前言
es是什么？
es是基于Apache Lucene的开源分布式（全文）搜索引擎，，提供简单的RESTful API来隐藏Lucene的复杂性。
es除了全文搜索引擎之外，还可以这样描述它：
1、分布式的实时文件存储，每个字段都被索引并可被搜索
2、分布式的实时分析搜索引擎
3、可以扩展到成百上千台服务器，处理PB级结构化或非结构化数据。ES的数据组织类比
| Relational DB | Elasticsearch |
|---|---|
| 数据库（database） | 索引（indices） |
| 表（tables） | types |
| 行（rows） | documents |
| 字段（columns） | fields |
- mac安装ES
- 1、更新brew
```brew update```
- 2、安装java1.8版本
```brew cask install homebrew/cask-versions/java8```
- 3、安装ES
```brew install elasticsearch```
- 4、启动本地ES
```brew services start elasticsearch```
- 5、本地访问9200端口查看ES安装
```http://localhost:9200```
- 6、安装kibana
```Kibana是ES的一个配套工具，可以让用户在网页中与ES进行交互```
```brew install kibana```
- 7、本地启动kibana
```brew services start kibana```
- 8、本地访问5601端口进入kibana交互界面
```http://localhost:5601```
一、 ES简单的增删改查
1、创建一篇文档（有则修改，无则创建)
PUT test/doc/2
{
"name":"wangfei",
"age":27,
"desc":"热天还不让后人不认同"
}
PUT test/doc/1
{
"name":"wangjifei",
"age":27,
"desc":"萨芬我反胃为范围额"
}
PUT test/doc/3
{
"name":"wangyang",
"age":30,
"desc":"点在我心内的几首歌"
}
2、查询指定索引信息
GET test
3、 查询指定文档信息
GET test/doc/1
GET test/doc/2
4、查询对应索引下所有数据
GET test/doc/_search
或
GET test/doc/_search
{
"query": {
"match_all": {}
}
}
5、删除指定文档
DELETE test/doc/3
6、删除索引
DELETE test
7、修改指定文档方式
- 修改时，不指定的属性会自动覆盖，只保留指定的属性(不正确的修改指定文档方式)
PUT test/doc/1
{
"name":"王计飞"
}
- 使用POST命令，在id后面跟_update，要修改的内容放到doc文档（属性）中(正确的修改指定文档方式)
POST test/doc/1/_update
{
"doc":{
"desc":"生活就像 茫茫海上"
}
}
二、ES查询的两种方式
1、查询字符串搜索
GET test/doc/_search?q=name:wangfei
2、结构化查询(单字段查询,不能多字段组合查询)
GET test/doc/_search
{
"query":{
"match":{
"name":"wang"
}
}
}
三、match系列之操作
1、match系列之match_all (查询全部)
GET test/doc/_search
{
"query":{
"match_all": {
}
}
}
2、match系列之match_phrase（短语查询）
准备数据
PUT test1/doc/1
{
"title": "中国是世界上人口最多的国家"
}
PUT test1/doc/2
{
"title": "美国是世界上军事实力最强大的国家"
}
PUT test1/doc/3
{
"title": "北京是中国的首都"
}
查询语句
GET test1/doc/_search
{
"query":{
"match":{
"title":"中国"
}
}
}
>>>输出结果
{
"took" : 241,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 3,
"max_score" : 0.68324494,
"hits" : [
{
"_index" : "test1",
"_type" : "doc",
"_id" : "1",
"_score" : 0.68324494,
"_source" : {
"title" : "中国是世界上人口最多的国家"
}
},
{
"_index" : "test1",
"_type" : "doc",
"_id" : "3",
"_score" : 0.5753642,
"_source" : {
"title" : "北京是中国的首都"
}
},
{
"_index" : "test1",
"_type" : "doc",
"_id" : "2",
"_score" : 0.39556286,
"_source" : {
"title" : "美国是世界上军事实力最强大的国家"
}
}
]
}
}
通过观察结果可以发现，虽然如期的返回了中国的文档。但是却把和美国的文档也返回了，这并不是我们想要的。是怎么回事呢？因为这是elasticsearch在内部对文档做分词的时候，对于中文来说，就是一个字一个字分的，所以，我们搜中国，中和国都符合条件，返回，而美国的国也符合。而我们认为中国是个短语，是一个有具体含义的词。所以elasticsearch在处理中文分词方面比较弱势。后面会讲针对中文的插件。但目前我们还有办法解决，那就是使用短语查询 用match_phrase
GET test1/doc/_search
{
"query":{
"match_phrase": {
"title": "中国"
}
}
}
>>>查询结果
{
"took" : 10,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 0.5753642,
"hits" : [
{
"_index" : "test1",
"_type" : "doc",
"_id" : "1",
"_score" : 0.5753642,
"_source" : {
"title" : "中国是世界上人口最多的国家"
}
},
{
"_index" : "test1",
"_type" : "doc",
"_id" : "3",
"_score" : 0.5753642,
"_source" : {
"title" : "北京是中国的首都"
}
}
]
}
}
我们搜索中国和世界这两个指定词组时，但又不清楚两个词组之间有多少别的词间隔。那么在搜的时候就要留有一些余地。这时就要用到了slop了。相当于正则中的中国.*?世界。这个间隔默认为0
GET test1/doc/_search
{
"query":{
"match_phrase": {
"title": {
"query": "中国世界",
"slop":2
}
}
}
}
>>>查询结果
{
"took" : 23,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.7445889,
