Elasticsearch5中term 查询和match 查询

偏执的太偏执、 2022-06-08 12:46 192阅读 0赞

### 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） ###

1.  准备数据：  
    
    
        POST /termtest/termtype/1
        {
          "content":"Name"
        }
    
        POST /termtest/termtype/2
        {
          "content":"name city"
        }
2.  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": "2", "_score": 1, "_source": { "content": "name city" } }, { "_index": "termtest", "_type": "termtype", "_id": "1", "_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

1.  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": "1", "_score": 0.2876821, "_source": { "content": "Name" } }, { "_index": "termtest", "_type": "termtype", "_id": "2", "_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][Link 1]   
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
    
    1.  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": "1", "_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": "1", "_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": "1", "_score": 0.51623213, "_source": { "full_text": "Quick Foxes!", "exact_value": "Quick Foxes!" } } ] } }

![save_snippets.png][]

> 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。

[Link 1]: https://www.elastic.co/guide/en/elasticsearch/reference/5.0/query-dsl-term-query.html
[save_snippets.png]: /images/20220608/4155f903570444838c63cd707072463d.png