ElasticSearch查询学习笔记章节5——geo_distance，geo_bounding_box，geo

ElasticSearch查询笔记目录

涉及的常用查询内容较多，将分多个章节进行笔记整理，具体如下：

ElasticSearch查询学习笔记章节1——term，terms，match，id查询

主要是依据精准的查询条件来查询，查询速度快，也是最常用的几类查询方式，具体种类如下:

term查询
terms查询
match_all查询
match查询
布尔match查询
multi_match查询
根据文档id查询（单个id）
根据文档ids查询（多个id）

ElasticSearch查询学习笔记章节2——prefix，fuzzy，wildcard，range，regexp查询

主要是涉及ElasticSearch查询条件相对模糊，查询速度相对慢，实时查询时应尽量避免这些方式，但是这些查询方式又具有自己独特不可代替的功能，还是还有必要，具体如下：

prefix查询
fuzzy查询
wildcard查询
range查询
regexp查询

ElasticSearch查询学习笔记章节3——scroll，delete-by-query，bool，boosting，filter，highlight查询**

主要涉及ElasticSearch的一些常用的杂项查询；

深分页scroll查询
delete-by-query
bool查询
boosting查询
filter查询
highlight(高亮)查询

ElasticSearch查询学习笔记章节4——cardinality，range，extended_stats聚合统计aggregations查询

主要涉及ES的聚合查询Aggregations；

cardinality(去重计数)查询
range(范围统计)查询
extended_stats(统计聚合)查询

ElasticSearch查询学习笔记章节5——geo_distance，geo_bounding_box，geo_polygon地图检索geo查询

. 主要涉及ES的地图检索geo相关的查询；

geo_distance查询
geo_bounding_box查询
geo_polygon查询

整体Java代码的测试用例项目

整个章节的Java代码放在CSDN资源ElasticSearch常用查询的Java实现；路径效果如下图，欢迎下载访问；

geo_point索引、数据准备

ES中特有的类型geo_point，用来存储地图类型，某平台外卖。快递体现的配送人员距离你多少米，就是这个数据类型做出来的。

新建索引map

RESTFul风格的创建索引my_badu_map如下；

PUT /my_badu_map
{"settings": {"number_of_replicas": 3,"number_of_shards": 5}, "mappings": {"properties": {"name":{"type": "text"},"location":{"type": "geo_point"}}}
}

RESTFul风格给索引my_badu_map添加数据；

PUT /my_badu_map/_doc/1
{"name":"天安门","location":{"lon":116.403981,"lat":39.914492}
}PUT /my_badu_map/_doc/2
{"name":"海淀公园","location":{"lon":116.302509,"lat":39.991152}
}PUT /my_badu_map/_doc/3
{"name":"北京动物园","location":{"lon":116.343184,"lat":39.947468}
}

RESTFul风格预览数据如下；

POST /my_badu_map/_search#数据结果如下
{"took" : 815,"timed_out" : false,"_shards" : {"total" : 5,"successful" : 5,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 3,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "my_badu_map","_type" : "_doc","_id" : "3","_score" : 1.0,"_source" : {"name" : "北京动物园","location" : {"lon" : 116.343184,"lat" : 39.947468}}},{"_index" : "my_badu_map","_type" : "_doc","_id" : "2","_score" : 1.0,"_source" : {"name" : "海淀公园","location" : {"lon" : 116.302509,"lat" : 39.991152}}},{"_index" : "my_badu_map","_type" : "_doc","_id" : "1","_score" : 1.0,"_source" : {"name" : "天安门","location" : {"lon" : 116.403981,"lat" : 39.914492}}}]}
}

ES的地图检索方式

ES支持的地图检索方式有以下几种；

geo_distance:直线距离检索，如给定点A，要求返回地图上距离点A三千米的商家（点外卖场景），如图1；

图1 geo_distance场景

geo_bounding_box:以两个点确定一个矩形，获取在矩形内的全部数据，如图2；

图2 geo_bounding_box场景

geo_polygon：以多个点，确定多边形，获取多边形内的全部数据，如图3；

图3 geo_polygon场景

基于RESTFul风格实现地图检索

geo_distance

geo_distance涉及的参数如下
location:确定一个点；
distance:确定一个半径，单位米
distance_type:确定一个图形的类型，一般是圆形,arc

实现要求，查找索引内距离北京站(116.433733,39.908404)3000米内的点;

RESTFUL代码如下；

POST /my_badu_map/_search
{"query": {"geo_distance":{"location":{"lon":116.433733,"lat":39.908404},"distance":3000,"distance_type":"arc"}}
}

RESTFUL代码效果如下,在3千米内，是包含了北京天安门的；

{"took" : 3,"timed_out" : false,"_shards" : {"total" : 5,"successful" : 5,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 1,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "my_badu_map","_type" : "_doc","_id" : "1","_score" : 1.0,"_source" : {"name" : "天安门","location" : {"lon" : 116.403981,"lat" : 39.914492}}}]}
}

geo_bounding_box

geo_bounding_box涉及的参数如下
top_left:左上角的矩形起始点经纬度；
bottom_right右下角的矩形结束点经纬度

实现要求，查找索引内位于中央民族大学(116.326943,39.95499)以及京站(116.433733,39.908404)矩形的点;

RESTFUL代码如下；

POST /my_badu_map/_search
{"query": {"geo_bounding_box":{"location":{"top_left":{"lon":116.326943,"lat":39.95499},"bottom_right":{"lon":116.433446,"lat":39.908737}}}}
}

