记录es的URI search搜索方式
Posted 泛舟五湖之间
tags:
篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了记录es的URI search搜索方式相关的知识，希望对你有一定的参考价值。
在前一篇的文章中我们讲述了关于es的一些关于文档、索引、集群的基本的应用，仅仅起了个开头的作用。
在这一篇中我们将学习es的一些更高级的用法以及更详细的用法。主要是包括搜索和聚合。其中是搜索有普通搜索，
结构化搜索、全文检索、join查询、复合查询、地理信息查询、对查询结果处理、调整相关度进行查询。
聚合主要包括桶聚合、指标聚合、管道聚合，可能会稍微介绍一下矩阵聚合。
接下里我们开始我们的学习之路！！！
一、 搜索
一、URI search
首先我们摘取从《elasticsearch技术解析与实战》中的一张关于URI search的参数表格
| 参数 | 解释 |
|---|---|
| q | 查询字符串，例如q=syslog |
| df | 当查询中没有定义前缀的时候默认使用的字段 |
| analyzer | 当分析查询字符串的时候使用的分词器 |
| lowercase_expanded_terms | 搜索的时候忽略大小写标志,默认为true |
| analyzer_wildcard | 通配符或者前缀查询是否被分析，默认为false |
| default_operator | 默认多个条件的关系，AND或者OR，默认OR |
| lenient | 如果设置为true，字段类型转换失败的时候将被忽略，默认为false |
| explain | 在每个返回结果中，将包含评分机制的解释 |
| _source | 是否包含元数据，同时支持_source_include和_source_exclude |
| fields | 只返回索引中指定的列，多个列中间用逗号分开 |
| sort | 根据字段名排序，例如fieldName:asc或者fieldName:desc |
| track_scores | 评分轨迹，当排序的时候，true表示返回评分的信息 |
| timeout | 超时的时间设置 |
| terminate_after | 在每个分片查询的最大条数，如果设置，返回结果中会有一个terminate_early字段 |
| from | 返回的索引匹配结果的开始值,默认为0 |
| size | 搜索结果返回的条数，默认为10 |
| search_type | 搜索的类型，可以是dfs_query_then_fetch、query_then_fetch,默认为query_then_fetch |
上述多个参数在使用的时候用&分开即可。下面我们开始学习使用URL search：
一、Query String Syntax的用法
GET bank/_search?q=Nanette
据说性能不好，因为它是对所有的文档进行了查询，搜索是否包含Nanette。
GET bank/_search?q=Nanette
"profile": "true"
此处仅仅添加了条件q=Nanette，所以查询的时候会去查询所有的字段中包含Nanette的信息，这一点我们通过profile返回的信息也可以看出来。
profile是在查询的时候可以输出查询的具体执行路径。即执行计划。由于篇幅的问题，我只在这一个查询中使用，后续的查询我们只学习查询的方式，不会再去查看具体的查询执行流程。自己学习的时候可以私下运行执行计划。
返回结果为:
"took" : 2,
"timed_out" : false,
"_shards" :
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
,
"hits" :
"total" :
"value" : 1,
"relation" : "eq"
,
"max_score" : 6.5042877,
"hits" : [
"_index" : "bank",
"_type" : "account",
"_id" : "13",
"_score" : 6.5042877,
"_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"
]
,
"profile" :
"shards" : [
"id" : "[jW8PbSdhTOOpESX13DRBJQ][bank][0]",
"searches" : [
"query" : [
"type" : "DisjunctionMaxQuery",
"description" : """(MatchNoDocsQuery("failed [account_number] query, caused by number_format_exception:[For input string: "Nanette"]") | firstname:nanette | address:nanette | gender:nanette | city:nanette | lastname.keyword:Nanette | gender.keyword:Nanette | city.keyword:Nanette | lastname:nanette | email.keyword:Nanette | firstname.keyword:Nanette | employer.keyword:Nanette | MatchNoDocsQuery("failed [balance] query, caused by number_format_exception:[For input string: "Nanette"]") | state.keyword:Nanette | employer:nanette | state:nanette | address.keyword:Nanette | email:nanette | MatchNoDocsQuery("failed [age] query, caused by number_format_exception:[For input string: "Nanette"]"))""",
"time_in_nanos" : 285257,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 1995,
"match" : 0,
"next_doc_count" : 1,
"score_count" : 1,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 8089,
"advance_count" : 1,
"score" : 6033,
"build_scorer_count" : 3,
"create_weight" : 157223,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 111917
,
"children" : [
"type" : "MatchNoDocsQuery",
"description" : """MatchNoDocsQuery("failed [account_number] query, caused by number_format_exception:[For input string: "Nanette"]")""",
"time_in_nanos" : 2115,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 1346,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 769
,
"type" : "TermQuery",
