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elasticsearch学习笔记高级篇十一多字段搜索下

jiezi

承接上一篇博客 https://segmentfault.com/a/11…

4、most_fields 查询

most_fields 是以字段为中心，这就使得它会查询最多匹配的字段。
假设我们有一个让用户搜索地址。其中有两个文档如下：

PUT /test_index/_create/1
{
    "street":   "5 Poland Street",
    "city":     "Poland",
    "country":  "United W1V",
    "postcode": "W1V 3DG"
}

PUT /test_index/_create/2
{
    "street":   "5 Poland Street W1V",
    "city":     "London",
    "country":  "United Kingdom",
    "postcode": "3DG"
}

使用 most_fields 进行查询：

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

我们发现对每个字段重复查询字符串很快就会显得冗长，此时用 multi_match 进行简化如下:

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

结果：

{
  "took" : 4,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 2.3835402,
    "hits" : [
      {
        "_index" : "test_index",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : 2.3835402,
        "_source" : {
          "street" : "5 Poland Street",
          "city" : "Poland",
          "country" : "United W1V",
          "postcode" : "W1V 3DG"
        }
      },
      {
        "_index" : "test_index",
        "_type" : "_doc",
        "_id" : "2",
        "_score" : 0.99938464,
        "_source" : {
          "street" : "5 Poland Street W1V",
          "city" : "London",
          "country" : "United Kingdom",
          "postcode" : "3DG"
        }
      }
    ]
  }
}

如果用 best_fields, 那么 doc2 会在 doc1 的前面

GET /test_index/_search
{
  "query": {
    "multi_match": {
      "query": "Poland Street W1V",
      "type": "best_fields", 
      "fields": ["street", "city", "country", "postcode"]
    }
  }
}

结果：

{
  "took" : 3,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 0.99938464,
    "hits" : [
      {
        "_index" : "test_index",
        "_type" : "_doc",
        "_id" : "2",
        "_score" : 0.99938464,
        "_source" : {
          "street" : "5 Poland Street W1V",
          "city" : "London",
          "country" : "United Kingdom",
          "postcode" : "3DG"
        }
      },
      {
        "_index" : "test_index",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : 0.6931472,
        "_source" : {
          "street" : "5 Poland Street",
          "city" : "Poland",
          "country" : "United W1V",
          "postcode" : "W1V 3DG"
        }
      }
    ]
  }
}

使用 most_fields 存在的问题

（1）它被设计用来找到匹配任意单词的多数字段，而不是找到跨越所有字段的最匹配的单词
（2）它不能使用 operator 或者 minimum_should_match 参数来减少低相关度结果带来的长尾效应
（3）每个字段的词条频度是不同的，会互相干扰最终得到较差的排序结果

5、全字段查询使用 copy_to 参数

上面那说了 most_fields 的问题，下面就来解决一下这个问题，解决这个问题的第一种方式就是使用 copy_to 参数。
我们可以用 copy_to 将多个 field 组合成一个 field
建立如下索引：

DELETE /test_index
PUT /test_index
{
  "mappings": {
    "properties": {
      "street": {
        "type": "text",
        "copy_to": "full_address"
      },
      "city": {
        "type": "text",
        "copy_to": "full_address"
      },
      "country": {
        "type": "text",
        "copy_to": "full_address"
      },
      "postcode": {
        "type": "text",
        "copy_to": "full_address"
      },
      "full_address": {"type": "text"}
    }
  }
}

插入之前的数据：

PUT /test_index/_create/1
{
    "street":   "5 Poland Street",
    "city":     "Poland",
    "country":  "United W1V",
    "postcode": "W1V 3DG"
}

PUT /test_index/_create/2
{
    "street":   "5 Poland Street W1V",
    "city":     "London",
    "country":  "United Kingdom",
    "postcode": "3DG"
}

查询：

GET /test_index/_search
{
  "query": {
    "match": {"full_address": "Poland Street W1V"}
  }
}

