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Elastic 文档 ›Elasticsearch 指南 [8.17] ›查询 DSL ›地理查询

地理网格查询

编辑

地理网格查询

编辑

匹配与 GeoGrid 聚合中的网格单元相交的 geo_point 和 geo_shape 值。

该查询旨在通过提供桶的键来匹配落在地理网格聚合桶内的文档。 对于 geohash 和 geotile 网格，该查询可以用于 geo_point 和 geo_shape 字段。 对于 geo_hex 网格，它只能用于 geo_point 字段。

示例

编辑

假设已索引以下文档

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 网格

编辑

使用 geohash_grid 聚合，可以根据文档的 geohash 值对文档进行分组

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
        }
      ]
    }
  }
}

我们可以通过使用以下语法，使用桶键执行 geo_grid 查询来提取其中一个桶中的文档

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 网格

编辑

使用 geotile_grid 聚合，可以根据文档的 geotile 值对文档进行分组

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
        }
      ]
    }
  }
}

我们可以通过使用以下语法，使用桶键执行 geo_grid 查询来提取其中一个桶中的文档

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 网格

编辑

使用 geohex_grid 聚合，可以根据文档的 geohex 值对文档进行分组

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
        }
      ]
    }
  }
}

我们可以通过使用以下语法，使用桶键执行 geo_grid 查询来提取其中一个桶中的文档

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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