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Elastic 文档 ›Elasticsearch 指南 [8.16] ›查询 DSL ›地理查询

地理网格查询

编辑

地理网格查询

编辑

匹配与地理网格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入地理网格聚合桶中的文档。对于 geohash 和 geotile 网格，该查询可用于 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，可以根据其 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执行地理网格查询并使用桶键来提取这些桶中的文档。

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，可以根据其 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执行地理网格查询并使用桶键来提取这些桶中的文档。

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，可以根据其 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执行地理网格查询并使用桶键来提取这些桶中的文档。

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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