• Elasticsearch中的评分排序--Function score query

    文章目录

    1. 背景

    实际开发中，使用elasticsearch做搜索时，难免会遇到以下需求：
    (假设，搜索"吴京"，同时去搜索contentName、actor、director三个字段)
    （1）场景1：三个字段中包含"吴京"的文档的排序：contentName>actor>director（即contenName包含吴京的文档在前，actor次之，director最后）
    （2）场景2：包含“吴京”的字段多的文档排序靠前，少的靠后

    2. 数据构建

    POST /_bulk
{"index":{"_index":"my-test2","_id":"1"}}
{"contentName":"战啥狼","actor":["吴啥京","张啥翰"],"director":["吴啥京"]}
{"index":{"_index":"my-test2","_id":"2"}}
{"contentName":"战神啥传奇","actor":["周杰啥伦","吴啥非"],"director":["吴啥京"]}
{"index":{"_index":"my-test2","_id":"3"}}
{"contentName":"狼图啥腾","actor":["吴啥京"],"director":[]}
{"index":{"_index":"my-test2","_id":"4"}}
{"contentName":"烽火啥狼烟","actor":["小啥明","王啥华"],"director":["这是战啥狼"]}
{"index":{"_index":"my-test2","_id":"5"}}
{"contentName":"吴啥京做客什么什么","actor":["吴啥京"],"director":["吴啥京"]}
{"index":{"_index":"my-test2","_id":"6"}}
{"contentName":"吴啥京","actor":["吴啥京"],"director":["吴啥京"]}
{"index":{"_index":"my-test2","_id":"7"}}
{"contentName":"又是一部啥电影","actor":["吴啥京"],"director":["吴啥京"]}
{"index":{"_index":"my-test2","_id":"8"}}
{"contentName":"又是一部啥电影2","actor":["哈哈喽"],"director":["吴啥京"]}
{"index":{"_index":"my-test2","_id":"9"}}
{"contentName":"战神杀杀杀狼无敌啊","actor":["吴啥京"],"director":["这是战啥狼"]}
{"index":{"_index":"my-test2","_id":"10"}}
{"contentName":"不知道起什么名字","actor":["这是战啥狼"],"director":["这也是战啥狼"]}

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    3. function score使用

    1. 在执行完一个查询后，function score允许我们修改文档的评分
    2. 使用function score，用户需要定义一个查询语句，还有一个或多个function用来计算一个新的评分（对于这个查询返回的每个文档）

    3.1 function score示例

    GET /_search
{
  "query": {
    "function_score": {
      "query": { "match_all": {} },
      "boost": "5", 
      "functions": [
        {
          "filter": { "match": { "test": "bar" } },
          "random_score": {}, 
          "weight": 23
        },
        {
          "filter": { "match": { "test": "cat" } },
          "weight": 42
        }
      ],
      "max_boost": 42,
      "score_mode": "max",
      "boost_mode": "multiply",
      "min_score": 42
    }
  }
}

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    3.2 参数说明

    1. score_mode：指定如何去组合计算评分
         (1) multiply：默认。相乘
     (2) sum：相加
     (3) avg：平均值
     (4) first：应用第一个function
     (5) max: 应用最大的
     (6) min：应用最小的

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    1. boost_mode：新的计算得分和原来的查询得分的结合方式
         (1) multiply：默认。query score和function score相乘
     (2) replace：仅使用function score，忽略query score
     (3) sum：query score和function score相加
     (3) avg：平均值
     (4) max: query score和function score中最大的
     (5) min： query score和function score中最小的

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    使用multi_match最初始的查询评分排序

    GET /my-test2/_search
{
  "size": 3, 
  "query": {
    "multi_match": {
      "query": "吴啥京",
      "fields": [
        "actor",
        "contentName",
        "director"
      ],
      "type": "phrase"
    }
  }
}

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    {
   ...
  "hits" : {
    "total" : {
      "value" : 8,
      "relation" : "eq"
    },
    "max_score" : 4.0664473,
    "hits" : [
      {
        "_index" : "my-test2",
        "_type" : "_doc",
        "_id" : "6",
        "_score" : 4.0664473,
        "_source" : {
          "contentName" : "吴啥京",
          "actor" : [
            "吴啥京"
          ],
          "director" : [
            "吴啥京"
          ]
        }
      },
      {
        "_index" : "my-test2",
        "_type" : "_doc",
        "_id" : "5",
        "_score" : 2.6965895,
        "_source" : {
          "contentName" : "吴啥京做客什么什么",
          "actor" : [
            "吴啥京"
          ],
          "director" : [
            "吴啥京"
          ]
        }
      },
      {
        "_index" : "my-test2",
        "_type" : "_doc",
        "_id" : "3",
        "_score" : 1.1954124,
        "_source" : {
          "contentName" : "狼图啥腾",
          "actor" : [
            "吴啥京"
          ],
          "director" : [ ]
        }
      }
    ]
  }
}