"hits" : [
{
"_index" : "test1",
"_type" : "doc",
"_id" : "1",
"_score" : 0.7445889,
"_source" : {
"title" : "中国是世界上人口最多的国家"
}
}
]
}
}
3、match系列之match_phrase_prefix（最左前缀查询）智能搜索--以什么开头
数据准备
PUT test2/doc/1
{
"title": "prefix1",
"desc": "beautiful girl you are beautiful so"
}
PUT test2/doc/2
{
"title": "beautiful",
"desc": "I like basking on the beach"
}
搜索特定英文开头的数据
查询语句
GET test2/doc/_search
{
"query": {
"match_phrase_prefix": {
"desc": "bea"
}
}
}
>>>查询结果（）
{
"took" : 5,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 0.39556286,
"hits" : [
{
"_index" : "test2",
"_type" : "doc",
"_id" : "1",
"_score" : 0.39556286,
"_source" : {
"title" : "prefix1",
"desc" : "beautiful girl you are beautiful so"
}
},
{
"_index" : "test2",
"_type" : "doc",
"_id" : "2",
"_score" : 0.2876821,
"_source" : {
"title" : "beautiful",
"desc" : "I like basking on the beach"
}
}
]
}
}
查询短语
GET test2/doc/_search
{
"query": {
"match_phrase_prefix": {
"desc": "you are bea"
}
}
}
>>>查询结果
{
"took" : 28,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.8630463,
"hits" : [
{
"_index" : "test2",
"_type" : "doc",
"_id" : "1",
"_score" : 0.8630463,
"_source" : {
"title" : "prefix1",
"desc" : "beautiful girl you are beautiful so"
}
}
]
}
}
max_expansions 参数理解 前缀查询会非常的影响性能，要对结果集进行限制，就加上这个参数。
GET test2/doc/_search
{
"query": {
"match_phrase_prefix": {
"desc": {
"query": "bea",
"max_expansions":1
}
}
}
}
4、match系列之multi_match（多字段查询)
- multi_match是要在多个字段中查询同一个关键字 除此之外，mulit_match甚至可以当做match_phrase和match_phrase_prefix使用，只需要指定type类型即可
GET test2/doc/_search
{
"query": {
"multi_match": {
"query": "beautiful",
"fields": ["title","desc"]
}
}
}
>>查询结果
{
"took" : 43,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 0.39556286,
"hits" : [
{
"_index" : "test2",
"_type" : "doc",
"_id" : "1",
"_score" : 0.39556286,
"_source" : {
"title" : "prefix1",
"desc" : "beautiful girl you are beautiful so"
}
},
{
"_index" : "test2",
"_type" : "doc",
"_id" : "2",
"_score" : 0.2876821,
"_source" : {
"title" : "beautiful",
"desc" : "I like basking on the beach"
}
}
]
}
}
- 当设置属性 type:phrase 时 等同于 短语查询
GET test1/doc/_search
{
"query": {
"multi_match": {
"query": "中国",
"fields": ["title"],
"type": "phrase"
}
}
}
>>>查询结果
{
"took" : 47,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 0.5753642,
"hits" : [
{
"_index" : "test1",
"_type" : "doc",
"_id" : "1",
"_score" : 0.5753642,
"_source" : {
"title" : "中国是世界上人口最多的国家"
}
},
{
"_index" : "test1",
"_type" : "doc",
"_id" : "3",
"_score" : 0.5753642,
"_source" : {
"title" : "北京是中国的首都"
}
}
]
}
}
- 当设置属性 type:phrase_prefix时 等同于 最左前缀查询
GET test2/doc/_search
{
"query": {
"multi_match": {
"query": "bea",
"fields": ["desc"],
"type": "phrase_prefix"
}
}
}
>>查询结果
{
"took" : 5,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 0.5753642,
"hits" : [
{
"_index" : "test1",
"_type" : "doc",
"_id" : "1",
"_score" : 0.5753642,
"_source" : {
"title" : "中国是世界上人口最多的国家"
}
},
{
"_index" : "test1",
"_type" : "doc",
"_id" : "3",
"_score" : 0.5753642,
"_source" : {
"title" : "北京是中国的首都"
}
}
]
}
}
match 查询相关总结
1、match：返回所有匹配的分词。
2、match_all：查询全部。
3、match_phrase：短语查询，在match的基础上进一步查询词组，可以指定slop分词间隔。
4、match_phrase_prefix：前缀查询，根据短语中最后一个词组做前缀匹配，可以应用于搜索提示，但注意和max_expanions搭配。其实默认是50.......
