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"]}

3. function score使用

在执行完一个查询后，function score允许我们修改文档的评分
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}}
}

3.2 参数说明

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

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

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"}}
}

{..."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" : [ ]}}]}
}

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"}}
}

{..."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"]}}]}
}

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"]}}]}
}

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

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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