Elasticsearch 5.0 中term 查询和match 查询（text和keyword）

Elasticsearch 5.0 关于term query和match query的认识

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"}

1. 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
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!"        }      }    ]  }}

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。