"description" : "firstname:nanette",
"time_in_nanos" : 35870,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 3,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 1,
"compute_max_score_count" : 3,
"compute_max_score" : 731,
"advance" : 863,
"advance_count" : 2,
"score" : 3138,
"build_scorer_count" : 3,
"create_weight" : 22778,
"shallow_advance" : 896,
"create_weight_count" : 1,
"build_scorer" : 7464
,
"type" : "TermQuery",
"description" : "address:nanette",
"time_in_nanos" : 7593,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 7119,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 474
,
"type" : "TermQuery",
"description" : "gender:nanette",
"time_in_nanos" : 1900,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 1560,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 340
,
"type" : "TermQuery",
"description" : "city:nanette",
"time_in_nanos" : 11893,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 11525,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 368
,
"type" : "TermQuery",
"description" : "lastname.keyword:Nanette",
"time_in_nanos" : 5070,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 4715,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 355
,
"type" : "TermQuery",
"description" : "gender.keyword:Nanette",
"time_in_nanos" : 1801,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 1526,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 275
,
"type" : "TermQuery",
"description" : "city.keyword:Nanette",
"time_in_nanos" : 7737,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 7355,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 382
,
"type" : "TermQuery",
"description" : "lastname:nanette",
"time_in_nanos" : 4524,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 4178,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 346
,
"type" : "TermQuery",
"description" : "email.keyword:Nanette",
"time_in_nanos" : 1986,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 1661,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 325
,
"type" : "TermQuery",
"description" : "firstname.keyword:Nanette",
"time_in_nanos" : 11564,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 3,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 1,
"compute_max_score_count" : 3,
"compute_max_score" : 2440,
"advance" : 497,
"advance_count" : 2,
"score" : 264,
"build_scorer_count" : 3,
"create_weight" : 5632,
"shallow_advance" : 455,
"create_weight_count" : 1,
"build_scorer" : 2276
,
"type" : "TermQuery",
"description" : "employer.keyword:Nanette",
"time_in_nanos" : 6058,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 5688,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 370
,
"type" : "MatchNoDocsQuery",
"description" : """MatchNoDocsQuery("failed [balance] query, caused by number_format_exception:[For input string: "Nanette"]")""",
"time_in_nanos" : 1217,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 479,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 738
,
"type" : "TermQuery",
"description" : "state.keyword:Nanette",
"time_in_nanos" : 4342,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 3525,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 817
,
"type" : "TermQuery",
"description" : "employer:nanette",
"time_in_nanos" : 6783,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 6042,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 741
