Elasticsearch 5.x 关于term query和match query的认识

http://blog.csdn.net/yangwenbo214/article/details/54142786

一、基本情况

前言：term query和match query牵扯的东西比较多，例如分词器、mapping、倒排索引等。我结合官方文档中的一个实例，谈谈自己对此处的理解

string类型在es5.*分为text和keyword。text是要被分词的，整个字符串根据一定规则分解成一个个小写的term，keyword类似es2.3中not_analyzed的情况。
string数据put到elasticsearch中，默认是text。

NOTE:默认分词器为standard analyzer。”Quick Brown Fox!”会被分解成[quick,brown,fox]写入倒排索引

term query会去倒排索引中寻找确切的term，它并不知道分词器的存在。这种查询适合keyword 、numeric、date
match query知道分词器的存在。并且理解是如何被分词的
总的来说有如下：
- term query 查询的是倒排索引中确切的term
- match query 会对filed进行分词操作，然后在查询

二、测试（1）

准备数据：
POST /termtest/termtype/1
{
  "content":"Name"
}

POST /termtest/termtype/2
{
  "content":"name city"
}

查看数据是否导入
GET /termtest/_search
{
  "query":
  {
    "match_all": {}
  }
}

结果：
{
  "took": 1,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 2,
    "max_score": 1,
    "hits": [
      {
        "_index": "termtest",
        "_type": "termtype",
        "_id": "",
        "_score": 1,
        "_source": {
          "content": "name city"
        }
      },
      {
        "_index": "termtest",
        "_type": "termtype",
        "_id": "",
        "_score": 1,
        "_source": {
          "content": "Name"
        }
      }
    ]
  }
}

如上说明，数据已经被导入。该处字符串类型是text，也就是默认被分词了

做如下查询：
POST /termtest/_search
{
  "query":{
    "term":{
      "content":"Name"
    }
  }
}

结果
{
  "took": 1,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 0,
    "max_score": null,
    "hits": []
  }
}

分析结果：因为是默认被standard analyzer分词器分词，大写字母全部转为了小写字母，并存入了倒排索引以供搜索。term是确切查询，
必须要匹配到大写的Name。所以返回结果为空

POST /termtest/_search
{
  "query":{
    "match":{
      "content":"Name"
    }
  }
}

结果
{
  "took": 2,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 2,
    "max_score": 0.2876821,
    "hits": [
      {
        "_index": "termtest",
        "_type": "termtype",
        "_id": "",
        "_score": 0.2876821,
        "_source": {
          "content": "Name"
        }
      },
      {
        "_index": "termtest",
        "_type": "termtype",
        "_id": "",
        "_score": 0.25811607,
        "_source": {
          "content": "name city"
        }
      }
    ]
  }
}



分析结果: 原因（1）：默认被standard analyzer分词器分词，大写字母全部转为了小写字母，并存入了倒排索引以供搜索，
原因（2）：match query先对filed进行分词，分词为”name”,再去匹配倒排索引中的term

三、测试（2）

下面是官网实例官网实例
1. 导入数据

PUT my_index
{
  "mappings": {
    "my_type": {
      "properties": {
        "full_text": {
          "type":  "text"
        },
        "exact_value": {
          "type":  "keyword"
        }
      }
    }
  }
}

PUT my_index/my_type/1
{
  "full_text":   "Quick Foxes!",
  "exact_value": "Quick Foxes!"
}



先指定类型，再导入数据

full_text: 指定类型为text，是会被分词
exact_value: 指定类型为keyword，不会被分词
full_text： 会被standard analyzer分词为如下terms [quick,foxes],存入倒排索引
exact_value： 只有[Quick Foxes!]这一个term会被存入倒排索引

做如下查询
GET my_index/my_type/_search
{
  "query": {
    "term": {
      "exact_value": "Quick Foxes!"
    }
  }
}

结果：

{
  "took": 1,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 1,
    "max_score": 0.2876821,
    "hits": [
      {
        "_index": "my_index",
        "_type": "my_type",
        "_id": "",
        "_score": 0.2876821,
        "_source": {
          "full_text": "Quick Foxes!",
          "exact_value": "Quick Foxes!"
        }
      }
    ]
  }
}

exact_value包含了确切的Quick Foxes!，因此被查询到

GET my_index/my_type/_search
{
  "query": {
    "term": {
      "full_text": "Quick Foxes!"
    }
  }
}
结果：

{
  "took": 4,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 0,
    "max_score": null,
    "hits": []
  }
}

full_text被分词了，倒排索引中只有quick和foxes。没有Quick Foxes!

GET my_index/my_type/_search
{
  "query": {
    "term": {
      "full_text": "foxes"
    }
  }
}

结果：

{
  "took": 2,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 1,
    "max_score": 0.25811607,
    "hits": [
      {
        "_index": "my_index",
        "_type": "my_type",
        "_id": "",
        "_score": 0.25811607,
        "_source": {
          "full_text": "Quick Foxes!",
          "exact_value": "Quick Foxes!"
        }
      }
    ]
  }
}

full_text被分词，倒排索引中只有quick和foxes，因此查询foxes能成功

GET my_index/my_type/_search
{
  "query": {
    "match": {
      "full_text": "Quick Foxes!"
    }
  }
}

结果：

{
  "took": 3,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 1,
    "max_score": 0.51623213,
    "hits": [
      {
        "_index": "my_index",
        "_type": "my_type",
        "_id": "",
        "_score": 0.51623213,
        "_source": {
          "full_text": "Quick Foxes!",
          "exact_value": "Quick Foxes!"
        }
      }
    ]
  }
}

match query会先对自己的query string进行分词。也就是”Quick Foxes!”先分词为quick和foxes。然后在去倒排索引中查询，此处full_text是text类型，被分词为quick和foxes
因此能匹配上。

参考文献:http://blog.csdn.net/yangwenbo214/article/details/54142786