5、multi_match：多字段查询，使用相当的灵活，可以完成match_phrase和match_phrase_prefix的工作。
四、ES的排序查询
es 6.8.4版本中，需要分词的字段不可以直接排序，比如：text类型，如果想要对这类字段进行排序，需要特别设置：对字段索引两次，一次索引分词（用于搜索）一次索引不分词（用于排序），es默认生成的text类型字段就是通过这样的方法实现可排序的。
倒叙排序
GET test/doc/_search
{
"query": {
"match_all": {}
},
"sort": [
{
"age": {
"order": "desc"
}
}
]
}
>>排序结果
{
"took" : 152,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 3,
"max_score" : null,
"hits" : [
{
"_index" : "test",
"_type" : "doc",
"_id" : "3",
"_score" : null,
"_source" : {
"name" : "wangyang",
"age" : 30,
"desc" : "点在我心内的几首歌"
},
"sort" : [
30
]
},
{
"_index" : "test",
"_type" : "doc",
"_id" : "2",
"_score" : null,
"_source" : {
"name" : "wangfei",
"age" : 27,
"desc" : "热天还不让后人不认同"
},
"sort" : [
27
]
},
{
"_index" : "test",
"_type" : "doc",
"_id" : "1",
"_score" : null,
"_source" : {
"name" : "wangjifei",
"age" : 27,
"desc" : "生活就像 茫茫海上"
},
"sort" : [
27
]
}
]
}
}
- 升序排序
GET test/doc/_search
{
"query": {
"match_all": {}
},
"sort": [
{
"age": {
"order": "asc"
}
}
]
}
五、ES的分页查询
- from：从哪开始查 size：返回几条结果
GET test/doc/_search
{
"query": {
"match_phrase_prefix": {
"name": "wang"
}
},
"from": 0,
"size": 1
}
>>查询结果
{
"took" : 3,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 3,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test",
"_type" : "doc",
"_id" : "2",
"_score" : 0.2876821,
"_source" : {
"name" : "wangfei",
"age" : 27,
"desc" : "热天还不让后人不认同"
}
}
]
}
}
六、ES的bool查询 (must、should)
- must (must字段对应的是个列表，也就是说可以有多个并列的查询条件，一个文档满足各个子条件后才最终返回)
#### 单条件查询
GET test/doc/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"name": "wangfei"
}
}
]
}
}
}
>>查询结果
{
"took" : 4,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test",
"_type" : "doc",
"_id" : "2",
"_score" : 0.2876821,
"_source" : {
"name" : "wangfei",
"age" : 27,
"desc" : "热天还不让后人不认同"
}
}
]
}
}
#### 多条件组合查询
GET test/doc/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"name": "wanggfei"
}
},{
"match": {
"age": 25
}
}
]
}
}
}
>>查询结果
{
"took" : 21,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 0,
"max_score" : null,
"hits" : [ ]
}
}
- should (只要符合其中一个条件就返回)
GET test/doc/_search
{
"query": {
"bool": {
"should": [
{
"match": {
"name": "wangjifei"
}
},{
"match": {
"age": 27
}
}
]
}
}
}
>>查询结果
{
"took" : 34,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 1.287682,
"hits" : [
{
"_index" : "test",
"_type" : "doc",
"_id" : "1",
"_score" : 1.287682,
"_source" : {
"name" : "wangjifei",
"age" : 27,
"desc" : "生活就像 茫茫海上"
}
},
{
"_index" : "test",
"_type" : "doc",
"_id" : "2",
"_score" : 1.0,
"_source" : {
"name" : "wangfei",
"age" : 27,
"desc" : "热天还不让后人不认同"
}
}
]
}
}
- must_not 顾名思义
GET test/doc/_search
{
"query": {
"bool": {
"must_not": [
{
"match": {
"name": "wangjifei"
}
},{
"match": {
"age": 27
}
}
]
}
}
}
>>查询结果
{
"took" : 13,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 1.0,
"hits" : [
{
"_index" : "test",
"_type" : "doc",
"_id" : "3",
"_score" : 1.0,
"_source" : {
"name" : "wangyang",
"age" : 30,
"desc" : "点在我心内的几首歌"
}
}
]
}
}
- filter(条件过滤查询，过滤条件的范围用range表示gt表示大于、lt表示小于、gte表示大于等于、lte表示小于等于)
GET test/doc/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"name": "wangjifei"
}
}
],
"filter": {
"range": {
"age": {
"gte": 10,
"lt": 27
}
}
}
}
}
}
>>查询结果
{
"took" : 33,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 0,
"max_score" : null,
"hits" : [ ]
}
}
bool查询总结
must：与关系，相当于关系型数据库中的 and。
should：或关系，相当于关系型数据库中的 or。
must_not：非关系，相当于关系型数据库中的 not。
filter：过滤条件。
range：条件筛选范围。
gt：大于，相当于关系型数据库中的 >。
gte：大于等于，相当于关系型数据库中的 >=。
lt：小于，相当于关系型数据库中的 <。
lte：小于等于，相当于关系型数据库中的 <=。
七、ES之查询结果过滤
####准备数据
PUT test3/doc/1
{
"name":"顾老二",
"age":30,
"from": "gu",
"desc": "皮肤黑、武器长、性格直",
"tags": ["黑", "长", "直"]
}
- 现在，在所有的结果中，我只需要查看name和age两个属性,提高查询效率
GET test3/doc/_search
{
"query": {
"match": {
"name": "顾"
}
},
"_source": ["name","age"]
}
>>查询结果
{
"took" : 58,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test3",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"name" : "顾老二",
"age" : 30
}
}
]
}
}
八、ES之查询结果高亮显示
- ES的默认高亮显示
GET test3/doc/_search
{
"query": {
"match": {
"name": "顾老二"
}
},
"highlight": {
"fields": {
"name": {}
}
}
}
>>查询结果
{
