文本和地理查询彼此分开执行。让我们举一个具体的例子：

PUT restaurants
{
  "mappings": {
    "properties": {
      "location": {
        "type": "geo_point"
      },
      "menu": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword"
          }
        }
      }
    }
  }
}

POST restaurants/_doc
{
  "name": "rest1",
  "location": {
    "lat": 40.739812,
    "lon": -74.006201
  },
  "menu": [
    "european",
    "french",
    "pizza"
  ]
}

POST restaurants/_doc
{
  "name": "rest2",
  "location": {
    "lat": 40.7403963,
    "lon": -73.9950026
  },
  "menu": [
    "pizza",
    "kebab"
  ]
}

然后您将match文本字段并应用geo_distance筛选器：

GET restaurants/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "menu": "pizza"
          }
        },
        {
          "geo_distance": {
            "distance": "0.5mi",
            "location": {
              "lat": 40.7388,
              "lon": -73.9982
            }
          }
        },
        {
          "function_score": {
            "query": {
              "match_all": {}
            },
            "boost_mode": "avg",
            "functions": [
              {
                "gauss": {
                  "location": {
                    "origin": {
                      "lat": 40.7388,
                      "lon": -73.9982
                    },
                    "scale": "0.5mi"
                  }
                }
              }
            ]
          }
        }
      ]
    }
  }
}

由于geo_distance查询只分配布尔值(-->Score=1；只检查位置是否在给定半径内)，因此您可能希望应用高斯function_score来提高离给定原点更近的位置。

最后，可以通过使用_geo_distance排序来覆盖这些分数，其中您可以按邻近度排序(当然，同时保持match查询不变)：

...
  "query: {...},
  "sort": [
    {
      "_geo_distance": {
        "location": {
          "lat": 40.7388,
          "lon": -73.9982
        },
        "order": "asc"
      }
    }
  ]
}