ElasticSearch7.2简单命令实操(postman版)

使用postman访问操作ElasticSearch数据库，数据格式均为json

目录

• 使用postman访问操作ElasticSearch数据库，数据格式均为json
  • 一、集群设置
    • 1、查看集群设置
    • 2、修改集群设置-自动创建索引
  • 二、索引操作-index
    • 1、创建索引
    • 2、获取索引
    • 3、关闭索引
    • 4、删除索引
  • 三、映射操作-mapping
    • 1、创建映射
    • 2、获取映射
  • 四、文档操作-doc
    • 1、新增文档-指定ID
  • 五、搜索
  • 六、分词器

---

一、集群设置

1、查看集群设置

GET http://elasticsearch-1:9200/_cluster/settings

2、修改集群设置-自动创建索引

当auto_create_index=false时，指定⼀个不存在的索引，新增文档会报错。

当auto_create_index=true时，指定⼀个不存在的索引，新增⽂档成功，并且会创建这个不存在的索引

PUT http://elasticsearch-1:9200/_cluster/settings

{
	"persistent": {
		"action.auto_create_index": "false"
	 }
}

二、索引操作-index

1、创建索引

PUT http://elasticsearch-1:9200/nba

{
    "mappings": {
        "properties": {
            "name": {
                "type": "text"
            },
            "team_name": {
                "type": "text"
            },
            "position": {
                "type": "text"
            },
            "play_year": {
                "type": "long"
            },
            "jerse_no": {
                "type": "keyword"
            }
        }
    }
}

2、获取索引

GET http://elasticsearch-1:9200/nba

3、关闭索引

POST http://elasticsearch-1:9200/nba/_close

4、删除索引

DELETE http://elasticsearch-1:9200/nba/

三、映射操作-mapping

1、创建映射

PUT http://elasticsearch-1:9200/nba/_mapping

{
	"properties": {
		"name": {
			"type": "text"
		 },
		"team_name": {
			"type": "text"
		 },
		"position": {
			"type": "keyword"
		 },
		"play_year": {
			"type": "keyword"
		 },
		"jerse_no": {
			"type": "keyword"
		 },
		 "country":{
		 	"type": "keyword"
		 }
	 }
}

2、获取映射

GET http://elasticsearch-1:9200/nba/_mapping

四、文档操作-doc

1、新增文档-指定ID

PUT http://elasticsearch-1:9200/nba/_doc/3

{
    "name": "詹姆斯",
    "team_name": "湖⼈",
    "position": "⼩前锋",
    "play_year": 15,
    "jerse_no": "23"
}

2、新增文档-不指定ID

POST http://elasticsearch-1:9200/nba/_doc

{
	"name":"哈登",
	"team_name":"⽕箭",
	"position":"得分后卫",
	"play_year":"10",
	"jerse_no":"13",
	"country":"庞各庄"
}

3、获取文档-byId

GET http://elasticsearch-1:9200/nba/_doc/1

4、新增文档-指定操作类型

生产过程当中根据业务去添加指定类型，防止数据重复，保证数据唯一性

PUT http://elasticsearch-1:9200/nba/_doc/1?op_type=create

{
	"name":"哈登",
	"team_name":"⽕箭",
	"position":"得分后卫",
	"play_year":"10",
	"jerse_no":"13"
}

5、获取文档-all

方式1：
http://elasticsearch-1:9200/_mget

{
    "docs": [
        {
            "_index": "nba",
            "_type": "_doc",
            "_id": "1"
        },
        {
            "_index": "nba",
            "_type": "_doc",
            "_id": "sBwWSW4BSS1Cuk2FoD69"
        }
    ]
}

方式2：
http://elasticsearch-1:9200/nba/_mget

{
    "docs": [
        {
            "_type": "_doc",
            "_id": "1"
        },
        {
            "_type": "_doc",
            "_id": "2"
        }
    ]
}

方式3：
http://elasticsearch-1:9200/nba/_docs/_mget

{
    "docs": [
        {
            "_id": "1"
        },
        {
            "_id": "2"
        }
    ]
}

方式4：
http://elasticsearch-1:9200/nba/_docs/_mget

{
    "ids": [
        "1",
        "sBwWSW4BSS1Cuk2FoD69"
    ]
}

6、修改文档-整条数据更新

POST http://elasticsearch-1:9200/nba/_update/2

{
    "doc": {
        "name": "库⾥",
        "team_name": "勇⼠",
        "position": "控球后卫",
        "play_year": 10,
        "jerse_no": "30",
        "title": "The best 3-points shooter is Curry!"
    }
}

7、新增字段

POST http://elasticsearch-1:9200/nba/_update/1

{
    "script": "ctx._source.age = 18"
}

8、更新指定文档字段

POST http://elasticsearch-1:9200/nba/_update/1

{
    "script": {
        "source": "ctx._source.age += params.age",
        "params": {
            "age": 4
        }
    }
}

9、删除字段

POST http://elasticsearch-1:9200/nba/_update/1

{
    "script": "ctx._source.remove(\"age\")"
}

10、upsert

upsert 当指定的⽂档不存在时，upsert参数包含的内容将会被插⼊到索引中，作为⼀个

新⽂档；如果指定的⽂档存在，ElasticSearch引擎将会执⾏指定的更新逻辑。

POST http://elasticsearch-1:9200/nba/_update/3

{
    "script": {
        "source": "ctx._source.allstar += params.allstar",
        "params": {
            "allstar": 4
        }
    },
    "upsert": {
        "allstar": 1
    }
}