"took" : 216,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.8630463,
"hits" : [
{
"_index" : "test3",
"_type" : "doc",
"_id" : "1",
"_score" : 0.8630463,
"_source" : {
"name" : "顾老二",
"age" : 30,
"from" : "gu",
"desc" : "皮肤黑、武器长、性格直",
"tags" : [
"黑",
"长",
"直"
]
},
"highlight" : {
"name" : [
"<em>顾</em><em>老</em><em>二</em>"
]
}
}
]
}
}
ES自定义高亮显示（在highlight中，pre_tags用来实现我们的自定义标签的前半部分，在这里，我们也可以为自定义的 标签添加属性和样式。post_tags实现标签的后半部分，组成一个完整的标签。至于标签中的内容，则还是交给fields来完成）
GET test3/doc/_search
{
"query": {
"match": {
"desc": "性格直"
}
},
"highlight": {
"pre_tags": "<b class='key' style='color:red'>",
"post_tags": "</b>",
"fields": {
"desc": {}
}
}
}
>>查询结果
{
"took" : 6,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.8630463,
"hits" : [
{
"_index" : "test3",
"_type" : "doc",
"_id" : "1",
"_score" : 0.8630463,
"_source" : {
"name" : "顾老二",
"age" : 30,
"from" : "gu",
"desc" : "皮肤黑、武器长、性格直",
"tags" : [
"黑",
"长",
"直"
]
},
"highlight" : {
"desc" : [
"皮肤黑、武器长、<b class='key' style='color:red'>性</b><b class='key' style='color:red'>格</b><b class='key' style='color:red'>直</b>"
]
}
}
]
}
}
十、ES之精确查询与模糊查询
- term查询查找包含文档精确的倒排索引指定的词条。也就是精确查找。
term和match的区别是：match是经过analyer的，也就是说，文档首先被分析器给处理了。根据不同的分析器，分析的结果也稍显不同，然后再根据分词结果进行匹配。term则不经过分词，它是直接去倒排索引中查找了精确的值了。
#### 准备数据
PUT w1
{
"mappings": {
"doc": {
"properties":{
"t1":{
"type": "text"
},
"t2": {
"type": "keyword"
}
}
}
}
}
PUT w1/doc/1
{
"t1": "hi single dog",
"t2": "hi single dog"
}
- 对比两者的不同 (结果就不展示出来了，只展示结果的文字叙述)
# t1类型为text，会经过分词，match查询时条件也会经过分词，所以下面两种查询都能查到结果
GET w1/doc/_search
{
"query": {
"match": {
"t1": "hi single dog"
}
}
}
GET w1/doc/_search
{
"query": {
"match": {
"t1": "hi"
}
}
}
# t2类型为keyword类型，不会经过分词，match查询时条件会经过分词，所以只能当值为"hi single dog"时能查询到
GET w1/doc/_search
{
"query": {
"match": {
"t2": "hi"
}
}
}
GET w1/doc/_search
{
"query": {
"match": {
"t2": "hi single dog"
}
}
}
# t1类型为text，会经过分词，term查询时条件不会经过分词，所以只有当值为"hi"时能查询到
GET w1/doc/_search
{
"query": {
"term": {
"t1": "hi single dog"
}
}
}
GET w1/doc/_search
{
"query": {
"term": {
"t1": "hi"
}
}
}
# t2类型为keyword类型，不会经过分词，term查询时条件不会经过分词，所以只能当值为"hi single dog"时能查询到
GET w1/doc/_search
{
"query": {
"term": {
"t2": "hi single dog"
}
}
}
GET w1/doc/_search
{
"query": {
"term": {
"t2": "hi"
}
}
}
- 查找多个精确值（terms）
#### 第一个查询方式
GET test/doc/_search
{
"query": {
"bool": {
"should": [
{
"term": {
"age":27
}
},{
"term":{
"age":28
}
}
]
}
}
}
# 第二个查询方式
GET test/doc/_search
{
"query": {
"terms": {
"age": [
"27",
"28"
]
}
}
}
>>>两种方式的查询结果都是一下结果
{
"took" : 10,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 1.0,
"hits" : [
{
"_index" : "test",
"_type" : "doc",
"_id" : "2",
"_score" : 1.0,
"_source" : {
"name" : "wangfei",
"age" : 27,
"desc" : "热天还不让后人不认同"
}
},
{
"_index" : "test",
"_type" : "doc",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"name" : "wangjifei",
"age" : 27,
"desc" : "生活就像 茫茫海上"
}
}
]
}
}
十一、ES的聚合查询avg、max、min、sum
#### 数据准备
PUT zhifou/doc/1
{
"name":"顾老二",
"age":30,
"from": "gu",
"desc": "皮肤黑、武器长、性格直",
"tags": ["黑", "长", "直"]
}
PUT zhifou/doc/2
{
"name":"大娘子",
"age":18,
"from":"sheng",
"desc":"肤白貌美，娇憨可爱",
"tags":["白", "富","美"]
}
PUT zhifou/doc/3
{
"name":"龙套偏房",
"age":22,
"from":"gu",
"desc":"mmp，没怎么看，不知道怎么形容",
"tags":["造数据", "真","难"]
}
PUT zhifou/doc/4
{
"name":"石头",
"age":29,
"from":"gu",
"desc":"粗中有细，狐假虎威",
"tags":["粗", "大","猛"]
}
PUT zhifou/doc/5
{
"name":"魏行首",
"age":25,
"from":"广云台",
"desc":"仿佛兮若轻云之蔽月,飘飘兮若流风之回雪,mmp，最后竟然没有嫁给顾老二！",
"tags":["闭月","羞花"]
}
GET zhifou/doc/_search
{
"query": {
"match_all": {}
}
}
- 需求1、查询from是gu的人的平均年龄。
GET zhifou/doc/_search
{
"query": {
"match": {
"from": "gu"
}
},
"aggs": {
"my_avg": {
"avg": {
"field": "age"
}
}
},
"_source": ["name", "age"]
}
>>>查询结果
{
"took" : 83,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 3,
"max_score" : 0.6931472,
"hits" : [
{
"_index" : "zhifou",
"_type" : "doc",
"_id" : "4",
"_score" : 0.6931472,
"_source" : {
"name" : "石头",
"age" : 29
}
},
{
"_index" : "zhifou",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"name" : "顾老二",
"age" : 30
}
},
{
"_index" : "zhifou",
"_type" : "doc",
"_id" : "3",
"_score" : 0.2876821,
"_source" : {
"name" : "龙套偏房",
"age" : 22
}
}
]
},
"aggregations" : {
"my_avg" : {
"value" : 27.0
