1、什么是近似匹配

两个句子

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.

match query，搜索java spark

{
	"match": {
		"content": "java spark"
	}
}

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，都会被返回回来。我们其实并不知道哪个doc，java和spark距离的比较近。如果我们就是希望搜索java spark，中间不能插入任何其他的字符，那这个时候match去做全文检索，就不行了。

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

如果说，要实现两个需求：

1、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
2、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

phrase match：短语匹配 ，proximity match：，近似匹配

这一讲，要学习的是phrase match，下一讲会讲proximity match，

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。

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，java spark，java的doc也会返回，spark的doc也会返回。

2、match_phrase

修改doc5的content，包含 java spark

POST /forum/article/5/_update
{
  "doc": {
    "content": "spark is best big data solution based on scala ,an programming language similar to java spark"
  }
}

响应结果

{
  "_index": "forum",
  "_type": "article",
  "_id": "5",
  "_version": 9,
  "result": "updated",
  "_shards": {
    "total": 2,
    "successful": 1,
    "failed": 0
  }
}

将一个doc的content设置为恰巧包含java spark这个短语

使用match

GET /forum/article/_search
{
  "query": {
    "match": {
      "content": "java spark"
    }
  }
}

响应结果

{
  "took": 0,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 2,
    "max_score": 0.68640786,
    "hits": [
      {
        "_index": "forum",
        "_type": "article",
        "_id": "2",
        "_score": 0.68640786,
        "_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": "5",
        "_score": 0.68324494,
        "_source": {
          "articleID": "DHJK-B-1395-#Ky5",
          "userID": 3,
          "hidden": false,
          "postDate": "2021-11-11",
          "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"
        }
      }
    ]
  }
}

单单包含java的doc也返回了，不是我们想要的结果

使用match_phrase语法

GET /forum/article/_search
{
    "query": {
        "match_phrase": {
            "content": "java spark"
        }
    }
}

响应结果

{
  "took": 1,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 1,
    "max_score": 0.5753642,
    "hits": [
      {
        "_index": "forum",
        "_type": "article",
        "_id": "5",
        "_score": 0.5753642,
        "_source": {
          "articleID": "DHJK-B-1395-#Ky5",
          "userID": 3,
          "hidden": false,
          "postDate": "2021-11-11",
          "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"
        }
      }
    ]
  }
}

成功了，只有包含java spark这个短语的doc才返回了，只包含java的doc不会返回

3、term position

doc1：hello world, java spark
doc2：hi, spark java

建立倒排索引并会记录这个关键字所在的position

hello doc1(0)
wolrd doc1(1)
java doc1(2) doc2(2)
spark doc1(3) doc2(1)

了解什么是分词后的position

GET _analyze
{
  "text": "hello world, java spark",
  "analyzer": "standard"
}

响应结果

{
  "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
    }
  ]
}

4、match_phrase的基本原理

索引中的position，match_phrase

doc1：hello world, java spark
doc2：hi, spark java

建立倒排索引并会记录这个关键字所在的position

hello doc1(0)
wolrd doc1(1)
java doc1(2) doc2(2)
spark doc1(3) doc2(1)

java spark 使用 match phrase搜索

java spark -->相当于搜索 java和spark

java --> doc1(2) doc2(2)
spark --> 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，并且java 的position比spark 的position小1，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

必须理解这块原理！！！！

因为后面的proximity match就是原理跟这个一模一样！！！

标签：java,18,doc1,doc,搜索,position,phrase,spark,match
来源： https://blog.csdn.net/m0_37450089/article/details/121442993