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）

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": "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

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

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。