插入测试数据

  1. POST nba/_bulk
  2. {"index":{"_index":"nba","_type":"_doc","_id":"1"}}
  3. {"countryEn":"United States","teamName":"老鹰","birthDay":831182400000,"country":"美国","teamCityEn":"Atlanta","code":"jaylen_adams","displayAffiliation":"United States","displayName":"杰伦 亚当斯","schoolType":"College","teamConference":"东部","teamConferenceEn":"Eastern","weight":"86.2 公斤","teamCity":"亚特兰大","playYear":1,"jerseyNo":"10","teamNameEn":"Hawks","draft":2018,"displayNameEn":"Jaylen Adams","heightValue":1.88,"birthDayStr":"1996-05-04","position":"后卫","age":23,"playerId":"1629121"}
  4. {"index":{"_index":"nba","_type":"_doc","_id":"2"}}
  5. {"countryEn":"New Zealand","teamName":"雷霆","birthDay":743140800000,"country":"新西兰","teamCityEn":"Oklahoma City","code":"steven_adams","displayAffiliation":"Pittsburgh/New Zealand","displayName":"斯蒂文 亚当斯","schoolType":"College","teamConference":"西部","teamConferenceEn":"Western","weight":"120.2 公斤","teamCity":"俄克拉荷马城","playYear":6,"jerseyNo":"12","teamNameEn":"Thunder","draft":2013,"displayNameEn":"Steven Adams","heightValue":2.13,"birthDayStr":"1993-07-20","position":"中锋","age":26,"playerId":"203500"}
  6. {"index":{"_index":"nba","_type":"_doc","_id":"5"}}
  7. {"countryEn":"United States","teamName":"马刺","birthDay":490593600000,"country":"美国","teamCityEn":"New Orleans","code":"lamarcus_aldridge","displayAffiliation":"Texas/United States","displayName":"拉马库斯 阿尔德里奇","schoolType":"College","teamConference":"西部","teamConferenceEn":"Western","weight":"117.9 公斤","teamCity":"圣安东尼奥","playYear":13,"jerseyNo":"12","teamNameEn":"Spurs","draft":2006,"displayNameEn":"LaMarcus Aldridge","heightValue":2.11,"birthDayStr":"1985-07-19","position":"中锋-前锋","age":34,"playerId":"200746"}
  8. {"index":{"_index":"nba","_type":"_doc","_id":"6"}}
  9. {"countryEn":"Canada","teamName":"鹈鹕","birthDay":887000400000,"country":"加拿大","teamCityEn":"New Orleans","code":"nickeil_alexander-walker","displayAffiliation":"Virginia Tech/Canada","displayName":"Nickeil Alexander-Walker","schoolType":"College","teamConference":"西部","teamConferenceEn":"Western","weight":"92.5 公斤","teamCity":"新奥尔良","playYear":0,"jerseyNo":"","teamNameEn":"Pelicans","draft":2019,"displayNameEn":"Nickeil Alexander-Walker","heightValue":1.96,"birthDayStr":"1998-02-09","position":"后卫","age":21,"playerId":"1629638"}
  10. {"countryEn":"United States","teamName":"尼克斯","birthDay":727074000000,"country":"美国","teamCityEn":"New York","code":"kadeem_allen","displayAffiliation":"Arizona/United States","displayName":"卡迪姆 艾伦","schoolType":"College","teamConference":"东部","teamConferenceEn":"Eastern","weight":"90.7 公斤","teamCity":"纽约","playYear":2,"jerseyNo":"0","teamNameEn":"Knicks","draft":2017,"displayNameEn":"Kadeem Allen","heightValue":1.9,"birthDayStr":"1993-01-15","position":"后卫","age":26,"playerId":"1628443"}

