十九、Elasticsearch基于slop参数实现近似匹配

1、基本语法

GET forum/article/_search
{
  "query": {
    "match_phrase": {
      "title": {
        "query": "java spark",
        "slop" : 1
      }
    }
  }
}

2、slop的含义

query string，搜索文本中的几个term，要经过几次移动才能与一个document匹配，这个移动的次数，就是slop

3、slop举例

一个query string经过几次移动之后可以匹配到一个document，然后设置slop

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

用match phrase去搜索java spark，是搜不到的

如果我们指定了slop，那么久允许java spark进行移动，来尝试与doc进行匹配。

java	is	very	good	spark	is
java	spark
java	--》	spark
java	--》	--》	spark
java	--》	--》	--》	spark

从表格中可以发现，我第一次移动了1位，spark到了very的位置，移动了三次后，恰巧到了对应的spark位置。所以这里slop就是3。因为java spark这个短语，spark移动了3次，就可以跟一个doc匹配上了。

slop的含义，不仅仅是说一个query string terms移动几次，跟一个doc匹配上，一个query string terms，最多可以移动几次去尝试跟一个doc匹配上。这里slop设置大于等于3就ok。

直接match_phrase搜索肯定是搜不到了，那么怎么才能搜到呢？

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

指定slop为大于等于3的数字就行了。原因我们已经在表格中体现了。

4、slop搜索下，关键词离得越近，relevance score分数就越高

GET /forum/article/_search
{
  "query": {
    "match_phrase": {
      "content": {
        "query": "java best",
        "slop": 15
      }
    }
  }
}

结果：

{
  "took": 3,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 2,
    "max_score": 0.65380025,
    "hits": [
      {
        "_index": "forum",
        "_type": "article",
        "_id": "2",
        "_score": 0.65380025,
        "_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": 0.07111243,
        "_source": {
          "articleID": "DHJK-B-1395-#Ky5",
          "userID": 3,
          "hidden": false,
          "postDate": "2017-03-01",
          "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"
        }
      }
    ]
  }
}

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