文章目錄

• 概述
• 官網
• slop 含義
• 例子
• • 示例一
  • 示例二
  • 示例三

概述

繼續跟中華石杉老師學習ES，第18篇

課程地址： https://www.roncoo.com/view/55

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接上篇博客 白話Elasticsearch17-match_phrase query 短語匹配搜索

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官網

https://www.elastic.co/guide/en/elasticsearch/reference/current/full-text-queries.html

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

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slop 含義

官網中我們可以看到

A phrase query matches terms up to a configurable slop (which defaults to 0) in any order. Transposed terms have a slop of 2.

slop是什么呢？

query string，搜索文本，中的幾個term，要經過幾次移動才能與一個document匹配，這個移動的次數，就是slop 。

• slop的phrase match，就是proximity match，近似匹配

• 如果我們指定了slop，那么就允許搜索關鍵詞進行移動，來嘗試與doc進行匹配

• 搜索關鍵詞k，可以有一定的距離，但是靠的越近，越先搜索出來，proximity match

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例子

一個query string經過幾次移動之后可以匹配到一個document，然后設置slop .

假設有個doc

hello world, java is very good, spark is also very good.

我們使用 match_phrase query 來搜索 java spark ，是肯定搜索不到的， 因為 match_phrase query 會將java spark 作為一個整體來查找。

如果我們指定了slop，那么就允許java spark進行移動，來嘗試與doc進行匹配

這里的slop，就是3，因為java spark這個短語，spark移動了3次，就可以跟一個doc匹配上了 。

slop的含義，不僅僅是說一個query string terms移動幾次，跟一個doc匹配上。一個query string terms，最多可以移動幾次去嘗試跟一個doc匹配上

slop，設置的是3，那么就ok

GET /forum/article/_search {"query": {"match_phrase": {"title": {"query": "java spark","slop": 3}}} }

就可以把剛才那個doc匹配上，那個doc會作為結果返回

但是如果slop設置的是2，那么java spark，spark最多只能移動2次，此時跟doc是匹配不上的，那個doc是不會作為結果返回的。

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示例一

我們那我們的測試數據來驗證下

GET /forum/article/_search {"query": {"match_phrase": {"content": {"query": "spark data","slop": 3}}} }

分析一下slop

data經過了3次移動才匹配到 spark data ,所以 slop設置為3即可，當然了設置成比3大的數字，肯定也是可以查詢到的，這里的slop設置為3 ，可以理解為至少移動3次。

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示例二

如果我們搜索data spark 呢？ 會不會匹配得到呢？ 答案是 ： 可以

來分析一下

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示例三

slop搜索下，關鍵詞離的越近，relevance score就會越高 .

GET /forum/article/_search {"query": {"match_phrase": {"title": {"query": "java blog","slop": 5}}} }

返回結果:

{"took": 2,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": 3,"max_score": 0.81487787,"hits": [{"_index": "forum","_type": "article","_id": "2","_score": 0.81487787,"_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","new_author_last_name": "Williams","new_author_first_name": "Smith"}},{"_index": "forum","_type": "article","_id": "1","_score": 0.31424814,"_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","new_author_last_name": "Smith","new_author_first_name": "Peter"}},{"_index": "forum","_type": "article","_id": "4","_score": 0.31424814,"_source": {"articleID": "QQPX-R-3956-#aD8","userID": 2,"hidden": true,"postDate": "2017-01-02","tag": ["java","elasticsearch"],"tag_cnt": 2,"view_cnt": 80,"title": "this is java, elasticsearch, hadoop blog","content": "elasticsearch and hadoop are all very good solution, i am a beginner","sub_title": "both of them are good","author_first_name": "Robbin","author_last_name": "Li","new_author_last_name": "Li","new_author_first_name": "Robbin"}}]} }

可以看到

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