在ElasticSearch中，地理位置通过geo_point这个数据类型来支持。地理位置的数据需要提供经纬度信息，当经纬度不合法时，ES会拒绝新增文档。这种类型的数据支持距离计算，范围查询等。在底层，索引使用Geohash实现。

1、创建索引

PUT创建一个索引cn_large_cities,mapping为city:

{"mappings": {"city": {"properties": {"city": {"type": "string"},"state": {"type": "string"},"location": {"type": "geo_point"}}}}
}

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geo_point类型必须显示指定，ES无法从数据中推断。在ES中，位置数据可以通过对象，字符串，数组三种形式表示，分别如下：

# "lat,lon"
"location":"40.715,-74.011""location": {"lat":40.715,"lon":-74.011
}# [lon ,lat]
"location":[-74.011,40.715]

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POST下面4条测试数据：

{"city": "Beijing", "state": "BJ","location": {"lat": "39.91667", "lon": "116.41667"}}{"city": "Shanghai", "state": "SH","location": {"lat": "34.50000", "lon": "121.43333"}}{"city": "Xiamen", "state": "FJ","location": {"lat": "24.46667", "lon": "118.10000"}}{"city": "Fuzhou", "state": "FJ","location": {"lat": "26.08333", "lon": "119.30000"}}{"city": "Guangzhou", "state": "GD","location": {"lat": "23.16667", "lon": "113.23333"}}

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查看全部文档：

curl -X GET "http://localhost:9200/cn_large_cities/city/_search?pretty=true"

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返回全部的5条数据，score均为1：

2、位置过滤

ES中有4中位置相关的过滤器，用于过滤位置信息：

• geo_distance: 查找距离某个中心点距离在一定范围内的位置
• geo_bounding_box: 查找某个长方形区域内的位置
• geo_distance_range: 查找距离某个中心的距离在min和max之间的位置
• geo_polygon: 查找位于多边形内的地点。

geo_distance

该类型过滤器查找的范围如下图：

下面是一个查询例子：

{"query":{"filtered":{"filter":{"geo_distance":"1km","location":{"lat":40.715,"lon": -73.988 }}}}
}

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以下查询，查找距厦门500公里以内的城市：

{"query":{"filtered":{"filter":{"geo_distance" : {"distance" : "500km","location" : { "lat" : 24.46667, "lon" : 118.10000 } }}}}
}

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geo_distance_range

{"query":{"filtered":{"filter":{"geo_distance_range":{"gte": "1km","lt":  "2km","location":{ "lat":40.715, "lon": -73.988 } }}}
}

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geo_bounding_box

{"query":{"filtered":{"filter":{"geo_bounding_box":{"location":{ "top_left":{ "lat": 40.8, "lon":-74.0 }, "bottom_right":{ "lat":40.715, "lon": -73.0 } } }}}
}

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3、按距离排序

接着我们按照距离厦门远近查找：

{"sort" : [{"_geo_distance" : {"location" : {"lat" : 24.46667,"lon" : 118.10000}, "order" : "asc","unit" : "km"}}],"query": {"filtered" : {"query" : {"match_all" : {}}}}
}

结果如下，依次是厦门、福州、广州…。符合我们的常识：

{"took": 8,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 5,"max_score": null,"hits": [{"_index": "us_large_cities","_type": "city","_id": "AVaiSGXXjL0tfmRppc_p","_score": null,"_source": {"city": "Xiamen","state": "FJ","location": {"lat": "24.46667","lon": "118.10000" }},"sort": [0]},{"_index": "us_large_cities","_type": "city","_id": "AVaiSSuNjL0tfmRppc_r","_score": null,"_source": {"city": "Fuzhou","state": "FJ","location": {"lat": "26.08333","lon": "119.30000" }},"sort": [216.61105485607183]},{"_index": "us_large_cities","_type": "city","_id": "AVaiSd02jL0tfmRppc_s","_score": null,"_source": {"city": "Guangzhou","state": "GD","location": {"lat": "23.16667","lon": "113.23333" }},"sort": [515.9964950041397]},{"_index": "us_large_cities","_type": "city","_id": "AVaiR7_5jL0tfmRppc_o","_score": null,"_source": {"city": "Shanghai","state": "SH","location": {"lat": "34.50000","lon": "121.43333" }},"sort": [1161.512141925948]},{"_index": "us_large_cities","_type": "city","_id": "AVaiRwLUjL0tfmRppc_n","_score": null,"_source": {"city": "Beijing","state": "BJ","location": {"lat": "39.91667","lon": "116.41667" }},"sort": [1725.4543712286697]}]}
}

