java - ElasticSearch - 在我的输入查询中没有 (*) 时 JavaApi 搜索不会发生

Question

我使用 java api 从 Elasticsearch 中获取文档，我的 Elasticsearch 文档中有以下代码，并尝试使用以下模式搜索它。

代码:MS-VMA1615-0D

Input : *VMA1615-0*     -- Am getting the results (MS-VMA1615-0D).
Input : MS-VMA1615-0D   -- Am getting the results (MS-VMA1615-0D).
Input : *VMA1615-0      -- Am getting the results (MS-VMA1615-0D).
Input : *VMA*-0*        -- Am getting the results (MS-VMA1615-0D).

但是，如果我给出如下所示的输入，则不会得到结果。

Input : VMA1615         -- Am not getting the results.

我希望返回代码MS-VMA1615-0D

请找到我正在使用的下面的java代码

private final String INDEX = "products";
private final String TYPE = "doc";
SearchRequest searchRequest = new SearchRequest(INDEX); 
    searchRequest.types(TYPE);
    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
    QueryStringQueryBuilder qsQueryBuilder = new QueryStringQueryBuilder(code); 

    qsQueryBuilder.defaultField("code");
    searchSourceBuilder.query(qsQueryBuilder);

    searchSourceBuilder.size(50);
    searchRequest.source(searchSourceBuilder);
    SearchResponse searchResponse = null;
    try {
         searchResponse = SearchEngineClient.getInstance().search(searchRequest);
    } catch (IOException e) {
        e.getLocalizedMessage();
    }
    Item item = null;
    SearchHit[] searchHits = searchResponse.getHits().getHits();

请查找我的映射详细信息:

PUT products
{
"settings": {
"analysis": {
  "analyzer": {
    "custom_analyzer": {
      "type": "custom",
      "tokenizer": "whitespace",
      "char_filter": [
        "html_strip"
      ],
      "filter": [
        "lowercase",
        "asciifolding"
      ]
    }
   }
  }
},
"mappings": {
"doc": {
  "properties": {
    "code": {
      "type": "text",
       "analyzer": "custom_analyzer"
      }
       }
  }
 }
}

Answer 1

要执行您正在寻找的操作，您可能必须更改您正在使用的标记生成器。目前您正在使用 whitespace 分词器，必须将其替换为 pattern 分词器。因此，您的新映射应如下所示:

PUT products
{
"settings": {
"analysis": {
  "analyzer": {
    "custom_analyzer": {
      "type": "custom",
      "tokenizer": "pattern",
      "char_filter": [
        "html_strip"
      ],
      "filter": [
        "lowercase",
        "asciifolding"
      ]
    }
   }
  }
},
"mappings": {
"doc": {
  "properties": {
    "code": {
      "type": "text",
       "analyzer": "custom_analyzer"
      }
    }
  }
 }
}

因此，更改映射后，对 VMA1615 的查询将返回 MS-VMA1615-0D。

这是因为它将字符串“MS-VMA1615-0D”标记为“MS”、“VMA1615”和“0D”。因此，只要在您的查询中遇到其中任何一个，它都会为您提供结果。

POST _analyze
{
  "tokenizer": "pattern",
  "text": "MS-VMA1615-0D"
}

将返回:

{
  "tokens": [
    {
      "token": "MS",
      "start_offset": 0,
      "end_offset": 2,
      "type": "word",
      "position": 0
    },
    {
      "token": "VMA1615",
      "start_offset": 3,
      "end_offset": 10,
      "type": "word",
      "position": 1
    },
    {
      "token": "0D",
      "start_offset": 11,
      "end_offset": 13,
      "type": "word",
      "position": 2
    }
  ]
}

根据您的评论:

It is not how elasticsearch works. Elasticsearch stores the terms and their corresponding documents in an inverted index data structure and by default the terms produced by a full text search is based on white-spaces, i.e. a text "Hi there I am a technocrat" would split up as ["Hi", "there", "I", "am", "a", "technocrat"]. So this implies that the terms which gets stored depends on how it is tokenized. After indexing when you query let's say in the above example if I query for "technocrat", I will get the result as the inverted index has that term associated with my document. So in your case "VMA" is not stored as a term.

为此，请使用以下映射:

PUT products
{
"settings": {
"analysis": {
  "analyzer": {
    "custom_analyzer": {
      "type": "custom",
      "tokenizer": "my_pattern_tokenizer",
      "char_filter": [
        "html_strip"
      ],
      "filter": [
        "lowercase",
        "asciifolding"
      ]
    }
   },
   "tokenizer": {
     "my_pattern_tokenizer": {
          "type": "pattern",
          "pattern": "-|\\d"
        }
   }
  }
},
"mappings": {
"doc": {
  "properties": {
    "code": {
      "type": "text",
       "analyzer": "custom_analyzer"
      }
    }
  }
 }
}

所以要检查:

POST products/_analyze
{
  "tokenizer": "my_pattern_tokenizer",
  "text": "MS-VMA1615-0D"
}

将产生:

{
  "tokens": [
    {
      "token": "MS",
      "start_offset": 0,
      "end_offset": 2,
      "type": "word",
      "position": 0
    },
    {
      "token": "VMA",
      "start_offset": 3,
      "end_offset": 6,
      "type": "word",
      "position": 1
    },
    {
      "token": "D",
      "start_offset": 12,
      "end_offset": 13,
      "type": "word",
      "position": 2
    }
  ]
}