3.elasticsearch文档查询dsl

Posted 2022-11-12 PacosonSWJTU

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【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