ElasticSearch扫盲之九:ElasticSearch结构化查询语句Query DSL之模糊查询即match查询

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模糊匹配一般适用于文本类型字段的处理，用于全文索引的字段

一、数据准备

先构建一些数据，用来做数据匹配试验

1.定义结构

定义一个基本的用户index

  1. PUT user
  2. {
  3.   "mappings": {
  4.     "_doc":{
  5.       "properties":{
  6.         "user_id":{
  7.           "type":"long"
  8.         },
  9.         "nickname":{
  10.           "type":"text"
  11.         },
  12.         "account":{
  13.           "type":"keyword"
  14.         },
  15.         "password":{
  16.           "type":"text",
  17.           "index":false
  18.         },
  19.         "email":{
  20.           "type":"text"
  21.         },
  22.         "avatar":{
  23.           "type":"text",
  24.           "index":false
  25.         },
  26.         "status":{
  27.           "type":"integer"
  28.         },
  29.         "tags":{
  30.           "type":"text"
  31.         },
  32.         "address":{
  33.           "type":"text"
  34.         },
  35.         "create_time":{
  36.           "type":"date"
  37.         }
  38.       }
  39.     }
  40.   }
  41. }
2.插入数据：
  1. POST _bulk
  2. {"create":{"_index":"user","_type":"_doc","_id":1}}
  3. {"user_id":1,"nickname":"shixinke","account":"shixinke","password":"abc","email":"i@withec.net","avatar":"http://avatar.shixinke.com/images/20190410457845781.png","status":1,"tags":["技术宅","文艺"], "address":"HangZhou,ZheJiang,China", "create_time":1554886662618}
  4. {"create":{"_index":"user","_type":"_doc","_id":2}}
  5. {"user_id":2,"nickname":"withec","account":"withec","password":"abceee","email":"withec@withec.com","avatar":"http://avatar.shixinke.com/images/20190410457145781.png","status":0,"tags":["活泼","运动型"],"address":"ShiYan,HuBei,China","create_time":1554886682618}
  6. {"create":{"_index":"user","_type":"_doc","_id":3}}
  7. {"user_id":3,"nickname":"lucy","account":"lucy","password":"abceee","email":"lucy@google.com","avatar":"http://avatar.google.com/images/20190410457145781.png","status":0,"tags":["安静","文艺"],"address":"JiNan,ShanDong,China","create_time":1554886782618}
  8. {"create":{"_index":"user","_type":"_doc","_id":4}}
  9. {"user_id":4,"nickname":"lilei","account":"lilei","password":"aebceeee","email":"lilei@live.com","avatar":"http://avatar.live.com/images/20190410457145781.png","status":1,"tags":["旅行","读书"],"address":"PuDong,ShangHai,China","create_time":1554889782618}
  10. {"create":{"_index":"user","_type":"_doc","_id":5}}
  11. {"user_id":5,"nickname":"jet","account":"jet","password":"aebceeee","email":"jet@sina.com","avatar":"http://avatar.sina.com/images/20190410457145781.png","status":1,"tags":["固执","文艺"],"address":"NingBo,ZheJiang,China","create_time":1554882782618}
  12. {"create":{"_index":"user","_type":"_doc","_id":6}}
  13. {"user_id":6,"nickname":"shixin","account":"shixin","password":"aebceeee","email":"shixinke@withec.net","avatar":"http://avatar.withec.com/images/20190410457145781.png","status":1,"tags":["天真","文艺"],"address":"SuZhou,JIangSu,China","create_time":1554882752618}

二.match使用

1.match使用
  • 功能：对搜索的字符串进行分词，从目标字段中的倒序索引(目标字段分词后的集合，并与文档主键形成的关联映射)中查找是否有其中某个分词．
  • 适用范围：
    • 多值的文本
    • 可分词的文本
(1)对于分词的字段
  • address 字段是text类型，默认是会分词，我们可以使用_analyze这个API来查看地址会被分成什么的样的
  1. POST _analyze
  2. {
  3.   "text":"HangZhou,ZheJiang,China"
  4. }

注：使用_analyze这个API，默认使用的是标准英文分词，如果是中文分词，需要指定分词器,以上的文本内容，根据,分成了三个词

  1. {
  2.   "tokens" : [
  3.     {
  4.       "token" : "hangzhou",
  5.       "start_offset" : 0,
  6.       "end_offset" : 8,
  7.       "type" : "<ALPHANUM>",
  8.       "position" : 0
  9.     },
  10.     {
  11.       "token" : "zhejiang",
  12.       "start_offset" : 9,
  13.       "end_offset" : 17,
  14.       "type" : "<ALPHANUM>",
  15.       "position" : 1
  16.     },
  17.     {
  18.       "token" : "china",
  19.       "start_offset" : 18,
  20.       "end_offset" : 23,
  21.       "type" : "<ALPHANUM>",
  22.       "position" : 2
  23.     }
  24.   ]
  25. }