RESTFUL代码效果如下，北京动物园和天安门在中央民族大学(116.326943,39.95499)以及京站(116.433733,39.908404)矩形内；

{"took" : 37,"timed_out" : false,"_shards" : {"total" : 5,"successful" : 5,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 2,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "my_badu_map","_type" : "_doc","_id" : "3","_score" : 1.0,"_source" : {"name" : "北京动物园","location" : {"lon" : 116.343184,"lat" : 39.947468}}},{"_index" : "my_badu_map","_type" : "_doc","_id" : "1","_score" : 1.0,"_source" : {"name" : "天安门","location" : {"lon" : 116.403981,"lat" : 39.914492}}}]}
}

geo_polygon

geo_polygon涉及的参数如下
points：是个数组，存储多变形定点的经纬度，每个点用大括号包起来

实现要求，查找索引内位于西苑桥(116.300209,40.003423)，巴沟山水园(116.29561,39.976004)以及北京科技大学(116.364528,39.996348)三角形内的点;

RESTFUL代码如下；

POST /my_badu_map/_search
{"query": {"geo_polygon":{"location":{"points":[{"lon":116.29561,"lat":39.976004},{"lon":116.364528,"lat":39.996348},{"lon":116.300209,"lat":40.003423}]}}}
}

RESTFUL代码效果如下，海淀公园位于西苑桥(116.300209,40.003423)，巴沟山水园(116.29561,39.976004)以及北京科技大学(116.364528,39.996348)三角形内；

{"took" : 204,"timed_out" : false,"_shards" : {"total" : 5,"successful" : 5,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 1,"relation" : "eq"},"max_score" : 1.0,"hits" : [{"_index" : "my_badu_map","_type" : "_doc","_id" : "2","_score" : 1.0,"_source" : {"name" : "海淀公园","location" : {"lon" : 116.302509,"lat" : 39.991152}}}]}
}

基于Java代码实现地图检索

geo_distance

geo_distance涉及的参数如下
location:确定一个点；
distance:确定一个半径，单位米
distance_type:确定一个图形的类型，一般是圆形,arc

实现要求，查找索引内距离北京站(116.433733,39.908404)3000米内的点;

Java代码如下；

    static RestHighLevelClient myClient= EsClient.getClient();  //获取操作ES的String index="sms-logs-index";String index2="my_badu_map";@Testpublic void geoDistance() throws IOException {//1.SearchRequestSearchRequest request=new SearchRequest(index2);//2.指定检索方式SearchSourceBuilder builder=new SearchSourceBuilder();  builder.query(QueryBuilders.geoDistanceQuery("location").distance("3000").point(39.908404,116.433733));request.source(builder);//3.执行查询SearchResponse resp = myClient.search(request, RequestOptions.DEFAULT);//4.返回结果for (SearchHit hit : resp.getHits().getHits()) {System.out.println(hit.getSourceAsMap());}}

Java代码实现geo_distance如下图4；

图4 Java实现geo_polygon的效果

geo_bounding_box

geo_bounding_box涉及的参数如下
top_left:左上角的矩形起始点经纬度；
bottom_right右下角的矩形结束点经纬度

实现要求，查找索引内位于中央民族大学(116.326943,39.95499)以及京站(116.433733,39.908404)矩形的点;

Java代码如下；

    static RestHighLevelClient myClient= EsClient.getClient();  //获取操作ES的String index="sms-logs-index";String index2="my_badu_map";@Testpublic void geoBoundingBox() throws IOException {//1.SearchRequestSearchRequest request=new SearchRequest(index2);//2.指定检索方式SearchSourceBuilder builder=new SearchSourceBuilder();builder.query(QueryBuilders.geoBoundingBoxQuery("location").setCorners(39.95499,116.326943,39.908737,116.433446));request.source(builder);//3.执行查询SearchResponse resp = myClient.search(request, RequestOptions.DEFAULT);//4.返回结果for (SearchHit hit : resp.getHits().getHits()) {System.out.println(hit.getSourceAsMap());}}

Java实现geo_bounding_box的效果如图5；

图5 Java实现geo_bounding_box的效果

geo_polygon

geo_polygon涉及的参数如下
points：是个数组，存储多变形定点的经纬度，每个点用大括号包起来

实现要求，查找索引内位于西苑桥(116.300209,40.003423)，巴沟山水园(116.29561,39.976004)以及北京科技大学(116.364528,39.996348)三角形内的点;

Java实现代码代码如下；

   static RestHighLevelClient myClient= EsClient.getClient();  //获取操作ES的String index="sms-logs-index";String index2="my_badu_map";@Testpublic void geoPolygon() throws IOException {//1.SearchRequestSearchRequest request=new SearchRequest(index2);//2.指定检索方式SearchSourceBuilder builder=new SearchSourceBuilder();List<GeoPoint> points=new ArrayList<>();//注意java里面是纬经度的顺序，不要搞反了points.add(new GeoPoint(39.976004,116.29561));points.add(new GeoPoint(39.996348,116.364528));points.add(new GeoPoint(40.003423,116.300209));builder.query(QueryBuilders.geoPolygonQuery("location",points));request.source(builder);//3.执行查询SearchResponse resp = myClient.search(request, RequestOptions.DEFAULT);//4.返回结果for (SearchHit hit : resp.getHits().getHits()) {System.out.println(hit.getSourceAsMap());}}

Java实现geo_polygon的效果如图6；

图6 Java实现geo_polygon的效果

更多的geo地图检索，可以参考官网geo-queries;