}
}
}
上例中，首先匹配查询from是gu的数据。在此基础上做查询平均值的操作，这里就用到了聚合函数，其语法被封装在aggs中，而my_avg则是为查询结果起个别名，封装了计算出的平均值。那么，要以什么属性作为条件呢？是age年龄，查年龄的什么呢？是avg，查平均年龄。
如果只想看输出的值，而不关心输出的文档的话可以通过size=0来控制
GET zhifou/doc/_search
{
"query": {
"match": {
"from": "gu"
}
},
"aggs":{
"my_avg":{
"avg": {
"field": "age"
}
}
},
"size":0,
"_source":["name","age"]
}
>>>查询结果
{
"took" : 35,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 3,
"max_score" : 0.0,
"hits" : [ ]
},
"aggregations" : {
"my_avg" : {
"value" : 27.0
}
}
}
- 需求2、查询年龄的最大值
GET zhifou/doc/_search
{
"query": {
"match_all": {}
},
"aggs": {
"my_max": {
"max": {
"field": "age"
}
}
},
"size": 0,
"_source": ["name","age","from"]
}
>>>查询结果
{
"took" : 10,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 5,
"max_score" : 0.0,
"hits" : [ ]
},
"aggregations" : {
"my_max" : {
"value" : 30.0
}
}
}
- 需求3、查询年龄的最小值
GET zhifou/doc/_search
{
"query": {
"match_all": {}
},
"aggs": {
"my_min": {
"min": {
"field": "age"
}
}
},
"size": 0,
"_source": ["name","age","from"]
}
>>>查询结果
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 5,
"max_score" : 0.0,
"hits" : [ ]
},
"aggregations" : {
"my_min" : {
"value" : 18.0
}
}
}
- 需求4、查询符合条件的年龄之和
GET zhifou/doc/_search
{
"query": {
"match": {
"from": "gu"
}
},
"aggs": {
"my_sum": {
"sum": {
"field": "age"
}
}
},
"size": 0,
"_source": ["name","age","from"]
}
>>>查询结果
{
"took" : 4,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 3,
"max_score" : 0.0,
"hits" : [ ]
},
"aggregations" : {
"my_sum" : {
"value" : 81.0
}
}
}
十二、ES的分组查询
- 需求： 要查询所有人的年龄段，并且按照1520，2025,25~30分组，并且算出每组的平均年龄。
GET zhifou/doc/_search
{
"size": 0,
"query": {
"match_all": {}
},
"aggs": {
"age_group": {
"range": {
"field": "age",
"ranges": [
{
"from": 15,
"to": 20
},
{
"from": 20,
"to": 25
},
{
"from": 25,
"to": 30
}
]
}
}
}
}
>>>查询结果
{
"took" : 9,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 5,
"max_score" : 0.0,
"hits" : [ ]
},
"aggregations" : {
"age_group" : {
"buckets" : [
{
"key" : "15.0-20.0",
"from" : 15.0,
"to" : 20.0,
"doc_count" : 1
},
{
"key" : "20.0-25.0",
"from" : 20.0,
"to" : 25.0,
"doc_count" : 1
},
{
"key" : "25.0-30.0",
"from" : 25.0,
"to" : 30.0,
"doc_count" : 2
}
]
}
}
}
上例中，在aggs的自定义别名age_group中，使用range来做分组，field是以age为分组，分组使用ranges来做，from和to是范围
- 接下来，我们就要对每个小组内的数据做平均年龄处理。
GET zhifou/doc/_search
{
"size": 0,
"query": {
"match_all": {}
},
"aggs": {
"age_group": {
"range": {
"field": "age",
"ranges": [
{
"from": 15,
"to": 20
},
{
"from": 20,
"to": 25
},
{
"from": 25,
"to": 30
}
]
},
"aggs": {
"my_avg": {
"avg": {
"field": "age"
}
}
}
}
}
}
>>>查询结果
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 5,
"max_score" : 0.0,
"hits" : [ ]
},
"aggregations" : {
"age_group" : {
"buckets" : [
{
"key" : "15.0-20.0",
"from" : 15.0,
"to" : 20.0,
"doc_count" : 1,
"my_avg" : {
"value" : 18.0
}
},
{
"key" : "20.0-25.0",
"from" : 20.0,
"to" : 25.0,
"doc_count" : 1,
"my_avg" : {
"value" : 22.0
}
},
{
"key" : "25.0-30.0",
"from" : 25.0,
"to" : 30.0,
"doc_count" : 2,
"my_avg" : {
"value" : 27.0
}
}
]
}
}
}
ES的聚合查询的总结：聚合函数的使用，一定是先查出结果，然后对结果使用聚合函数做处理
avg：求平均
max：最大值
min：最小值
sum：求和
十三、ES之Mappings
GET test
>>>查询结果
{
"test" : {
"aliases" : { },
"mappings" : {
"doc" : {
"properties" : {
"age" : {
"type" : "long"
},
"desc" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"name" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
}
}
}
},
"settings" : {
"index" : {
"creation_date" : "1569133097594",
"number_of_shards" : "5",
"number_of_replicas" : "1",
"uuid" : "AztO9waYQiyHvzP6dlk4tA",
"version" : {
"created" : "6080299"
},
"provided_name" : "test"
}
}
}
}
由返回结果可以看到，分为两大部分：
第一部分关于t1索引类型相关的，包括该索引是否有别名aliases，然后就是mappings信息，
包括索引类型doc，各字段的详细映射关系都收集在properties中。
另一部分是关于索引t1的settings设置。包括该索引的创建时间，主副分片的信息，UUID等等。
1. mappings 是什么？
映射就是在创建索引的时候，有更多定制的内容，更加的贴合业务场景。
用来定义一个文档及其包含的字段如何存储和索引的过程。
2. 字段的数据类型
简单类型如文本（text）、关键字（keyword）、日期（data）、整形（long）、双精度
（double）、布尔（boolean）或ip。 可以是支持JSON的层次结构性质的类型，如对象或嵌套。
或者一种特殊类型，如geo_point、geo_shape或completion。为了不同的目的，
以不同的方式索引相同的字段通常是有用的。例如，字符串字段可以作为全文搜索的文本字段进行索引，
也可以作为排序或聚合的关键字字段进行索引。或者，可以使用标准分析器、英语分析器和
法语分析器索引字符串字段。这就是多字段的目的。大多数数据类型通过fields参数支持多字段。
- 一个简单的映射示例
PUT mapping_test
{
"mappings": {
"test1":{
