14.multi_match+most-fields策略

主要知识点

• most-fields策略的用法
• most-fields策略和best-fields的比较

 
 

 
 

best-fields策略：将某一个field匹配尽可能多的关键词的doc优先返回回来，也就是说如果这个filed匹配了更多的关键词，那么这个doc的分数就会更高。

most-fields策略：尽可能返回更多field匹配到某个关键词的doc，优先返回回来。也就是说，如果一个doc中有更多的field匹配到了关键词，那么这个doc的得分会更高。

 
 

一、准备数据

1、建立一个自定义mapping的index

POST /forum/_mapping/article

{

"properties": {

"sub_title": {

"type": "string",

"analyzer": "english",

"fields": {

"std": {

"type": "string",

"analyzer": "standard"

}

}

}

}

}

 
 

2、插入数据

POST /forum/article/_bulk

{ "update": { "_id": "1"} }

{ "doc" : {"sub_title" : "learning more courses"} }

{ "update": { "_id": "2"} }

{ "doc" : {"sub_title" : "learned a lot of course"} }

{ "update": { "_id": "3"} }

{ "doc" : {"sub_title" : "we have a lot of fun"} }

{ "update": { "_id": "4"} }

{ "doc" : {"sub_title" : "both of them are good"} }

{ "update": { "_id": "5"} }

{ "doc" : {"sub_title" : "haha, hello world"} }

 
 

3、进行match搜索

GET /forum/article/_search

{

"query": {

"match": {

"sub_title": "learning courses"

}

}

}

执行结果是：

{

"took": 3,

"timed_out": false,

"_shards": {

"total": 5,

"successful": 5,

"failed": 0

},

"hits": {

"total": 2,

"max_score": 1.219939,

"hits": [

{

"_index": "forum",

"_type": "article",

"_id": "2",

"_score": 1.219939,

"_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"

}

},

{

"_index": "forum",

"_type": "article",

"_id": "1",

"_score": 0.5063205,

"_source": {

"articleID": "XHDK-A-1293-#fJ3",

"userID": 1,

"hidden": false,

"postDate": "2017-01-01",

"tag": [

"java",

"hadoop"

],

"tag_cnt": 2,

"view_cnt": 30,

"title": "this is java and elasticsearch blog",

"content": "i like to write best elasticsearch article",

"sub_title": "learning more courses"

}

}

]

}

}

 
 

如果我们用的是类似于english analyzer这种分词器的话，就会将单词还原为其最基本的形态，这个还原单词的过程叫做stemmer。这样可能{ "doc" : {"sub_title" : "learned a lot of course"} }，就排在了{ "doc" : {"sub_title" : "learning more courses"} }的前面

learning --> learn

learned --> learn

courses --> course

sub_titile: learning coureses --> learn course

4、加上standerd分词器，

standerd不会去还原单词的基本形态，会保留单词的原样。

POST /forum/_mapping/article

{

"properties": {

"sub_title": {

"type": "string",

"analyzer": "english",

"fields": {

"std": {

"type": "string",

"analyzer": "standard"

}

}

}

}

}

 
 

 
 

、进行multi_match进行搜索。

GET /forum/article/_search

{

"query": {

"multi_match": {

"query": "learning courses",

"type": "most_fields",

"fields": [ "sub_title", "sub_title.std" ]

}

}

}

执行结果是：

{

"took": 2,

"timed_out": false,

"_shards": {

"total": 5,

"successful": 5,

"failed": 0

},

"hits": {

"total": 2,

"max_score": 1.219939,

"hits": [

{

"_index": "forum",

"_type": "article",

"_id": "2",

"_score": 1.219939,

"_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"

}

},

{

"_index": "forum",

"_type": "article",

"_id": "1",

"_score": 1.012641,

"_source": {

"articleID": "XHDK-A-1293-#fJ3",

"userID": 1,

"hidden": false,

"postDate": "2017-01-01",

"tag": [

"java",

"hadoop"

],

"tag_cnt": 2,

"view_cnt": 30,

"title": "this is java and elasticsearch blog",

"content": "i like to write best elasticsearch article",

"sub_title": "learning more courses"

}

}

]

}

}

 
 

你问我，具体的分数怎么算出来的，很难说，因为这个东西很复杂，
还不只是TF/IDF算法。因为不同的query，不同的语法，都有不同的计算score的细节。

 
 

与best_fields的区别

 
 

（1）best_fields，是对多个field进行搜索，挑选某个field匹配度最高的那个分数，同时在多个query最高分相同的情况下，在一定程度上考虑其他query的分数。简单来说，你对多个field进行搜索，就想搜索到某一个field尽可能包含更多关键字的数据

 
 

优点：通过best_fields策略，以及综合考虑其他field，还有minimum_should_match支持，可以尽可能精准地将匹配的结果推送到最前面

缺点：除了那些精准匹配的结果，其他差不多大的结果，排序结果不是太均匀，没有什么区分度了

 
 

实际的例子：百度之类的搜索引擎，最匹配的到最前面，但是其他的就没什么区分度了

 
 

（2）most_fields，综合多个field一起进行搜索，尽可能多地让所有field的query参与到总分数的计算中来，此时就会是个大杂烩，出现类似best_fields案例最开始的那个结果，结果不一定精准，某一个document的一个field包含更多的关键字，但是因为其他document有更多field匹配到了，所以排在了前面；所以需要建立类似sub_title.std这样的field，尽可能让某一个field精准匹配query string，贡献更高的分数，将更精准匹配的数据排到前面

 
 

优点：将尽可能匹配更多field的结果推送到最前面，整个排序结果是比较均匀的

缺点：可能那些精准匹配的结果，无法推送到最前面

 
 

实际的例子：wiki，明显的most_fields策略，搜索结果比较均匀，但是的确要翻好几页才能找到最匹配的结果