跨字段型实体的搜索

现在我们来讨论一个更常见的情形：对跨字段型的实体进行搜索。像人，产品或者地址等实体，它们的标识信息会被分开存储到多个字段中。比如我们有一个表示人的数据，会被这样切分成两个字段来入索引：

{
    "firstname":  "Peter",
    "lastname":   "Smith"
}

或者一个这样子的地址：

{
    "street":   "5 Poland Street",
    "city":     "London",
    "country":  "United Kingdom",
    "postcode": "W1V 3DG"
}

这看上去有点像我们在 Multiple Query Strings 章节中讨论的，但其实两者之间区别很大。在 Multiple Query Strings 中，我们对每个查询的字段都指定了各自的搜索关键词，而在此我们需要使用相同的关键字来搜索每个字段。

我们的用户可能搜索一个叫 “Peter Smith” 的人或者一个叫 “Poland Street W1V.” 的地址。这些关键字对应的数据都被拆分存储在不同的字段里，所以采用 dis_max 或者 best_fields 查询来应对这一场景明显都不合适。~~我怎么觉得 best_fields 其实貌似也行。。。~~

一个拍着屁股想出来的办法

既然我们想要在多个字段里搜索同一个关键词，我们直接想到的做法或许是像下面这样的，在一个bool查询里嵌套多个 should 子查询，每个should子查询都这个关键词对不同字段的 match 查询：

{
  "query": {
    "bool": {
      "should": [
        { "match": { "street":    "Poland Street W1V" }},
        { "match": { "city":      "Poland Street W1V" }},
        { "match": { "country":   "Poland Street W1V" }},
        { "match": { "postcode":  "Poland Street W1V" }}
      ]
    }
  }
}

当然这么编码看上去有点蠢，你可以用 most_fields 类型的 multi_match 来简写上面这个 bool 查询：~~所以我说 most_fields 好像也行嘛~~

{
  "query": {
    "multi_match": {
      "query":       "Poland Street W1V",
      "type":        "most_fields",
      "fields":      [ "street", "city", "country", "postcode" ]
    }
  }
}

使用 most_fields 的弊端

~~瞬间被打脸~~

使用 most_fields 方式来应对这一场景有着一些难以察觉的潜在风险点：

most_fields 查询在判断一个字段是否与查询关键词匹配的时候，判断的依据是字段是否含有查询关键词分完词之后的任意一个词元，然后再根据匹配的字段数来判断一个文档的相关度，而不会去判断文档的这个字段是否尽可能得匹配整个关键词分出来的所有词元。
它没法使用 minimum_should_match 参数来减少那些匹配度极低的长尾搜索结果
每个字段的词元匹配度都是不同的，而 most_fields 查询不会在意这些细节，所以可能导致查询结果的排序不符合用户的查询意图。~~这和第一条有点意思相近~~

反正大致意思是排序会有问题，然后也不支持 minimum_should_match 参数，归根结底还是因为它本身就不是被设计成用来应对这种业务场景的，它只是被用来处理那些使用了相同信息但是不同解析器索引出来的不同字段，而不是不同的信息相同的解析器索引出来的不同字段

Cross-fields Entity Search

Now we come to a common pattern: cross-fields entity search. With entities like person, product, or address, the identifying information is spread across several fields. We may have a person indexed as follows:

{
    "firstname":  "Peter",
    "lastname":   "Smith"
}

Or an address like this:

{
    "street":   "5 Poland Street",
    "city":     "London",
    "country":  "United Kingdom",
    "postcode": "W1V 3DG"
}

This sounds a lot like the example we described in Multiple Query Strings, but there is a big difference between these two scenarios. In Multiple Query Strings, we used a separate query string for each field. In this scenario, we want to search across multiple fields with a single query string.

Our user might search for the person “Peter Smith” or for the address “Poland Street W1V.” Each of those words appears in a different field, so using a dis_max / best_fields query to find the single best-matching field is clearly the wrong approach.

A Naive Approach

Really, we want to query each field in turn and add up the scores of every field that matches, which sounds like a job for the bool query:

{
  "query": {
    "bool": {
      "should": [
        { "match": { "street":    "Poland Street W1V" }},
        { "match": { "city":      "Poland Street W1V" }},
        { "match": { "country":   "Poland Street W1V" }},
        { "match": { "postcode":  "Poland Street W1V" }}
      ]
    }
  }
}

Repeating the query string for every field soon becomes tedious. We can use the multi_match query instead, and set the type to most_fields to tell it to combine the scores of all matching fields:

{
  "query": {
    "multi_match": {
      "query":       "Poland Street W1V",
      "type":        "most_fields",
      "fields":      [ "street", "city", "country", "postcode" ]
    }
  }
}

Problems with the most_fields Approach

The most_fields approach to entity search has some problems that are not immediately obvious:

It is designed to find the most fields matching any words, rather than to find the most matching words across all fields.
It can’t use the operator or minimum_should_match parameters to reduce the long tail of less-relevant results.
Term frequencies are different in each field and could interfere with each other to produce badly ordered results.

4.3.7 Cross-fields Entity Search 对跨字段型实体进行搜索