"properties":{
"name":{"type": "text"},
"age":{"type":"long"}
}
}
}
}
我们在创建索引PUT mapping_test1的过程中，为该索引定制化类型（设计表结构），添加一个映射类型test1；指定字段或者属性都在properties内完成。
GET mapping_test
>>>查询结果
{
"mapping_test" : {
"aliases" : { },
"mappings" : {
"test1" : {
"properties" : {
"age" : {
"type" : "long"
},
"name" : {
"type" : "text"
}
}
}
},
"settings" : {
"index" : {
"creation_date" : "1570794586526",
"number_of_shards" : "5",
"number_of_replicas" : "1",
"uuid" : "P4-trriPTxq-nJj89iYXZA",
"version" : {
"created" : "6080299"
},
"provided_name" : "mapping_test"
}
}
}
}
返回的结果中你肯定很熟悉！映射类型是test1，具体的属性都被封装在properties中。
3. ES mappings之dynamic的三种状态
- 一般的，mapping则又可以分为动态映射（dynamic mapping）和静态（显示）映射（explicit mapping）和精确（严格）映射（strict mappings），具体由dynamic属性控制。默认为动态映射
##### 默认为动态映射
PUT test4
{
"mappings": {
"doc":{
"properties": {
"name": {
"type": "text"
},
"age": {
"type": "long"
}
}
}
}
}
GET test4/_mapping
>>>查询结果
{
"test4" : {
"mappings" : {
"doc" : {
"properties" : {
"age" : {
"type" : "long"
},
"name" : {
"type" : "text"
},
"sex" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
}
}
}
}
}
}
#####添加数据
PUT test4/doc/1
{
"name":"wangjifei",
"age":"18",
"sex":"不详"
}
#####查看数据
GET test4/doc/_search
{
"query": {
"match_all": {}
}
}
>>>查询结果
{
"took" : 8,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 1.0,
"hits" : [
{
"_index" : "test4",
"_type" : "doc",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"name" : "wangjifei",
"age" : "18",
"sex" : "不详"
}
}
]
}
}
- 测试静态映射：当elasticsearch察觉到有新增字段时，因为dynamic:false的关系，会忽略该字段，但是仍会存储该字段。
#####创建静态mapping
PUT test5
{
"mappings": {
"doc":{
"dynamic":false,
"properties": {
"name": {
"type": "text"
},
"age": {
"type": "long"
}
}
}
}
}
#####插入数据
PUT test5/doc/1
{
"name":"wangjifei",
"age":"18",
"sex":"不详"
}
####条件查询
GET test5/doc/_search
{
"query": {
"match": {
"sex": "不详"
}
}
}
>>>查询结果
{
"took" : 9,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 0,
"max_score" : null,
"hits" : [ ]
}
}
#####查看所有数据
GET /test5/doc/_search
{
"query": {
"match_all": {}
}
}
>>>查询结果
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 1.0,
"hits" : [
{
"_index" : "test5",
"_type" : "doc",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"name" : "wangjifei",
"age" : "18",
"sex" : "不详"
}
}
]
}
}
- 测试严格映射：当elasticsearch察觉到有新增字段时，因为dynamic:strict 的关系，就会报错，不能插入成功。
#####创建严格mapping
PUT test6
{
"mappings": {
"doc":{
"dynamic":"strict",
"properties": {
"name": {
"type": "text"
},
"age": {
"type": "long"
}
}
}
}
}
#####插入数据
PUT test6/doc/1
{
"name":"wangjifei",
"age":"18",
"sex":"不详"
}
>>>插入结果
{
"error": {
"root_cause": [
{
"type": "strict_dynamic_mapping_exception",
"reason": "mapping set to strict, dynamic introduction of [sex] within [doc] is not allowed"
}
],
"type": "strict_dynamic_mapping_exception",
"reason": "mapping set to strict, dynamic introduction of [sex] within [doc] is not allowed"
},
"status": 400
}
小结： 动态映射（dynamic：true）：动态添加新的字段（或缺省）。 静态映射（dynamic：false）：忽略新的字段。在原有的映射基础上，当有新的字段时，不会主动的添加新的映射关系，只作为查询结果出现在查询中。 严格模式（dynamic：strict）：如果遇到新的字段，就抛出异常。一般静态映射用的较多。就像HTML的img标签一样，src为自带的属性，你可以在需要的时候添加id或者class属性。当然，如果你非常非常了解你的数据，并且未来很长一段时间不会改变，strict不失为一个好选择。
4. ES之mappings的 index 属性
- index属性默认为true，如果该属性设置为false，那么，elasticsearch不会为该属性创建索引，也就是说无法当做主查询条件。
PUT test7
{
"mappings": {
"doc": {
"properties": {
"name": {
"type": "text",
"index": true
},
"age": {
"type": "long",
"index": false
}
}
}
}
}
####插入数据
PUT test7/doc/1
{
"name":"wangjifei",
"age":18
}
####条件查询数据
GET test7/doc/_search
{
"query": {
"match": {
"name": "wangjifei"
}
}
}
>>>查询结果
{
"took" : 18,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test7",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"name" : "wangjifei",
"age" : 18
}
}
]
}
}
#####条件查询
GET test7/doc/_search
{
"query": {
"match": {
"age": 18
}
}
}
>>>查询结果
{
"error": {
"root_cause": [
{
"type": "query_shard_exception",
"reason": "failed to create query: {\n \"match\" : {\n \"age\" : {\n \"query\" : 18,\n \"operator\" : \"OR\",\n \"prefix_length\" : 0,\n \"max_expansions\" : 50,\n \"fuzzy_transpositions\" : true,\n \"lenient\" : false,\n \"zero_terms_query\" : \"NONE\",\n \"auto_generate_synonyms_phrase_query\" : true,\n \"boost\" : 1.0\n }\n }\n}",
