按算分排序

• 距离的单位是 km；

GET /shop/_search 
{
  "query":{
    "match": {
      "name": "凯悦"
    }
  },
  "_source": "*",
  "script_fields": {
    "distance": {
      "script": {
        "source": "haversin(lat,lon,doc['location'].lat,doc['location'].lon)",
        "lang": "expression",
        "params": {"lat":43.84038035,"lon":87.56498774}
      }
    }
  }
}

按距离排序

• 要给一个入参，表明用户的位置；
• "distance_type": "arc" 意思是球面的意思；

GET /shop/_search 
{
  "query":{
    "match": {
      "name": "凯悦"
    }
  },
  "_source": "*",
  "script_fields": {
    "distance": {
      "script": {
        "source": "haversin(lat,lon,doc['location'].lat,doc['location'].lon)",
        "lang": "expression",
        "params": {"lat":43.84038035,"lon":87.56498774}
      }
    }
  },
  "sort": [
    {
      "_geo_distance": {
        "location": {
          "lat": 43.84038035,
          "lon": 87.56498774
        },
        "order": "asc",
        "unit": "km",
        "distance_type": "arc"
      }
    }
  ]
}

基于 Function Query 的默认排序规则

GET /shop/_search 
{
  "explain": true, 
  "_source": "*",
  "script_fields": {
    "distance": {
      "script": {
        "source": "haversin(lat,lon,doc['location'].lat,doc['location'].lon)",
        "lang": "expression",
        "params": {"lat":31.23916171,"lon":121.48789949}
      }
    }
  },
  "query": {
    "function_score": {
      "query": {
        "bool": {
          "must": [
            {"match": {"name": {"query": "凯悦","boost": 0.1}}},
            {"term": {"seller_disabled_flag": 0}}
          ]
        }
      },
      "functions": [
        {
          "gauss": {
            "location": {
              "origin": "31.23916171,121.48789949",
              "scale": "100km",
              "offset": "0km",
              "decay": 0.5
            }
          },
          "weight": 9
        },
        {
          "field_value_factor": {
            "field": "remark_score"
          },
          "weight": 0.2
        },
        {
          "field_value_factor": {
            "field": "seller_remark_score"
          },
          "weight": 0.1
        }
      ],
      "score_mode": "sum",
      "boost_mode": "sum"
    }
  },
  "sort": [ 
    {
      "_score": {
        "order":"desc"
      }
    }
  ]
}

基于 Function Query 的低价排序规则

GET /shop/_search 
{
  "_source": "*",
  "script_fields": {
    "distance": {
      "script": {
        "source": "haversin(lat,lon,doc['location'].lat,doc['location'].lon)",
        "lang": "expression",
        "params": {"lat":31.23916171,"lon":121.48789949}
      }
    }
  },
  "query": {
    "function_score": {
      "query": {
        "bool": {
          "must": [
            {"match": {"name": {"query": "凯悦","boost": 0.1}}},
            {"term": {"seller_disabled_flag": 0}}
          ]
        }
      },
      "functions": [
        {
          "field_value_factor": {
            "field": "price_per_man"
          },
          "weight": 1
        }
      ],
      "score_mode": "sum",
      "boost_mode": "replace"
    }
  },
  "sort": [ 
    {
      "_score": {
        "order":"asc"
      }
    }
  ]
}

在 Function Query 的基础上增加标签的聚合

GET /shop/_search 
{
  "_source": "*",
  "script_fields": {
    "distance": {
      "script": {
        "source": "haversin(lat,lon,doc['location'].lat,doc['location'].lon)",
        "lang": "expression",
        "params": {"lat":31.23916171,"lon":121.48789949}
      }
    }
  },
  "query": {
    "function_score": {
      "query": {
        "bool": {
          "must": [
            {"match": {"name": {"query": "凯悦","boost": 0.1}}},
            {"term": {"seller_disabled_flag": 0}},
            {"term": {"tags": "落地大窗"}}
          ]
        }
      },
      "functions": [
        {
          "gauss": {
            "location": {
              "origin": "31.23916171,121.48789949",
              "scale": "100km",
              "offset": "0km",
              "decay": 0.5
            }
          },
          "weight": 9
        },
        {
          "field_value_factor": {
            "field": "remark_score"
          },
          "weight": 0.2
        },
        {
          "field_value_factor": {
            "field": "seller_remark_score"
          },
          "weight": 0.1
        }
      ],
      "score_mode": "sum",
      "boost_mode": "sum"
    }
  },
  "sort": [ 
    {
      "_score": {
        "order":"desc"
      }
    }
  ],
  "aggs": {
    "group_by_tags": {
      "terms": {
        "field": "tags"
      }
    }
  }
}