":3}}
{"location": "POINT (4.914722 52.371667)", "name": "Nederlands Scheepvaartmuseum"}
{"index":{"_id":4}}
{"location": "POINT (4.405200 51.222900)", "name": "Letterenhuis"}
{"index":{"_id":5}}
{"location": "POINT (2.336389 48.861111)", "name": "Musée du Louvre"}
{"index":{"_id":6}}
{"location": "POINT (2.327000 48.860000)", "name": "Musée d'Orsay"}
POST /museums/_search?size=0
{
"aggregations": {
"large-grid": {
"geotile_grid": {
"field": "location",
"precision": 8
}
}
}
}
响应
{
...
"aggregations": {
"large-grid": {
"buckets": [
{
"key": "8/131/84",
"doc_count": 3
},
{
"key": "8/129/88",
"doc_count": 2
},
{
"key": "8/131/85",
"doc_count": 1
}
]
}
}
}
高精度请求
编辑当请求详细的桶（通常用于显示“放大”的地图）时，应应用诸如 geo_bounding_box 之类的过滤器来缩小主题区域。否则，可能会创建和返回数百万个桶。
resp = client.search(
index="museums",
size="0",
aggregations={
"zoomed-in": {
"filter": {
"geo_bounding_box": {
"location": {
"top_left": "POINT (4.9 52.4)",
"bottom_right": "POINT (5.0 52.3)"
}
}
},
"aggregations": {
"zoom1": {
"geotile_grid": {
"field": "location",
"precision": 22
}
}
}
}
},
)
print(resp)
response = client.search(
index: 'museums',
size: 0,
body: {
aggregations: {
"zoomed-in": {
filter: {
geo_bounding_box: {
location: {
top_left: 'POINT (4.9 52.4)',
bottom_right: 'POINT (5.0 52.3)'
}
}
},
aggregations: {
"zoom1": {
geotile_grid: {
field: 'location',
precision: 22
}
}
}
}
}
}
)
puts response
const response = await client.search({
index: "museums",
size: 0,
aggregations: {
"zoomed-in": {
filter: {
geo_bounding_box: {
location: {
top_left: "POINT (4.9 52.4)",
bottom_right: "POINT (5.0 52.3)",
},
},
},
aggregations: {
zoom1: {
geotile_grid: {
field: "location",
precision: 22,
},
},
},
},
},
});
console.log(response);
POST /museums/_search?size=0
{
"aggregations": {
"zoomed-in": {
"filter": {
"geo_bounding_box": {
"location": {
"top_left": "POINT (4.9 52.4)",
"bottom_right": "POINT (5.0 52.3)"
}
}
},
"aggregations": {
"zoom1": {
"geotile_grid": {
"field": "location",
"precision": 22
}
}
}
}
}
}
响应
{
...
"aggregations": {
"zoomed-in": {
"doc_count": 3,
"zoom1": {
"buckets": [
{
"key": "22/2154412/1378379",
"doc_count": 1
},
{
"key": "22/2154385/1378332",
"doc_count": 1
},
{
"key": "22/2154259/1378425",
"doc_count": 1
}
]
}
}
}
}
使用其他边界框筛选的请求
编辑geotile_grid 聚合支持可选的 bounds 参数，该参数将考虑的单元格限制为与提供的边界相交的单元格。bounds 参数接受与地理边界框查询相同的边界框格式。此边界框可以与额外的 geo_bounding_box 查询一起使用，也可以不使用，以便在聚合之前过滤点。它是一个独立的边界框，可以与聚合上下文中定义的任何其他 geo_bounding_box 查询相交、相等或不相交。
resp = client.search(
index="museums",
size="0",
aggregations={
"tiles-in-bounds": {
"geotile_grid": {
"field": "location",
"precision": 22,
"bounds": {
"top_left": "POINT (4.9 52.4)",
"bottom_right": "POINT (5.0 52.3)"
}
}
}
},
)
print(resp)
response = client.search(
index: 'museums',
size: 0,
body: {
aggregations: {
"tiles-in-bounds": {
geotile_grid: {
field: 'location',
precision: 22,
bounds: {
top_left: 'POINT (4.9 52.4)',
bottom_right: 'POINT (5.0 52.3)'
}
}
}
}
}
)
puts response
const response = await client.search({
index: "museums",
size: 0,
aggregations: {
"tiles-in-bounds": {
geotile_grid: {
field: "location",
precision: 22,
bounds: {
top_left: "POINT (4.9 52.4)",
bottom_right: "POINT (5.0 52.3)",
},
},
},
},
});
console.log(response);
POST /museums/_search?size=0
{
"aggregations": {
"tiles-in-bounds": {
"geotile_grid": {
"field": "location",
"precision": 22,
"bounds": {
"top_left": "POINT (4.9 52.4)",
"bottom_right": "POINT (5.0 52.3)"
}
}
}
}
}
响应
{
...
"aggregations": {
"tiles-in-bounds": {
"buckets": [
{
"key": "22/2154412/1378379",
"doc_count": 1
},
{
"key": "22/2154385/1378332",
"doc_count": 1
},
{
"key": "22/2154259/1378425",
"doc_count": 1
}
]
}
}
}
聚合 geo_shape 字段
编辑对 Geoshape 字段进行聚合的工作方式与点几乎相同，只是单个形状可以在多个切片中进行计数。如果形状的任何部分与该切片相交，则该形状将计入匹配值的计数。下面是演示此情况的图像
选项
编辑|
field |
（必需，字符串）包含已索引的地理点或地理形状值的字段。必须显式映射为 |
|
precision |
（可选，整数）用于定义结果中单元格/桶的键的整数缩放。默认为 |
|
bounds |
（可选，对象）用于筛选每个桶中的地理点或地理形状的边界框。接受与地理边界框查询相同的边界框格式。 |
|
size |
（可选，整数）要返回的最大桶数。默认为 10,000。当结果被截断时，桶会根据其中包含的文档量进行优先级排序。 |
|
shard_size |
（可选，整数）从每个分片返回的桶数。默认为 |