,
"type" : "TermQuery",
"description" : "state:nanette",
"time_in_nanos" : 3225,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 2904,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 321
,
"type" : "TermQuery",
"description" : "address.keyword:Nanette",
"time_in_nanos" : 2001,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 1614,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 387
,
"type" : "TermQuery",
"description" : "email:nanette",
"time_in_nanos" : 7383,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 7023,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 360
,
"type" : "MatchNoDocsQuery",
"description" : """MatchNoDocsQuery("failed [age] query, caused by number_format_exception:[For input string: "Nanette"]")""",
"time_in_nanos" : 852,
"breakdown" :
"set_min_competitive_score_count" : 0,
"match_count" : 0,
"shallow_advance_count" : 0,
"set_min_competitive_score" : 0,
"next_doc" : 0,
"match" : 0,
"next_doc_count" : 0,
"score_count" : 0,
"compute_max_score_count" : 0,
"compute_max_score" : 0,
"advance" : 0,
"advance_count" : 0,
"score" : 0,
"build_scorer_count" : 2,
"create_weight" : 410,
"shallow_advance" : 0,
"create_weight_count" : 1,
"build_scorer" : 442
]
],
"rewrite_time" : 4769,
"collector" : [
"name" : "SimpleTopScoreDocCollector",
"reason" : "search_top_hits",
"time_in_nanos" : 15204
]
],
"aggregations" : [ ]
]
二、GET bank/_search?q=+Nanette
q=+Nanette的意思就是搜索一定包含Nanette这个词的文档，相反q=-Nanette的意思就是一定不包含Nanette这个词的文档。
由于q=-Nanette输出的文档过多，这里就不展示了，读者自行测试即可
返回结果:
"took" : 2,
"timed_out" : false,
"_shards" :
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
,
"hits" :
"total" :
"value" : 1,
"relation" : "eq"
,
"max_score" : 6.5042877,
"hits" : [
"_index" : "bank",
"_type" : "account",
"_id" : "13",
"_score" : 6.5042877,
"_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"
]
三、GET bank/_search?q=firstname:Nanette
GET bank/_search?q=firstname:Nanette
其实就是指定firstname这个字段进行查询
返回结果为:
"took" : 1,
"timed_out" : false,
"_shards" :
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
,
"hits" :
"total" :
"value" : 1,
"relation" : "eq"
,
"max_score" : 6.5042877,
"hits" : [
"_index" : "bank",
"_type" : "account",
"_id" : "13",
"_score" : 6.5042877,
"_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"
]
四、GET bank/_search?q=address:“Hutchinson Court”
GET bank/_search?q=address:"Hutchinson Court"
加双引号的是Phrase查询，即短语查询，可以通过执行计划看出来，底层使用的是短语查询。
GET bank/_search?q=address:(Hutchinson AND Court)
这里底层进行的是boolean查询，也可以自己通过执行计划看出来。
其中比较重要的一点就是短语查询的话，必须保证词语的顺序，不然查询不出来。
两者都实现了AND功能。但是底层机制是不一样的。
返回结果：
"took" : 1,
"timed_out" : false,
"_shards" :
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
,
"hits" :
"total" :
"value" : 1,
"relation" : "eq"
,
"max_score" : 8.628372,
"hits" : [
"_index" : "bank",
"_type" : "account",
"_id" : "18",
"_score" : 8.628372,
"_source" :
"account_number" : 18,
"balance" : 4180,
"firstname" : "Dale",
"lastname" : "Adams",
"age" : 33,
"gender" : "M",
"address" : "467 Hutchinson Court",
"employer" : "Boink",
"email" : "daleadams@boink.com",
"city" : "Orick",
"state" : "MD"
]
五、GET bank/_search?q=address:Hutchinson Court
GET bank/_search?q=address:Hutchinson Court
这里其实是一个term查询，通过执行计划可以看出来
GET bank/_search?q=address:(Hutchinson Court)
这里又是一个boolean查询，
虽然这两个都实现了OR查询，即address字段中包含Hutchinson或者Court，但是两者底层查询的方式是不一样。可以自己执行，
查看他们的具体执行计划
返回结果:
"took" : 1,
