概述

继续跟中华石杉老师学习ES，第26篇

课程地址： https://www.roncoo.com/view/55

官方说明

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-function-score-query.html

简单来说： 自定义一个function_score函数，自己将某个field的值，跟es内置算出来的分数进行运算，然后由自己指定的field来进行分数的增强

例子

需求： 看帖子的人越多，那么帖子的分数就越高

先给所有的帖子数据增加follower数量 ， 将对帖子搜索得到的分数，跟follower_num进行运算，由follower_num在一定程度上增强帖子的分数
看帖子的人越多，那么帖子的分数就越高

POST /forum/article/_bulk
{ "update": { "_id": "1"} }
{ "doc" : {"follower_num" : 5} }
{ "update": { "_id": "2"} }
{ "doc" : {"follower_num" : 10} }
{ "update": { "_id": "3"} }
{ "doc" : {"follower_num" : 25} }
{ "update": { "_id": "4"} }
{ "doc" : {"follower_num" : 3} }
{ "update": { "_id": "5"} }
{ "doc" : {"follower_num" : 60} }

  

 

  
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DSL

GET /forum/article/_search
{
  "query": {
    "function_score": {
      "query": {
        "multi_match": {
          "query": "java spark",
          "fields": ["tile", "content"]
        }
      },
      "field_value_factor": {
        "field": "follower_num",
        "modifier": "log1p",
        "factor": 0.5
      },
      "boost_mode": "sum",
      "max_boost": 5
    }
  }
}

  

 

  
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• 如果只有field，那么会将每个doc的分数都乘以follower_num，如果有的doc follower是0，那么分数就会变为0，效果很不好。

• 因此一般会加个log1p函数，公式会变为，new_score = old_score * log(1 + number_of_votes)，这样出来的分数会比较合理 。
  

• 再加个factor，可以进一步影响分数，new_score = old_score * log(1 + factor * number_of_votes)

• boost_mode，可以决定分数与指定字段的值如何计算 : multiply，replace, sum，min，max，avg

• max_boost，限制计算出来的分数不要超过max_boost指定的值
  

返回结果：

{
  "took": 87,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 2,
    "max_score": 3.8050528,
    "hits": [
      {
        "_index": "forum",
        "_type": "article",
        "_id": "5",
        "_score": 3.8050528,
        "_source": {
          "articleID": "DHJK-B-1395-#Ky5",
          "userID": 3,
          "hidden": false,
          "postDate": "2019-05-01",
          "tag": [
            "elasticsearch"
          ],
          "tag_cnt": 1,
          "view_cnt": 10,
          "title": "this is spark blog",
          "content": "spark is best big data solution based on scala ,an programming language similar to java spark",
          "sub_title": "haha, hello world",
          "author_first_name": "Tonny",
          "author_last_name": "Peter Smith",
          "new_author_last_name": "Peter Smith",
          "new_author_first_name": "Tonny",
          "follower_num": 60
        }
      },
      {
        "_index": "forum",
        "_type": "article",
        "_id": "2",
        "_score": 1.7247463,
        "_source": {
          "articleID": "KDKE-B-9947-#kL5",
          "userID": 1,
          "hidden": false,
          "postDate": "2017-01-02",
          "tag": [
            "java"
          ],
          "tag_cnt": 1,
          "view_cnt": 50,
          "title": "this is java blog",
          "content": "i think java is the best programming language",
          "sub_title": "learned a lot of course",
          "author_first_name": "Smith",
          "author_last_name": "Williams",
          "new_author_last_name": "Williams",
          "new_author_first_name": "Smith",
          "follower_num": 10
        }
      }
    ]
  }
}

  

 

  
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文章来源: artisan.blog.csdn.net，作者：小小工匠，版权归原作者所有，如需转载，请联系作者。

原文链接：artisan.blog.csdn.net/article/details/98673569