概述
继续跟中华石杉老师学习ES，第17篇
课程地址： https://www.roncoo.com/view/55
官网
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
近似匹配
假设content字段中有2个语句
java is my favourite programming language, and I also think spark is a very good big data system. java spark are very related, because scala is spark's programming language and scala is also based on jvm like java.
使用match query , 搜索java spark ,DSL 大致如下
{ "match": { "content": "java spark" } }
content 被拆分为两个单词 java 和 spark去匹配，所以如上两个doc都能被查询出来。
match query，只能搜索到包含java和spark的document，但是不知道java和spark是不是离的很近. 包含java或包含spark，或包含java和spark的doc，都会被查询出来。我们其实并不知道哪个doc，java和spark距离的比较近。
如果我们希望搜索java spark，中间不能插入任何其他的字符， 这个时候match就无能为力了 。
再比如 ， 如果我们要尽量让java和spark离的很近的document优先返回，要给它一个更高的relevance score，这就涉及到了proximity match，近似匹配.
例子
假设要实现两个需求：
java spark，就靠在一起，中间不能插入任何其他字符，就要搜索出来这种doc
java spark，但是要求，java和spark两个单词靠的越近，doc的分数越高，排名越靠前
要实现上述两个需求，用match做全文检索，是搞不定的，必须得用proximity match，近似匹配
phrase match：短语匹配
proximity match：近似匹配
这里我们要学习的是phrase match，就是仅仅搜索出java和spark靠在一起的那些doc，比如有个doc，是java use’d spark，不行。必须是比如java spark are very good friends，是可以搜索出来的。
match phrase query，就是要去将多个term作为一个短语，一起去搜索，只有包含这个短语的doc才会作为结果返回。
不像是match query，java spark，java的doc也会返回，spark的doc也会返回。
match query
为了做比对，我们先看下match query的查询结果
GET /forum/article/_search { "query": { "match": { "content": "java spark" } } }
返回结果
{ "took": 40, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": 2, "max_score": 1.8166281, "hits": [ { "_index": "forum", "_type": "article", "_id": "5", "_score": 1.8166281, "_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 spark", "sub_title": "haha, hello world", "author_first_name": "Tonny", "author_last_name": "Peter Smith", "new_author_last_name": "Peter Smith", "new_author_first_name": "Tonny" } }, { "_index": "forum", "_type": "article", "_id": "2", "_score": 0.7721133, "_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" } } ] } }
可以看到单单包含java的doc也返回了，不是我们想要的结果 。
match phrase query
为了演示match phrase query的功能，我们先调整一下测试数据
POST /forum/article/5/_update { "doc": { "content":"spark is best big data solution based on scala ,an programming language similar to java spark" } }
将id=5的doc的content设置为恰巧包含java spark这个短语 。
GET /forum/article/_search { "query": { "match_phrase": { "content": "java spark" } } }
返回结果
{ "took": 47, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": 1, "max_score": 1.4302213, "hits": [ { "_index": "forum", "_type": "article", "_id": "5", "_score": 1.4302213, "_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 spark", "sub_title": "haha, hello world", "author_first_name": "Tonny", "author_last_name": "Peter Smith", "new_author_last_name": "Peter Smith", "new_author_first_name": "Tonny" } } ] } }
从结果中可以看到只有包含java spark这个短语的doc才返回，只包含java的doc不会返回
term position
分词后，每个单词就是一个term
分词后 ， es还记录了 每个field的位置。
举个例子 两个doc 如下：
hello world, java spark doc1
hi, spark java doc2
建立倒排索引后
可以通过如下API来看下
GET _analyze { "text": "hello world, java spark", "analyzer": "standard" }
返回：
{ "tokens": [ { "token": "hello", "start_offset": 0, "end_offset": 5, "type": "<ALPHANUM>", "position": 0 }, { "token": "world", "start_offset": 6, "end_offset": 11, "type": "<ALPHANUM>", "position": 1 }, { "token": "java", "start_offset": 13, "end_offset": 17, "type": "<ALPHANUM>", "position": 2 }, { "token": "spark", "start_offset": 18, "end_offset": 23, "type": "<ALPHANUM>", "position": 3 } ] }
通过position 可以看到位置信息 。
match_phrase的基本原理
理解下索引中的position，match_phrase
两个doc 如下
hello world, java spark doc1 hi, spark java doc2
java spark , 采用match phrase来查询
- 首先 java spark 被拆成 java和spark ，分别取索引中查找
java 出现在 doc1(2) doc2(2) spark 出现在 doc1(3) doc2(1)
要找到每个term都在的一个共有的那些doc，就是要求一个doc，必须包含每个term，才能拿出来继续计算
doc1 --> java和spark --> spark position恰巧比java大1 --> java的position是2，spark的position是3，恰好满足条件
doc1符合条件
doc2 --> java和spark --> java position是2，spark position是1，spark position比java position小1，而不是大1 --> 光是position就不满足，那么doc2不匹配 .