"timed_out" : false,
"_shards" :
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
,
"hits" :
"total" :
"value" : 119,
"relation" : "eq"
,
"max_score" : 8.628372,
"hits" : [
"_index" : "bank",
"_type" : "account",
"_id" : "18",
"_score" : 8.628372,
"_source" :
"account_number" : 18,
"balance" : 4180,
"firstname" : "Dale",
"lastname" : "Adams",
"age" : 33,
"gender" : "M",
"address" : "467 Hutchinson Court",
"employer" : "Boink",
"email" : "daleadams@boink.com",
"city" : "Orick",
"state" : "MD"
,
"_index" : "bank",
"_type" : "account",
"_id" : "56",
"_score" : 2.1258574,
"_source" :
"account_number" : 56,
"balance" : 14992,
"firstname" : "Josie",
"lastname" : "Nelson",
"age" : 32,
"gender" : "M",
"address" : "857 Tabor Court",
"employer" : "Emtrac",
"email" : "josienelson@emtrac.com",
"city" : "Sunnyside",
"state" : "UT"
]
六、GET bank/_search?q=address:(Hutchinson NOT Tabor)
GET bank/_search?q=address:(Hutchinson NOT Tabor)
表示包含Hutchinson不包含Tabor
返回结果:
"took" : 1,
"timed_out" : false,
"_shards" :
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
,
"hits" :
"total" :
"value" : 1,
"relation" : "eq"
,
"max_score" : 6.5025153,
"hits" : [
"_index" : "bank",
"_type" : "account",
"_id" : "18",
"_score" : 6.5025153,
"_source" :
"account_number" : 18,
"balance" : 4180,
"firstname" : "Dale",
"lastname" : "Adams",
"age" : 33,
"gender" : "M",
"address" : "467 Hutchinson Court",
"employer" : "Boink",
"email" : "daleadams@boink.com",
"city" : "Orick",
"state" : "MD"
]
七、GET bank/_search?q=age:<36
GET bank/_search?q=age:<36
表示的是范围查询，当然范围查询还有另一种形式:
GET bank/_search?q=age:30 TO 36;开区间
GET bank/_search?q=age:[* TO 36];闭区间
返回结果:
"took" : 1,
"timed_out" : false,
"_shards" :
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
,
"hits" :
"total" :
"value" : 762,
"relation" : "eq"
,
"max_score" : 1.0,
"hits" : [
"_index" : "bank",
"_type" : "account",
"_id" : "13",
"_score" : 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"
,
"_index" : "bank",
"_type" : "account",
"_id" : "18",
"_score" : 1.0,
"_source" :
"account_number" : 18,
"balance" : 4180,
"firstname" : "Dale",
"lastname" : "Adams",
"age" : 33,
"gender" : "M",
"address" : "467 Hutchinson Court",
"employer" : "Boink",
"email" : "daleadams@boink.com",
"city" : "Orick",
"state" : "MD"
]
八、GET bank/_search?q=firstname:De*
GET bank/_search?q=firstname:De*
表示的是通配符查询,当然还有?,问号表示的是一个字符，而*表示的是0个或者多个字符
返回结果:
"took" : 1,
"timed_out" : false,
"_shards" :
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
,
"hits" :
"total" :
"value" : 12,
"relation" : "eq"
,
"max_score" : 1.0,
"hits" : [
"_index" : "bank",
"_type" : "account",
"_id" : "70",
"_score" : 1.0,
"_source" :
"account_number" : 70,
"balance" : 38172,
"firstname" : "Deidre",
"lastname" : "Thompson",
"age" : 33,
"gender" : "F",
"address" : "685 School Lane",
"employer" : "Netplode",
"email" : "deidrethompson@netplode.com",
"city" : "Chestnut",
"state" : "GA"
,
"_index" : "bank",
"_type" : "account",
"_id" : "102",
"_score" : 1.0,
"_source" :
"account_number" : 102,
"balance" : 29712,
"firstname" : "Dena",
"lastname" : "Olson",
"age" : 27,
"gender" : "F",
"address" : "759 Newkirk Avenue",
"employer" : "Hinway",
"email" : "denaolson@hinway.com",
"city" : "Choctaw",
"state" : "NJ"
]
九、GET bank/_search?q=firstname:Deidr~2
GET bank/_search?q=firstname:Deidr~2
模糊查询，但是我也不知道这个后面的2是什么意思，看了一圈也没几个解释清楚的，也尝试自己改变了这个值，但是还是捉摸不透，
