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