结果：

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 0.68370587,
    "hits" : [
      {
        "_index" : "test_index",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : 0.68370587,
        "_source" : {
          "street" : "5 Poland Street",
          "city" : "Poland",
          "country" : "United W1V",
          "postcode" : "W1V 3DG"
        }
      },
      {
        "_index" : "test_index",
        "_type" : "_doc",
        "_id" : "2",
        "_score" : 0.5469647,
        "_source" : {
          "street" : "5 Poland Street W1V",
          "city" : "London",
          "country" : "United Kingdom",
          "postcode" : "3DG"
        }
      }
    ]
  }
}

我们可以发现这样变成一个字段 full_address 之后，就可以解决 most_fields 的问题了。

5、cross_fields 查询

解决 most_fields 的问题的第二种方式就是使用 cross_fields 查询。
如果我们在索引文档之前都能够使用_all 或是提前定义好 copy_to 的话，那就没什么问题。但是，Elasticsearch 同时也提供了一个搜索期间的解决方案就是使用 cross_fields 查询。cross_fields 采用了一种以词条为中心的方法，这种方法和 best_fields 以及 most_fields 采用的以字段为中心的方法有很大的区别。它将所有的字段视为一个大的字段，然后在任一字段中搜索每个词条。
下面解释一下以字段为中心和以词条为中心的区别。

以字段为中心

通过查询：

GET /test_index/_validate/query?explain
{
  "query": {
    "multi_match": {
      "query": "Poland Street W1V",
      "type": "best_fields",
      "fields": ["street", "city", "country", "postcode"]
    }
  }
}

得到：

{
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "failed" : 0
  },
  "valid" : true,
  "explanations" : [
    {
      "index" : "test_index",
      "valid" : true,
      "explanation" : "((postcode:poland postcode:street postcode:w1v) | (country:poland country:street country:w1v) | (city:poland city:street city:w1v) | (street:poland street:street street:w1v))"
    }
  ]
}

((postcode:poland postcode:street postcode:w1v) |
(country:poland country:street country:w1v) |
(city:poland city:street city:w1v) |
(street:poland street:street street:w1v))
这个就是规则。
将 operator 设置成 and 就变成
((+postcode:poland +postcode:street +postcode:w1v) |
(+country:poland +country:street +country:w1v) |
(+city:poland +city:street +city:w1v) |
(+street:poland +street:street +street:w1v))
标识四个词条都需要出现在相同的字段中

以词条为中心

通过查询

GET /test_index/_validate/query?explain
{
  "query": {
    "multi_match": {
      "query": "Poland Street W1V",
      "type": "cross_fields", 
      "operator": "and", 
      "fields": ["street", "city", "country", "postcode"]
    }
  }
}

得到：

{
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "failed" : 0
  },
  "valid" : true,
  "explanations" : [
    {
      "index" : "test_index",
      "valid" : true,
      "explanation" : "+blended(terms:[postcode:poland, country:poland, city:poland, street:poland]) +blended(terms:[postcode:street, country:street, city:street, street:street]) +blended(terms:[postcode:w1v, country:w1v, city:w1v, street:w1v])"
    }
  ]
}

+blended(terms:[postcode:poland, country:poland, city:poland, street:poland]) +blended(terms:[postcode:street, country:street, city:street, street:street]) +blended(terms:[postcode:w1v, country:w1v, city:w1v, street:w1v])
这个是规则。换言之所有的词必须出现在任意字段中。
cross_fields 类型首先会解析查询字符串来得到一个词条列表，然后在任一字段中搜索每个词条。通过混合字段的倒排文档频度来解决词条频度问题。从而完美结局了 most_fields 的问题。
使用 cross_fields 相比较于 copy_to，可以在查询期间对个别字段进行加权。
示例：

GET /test_index/_search
{
  "query": {
    "multi_match": {
      "query": "Poland Street W1V",
      "type": "cross_fields", 
      "fields": ["street^2", "city", "country", "postcode"]
    }
  }
}

这样 street 字段的 boost 就是 2，其它字段都为 1

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