elasticsearch(14) best_field策略和most_field策略
從best-fields換成most-fields策略 best-fields策略，主要是說將某一個field匹配儘可能多的關鍵詞的doc優先返回回來 most-fields策略，主要是說盡可能返回更多field匹配到某個關鍵詞的doc，優先返回回來
POST /forum/_mapping/article { "properties": { "sub_title": { "type": "string", "analyzer": "english", "fields": { "std": { "type": "string", "analyzer": "standard" } } } } }
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"} }
GET /forum/article/_search { "query": { "match": { "sub_title": "learning courses" } } }
{ "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" } } ] } }
sub_title用的是enligsh analyzer，所以還原了單詞
為什麼，因為如果我們用的是類似於english analyzer這種分詞器的話，就會將單詞還原為其最基本的形態，stemmer learning --> learn learned --> learn courses --> course
sub_titile: learning coureses --> learn course
{ "doc" : {"sub_title" : "learned a lot of course"} }，就排在了{ "doc" : {"sub_title" : "learning more courses"} }的前面
GET /forum/article/_search { "query": { "match": { "sub_title": "learning courses" } } }
很繞。。。。我自己都覺得很繞
很多東西，你看文字就覺得很繞，然後用語言去表述，也很繞，但是我覺得，用語言去說，相對來說會好一點點
GET /forum/article/_search { "query": { "multi_match": { "query": "learning courses", "type": "most_fields", "fields": [ "sub_title", "sub_title.std" ] } } }
{ "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" } } ] } }
你問我，具體的分數怎麼算出來的，很難說，因為這個東西很複雜， 還不只是TF/IDF演算法。因為不同的query，不同的語法，都有不同的計算score的細節。
與best_fields的區別
（1）best_fields，是對多個field進行搜尋，挑選某個field匹配度最高的那個分數，同時在多個query最高分相同的情況下，在一定程度上考慮其他query的分數。簡單來說，你對多個field進行搜尋，就想搜尋到某一個field儘可能包含更多關鍵字的資料
優點：通過best_fields策略，以及綜合考慮其他field，還有minimum_should_match支援，可以儘可能精準地將匹配的結果推送到最前面 缺點：除了那些精準匹配的結果，其他差不多大的結果，排序結果不是太均勻，沒有什麼區分度了
實際的例子：百度之類的搜尋引擎，最匹配的到最前面，但是其他的就沒什麼區分度了
（2）most_fields，綜合多個field一起進行搜尋，儘可能多地讓所有field的query參與到總分數的計算中來，此時就會是個大雜燴，出現類似best_fields案例最開始的那個結果，結果不一定精準，某一個document的一個field包含更多的關鍵字，但是因為其他document有更多field匹配到了，所以排在了前面；所以需要建立類似sub_title.std這樣的field，儘可能讓某一個field精準匹配query string，貢獻更高的分數，將更精準匹配的資料排到前面
優點：將盡可能匹配更多field的結果推送到最前面，整個排序結果是比較均勻的 缺點：可能那些精準匹配的結果，無法推送到最前面
實際的例子：wiki，明顯的most_fields策略，搜尋結果比較均勻，但是的確要翻好幾頁才能找到最匹配的結果