ElasticSearch位置搜索_elasticsearch wildcard 怎么指定位置-优快云博客

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"}
            }
        }
    }
}

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]

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"}}

查看全部文档：

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

返回全部的5条数据，score均为1：

这里写图片描述

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 }
      }
    }
  }
}

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 } }
        }
    }

    } 
}

geo_distance_range

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

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 } } }
    }
  }
}

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
        ]
      }
    ]
  }
}

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}
                ]
            }


        }
    }

}

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
        }
      ]
    }
  }

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
            }


        }
    }

}

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
        }
      ]
    }
  }

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
        }
      ]
    }
  }

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"
            }

        }
    }

}

结果如下：

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

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
        }
      }
    }
  }