ElasticSearch使用most_fields策略进行cross-fields search

概述

cross-fields搜索，一个唯一标识，跨了多个field。

比如一个人，标识，是姓名；一个建筑，它的标识是地址。姓名可以散落在多个field中，比如first_name和last_name中，地址可以散落在country，province，city中。

跨多个field搜索一个标识，比如搜索一个人名，或者一个地址，就是cross-fields搜索

初步来说，如果要实现，可能用most_fields比较合适。因为best_fields是优先搜索单个field最匹配的结果，cross-fields本身就不是一个field的问题了。

存在的问题：

只是找到尽可能多的field匹配的doc，而不是某个field完全匹配的doc

most_fields，没办法用minimum_should_match去掉长尾数据，就是匹配的特别少的结果

TF/IDF算法，比如Peter Smith和Smith Williams，搜索Peter Smith的时候，由于first_name中很少有Smith的，所以query在所有document中的频率很低，得到的分数很高，可能Smith Williams反而会排在Peter Smith前面

例子

增加属性：author_first_name、author_last_name

POST /forum/article/_bulk{ "update": { "_id": "1"} }{ "doc" : {
  "author_first_name" : "Peter", "author_last_name" : "Smith"} }{ "update": { "_id": "2"} }{ "doc" : {
  "author_first_name" : "Smith", "author_last_name" : "Williams"} }{ "update": { "_id": "3"} }{ "doc" : {
  "author_first_name" : "Jack", "author_last_name" : "Ma"} }{ "update": { "_id": "4"} }{ "doc" : {
  "author_first_name" : "Robbin", "author_last_name" : "Li"} }{ "update": { "_id": "5"} }{ "doc" : {
  "author_first_name" : "Tonny", "author_last_name" : "Peter Smith"} }

most_fields方式实现查询：

GET /forum/article/_search{  "query": {    "multi_match": {      "query":       "Peter Smith",      "type":        "most_fields",      "fields":      [ "author_first_name", "author_last_name" ]    }  }}

查询结果：

{  "took": 2,  "timed_out": false,  "_shards": {    "total": 5,    "successful": 5,    "failed": 0  },  "hits": {    "total": 3,    "max_score": 0.6931472,    "hits": [      {        "_index": "forum",        "_type": "article",        "_id": "2",        "_score": 0.6931472,        "_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"        }      },      {        "_index": "forum",        "_type": "article",        "_id": "1",        "_score": 0.5753642,        "_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",          "author_first_name": "Peter",          "author_last_name": "Smith"        }      },      {        "_index": "forum",        "_type": "article",        "_id": "5",        "_score": 0.51623213,        "_source": {          "articleID": "DHJK-B-1395-#Ky5",          "userID": 3,          "hidden": false,          "postDate": "2017-03-01",          "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",          "sub_title": "haha, hello world",          "author_first_name": "Tonny",          "author_last_name": "Peter Smith"        }      }    ]  }}

查询结果的排序 不是我们想要的。

Peter Smith，匹配author_first_name，匹配到了Smith，这时候它的分数很高，为什么。

因为IDF分数高，IDF分数要高，那么这个匹配到的term（Smith），在所有doc中的出现频率要低，author_first_name field中，Smith就出现过1次

Peter Smith这个人，doc 1，Smith在author_last_name中，但是author_last_name出现了两次Smith，所以导致doc 1的IDF分数较低

ElasticSearch使用most_fields策略进行cross-fields search

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概述

存在的问题：

例子

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