【README】

1.本文elasticsearch版本是 7.2.1；

2.文档查询语句叫做 DSL， domain structure language， 领域特定语言；dsl，参见 Query DSL | Elasticsearch Guide [7.2] | Elastic

3.elasticsearch 基于json 提供了完整的查询 DSL 语句（Domain Specific Language-领域特定语言）来定义查询，把查询的dsl看做一种查询的抽象语法树，包含两种类型的查询；

• 1.叶子查询子句：叶子查询子句在特定字段中寻找特定值，如匹配（match），术语（term）或范围查询（range） ；
• 2.复合查询子句： 复合查询子句包裹了其他叶子查询或复合查询，并以逻辑方式组合多个查询（如bool，dis_max 查询），或变更他们的行为（如 constant_score 不计算评分）；

4.查询子句的行为不同，具体取决于它们是用于查询上下文还是过滤器上下文。

• 查询上下文：若查询条件的字段类型是字符串，则计算文档相关性分数；
• 过滤器上下文：若查询条件的字段类型是字符串，则不计算文档分数；

5.查询dsl api分类（包括但不限于，本文仅列出常用查询api）；

• match_all，查询所有文档；
• match，全文检索（若字段是非字符串，则精确匹配，若是字符串类型，则是模糊匹配）；
• match_phrase，短语匹配；
• multi_match，多字段匹配；
• bool，组合多个查询子句的复合查询，子句包括 must, must_not, should, filter 等；
• filter过滤器查询（不计算文档评分）；
• term，术语查询（精确匹配）；
• constant_score，常量分数查询（不计算文档分数）；

6.本文es文档数据来源于  content-elasticsearch-deep-dive/accounts.json at master · linuxacademy/content-elasticsearch-deep-dive · GitHubMyles Elastic Certified Engineer Course. Contribute to linuxacademy/content-elasticsearch-deep-dive development by creating an account on GitHub.https://github.com/linuxacademy/content-elasticsearch-deep-dive/blob/master/sample_data/accounts.json，批量导入，参见 2.elasticsearch文档批量操作-bulk api_PacosonSWJTU的博客-CSDN博客

中的  “【2】bulk 批量导入样本数据章节”；

7.elasticsearch查询 dsl api 是否计算文档分数统计表：

---

【1】match_all ，查询所有文档

0）match_all 查询所有文档，给定文档分数为1.0 （不计算文档分数，给定1.0）；

1）分页查询银行账户索引文档；

// 【sql】
Select firstname, balance
From bank
Order by balance desc
Limit 5 offset 0/5 // elasticsearch match_all api 
Post localhost:9200/bank/_search{"_source":["firstname", "balance"],"query":{"match_all":{}}, "sort":[{"balance":"desc"}], "from":5/0 // 文档偏移量为5 或者 0 , "size":5  // 每页5个文档 
}

---

【2】match-全文检索

match匹配结果：返回与提供的文本、数字、日期或布尔值匹配的文档。

• 若匹配的字段类型是字符串，则在匹配之前会对查询条件的字符串进行分词（如hello world 会分词为 hello和 world两个单词）；
• 匹配查询是执行全文搜索的标准查询，包括模糊匹配选项。

【2.1】精确匹配

1）match 匹配非字符串类型字段，就是精确匹配；

• 因为非字符串类型字段的值没有建立倒排索引（分词），值就是它本身；

2）查询余额等于  49568 的客户信息文档

Post localhost:9200/bank/_search
{"_source":["firstname", "balance"],"query":{"match":{"balance":49568}}   
}{"took": 18,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 1,"relation": "eq"},"max_score": 1.0, // 评分默认为1，没有计算文档评分"hits": [{"_index": "bank","_type": "account","_id": "168","_score": 1.0, // 评分默认为1，没有计算文档评分 "_source": {"firstname": "Carissa","balance": 49568}}]}
}

【2.2】match-模糊匹配（计算文档评分）

0）match匹配字符串类型字段，就是模糊匹配；

1）查询地址包含 Kings 的银行客户文档

Post  localhost:9200/bank/_search {"_source":["firstname", "balance","address"],"query":{"match":{"address":"Kings"}}   
}{"took": 7,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 2,"relation": "eq"},"max_score": 5.9908285,// 显然计算了文档评分 "hits": [{"_index": "bank","_type": "account","_id": "20","_score": 5.9908285,"_source": {"firstname": "Elinor","address": "282 Kings Place","balance": 16418}},{"_index": "bank","_type": "account","_id": "722","_score": 5.9908285,"_source": {"firstname": "Roberts","address": "305 Kings Hwy","balance": 27256}}]}
}