"index_uuid": "fzN9frSZRy2OzinRjeMKGA",
"index": "test7"
}
],
"type": "search_phase_execution_exception",
"reason": "all shards failed",
"phase": "query",
"grouped": true,
"failed_shards": [
{
"shard": 0,
"index": "test7",
"node": "INueKtviRpO1dbNWngcjJA",
"reason": {
"type": "query_shard_exception",
"reason": "failed to create query: {\n \"match\" : {\n \"age\" : {\n \"query\" : 18,\n \"operator\" : \"OR\",\n \"prefix_length\" : 0,\n \"max_expansions\" : 50,\n \"fuzzy_transpositions\" : true,\n \"lenient\" : false,\n \"zero_terms_query\" : \"NONE\",\n \"auto_generate_synonyms_phrase_query\" : true,\n \"boost\" : 1.0\n }\n }\n}",
"index_uuid": "fzN9frSZRy2OzinRjeMKGA",
"index": "test7",
"caused_by": {
"type": "illegal_argument_exception",
"reason": "Cannot search on field [age] since it is not indexed."
}
}
}
]
},
"status": 400
}
5. ES 之 mappings 的copy_to属性
PUT test8
{
"mappings": {
"doc": {
"dynamic":false,
"properties": {
"first_name":{
"type": "text",
"copy_to": "full_name"
},
"last_name": {
"type": "text",
"copy_to": "full_name"
},
"full_name": {
"type": "text"
}
}
}
}
}
#####插入数据
PUT test8/doc/1
{
"first_name":"tom",
"last_name":"ben"
}
PUT test8/doc/2
{
"first_name":"john",
"last_name":"smith"
}
#####查询所有
GET test8/doc/_search
{
"query": {
"match_all": {}
}
}
>>>查询结果
{
"took" : 4,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 1.0,
"hits" : [
{
"_index" : "test8",
"_type" : "doc",
"_id" : "2",
"_score" : 1.0,
"_source" : {
"first_name" : "john",
"last_name" : "smith"
}
},
{
"_index" : "test8",
"_type" : "doc",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"first_name" : "tom",
"last_name" : "ben"
}
}
]
}
}
#####条件查询
GET test8/doc/_search
{
"query": {
"match": {
"first_name": "tom"
}
}
}
>>>查询结果
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test8",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"first_name" : "tom",
"last_name" : "ben"
}
}
]
}
}
######条件查询
GET test8/doc/_search
{
"query": {
"match": {
"full_name": "ben"
}
}
}
>>>查询结果
{
"took" : 3,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test8",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"first_name" : "tom",
"last_name" : "ben"
}
}
]
}
}
上例中，我们将first_name和last_name都复制到full_name中。并且使用full_name查询也返回了结果
- 既要查询tom还要查询smith该怎么办？
GET test8/doc/_search
{
"query": {
"match": {
"full_name": {
"query": "tom smith",
"operator": "or"
}
}
}
}
>>>查询结果
{
"took" : 3,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test8",
"_type" : "doc",
"_id" : "2",
"_score" : 0.2876821,
"_source" : {
"first_name" : "john",
"last_name" : "smith"
}
},
{
"_index" : "test8",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"first_name" : "tom",
"last_name" : "ben"
}
}
]
}
}
operator参数为多个条件的查询关系也可以是and
- 上面的查询还可以简写成一下：
GET test8/doc/_search
{
"query": {
"match": {
"full_name": "tom smith"
}
}
}
- copy_to还支持将相同的属性值复制给不同的字段。
PUT test9
{
"mappings": {
"doc": {
"dynamic":false,
"properties": {
"first_name":{
"type": "text",
"copy_to": ["full_name1","full_name2"]
},
"last_name": {
"type": "text",
"copy_to": ["full_name1","full_name2"]
},
"full_name1": {
"type": "text"
},
"full_name2":{
"type":"text"
}
}
}
}
}
####插入数据
PUT test9/doc/1
{
"first_name":"tom",
"last_name":"ben"
}
PUT test9/doc/2
{
"first_name":"john",
"last_name":"smith"
}
####条件查询
GET test9/doc/_search
{
"query": {
"match": {
"full_name1": "tom smith"
}
}
}
>>>查询结果
{
"took" : 7,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test9",
"_type" : "doc",
"_id" : "2",
"_score" : 0.2876821,
"_source" : {
"first_name" : "john",
"last_name" : "smith"
}
},
{
"_index" : "test9",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"first_name" : "tom",
"last_name" : "ben"
}
}
]
}
}
#####条件查询
GET test9/doc/_search
{
"query": {
"match": {
"full_name2": "tom smith"
}
}
}
>>>查询结果
{
"took" : 7,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 2,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test9",
"_type" : "doc",
"_id" : "2",
"_score" : 0.2876821,
"_source" : {
"first_name" : "john",
"last_name" : "smith"
}
},
{
"_index" : "test9",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"first_name" : "tom",
