# Geo Distance Aggregation(地理距离聚合) 原文链接 : [https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-geodistance-aggregation.html](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-geodistance-aggregation.html) 译文链接 : [Geo Distance Aggregation(地理距离聚合)](http://apache.wiki/pages/viewpage.action?pageId=10028788&src=contextnavpagetreemode) 贡献者 : @于永超，[ApacheCN](/display/~apachecn)，[Apache中文网](/display/~apachechina) ## Geo Distance Aggregation 在geo_point字段上工作的多bucket聚合和概念上的工作非常类似于[range](https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-range-aggregation.html)(范围)聚合.用户可以定义原点的点和距离范围的集合。聚合计算每个文档值与原点的距离，并根据范围确定其所属的bucket(桶)（如果文档和原点之间的距离落在bucket(桶)的距离范围内，则文档属于bucket(桶) ) ``` PUT /museums { "mappings": { "doc": { "properties": { "location": { "type": "geo_point" } } } } } POST /museums/doc/_bulk?refresh {"index":{"_id":1}} {"location": "52.374081,4.912350", "name": "NEMO Science Museum"} {"index":{"_id":2}} {"location": "52.369219,4.901618", "name": "Museum Het Rembrandthuis"} {"index":{"_id":3}} {"location": "52.371667,4.914722", "name": "Nederlands Scheepvaartmuseum"} {"index":{"_id":4}} {"location": "51.222900,4.405200", "name": "Letterenhuis"} {"index":{"_id":5}} {"location": "48.861111,2.336389", "name": "Musée du Louvre"} {"index":{"_id":6}} {"location": "48.860000,2.327000", "name": "Musée d'Orsay"} POST /museums/_search?size=0 { "aggs" : { "rings_around_amsterdam" : { "geo_distance" : { "field" : "location", "origin" : "52.3760, 4.894", "ranges" : [ { "to" : 100000 }, { "from" : 100000, "to" : 300000 }, { "from" : 300000 } ] } } } } ``` 响应结果： ``` { ... "aggregations": { "rings_around_amsterdam" : { "buckets": [ { "key": "*-100000.0", "from": 0.0, "to": 100000.0, "doc_count": 3 }, { "key": "100000.0-300000.0", "from": 100000.0, "to": 300000.0, "doc_count": 1 }, { "key": "300000.0-*", "from": 300000.0, "doc_count": 2 } ] } } } ``` 指定的字段必须是geo_point类型（只能在映射中显式设置）。它还可以保存一个geo_point字段的数组，在这种情况下，在聚合期间将考虑所有这些字段。原点可以接受[geo_point](https://www.elastic.co/guide/en/elasticsearch/reference/current/geo-point.html)类型支持的所有格式: * 对象格式：{ "lat" : 52.3760, "lon" : 4.894 }－ 这是最安全的格式，因为它是最明确的lat (纬度)& lon(经度)值 * 字符串格式："52.3760, 4.894"  － 第一个数值是lat(纬度)，第二个是lon(经度) * 数组格式：[4.894, 52.3760]  － 它基于GeoJson标准，第一个数字是lon(经度)，第二个数字是lat(纬度) 在默认情况下，距离单位是m(米)，但它也可以接受:mi(英里)，in(英寸)，yd(码)，km(公里)，cm(厘米)，毫米(毫米)。 ``` POST /museums/_search?size=0 { "aggs" : { "rings" : { "geo_distance" : { "field" : "location", "origin" : "52.3760, 4.894", "unit" : "km", ＃1 "ranges" : [ { "to" : 100 }, { "from" : 100, "to" : 300 }, { "from" : 300 } ] } } } } ``` ＃1   距离将以公里计算 有两种距离计算模式：arc(默认) 和 plane， arc(电弧)计算模式是最准确的，plane模式是最快的，但是最不准确。当考虑搜索上下文是“narrow”,跨越较小的地理区域（约5km）可以用plane，plane将为非常大的区域（例如跨大陆搜索）的搜索返回更高的误差区间。距离计算类型可以使用distance_type参数设置。 ``` POST /museums/_search?size=0 { "aggs" : { "rings" : { "geo_distance" : { "field" : "location", "origin" : "52.3760, 4.894", "unit" : "km", "distance_type" : "plane", "ranges" : [ { "to" : 100 }, { "from" : 100, "to" : 300 }, { "from" : 300 } ] } } } } ``` ### Keyed Response 将keyed标志设置为true会将一个惟一的字符串键与每个bucket(桶)关联起来，并将范围作为散列而不是数组返回: ``` POST /museums/_search?size=0 { "aggs" : { "rings_around_amsterdam" : { "geo_distance" : { "field" : "location", "origin" : "52.3760, 4.894", "ranges" : [ { "to" : 100000 }, { "from" : 100000, "to" : 300000 }, { "from" : 300000 } ], "keyed": true } } } } ``` 返回结果： ``` { ... "aggregations": { "rings_around_amsterdam" : { "buckets": { "*-100000.0": { "from": 0.0, "to": 100000.0, "doc_count": 3 }, "100000.0-300000.0": { "from": 100000.0, "to": 300000.0, "doc_count": 1 }, "300000.0-*": { "from": 300000.0, "doc_count": 2 } } } } } ``` 也可以为每个范围自定义key ``` POST /museums/_search?size=0 { "aggs" : { "rings_around_amsterdam" : { "geo_distance" : { "field" : "location", "origin" : "52.3760, 4.894", "ranges" : [ { "to" : 100000, "key": "first_ring" }, { "from" : 100000, "to" : 300000, "key": "second_ring" }, { "from" : 300000, "key": "third_ring" } ], "keyed": true } } } } ``` 返回结果： ``` { ... "aggregations": { "rings_around_amsterdam" : { "buckets": { "first_ring": { "from": 0.0, "to": 100000.0, "doc_count": 3 }, "second_ring": { "from": 100000.0, "to": 300000.0, "doc_count": 1 }, "third_ring": { "from": 300000.0, "doc_count": 2 } } } } } ```