因此只要查询的条件中，将条件的内容再进行分词，与目标字段内容的分词词组进行对比

  1. GET user/_doc/_search
  2. {
  3.   "_source":["user_id","address"],
  4.   "query":{
  5.     "match":{
  6.       "address":"zhejiang"
  7.     }
  8.   }
  9. }

得出以下的结果：

  1. {
  2.   "took" : 0,
  3.   "timed_out" : false,
  4.   "_shards" : {
  5.     "total" : 5,
  6.     "successful" : 5,
  7.     "skipped" : 0,
  8.     "failed" : 0
  9.   },
  10.   "hits" : {
  11.     "total" : 2,
  12.     "max_score" : 0.2876821,
  13.     "hits" : [
  14.       {
  15.         "_index" : "user",
  16.         "_type" : "_doc",
  17.         "_id" : "5",
  18.         "_score" : 0.2876821,
  19.         "_source" : {
  20.           "address" : "NingBo,ZheJiang,China",
  21.           "user_id" : 5
  22.         }
  23.       },
  24.       {
  25.         "_index" : "user",
  26.         "_type" : "_doc",
  27.         "_id" : "1",
  28.         "_score" : 0.2876821,
  29.         "_source" : {
  30.           "address" : "HangZhou,ZheJiang,China",
  31.           "user_id" : 1
  32.         }
  33.       }
  34.     ]
  35.   }
  36. }
(2)对于多值的字段

本例中tags字段是一个多值的字段

  1. GET user/_doc/_search
  2. {
  3.   "query":{
  4.     "match":{
  5.       "tags":"运"
  6.     }
  7.   }
  8. }

搜索结果：

  1. {
  2.   "took" : 0,
  3.   "timed_out" : false,
  4.   "_shards" : {
  5.     "total" : 5,
  6.     "successful" : 5,
  7.     "skipped" : 0,
  8.     "failed" : 0
  9.   },
  10.   "hits" : {
  11.     "total" : 1,
  12.     "max_score" : 0.9227539,
  13.     "hits" : [
  14.       {
  15.         "_index" : "user",
  16.         "_type" : "_doc",
  17.         "_id" : "2",
  18.         "_score" : 0.9227539,
  19.         "_source" : {
  20.           "user_id" : 2,
  21.           "nickname" : "withec",
  22.           "account" : "withec",
  23.           "password" : "abceee",
  24.           "email" : "withec@withec.com",
  25.           "avatar" : "http://avatar.shixinke.com/images/20190410457145781.png",
  26.           "status" : 0,
  27.           "tags" : [
  28.             "活泼",
  29.             "运动型"
  30.           ],
  31.           "address" : "ShiYan,HuBei,China",
  32.           "create_time" : 1554886682618
  33.         }
  34.       }
  35.     ]
  36.   }
  37. }

注：因为我们在建立mapping时，没有指定分词器，默认的分词器会把中文按字分割

(3)match的复杂用法

match条件还支持以下参数：

  • query : 指定匹配的值
  • operator : 匹配条件类型
    • and : 条件分词后都要匹配
    • or : 条件分词后有一个匹配即可(默认)
  • minmum_should_match : 指定最小匹配的数量

A.默认情况下或operator为or的情况是只要包含其中一个条件即可

  1. GET user/_doc/_search
  2. {
  3.   "_source":["user_id", "tags"],
  4.   "query":{
  5.     "match":{
  6.       "tags":{
  7.         "query":"运,艺"
  8.       }
  9.     }
  10.   }
  11. }
  • 条件中的”运,艺”会被分词为[“运”,”艺”]这样的集合
  • tags字段会根据默认分词，形成一个词组集合，如 [“活泼”,”运动型”]会被分词为[“活”,”动”,”运”,”动”,”型”]这样的词组集合
  • 因此只需要比较条件中的词组集合和目标字段中的词组集合即可，即tags字段中只要包含”运”或者”艺”即可

