Wilcard搜索或Elastic搜索中的部分匹配 [英] Wilcard search or partial matching in Elastic search

问题描述

我正在尝试向最终用户提供搜索类型，因为它们更像是sqlserver。
我能够为给定的sql方案实现ES查询：

I am trying to provide the search to end user with type as they go which is is more like sqlserver. I was able to implement ES query for the given sql scenario:

 select * from table where name like '%pete%' and type != 'xyz and type!='abc'

但ES查询不为这个sql查询工作

But the ES query doesnt work for this sql query

  select * from table where name like '%peter tom%' and type != 'xyz and type!='abc'

在我的弹性搜索中，通配符查询我还需要执行一些布尔过滤查询

In my elastic search alongwith the wildcard query i also need to perform some boolean filtered query

{
"query": {
"filtered": {
"filter": {
"bool": {
"should": [
{
"query": {
"wildcard": {
"name":
{ "value": "*pete*" }
}
}
}
],
"must_not": [
{
"match":
{ "type": "xyz" }
}, {
"match":
{ "type": "abc" }
}
]
}
}
}
}
}

上述弹性查询与通配符搜索工作正常得到我所有与pete匹配的文档，不是xyz和abc类型的文档。但是当我尝试使用2分隔字符的空格执行通配符时，相同的查询将返回空白，如下所示。例如

The above elastic query with wildcard search works fine and gets me all the documents that matches pete and are not of type xyz and abc .But when i try perform the wildcard with 2 seprate words seprated by space then the same query returns me empty as shown below.For example

{
    "query": {
    "filtered": {
    "filter": {
    "bool": {
    "should": [
    {
    "query": {
    "wildcard": {
    "name":
    { "value": "*peter tom*" }
    }
    }
    }
    ],
    "must_not": [
    {
    "match":
    { "type": "xyz" }
    }, {
    "match":
    { "type": "abc" }
    }
    ]
    }
    }
    }
    }
    }

我的映射如下：

{
  "properties": {
     "name": {
      "type": "string"
    }
    "type": {
      "type": "string"
    }
  }
}

为了让空格分隔的单词可以进行通配符搜索，我应该使用t查询。

What query should i use in order to make wild card search possible for words seprated by spaces

推荐答案

最有效的解决方案是利用 ngram tokenizer 以便标记部分名称字段。例如，如果您有一个名称，如 peter tomson ，则ngram tokenizer将进行标记和索引，如下所示：

The most efficient solution involves leveraging an ngram tokenizer in order to tokenize portions of your name field. For instance, if you have a name like peter tomson, the ngram tokenizer will tokenize and index it like this:

• pe


• pet


• pete


• peter


• peter t


• peter to


• 彼得·汤姆


• 彼得·托姆斯>
  
• peter tomso


• eter tomson


• ter tomson


• er tomson


• r tomson


• tomson


• tomson


• omson
• mson


• 儿子


• on

• pe
• pet
• pete
• peter
• peter t
• peter to
• peter tom
• peter toms
• peter tomso
• eter tomson
• ter tomson
• er tomson
• r tomson
• tomson
• tomson
• omson
• mson
• son
• on

所以，当这被索引时，搜索任何这些令牌将检索您的文档，其中包含 peter thomson 。

So, when this has been indexed, searching for any of those tokens will retrieve your document with peter thomson in it.

让我们创建索引：

PUT likequery
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_ngram_analyzer": {
          "tokenizer": "my_ngram_tokenizer"
        }
      },
      "tokenizer": {
        "my_ngram_tokenizer": {
          "type": "nGram",
          "min_gram": "2",
          "max_gram": "15"
        }
      }
    }
  },
  "mappings": {
    "typename": {
      "properties": {
        "name": {
          "type": "string",
          "fields": {
            "search": {
              "type": "string",
              "analyzer": "my_ngram_analyzer"
            }
          }
        },
        "type": {
          "type": "string",
          "index": "not_analyzed"
        }
      }
    }
  }
}

然后，您可以使用简单高效的术语查询：

You'll then be able to search like this with a simple and very efficient term query:

POST likequery/_search
{
  "query": {
    "bool": {
      "should": [
        {
          "term": {
            "name.search": "peter tom"
          }
        }
      ],
      "must_not": [
        {
          "match": {
            "type": "xyz"
          }
        },
        {
          "match": {
            "type": "abc"
          }
        }
      ]
    }
  }
}

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