【小结】match的字段类型是字符串时 （注意match的字段类型是非字符串，则是精确匹配）

• 全文检索按照评分进行排序；
• 全文检索会对检索条件进行分词匹配； 

---

【3】match_phrase 短语匹配 （不分词）

1）match_phrase短语匹配：把需要匹配的值当做一个整体单词（不分词）进行检索；

• 而match 模糊匹配： 把需要匹配的值先进行分词，然后检索；

2）match 全文检索例子（查询结果有19个文档）

Post  localhost:9200/bank/_search 
{"_source":["firstname", "balance","address"],"query":{"match":{"address":"mill lane"}}   
}

【解析】

• match 全文检索，会把 mill lane 分为2个单词，查询出包含2个单词中的一个即可；

3）match_phrase：短语匹配例子 （查询结果只有1个文档）

Post localhost:9200/bank/_search
{"_source":["firstname", "balance","address"],"query":{"match_phrase":{"address":"mill lane"}}   
}{"took": 2,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 1,"relation": "eq"},"max_score": 9.507477,"hits": [{"_index": "bank","_type": "account","_id": "136","_score": 9.507477,"_source": {"firstname": "Winnie","address": "198 Mill Lane","balance": 45801}}]}
}

【解析】match_phrase：把 mill lane 当做一个整体，不分词，送入es查询包含 mill lane的文档；

---

【4】multi_match 多字段匹配

1）场景： 查询出 state或address字段包含 mill 的文档；

Post  localhost:9200/bank/_search 
{"_source":["firstname", "balance","address","state"],"query":{"multi_match":{"query":"mill", "fields":["state", "address"]}}   
}// 查询结果
{"took": 2,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 4,"relation": "eq"},"max_score": 5.4032025,"hits": [{"_index": "bank","_type": "account","_id": "970","_score": 5.4032025,"_source": {"firstname": "Forbes","address": "990 Mill Road","balance": 19648,"state": "AK"}},
...... 
}

【4.1】多字段匹配给定的分词条件

1）场景： 查询出 state或address字段包含 mill 或 KY的文档；  

{"_source":["firstname", "balance","address","state"],"query":{"multi_match":{"query":"mill KY", "fields":["state", "address"]}}   
}

---

【5】bool复合查询（组合多个查询条件 ）

【5.1】定义

1）bool 用来做复合查询；

• bool复合查询可以合并任何其他查询语句，包括复合语句；
• 复合语句之间可以相互嵌套，可以表达非常复杂的逻辑；  

2）bool 复合查询可以包含的查询子句

• ① must（计算文档分数）
• ② must_not（不计算文档分数）
• ③ should（计算文档分数）
• ④ filter （不计算文档分数）

【5.2】bool复合查询例子

1）场景： 查询gender等于M，state等于KY，且 age不等于28，或者 lastname等于 Hancock的文档；

• should表示或者，如果匹配，则评分更高；

Post  localhost:9200/bank/_search  
{"query":{"bool":{"must":[{"match":{"gender":"M"}},{"match":{"state":"KY"}}],"must_not":[{"match":{"age":"28"}}],"should":[{"match":{"lastname":"Hancock"}}]}}
}// 查询结果 
{"took": 8,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 6,"relation": "eq"},"max_score": 11.173532,"hits": [{"_index": "bank","_type": "account","_id": "640","_score": 11.173532, // 计算文档得分 "_source": {"account_number": 640,"balance": 35596,"firstname": "Candace","lastname": "Hancock","age": 25,"gender": "M","address": "574 Riverdale Avenue","employer": "Animalia","email": "candacehancock@animalia.com","city": "Blandburg","state": "KY"}},
............................ 