11、删除文档-byId

DELETE http://elasticsearch-1:9200/nba/_doc/3

五、搜索

1、term(词条)查询-精确查询

GET http://elasticsearch-1:9200/nba/_search

词条查询：词条查询不会分析查询条件，只有当词条和查询字符串完全匹配时，才匹配搜索。

单条查询：

{
    "query": {
        "term": {
            "jerse_no": "23"
        }
    }
}

多条查询：

{
    "query": {
        "terms": {
            "jerse_no": [
                "23",
                "13"
            ]
        }
    }
}

2、match_all全文查询-单字段查询

GET http://elasticsearch-1:9200/nba/_search

full text(全⽂)查询

全⽂查询：ElasticSearch引擎会先分析查询字符串，将其拆分成多个分词，只要已分析的字段中包含词条的任意⼀个，或全部包含，就匹配查询条件，返回该⽂档；如果不包含任意⼀个分词，表示没有任何⽂档匹配查询条件。

注意：每次搜索默认查询出10条，可以通过from和size设定从第几条开始查询初多少条。也就是分页

无条件全量查询：

{
    "query": {
        "match_all": {}
    },
    "from": 0,
    "size": 100
}

根据查询条件分词查询：

{
    "query": {
        "match": {
            "name": "库baby"
        }
    },
    "from": 0,
    "size": 100
}

3、multi_match全文查询-多字段查询

一个参数在多个字段中查询，字段拼接为or，非and。也就是 title='shooter' or name='shooter'

GET http://elasticsearch-1:9200/nba/_search

{
    "query": {
        "multi_match": {
            "query": "shooter",
            "fields": [
                "title",
                "name"
            ]
        }
    }
}

4、match_phrase(词条)-精确查询

GET http://elasticsearch-1:9200/nba/_search

{
    "query": {
        "match_phrase": {
            "position": "得分后"
        }
    }
}

5、match_phrase_prefix(词条)-左前缀查询

GET http://elasticsearch-1:9200/nba/_search

{
    "query": {
        "match_phrase_prefix": {
            "title": "the best s"
        }
    }
}

六、分词器

1、standard-analyzer：默认,标准分词器

标准分析器是默认分词器，如果未指定，则使⽤该分词器。

POST http://elasticsearch-1:9200/_analyze

{
    "analyzer": "standard",
    "text": "The best 3-points shooter is Curry!"
}

2、simple-analyzer：简单分词器

simple 分析器当它遇到只要不是字⺟的字符，就将⽂本解析成term，⽽且所有的term都是⼩写的。

POST http://elasticsearch-1:9200/_analyze

{
    "analyzer": "simple",
    "text": "The best 3-points shooter is Curry!"
}

3、whitespace-analyzer：空白字符分词器

whitespace 分析器，当它遇到空⽩字符时，就将⽂本解析成terms

POST http://elasticsearch-1:9200/_analyze

{
    "analyzer": "whitespace",
    "text": "The best 3-points shooter is Curry!"
}

4、stop-analyzer

stop 分析器 和 simple分析器很像，唯⼀不同的是，stop分析器增加了对删除停⽌词的⽀持，默认使⽤了english停⽌词

stopwords 预定义的停⽌词列表，⽐如 (the,a,an,this,of,at)等等

POST http://elasticsearch-1:9200/_analyze

{
    "analyzer": "stop",
    "text": "The best 3-points shooter is Curry!"
}

5、language-analyzer：语言分词器

（特定的语⾔的分词器，⽐如说，english，英语分词器),内置语⾔：arabic, armenian,basque, bengali, brazilian, bulgarian, catalan, cjk, czech, danish, dutch, english, finnish,french, galician, german, greek, hindi, hungarian, indonesian, irish, italian, latvian,lithuanian, norwegian, persian, portuguese, romanian,russian, sorani, spanish,swedish, turkish, thai）

POST http://elasticsearch-1:9200/_analyze

{
    "analyzer": "english",
    "text": "The best 3-points shooter is Curry!"
}

6、pattern-analyzer：正则分词器

⽤正则表达式来将⽂本分割成terms，默认的正则表达式是\W+（⾮单词字符）

POST http://elasticsearch-1:9200/_analyze

{
    "analyzer": "pattern",
    "text": "The best 3-points shooter is Curry!"
}

7、IK-analyzer：推荐使用

POST http://elasticsearch-1:9200/_analyze

{
    "analyzer": "ik_max_word",
    "text": "2019年双十一再一次证明中国老百姓的财富时取之不尽用之不竭的"
}

8、创建索引时-设置分词器

PUT http://elasticsearch-1:9200/analyzer_index

{
    "settings": {
        "analysis": {
            "analyzer": {
                "my_analyzer": {
                    "type": "whitespace"
                }
            }
        }
    },
    "mappings": {
        "properties": {
            "name": {
                "type": "text"
            },
            "team_name": {
                "type": "text"
            },
            "position": {
                "type": "text"
            },
            "play_year": {
                "type": "long"
            },
            "jerse_no": {
                "type": "keyword"
            },
            "title": {
                "type": "text",
                "analyzer": "my_analyzer"
            }
        }
    }
}

https://blog.csdn.net/qq_22049773/article/details/103013150?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-3.channel_param&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-3.channel_param