elasticsearch 经纬度查询
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 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-XPUThttp: //localhost:9200/shop/shop/1-d' { "name" : "北京" , "location" : "39.9047253699,116.4072154982" } '; curl-XPUThttp: //localhost:9200/shop/shop/2-d' { "name" : "顺义" , "location" : "40.1299127031,116.6569478577" } '; curl-XPUThttp: //localhost:9200/shop/shop/3-d' { "name" : "天津" , "location" : "39.0850853357,117.1993482089" } '; curl-XPUThttp: //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" : "return0" } } },{ "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-XPUThttp: //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