因此上面的条件会以下结果：

  1. {
  2.   "took" : 4,
  3.   "timed_out" : false,
  4.   "_shards" : {
  5.     "total" : 5,
  6.     "successful" : 5,
  7.     "skipped" : 0,
  8.     "failed" : 0
  9.   },
  10.   "hits" : {
  11.     "total" : 5,
  12.     "max_score" : 1.0126973,
  13.     "hits" : [
  14.       {
  15.         "_index" : "user",
  16.         "_type" : "_doc",
  17.         "_id" : "6",
  18.         "_score" : 1.0126973,
  19.         "_source" : {
  20.           "user_id" : 6,
  21.           "tags" : [
  22.             "天真",
  23.             "文艺"
  24.           ]
  25.         }
  26.       },
  27.       {
  28.         "_index" : "user",
  29.         "_type" : "_doc",
  30.         "_id" : "2",
  31.         "_score" : 0.9227539,
  32.         "_source" : {
  33.           "user_id" : 2,
  34.           "tags" : [
  35.             "活泼",
  36.             "运动型"
  37.           ]
  38.         }
  39.       },
  40.       {
  41.         "_index" : "user",
  42.         "_type" : "_doc",
  43.         "_id" : "5",
  44.         "_score" : 0.2876821,
  45.         "_source" : {
  46.           "user_id" : 5,
  47.           "tags" : [
  48.             "固执",
  49.             "文艺"
  50.           ]
  51.         }
  52.       },
  53.       {
  54.         "_index" : "user",
  55.         "_type" : "_doc",
  56.         "_id" : "1",
  57.         "_score" : 0.2876821,
  58.         "_source" : {
  59.           "user_id" : 1,
  60.           "tags" : [
  61.             "技术宅",
  62.             "文艺"
  63.           ]
  64.         }
  65.       },
  66.       {
  67.         "_index" : "user",
  68.         "_type" : "_doc",
  69.         "_id" : "3",
  70.         "_score" : 0.2876821,
  71.         "_source" : {
  72.           "user_id" : 3,
  73.           "tags" : [
  74.             "安静",
  75.             "文艺"
  76.           ]
  77.         }
  78.       }
  79.     ]
  80.   }
  81. }

B.通过operator中的and控制匹配结果

  1. GET user/_doc/_search
  2. {
  3.   "_source":["user_id", "tags"],
  4.   "query":{
  5.     "match":{
  6.       "tags":{
  7.         "query":"天,艺",
  8.         "operator":"and"
  9.       }
  10.     }
  11.   }
  12. }
  • 条件中的”天,艺”会被分词为[“天”,”艺”]这样的集合
  • tags字段会根据默认分词形成一个词组集合
  • 因为operator是and,所有tags的词组集合必须包含条件中两个词组才能满足条件
  1. {
  2.   "took" : 1,
  3.   "timed_out" : false,
  4.   "_shards" : {
  5.     "total" : 5,
  6.     "successful" : 5,
  7.     "skipped" : 0,
  8.     "failed" : 0
  9.   },
  10.   "hits" : {
  11.     "total" : 1,
  12.     "max_score" : 2.0253947,
  13.     "hits" : [
  14.       {
  15.         "_index" : "user",
  16.         "_type" : "_doc",
  17.         "_id" : "6",
  18.         "_score" : 2.0253947,
  19.         "_source" : {
  20.           "user_id" : 6,
  21.           "tags" : [
  22.             "天真",
  23.             "文艺"
  24.           ]
  25.         }
  26.       }
  27.     ]
  28.   }
  29. }

C.通过minimum_shoud_match控制匹配结果

  1. GET user/_doc/_search
  2. {
  3.   "_source":["user_id", "tags"],
  4.   "query":{
  5.     "match":{
  6.       "tags":{
  7.         "query":"天,艺,运",
  8.         "minimum_should_match":2
  9.       }
  10.     }
  11.   }
  12. }
  • 条件中的”天,艺,运”会被分词为[“天”,”艺”,”运”]这样的集合
  • tags字段会根据默认分词形成一个词组集合
  • 因此只要tags字段的分词后的词组至少包含[“天”,”艺”,”运”]这个集合其中两个词就可满足条件
  1. {
  2.   "took" : 1,
  3.   "timed_out" : false,
  4.   "_shards" : {
  5.     "total" : 5,
  6.     "successful" : 5,
  7.     "skipped" : 0,
  8.     "failed" : 0
  9.   },
  10.   "hits" : {
  11.     "total" : 1,
  12.     "max_score" : 2.0253947,
  13.     "hits" : [
  14.       {
  15.         "_index" : "user",
  16.         "_type" : "_doc",
  17.         "_id" : "6",
  18.         "_score" : 2.0253947,
  19.         "_source" : {
  20.           "user_id" : 6,
  21.           "tags" : [
  22.             "天真",
  23.             "文艺"
  24.           ]
  25.         }
  26.       }
  27.     ]
  28.   }
  29. }
2.match_pharse
  • 功能：完全包含搜索的内容
  1. GET user/_doc/_search
  2. {
  3.   "_source":["user_id","tags"],
  4.   "query":{
  5.     "match_phrase":{
  6.       "tags":"艺术"
  7.     }
  8.   }
  9. }
  • 查找必须包含”艺术”这个词,与match区别是比较大的(个人理解，目标的字段必须完全包含搜索的词语，而不是分词后的匹配结果)