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结果返回的sort字段是指公里数。加上限制条件，只返回最近的一个城市：

{"from":0,"size":1,"sort" : [{"_geo_distance" : {"location" : {"lat" : 24.46667,"lon" : 118.10000}, "order" : "asc","unit" : "km"}}],"query": {"filtered" : {"query" : {"match_all" : {}}}}
}

4、地理位置聚合

ES提供了3种位置聚合：

• geo_distance: 根据到特定中心点的距离聚合
• geohash_grid: 根据Geohash的单元格（cell）聚合
• geo_bounds: 根据区域聚合

4.1 geo_distance聚合

下面这个查询根据距离厦门的距离来聚合，返回0-500，500-8000km的聚合：

{"query":{"filtered":{"filter":{"geo_distance" : {"distance" : "10000km","location" : { "lat" : 24.46667, "lon" : 118.10000 } }}}},"aggs":{"per_ring":{"geo_distance":{"field": "location","unit":  "km","origin":{"lat" : 24.46667,"lon" : 118.10000 },"ranges":[{"from": 0 , "to":500},{"from": 500 , "to":8000}]}}}}

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返回的聚合结果如下;

"aggregations": {"per_ring": {"buckets": [{"key": "*-500.0","from": 0,"from_as_string": "0.0","to": 500,"to_as_string": "500.0","doc_count": 2},{"key": "500.0-8000.0","from": 500,"from_as_string": "500.0","to": 8000,"to_as_string": "8000.0","doc_count": 3}]}}

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可以看到，距离厦门0-500km的城市有2个，500-8000km的有3个。

4.2 geohash_grid聚合

该聚合方式根据geo_point数据对应的geohash值所在的cell进行聚合，cell的划分精度通过precision属性来控制，精度是指cell划分的次数。

{"query":{"filtered":{"filter":{"geo_distance" : {"distance" : "10000km","location" : { "lat" : 24.46667, "lon" : 118.10000 } }}}},"aggs":{"grid_agg":{"geohash_grid":{"field": "location","precision":  2}}}}

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聚合结果如下：

"aggregations": {"grid_agg": {"buckets": [{"key": "ws","doc_count": 3},{"key": "wx","doc_count": 1},{"key": "ww","doc_count": 1}]}}

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可以看到，有3个城市的的geohash值为ws。将精度提高到5，聚合结果如下：

"aggregations": {"grid_agg": {"buckets": [{"key": "wx4g1","doc_count": 1},{"key": "wwnk7","doc_count": 1},{"key": "wssu6","doc_count": 1},{"key": "ws7gp","doc_count": 1},{"key": "ws0eb","doc_count": 1}]}}

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4.3 geo_bounds聚合

这个聚合操作计算能够覆盖所有查询结果中geo_point的最小区域，返回的是覆盖所有位置的最小矩形：

{"query":{"filtered":{"filter":{"geo_distance" : {"distance" : "10000km","location" : { "lat" : 24.46667, "lon" : 118.10000 } }}}},"aggs":{"map-zoom":{"geo_bounds":{"field": "location"}}}}

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结果如下：

 "aggregations": {"map-zoom": {"bounds": {"top_left": {"lat": 39.91666993126273,"lon": 113.2333298586309},"bottom_right": {"lat": 23.16666992381215,"lon": 121.43332997336984}}}}

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也就是说，这两个点构成的矩形能够包含所有到厦门距离10000km的区域。我们把距离调整为500km，此时覆盖这些城市的矩形如下：

"aggregations": {"map-zoom": {"bounds": {"top_left": {"lat": 26.083329990506172,"lon": 118.0999999679625},"bottom_right": {"lat": 24.46666999720037,"lon": 119.29999999701977}}}}

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5、参考资料

图解 MongoDB 地理位置索引的实现原理：http://blog.nosqlfan.com/html/1811.html
Geopoint数据类型：https://www.elastic.co/guide/en/elasticsearch/reference/current/geo-point.html