文章目錄

• 概述
• 官網(wǎng)
• 示例

概述

繼續(xù)跟中華石杉老師學習ES，第十四篇

課程地址： https://www.roncoo.com/view/55

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官網(wǎng)

https://www.elastic.co/guide/en/elasticsearch/reference/7.2/query-dsl-multi-match-query.html

cross-fields搜索，一個唯一標識，跨了多個field。

比如一個人，標識，是姓名；一個建筑，它的標識是地址。

姓名可以散落在多個field中，比如first_name和last_name中，地址可以散落在country，province，city中。

跨多個field搜索一個標識，比如搜索一個人名，或者一個地址，就是cross-fields搜索

初步來說，如果要實現(xiàn)，可能用most_fields比較合適。因為best_fields是優(yōu)先搜索單個field最匹配的結果，cross-fields本身就不是一個field的問題了。

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示例

構造數(shù)據(jù)

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"} }

執(zhí)行查詢

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"]}} }

返回結果

{"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版本中可能會出現(xiàn)： Peter Smith，匹配author_first_name，匹配到了Smith，這時候它的分數(shù)很高，為什么？？？
因為IDF分數(shù)高，IDF分數(shù)要高，那么這個匹配到的term（Smith），在所有doc中的出現(xiàn)頻率要低，author_first_name field中，Smith就出現(xiàn)過1次
Peter Smith這個人，doc 1，Smith在author_last_name中，但是author_last_name出現(xiàn)了兩次Smith，所以導致doc 1的IDF分數(shù)較低

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cross-fields弊端

• 問題1：只是找到盡可能多的field匹配的doc，而不是某個field完全匹配的doc
• 問題2：most_fields，沒辦法用minimum_should_match去掉長尾數(shù)據(jù)，就是匹配的特別少的結果
• 問題3：TF/IDF算法，比如Peter Smith和Smith Williams，搜索Peter Smith的時候，由于first_name中很少有Smith的，所以query在所有document中的頻率很低，得到的分數(shù)很高，可能Smith Williams反而會排在Peter Smith前面

總結

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