"last_name" : "ben"
}
}
]
}
}
full_name1 full_name2两个字段都可以查出来
6. ES 之mappings的对象属性
- 首先先看看ES自动创建的mappings
PUT test10/doc/1
{
"name":"wangjifei",
"age":18,
"info":{
"addr":"北京",
"tel":"18500327026"
}
}
GET test10
>>>查询结果
{
"test10" : {
"aliases" : { },
"mappings" : {
"doc" : {
"properties" : {
"age" : {
"type" : "long"
},
"info" : {
"properties" : {
"addr" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"tel" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
}
}
},
"name" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
}
}
}
},
"settings" : {
"index" : {
"creation_date" : "1570975011394",
"number_of_shards" : "5",
"number_of_replicas" : "1",
"uuid" : "YvMGDHxkSri0Lgx6GGXiNw",
"version" : {
"created" : "6080299"
},
"provided_name" : "test10"
}
}
}
}
- 现在如果要以info中的tel为条件怎么写查询语句呢？
GET test10/doc/_search
{
"query": {
"match": {
"info.tel": "18500327026"
}
}
}
>>>查询结果
{
"took" : 5,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test10",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"name" : "wangjifei",
"age" : 18,
"info" : {
"addr" : "北京",
"tel" : "18500327026"
}
}
}
]
}
}
info既是一个属性，也是一个对象，我们称为info这类字段为对象型字段。该对象内又包含addr和tel两个字段，如上例这种以嵌套内的字段为查询条件的话，查询语句可以以字段点子字段的方式来写即可
7. ES之mappings的settings 设置
- 在创建一个索引的时候，我们可以在settings中指定分片信息：
PUT test11
{
"mappings": {
"doc": {
"properties": {
"name": {
"type": "text"
}
}
}
},
"settings": {
"number_of_replicas": 1,
"number_of_shards": 5
}
}
number_of_shards是主分片数量（每个索引默认5个主分片），而number_of_replicas是复制分片，默认一个主分片搭配一个复制分片。
8. ES 之mappings的ignore_above参数
- ignore_above参数仅针对于keyword类型有用
# 这样设置是会报错的
PUT test12
{
"mappings": {
"doc": {
"properties": {
"name": {
"type": "text",
"ignore_above":5
}
}
}
}
}
>>>显示结果
{
"error": {
"root_cause": [
{
"type": "mapper_parsing_exception",
"reason": "Mapping definition for [name] has unsupported parameters: [ignore_above : 5]"
}
],
"type": "mapper_parsing_exception",
"reason": "Failed to parse mapping [doc]: Mapping definition for [name] has unsupported parameters: [ignore_above : 5]",
"caused_by": {
"type": "mapper_parsing_exception",
"reason": "Mapping definition for [name] has unsupported parameters: [ignore_above : 5]"
}
},
"status": 400
}
##### 正确的打开方式
PUT test12
{
"mappings": {
"doc": {
"properties": {
"name": {
"type": "keyword",
"ignore_above":5
}
}
}
}
}
PUT test12/doc/1
{
"name":"wangjifei"
}
##### 这样查询能查出结果
GET test12/doc/_search
{
"query": {
"match_all": {}
}
}
>>>查询结果
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 1.0,
"hits" : [
{
"_index" : "test12",
"_type" : "doc",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"name" : "wangjifei"
}
}
]
}
}
######这样查询不能查询出结果
GET test12/doc/_search
{
"query": {
"match": {
"name": "wangjifei"
}
}
}
>>>查询结果
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 0,
"max_score" : null,
"hits" : [ ]
}
}
上面的例子证明超过ignore_above设定的值后会被存储但不会建立索引
- 那么如果字符串的类型是text时能用ignore_above吗，答案是能，但要特殊设置：
PUT test13
{
"mappings": {
"doc":{
"properties":{
"name1":{
"type":"keyword",
"ignore_above":5
},
"name2":{
"type":"text",
"fields":{
"keyword":{
"type":"keyword",
"ignore_above": 10
}
}
}
}
}
}
}
PUT test13/doc/1
{
"name1":"wangfei",
"name2":"wangjifei hello"
}
##### 能查出来
GET test13/doc/_search
{
"query": {
"match_all": {}
}
}
>>>查询结果
{
"took" : 4,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 1.0,
"hits" : [
{
"_index" : "test13",
"_type" : "doc",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"name1" : "wangfei",
"name2" : "wangjifei hello"
}
}
]
}
}
##### 通过name1 字段查不出来，因为设置的是keyword类型 限制了5个字符的长度，
##### 存储的值超过了最大限制
GET test13/doc/_search
{
"query": {
"match": {
"name1": "wangfei"
}
}
}
>>>查询结果
{
"took" : 2,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 0,
"max_score" : null,
"hits" : [ ]
}
}
##### 通过name2 字段能查出来，虽然限制了5个字符的长度，存储的值超过了最大限制，但是，
##### 当字段类型设置为text之后，ignore_above参数的限制就失效了。（了解就好，意义不大）
GET test13/doc/_search
{
"query": {
"match": {
"name2": "wangjifei"
}
}
}
>>>查询结果
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : 1,
"max_score" : 0.2876821,
"hits" : [
{
"_index" : "test13",
"_type" : "doc",
"_id" : "1",
"_score" : 0.2876821,
"_source" : {
"name1" : "wangfei",
"name2" : "wangjifei hello"
}
}
]
}
}