等后面越来越熟悉了再回来编辑一波。如果有知道的小伙伴，可以在下方留言告知我一声，我直接编辑。
返回结果:
"took" : 2,
"timed_out" : false,
"_shards" :
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
,
"hits" :
"total" :
"value" : 2,
"relation" : "eq"
,
"max_score" : 5.2034307,
"hits" : [
"_index" : "bank",
"_type" : "account",
"_id" : "70",
"_score" : 5.2034307,
"_source" :
"account_number" : 70,
"balance" : 38172,
"firstname" : "Deidre",
"lastname" : "Thompson",
"age" : 33,
"gender" : "F",
"address" : "685 School Lane",
"employer" : "Netplode",
"email" : "deidrethompson@netplode.com",
"city" : "Chestnut",
"state" : "GA"
,
"_index" : "bank",
"_type" : "account",
"_id" : "463",
"_score" : 3.902573,
"_source" :
"account_number" : 463,
"balance" : 36672,
"firstname" : "Heidi",
"lastname" : "Acosta",
"age" : 20,
"gender" : "F",
"address" : "692 Kenmore Terrace",
"employer" : "Elpro",
"email" : "heidiacosta@elpro.com",
"city" : "Ezel",
"state" : "SD"
]
小结
上面的Query String Syntax用法基本都讲到了，下面我们就把上面的基本用法结合一下，然后搜索一下。
GET bank/_search?q=firstname:De~2 AND age:30 TO 36] AND address:Court* AND employer:(Entropix Eplosion Boink)
AND gender:(NOT M) OR state:AL
返回结果:
"took" : 2,
"timed_out" : false,
"_shards" :
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
,
"hits" :
"total" :
"value" : 2,
"relation" : "eq"
,
"max_score" : 12.178019,
"hits" : [
"_index" : "bank",
"_type" : "account",
"_id" : "903",
"_score" : 12.178019,
"_source" :
"account_number" : 903,
"balance" : 10238,
"firstname" : "Wade",
"lastname" : "Page",
"age" : 35,
"gender" : "F",
"address" : "685 Waldorf Court",
"employer" : "Eplosion",
"email" : "wadepage@eplosion.com",
"city" : "Welda",
"state" : "AL"
,
"_index" : "bank",
"_type" : "account",
"_id" : "761",
"_score" : 8.506945,
"_source" :
"account_number" : 761,
"balance" : 7663,
"firstname" : "Rae",
"lastname" : "Juarez",
"age" : 34,
"gender" : "F",
"address" : "560 Gilmore Court",
"employer" : "Entropix",
"email" : "raejuarez@entropix.com",
"city" : "Northchase",
"state" : "ID"
]
所以相对来讲Query String Syntax的用法不仅挺多的，而且还非常的灵活的。
基本上关于Query String Syntax的相关的用法大概就这么多吧，本来想用一篇博客把其他参数的用法也学习的，但是看着篇幅，我觉得用一篇来写，可能就太多了，所以后面其他参数的使用我会另外开一篇博客来写。
es 基础概念总结 —— URI Search
搜索 API
GET /<index>/_search POST /<index>/_search GET /_search POST /_search
示例：
# 单个索引： GET /twitter/_search?q=tag:wow # 多个索引： GET /kimchy,elasticsearch/_search?q=user:kimchy # 全部索引： GET /_search?q=user:kimchy GET /_all/_search?q=user:kimchy GET /*/_search?q=user:kimchy
URI Search
GET /<index>/_search?q=<parameter>
指定字段
GET /movies/_search?q=title:2012
{
"profile":"true"
}
泛查询
GET /movies/_search?q=2012
{
"profile":"true"
}
phrase 查询
GET /movies/_search?q=title:"Beautiful Mind"
{
"profile":"true"
}
分组查询（非泛查询
GET /movies/_search?q=title:(Beautiful Mind)
{
"profile":"true"
}
boolean 查询
GET /movies/_search?q=title:(Beautiful AND Mind)
{
"profile":"true"
}
GET /movies/_search?q=title:(Beautiful NOT Mind)
{
"profile":"true"
}
GET /movies/_search?q=title:(Beautiful %2BMind)
{
"profile":"true"
}
通配符查询
GET /movies/_search?q=title:b*
{
"profile":"true"
}
模糊查询
GET /movies/_search?q=title:beautifl~1
{
"profile":"true"
}
GET /movies/_search?q=title:"Lord Rings"~2
{
"profile":"true"
}
范围查询，区间写法
GET /movies/_search?q=title:beautiful AND year:[2002 TO 2018%7D
{
"profile":"true"
}
相关文档
https://www.elastic.co/guide/en/elasticsearch/reference/7.6/search-uri-request.html
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