elasticsearch 经纬度查询

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curl -XPUT "http://localhost:9200/shop/" -d '{
  "mappings": {
    "shop": {
      "properties": {
        "name": {
          "type""string"
        },
        "location": {
          "type""geo_point",//经纬度类型
"lat_lon"true,
"fielddata": {
      "format":    "compressed"//压缩模式,节省内存
      "precision""3m"
    }
        }
      }
    }
  }
}';
 
 
curl -XPUT http://localhost:9200/shop/shop/1 -d'
{"name""北京""location" "39.9047253699,116.4072154982"}
';
 
curl -XPUT http://localhost:9200/shop/shop/2 -d'
{"name""顺义""location" "40.1299127031,116.6569478577"}
';
 
curl -XPUT http://localhost:9200/shop/shop/3 -d'
{"name""天津""location" "39.0850853357,117.1993482089"}
';
 
curl -XPUT http://localhost:9200/shop/shop/4 -d'
{"name""上海""location" "31.2304324029,121.4737919321"}
';
 
 
curl -XGET "http://localhost:9200/shop/shop/_search?pretty" -d '{
  "query": {
    "filtered": {
      "filter": {
        "geo_distance": {
          "distance""28km",
          "type":    "indexed",
          "distance_type""sloppy_arc",
          "location": { 
            "lat":  39.9682060617,
            "lon":  116.4107280170
          }
        }
      }
    }
  },
  "sort": [     //按距离排序
    {
      "_geo_distance": {
        "location": { 
          "lat":  39.9682060617,
          "lon":  116.4107280170
        },
        "order":         "asc",
        "unit":          "km"
        "distance_type""sloppy_arc" //推荐适应此模式 plane 不准,精度太差
      }
    }
  ]
}'
 
 
再举一个更加实际的例子
 
curl -XGET "http://localhost:9200/shop/shop/_search?pretty" -d '{
  "query" : {
    "function_score" : {
      "query" : {
        "bool" : {
          "filter" : {
            "geo_distance": {
            "distance""28km",
            "type":    "indexed",
            "distance_type""sloppy_arc",
            "location": { 
            "lat":  39.9682060617,
            "lon":  116.4107280170
}
            }
          }
        }
      },
      "functions" : [ {
        "script_score" : {
          "script" : {
            "inline" "return 0"
          }
        }
      },{
         "gauss": {   //按举例远近打分
                  "location": {
                     "origin""39.9682060617,116.4107280170",
                     "scale""5km",
                     "offset""0",
                     "decay"0.5
                  }
               },
               "weight""1"
      } ],
      "score_mode" "sum",
      "boost_mode" "replace"
    }
  }
}'
 
此时你会看到北京的得分是0.25分,因为差5公里,衰减0.5, 北京距此坐标7公里,所以取值0.25.
 
 
2.3 版本的mapping 可以写成
 
curl -XPUT "http://localhost:9200/shop/" -d '{
  "mappings": {
    "shop": {
      "properties": {
        "name": {
          "type""string"
        },
        "location": {
          "type""geo_point",
"lat_lon"true//倒排索引
"geohash"true,
"geohash_prefix":     true
"geohash_precision":  "50m" 
        }
      }
    }
  }
}';
 
其实它还支持多个地址,例如
curl -XPUT http://localhost:9200/ext-shop/ext-shop/5 -d'
{"name""京津冀""location" : [[116.4072154982,39.9047253699],[116.6569478577,40.1299127031]]}
';
 
佩服ES的强大



本文转自whk66668888 51CTO博客,原文链接:http://blog.51cto.com/12597095/2048249


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