概述
继续跟中华石杉老师学习ES，第十四篇
课程地址： https://www.roncoo.com/view/55
官网
https://www.elastic.co/guide/en/elasticsearch/reference/7.2/query-dsl-multi-match-query.html
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。
示例
构造数据
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"} }
执行查询
GET /forum/article/_search { "query": { "multi_match": { "query": "Peter Smith", "type": "cross_fields", "fields": [ "author_first_name", "author_last_name" ] } } }
等同于 most_fileds
GET /forum/article/_search { "query": { "multi_match": { "query": "Peter Smith", "type": "most_fields", "fields": [ "author_first_name", "author_last_name" ] } } }
返回结果
{ "took": 2, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": 3, "max_score": 2.3258216, "hits": [ { "_index": "forum", "_type": "article", "_id": "1", "_score": 2.3258216, "_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": 1.7770995, "_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", "sub_title": "haha, hello world", "author_first_name": "Tonny", "author_last_name": "Peter Smith" } }, { "_index": "forum", "_type": "article", "_id": "2", "_score": 0.5389965, "_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" } } ] } }
5.x版本中可能会出现： 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
cross-fields弊端
问题1：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
问题2：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
问题3：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