下面与match比较一下：

  1. GET user/_doc/_search
  2. {
  3.   "_source":["user_id","tags"],
  4.   "query":{
  5.     "match":{
  6.       "tags":{
  7.         "query":"艺术",
  8.         "operator":"and"
  9.       }
  10.     }
  11.   }
  12. }
  • match可以查找到tags包含”艺”和”术”的记录
3.multi_match
  • 功能：用于搜索多个字段匹配同一个内容(内容会被分词)
  • 参数：
    • query : 匹配的值
    • fields : 查找的字段范围
    • type :　过滤筛选的类型
      • best_fields : 只要匹配任意一个字段即可，使用最匹配的那个字段的相关度评分
      • most_fields : 只要匹配做任意一个字段，但会将匹配度的得分进行组合
      • corss_fields : 使用相同的分词器，只要有一个字段匹配即可
      • phrase : 最匹配的字段要完全匹配搜索的内容
      • phrase_prefix : 最匹配的字段要完全匹配搜索的内容(包含搜索的的内容)
    • operator :　匹配的字段关系
      • and : 所有字段都匹配
      • or : 只要一个字段匹配即可
  1. GET user/_doc/_search
  2. {
  3.   "_source":["user_id","nickname","email"],
  4.   "query":{
  5.     "multi_match":{
  6.       "query":"shixinke",
  7.       "fields":["email","nickname"]
  8.     }
  9.   }
  10. }
  • 从用户index的email,nickname等字段中查找”shixinke”这个关键词

三、prefix

请求：

  1. GET user/_doc/_search
  2. {
  3.   "_source":["user_id","email"],
  4.   "query":{
  5.     "prefix":{
  6.       "email":"shixin"
  7.     }
  8.   }
  9. }

匹配email字段以shixin为前缀的记录

查询结果：

  1. {
  2.   "took" : 10,
  3.   "timed_out" : false,
  4.   "_shards" : {
  5.     "total" : 5,
  6.     "successful" : 5,
  7.     "skipped" : 0,
  8.     "failed" : 0
  9.   },
  10.   "hits" : {
  11.     "total" : 1,
  12.     "max_score" : 1.0,
  13.     "hits" : [
  14.       {
  15.         "_index" : "user",
  16.         "_type" : "_doc",
  17.         "_id" : "6",
  18.         "_score" : 1.0,
  19.         "_source" : {
  20.           "user_id" : 6,
  21.           "email" : "shixinke@withec.net"
  22.         }
  23.       }
  24.     ]
  25.   }
  26. }

四、regexp正则匹配

1.功能

使用正则表达式来搜索指定的内容

2.用法
  • 参数：
    • value : 正则表达式
    • flags : 启用的选项
    • ALL: 启用所有选项
    • ANYSTRING : 使用@表示整个字符串
    • AUTOMATION : 启用保留字符,开始<和>的转义
    • COMPLEMENT : 使用~表示任意长度且不是~后面的字符的
    • EMPTY : 开启#转义
    • INTERSECTION : 启用&连接两个模式，必须同时匹配两个模式
    • INTERNAL : 启用<>括起来的数字范围
  1. GET user/_doc/_search
  2. {
  3.   "_source":["user_id","nickname","email"],
  4.   "query":{
  5.     "regexp":{
  6.       "nickname":{
  7.         "value":"l.*"
  8.       }
  9.     }
  10.   }
  11. }
  • l.表示以l开始后面跟任意字符(.表示任意字符，表示.表示的字符出现0次、1次、1次以上)

查询结果：

  1. {
  2.   "took" : 5,
  3.   "timed_out" : false,
  4.   "_shards" : {
  5.     "total" : 5,
  6.     "successful" : 5,
  7.     "skipped" : 0,
  8.     "failed" : 0
  9.   },
  10.   "hits" : {
  11.     "total" : 2,
  12.     "max_score" : 1.0,
  13.     "hits" : [
  14.       {
  15.         "_index" : "user",
  16.         "_type" : "_doc",
  17.         "_id" : "4",
  18.         "_score" : 1.0,
  19.         "_source" : {
  20.           "user_id" : 4,
  21.           "nickname" : "lilei",
  22.           "email" : "lilei@live.com"
  23.         }
  24.       },
  25.       {
  26.         "_index" : "user",
  27.         "_type" : "_doc",
  28.         "_id" : "3",
  29.         "_score" : 1.0,
  30.         "_source" : {
  31.           "user_id" : 3,
  32.           "nickname" : "lucy",
  33.           "email" : "lucy@google.com"
  34.         }
  35.       }
  36.     ]
  37.   }
  38. }

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