---

【6】filter过滤器（不计算文档分数）

1）filter 不计算文档分数 ；  

• Bool复合查询中的must，should，must_not 被称为查询子句；
• 其中 must或should会计算相关性评分以表示一个文档对查询条件的匹配程度；分数越高，文档就越匹配查询条件；
• 但 must_not 被当做一个过滤器，过滤器不会计算文档匹配的分数；

【6.1】不用filter过滤器查询例子 （计算评分）

1）不用filter的查询（计算分数）

场景：查询年龄大于等于18，且小于等于30，且address包含mill的文档；

注意： match:{"address":"mill"} 表示的是 address包含mill，并不是address等于mill；

Post  localhost:9200/bank/_search
{"query":{"bool":{"must":[{"range":{"age":{"gte":18,"lte":30}}}, {"match":{"address":"mill"}}]}}
}// 查询结果
{"took": 2,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 1,"relation": "eq"},"max_score": 6.4032025,"hits": [{"_index": "bank","_type": "account","_id": "970","_score": 6.4032025,// 计算分数 "_source": {"account_number": 970,"balance": 19648,"firstname": "Forbes","lastname": "Wallace","age": 28,"gender": "M","address": "990 Mill Road","employer": "Pheast","email": "forbeswallace@pheast.com","city": "Lopezo","state": "AK"}}]}
}

---

【6.2】用filter过滤器查询（不计算分数）

1）用filter过滤器查询，就不会计算文档的相关性分数；

• 因为filter不计算分数，filter的查询性能优于 match匹配查询；

2）场景：查询年龄大于等于18，且小于等于30，且address包含mill的文档；

查询结果的分数为0.0，显然 filter不会计算分数；

Post  localhost:9200/bank/_search 
{"query":{"bool":{"filter":[{"range":{"age":{"gte":18,"lte":30}}}, {"match":{"address":"mill"}}]}}
}{"took": 2,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 1,"relation": "eq"},"max_score": 0.0,"hits": [{"_index": "bank","_type": "account","_id": "970","_score": 0.0,  // 没有计算得分 "_source": {"account_number": 970,"balance": 19648,"firstname": "Forbes","lastname": "Wallace","age": 28,"gender": "M","address": "990 Mill Road","employer": "Pheast","email": "forbeswallace@pheast.com","city": "Lopezo","state": "AK"}}]}
}

---

【6.3】把 filter 作为bool的查询子句

1）场景：查询 gender等于M，且state等于KY，且age不等于28，或者 lastname等于 Hancock，且 age在25到30之间的文档；

• 其中 must，should子句计算评分， must_not， filter 不计算评分； 

Post  localhost:9200/bank/_search 
{"query":{"bool":{"must":[{"match":{"gender":"M"}},{"match":{"state":"KY"}}],"must_not":[{"match":{"age":"28"}}],"should":[{"match":{"lastname":"Hancock"}}], "filter":{"range":{"age":{"gte":"25","lte":"30"}}}}}
}
// 查询结果 
{"took": 4,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 1,"relation": "eq"},"max_score": 11.173532,"hits": [{"_index": "bank","_type": "account","_id": "640","_score": 11.173532,"_source": {"account_number": 640,"balance": 35596,"firstname": "Candace","lastname": "Hancock","age": 25,"gender": "M","address": "574 Riverdale Avenue","employer": "Animalia","email": "candacehancock@animalia.com","city": "Blandburg","state": "KY"}}]}
}

2）如何使得上述bool查询中的must，should 不计算分数呢 ？将其嵌套在 filter 里面，如下：

post localhost:9200/bank/_search
{"query":{"bool":{"filter":{"bool":{"must":[{"match":{"gender":"M"}},{"match":{"state":"KY"}}],"must_not":[{"match":{"age":"28"}}],"should":[{"match":{"lastname":"Hancock"}}], "filter":{"range":{"age":{"gte":"25","lte":"30"}}}}}}}
}// 查询结果
{"took": 368,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 1,"relation": "eq"},"max_score": 0.0,"hits": [{"_index": "bank","_type": "account","_id": "640","_score": 0.0, // 显然没有计算文档分数"_source": {"account_number": 640,"balance": 35596,"firstname": "Candace","lastname": "Hancock","age": 25,"gender": "M","address": "574 Riverdale Avenue","employer": "Animalia","email": "candacehancock@animalia.com","city": "Blandburg","state": "KY"}}]}
}