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    使用function_score（score_mode=sum boost_mode=multiply）

    GET /my-test2/_search
{
  "size": 3, 
  "query": {
    "function_score": {
      "query": {
        "multi_match": {
          "query": "吴啥京",
          "fields": [
            "actor",
            "contentName",
            "director"
          ],
          "type": "phrase"
        }
      },
      "functions": [
        {
          "filter": {
            "match_phrase": {
              "contentName": "吴啥京"
            }
          },
          "weight": 2
        },
        {
          "filter": {
            "match_phrase": {
              "actor": "吴啥京"
            }
          },
          "weight": 3
        },
        {
          "filter": {
            "match_phrase": {
              "director": "吴啥京"
            }
          },
          "weight": 4
        }
      ],
      "score_mode": "sum",
      "boost_mode": "multiply"
    }
  }
}

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    {
  ...
  "hits" : {
    "total" : {
      "value" : 8,
      "relation" : "eq"
    },
    "max_score" : 36.598026,
    "hits" : [
      {
        "_index" : "my-test2",
        "_type" : "_doc",
        "_id" : "6",
        "_score" : 36.598026,
        "_source" : {
          "contentName" : "吴啥京",
          "actor" : [
            "吴啥京"
          ],
          "director" : [
            "吴啥京"
          ]
        }
      },
      {
        "_index" : "my-test2",
        "_type" : "_doc",
        "_id" : "5",
        "_score" : 24.269306,
        "_source" : {
          "contentName" : "吴啥京做客什么什么",
          "actor" : [
            "吴啥京"
          ],
          "director" : [
            "吴啥京"
          ]
        }
      },
      {
        "_index" : "my-test2",
        "_type" : "_doc",
        "_id" : "7",
        "_score" : 8.367887,
        "_source" : {
          "contentName" : "又是一部啥电影",
          "actor" : [
            "吴啥京"
          ],
          "director" : [
            "吴啥京"
          ]
        }
      }
    ]
  }
}

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    从返回的_score字段可以看出，最新的评分 = 将计算评分相加后，又乘以原始的搜索评分（eg: 36.598026 ≈ (2+3+4)*4.0664473 ）

    使用function_score（score_mode=multiply boost_mode=multiply）

    {
   ...
  "hits" : {
    "total" : {
      "value" : 8,
      "relation" : "eq"
    },
    "max_score" : 97.594734,
    "hits" : [
      {
        "_index" : "my-test2",
        "_type" : "_doc",
        "_id" : "6",
        "_score" : 97.594734,
        "_source" : {
          "contentName" : "吴啥京",
          "actor" : [
            "吴啥京"
          ],
          "director" : [
            "吴啥京"
          ]
        }
      },
      {
        "_index" : "my-test2",
        "_type" : "_doc",
        "_id" : "5",
        "_score" : 64.71815,
        "_source" : {
          "contentName" : "吴啥京做客什么什么",
          "actor" : [
            "吴啥京"
          ],
          "director" : [
            "吴啥京"
          ]
        }
      },
      {
        "_index" : "my-test2",
        "_type" : "_doc",
        "_id" : "7",
        "_score" : 14.344949,
        "_source" : {
          "contentName" : "又是一部啥电影",
          "actor" : [
            "吴啥京"
          ],
          "director" : [
            "吴啥京"
          ]
        }
      }
    ]
  }
}

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    从返回的_score字段可以看出，最新的评分 = 计算评分相乘，又乘以原始的搜索评分（eg: 97.594734 ≈ (234)*4.0664473 ）

    经测试，score_mode是用来控制functions中的评分关系，是”相乘“还是”相加“等；boost_mode是用来控制functions中的评分关系与原始搜索评分的关系

    1. min_score：默认情况下，评分修改不会改变匹配的文档. 想要排除不满足某个评分阈值的文档，该参数(min_score)可以设置为需要的评分阈值
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  • 原文地址：https://blog.csdn.net/qq_39198749/article/details/126639682