---

【7】term查询（术语查询），精确匹配

1）定义：

• term查询返回给定字段包含精确值的文档；

2）注意：

• ① 避免对 text 字段进行term查询；
• ② 默认情况下，Elasticsearch 会在分析过程中更改文本字段的值（分词）。 这会使查找文本字段值的精确匹配变得困难。
• ③ 要查询 text字段，使用match（模糊查询）；

3）term 与 match 区别：

• ① term： 精确查询（不分词）；
• ② match： 模糊查询（查询字段的类型是字符串，要分词）；

---

【7.1】term查询例子

1）场景1：查询 address 等于 574 Riverdale Avenue 的文档 ;
es文档中的address被分词了，而term查询对 574 Riverdale Avenue 进行精确查询（不分词），所以查无记录。

{"query":{"term":{"address":"574 Riverdale Avenue"}}
}// 为空。

2）场景2：查询 address.keyword 等于 574 Riverdale Avenue 的文档 （查询 address不分词时 等于 574 Riverdale Avenue  的文档 ）

{"query":{"term":{"address.keyword":"574 Riverdale Avenue" // 这里匹配的是 address的keyword属性 }}
}{"took": 1,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 1,"relation": "eq"},"max_score": 6.5032897,"hits": [{"_index": "bank","_type": "account","_id": "640","_score": 6.5032897,// 显然 term 要评分"_source": {"account_number": 640,"balance": 35596,"firstname": "Candace","lastname": "Hancock","age": 25,"gender": "M","address": "574 Riverdale Avenue","employer": "Animalia","email": "candacehancock@animalia.com","city": "Blandburg","state": "KY"}}]}
}

【结果分析】

• 显然term精确匹配会计算评分，其经常嵌套在 filter里面，以不计算评分；

---

【7.2】address.keyword 与 match_phrase 区别

1）address.keyword 查无记录；因为是精确匹配（不分词）；

场景：查询地址等于 574 Riverdale 的文档；

{"query":{"term":{"address.keyword":"574 Riverdale"}}
}
// 查无记录 

2）match_phrase 是部分匹配，包含 574 Riverdale  即可（有值）；

{"query":{"match_phrase":{"address":"574 Riverdale"}}
}// 查询结果 
{"took": 1,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 1,"relation": "eq"},"max_score": 12.492344,"hits": [{"_index": "bank","_type": "account","_id": "640","_score": 12.492344,"_source": {"account_number": 640,"balance": 35596,"firstname": "Candace","lastname": "Hancock","age": 25,"gender": "M","address": "574 Riverdale Avenue","employer": "Animalia","email": "candacehancock@animalia.com","city": "Blandburg","state": "KY"}}]}
}

【小结】

• 若是查询非 text字段，则使用term做精确查询；
• 若是查询 text字段，则使用 match来全文检索；
• 若使用match做精确匹配，则使用 field.keyword 进行；
• 若是查询 text字段的部分匹配（短语匹配，不对字符串不分词），则使用 match_phrase ；

---

【8】constant_socre 常量分数查询

1）constant_score 不计算分数；

2）constant_score 参数有2个：filter 和 boost；

• filter：必须有；filter查询不会计算相关性分数；为了加速性能，elasticsearch自动缓存频繁使用的 filter查询；
• boost：可选，浮点型，用于指定每个文档的分数，默认为1.0；

场景：查询年龄在大于等于25，且小于等于30的文档；

post localhost:9200/bank/_search {"query":{"constant_score":{"filter":{"range":{"age":{"gte":"25","lte":"30"}}}           }}
}// 查询结果 
{"took": 4,"timed_out": false,"_shards": {"total": 1,"successful": 1,"skipped": 0,"failed": 0},"hits": {"total": {"value": 273,"relation": "eq"},"max_score": 1.0,"hits": [{"_index": "bank","_type": "account","_id": "13","_score": 1.0, // 显然，文档分数默认为1.0 "_source": {"account_number": 13,"balance": 32838,"firstname": "Nanette","lastname": "Bates","age": 28,"gender": "F","address": "789 Madison Street","employer": "Quility","email": "nanettebates@quility.com","city": "Nogal","state": "VA"}},
......
} 

参考自： Constant score query | Elasticsearch Guide [7.2] | Elastichttps://www.elastic.co/guide/en/elasticsearch/reference/7.2/query-dsl-constant-score-query.html