单字段检索

只要分词后能匹配到了，那么即命中，单词顺序不影响查询结果，也不会影响最终分数。
如下 2 种写法等价，第二种写法扩展性更好，第一种写法更简洁。

  1. GET /nba/_search
  2. {
  3.   "query": {
  4.     "match": {
  5.       "teamCityEn": "New York" #  "York New"
  6.     }
  7.   }
  8. }
  10. GET /nba/_search
  11. {
  12.   "query": {
  13.     "match": {
  14.       "teamCityEn": {
  15.         "query": "New York"
  16.       }
  17.     }
  18.   }
  19. }

operator

支持and和or,默认是or即只包含任何一个分词后的结果，那么即匹配。如果要全部匹配。建议使用and

  1. GET /nba/_search
  2. {
  3.   "query": {
  4.     "match": {
  5.       "teamCityEn": {
  6.         "query": "New York",
  7.         "operator": "and"
  8.       }
  9.     }
  10.   }
  11. }

minimum_should_match

and的粒度太粗了，必须要全部满足。当需求要求的是部分满足的时候，可以使用：minimum_should_match
该选项表示：至少匹配多少个单词。比如如下案例，表示至少要匹配到New/York/States中的 2 个单词。

  1. GET /nba/_search
  2. {
  3.   "query": {
  4.     "match": {
  5.       "teamCityEn": 
  6.       {
  7.         "query": "New York States",
  8.         "minimum_should_match": 2
  9.       }
  10.     }
  11.   }
  12. }

上述写法除了具体的数值外也支持百分数，比如下面这写法

  1. GET /nba/_search
  2. {
  3.   "query": {
  4.     "match": {
  5.       "teamCityEn": 
  6.       {
  7.         "query": "New York States",
  8.         "minimum_should_match": "50%"
  9.       }
  10.     }
  11.   }
  12. }

表示至少匹配 3 个单词中50%，此处有个坑，3 个单词的 50%是1.5，但是 ES会向下取整即 1，所以只要匹配到1 个分词后的结果，即会展示在结果中。

多字段不同内容搜索

如下例子中是多字段且不同字段带有不同的权重

  1. GET /nba/_search
  2. {
  3.   "query": {
  4.     "bool": {
  5.       "should": [
  6.         {
  7.           "match": {
  8.             "teamCity": {
  9.               "query": "New York",
  10.               "boost": 2
  11.             }
  12.           }
  13.         },
  14.         {
  15.           "match": {
  16.             "displayName": {
  17.               "query": "亚当斯",
  18.               "boost": 5
  19.             }
  20.           }
  21.         }
  22.       ]
  23.     }
  24.   }
  25. }

多字段相同内容搜索

该需求是最近似我们通过搜索引擎搜索的一个场景。
在搜索的时候，我们通常会遇到一个内容，匹配多个字段，比如输入”亚当斯”，需要从：displayName、teamName、country三个字段中搜索出相似内容。

基本搜索

不做其他处理的情况下可以使用下面 2 种写法。

  1. GET /nba/_search
  2. {
  3.   "query": {
  4.     "bool": {
  5.       "should": [
  6.         {
  7.           "match": {
  8.             "teamCity": {
  9.               "query": "亚当斯"
  10.             }
  11.           }
  12.         },
  13.         {
  14.           "match": {
  15.             "displayName": {
  16.               "query": "亚当斯"
  17.             }
  18.           }
  19.         },
  20.         {
  21.           "match": {
  22.             "country": {
  23.               "query": "亚当斯"
  24.             }
  25.           }
  26.         }
  27.       ]
  28.     }
  29.   }
  30. }
  32. # 方法 2，后续详细讲解
  33. GET /nba/_search
  34. {
  35.   "query": {
  36.     "multi_match": {
  37.       "query": "亚当斯",
  38.       "fields": [
  39.         "displayName",
  40.         "teamName",
  41.         "country"]
  42.     }
  43.   }
  44. }

一种是非常简洁的multi_match，另外一种是bool-should-match。

dis_max

上述查询中，会将所有字段匹配后，再显示结果，假如搜索项在其中某一个字段的分值非常高，但是其他 2 项的分值很低，就会拉低平均值。
但是通常我们希望找到的是某一项最高的某个项目，3 个字段中任何一个符合都可以，此时就需要使用dis_max了。

  1. GET /nba/_search
  2. {
  3.   "query": {
  4.     "dis_max": {
  5.       "queries": [
  6.         {
  7.           "match": {
  8.             "teamCity": {
  9.               "query": "亚当斯"
  10.             }
  11.           }
  12.         },
  13.         {
  14.           "match": {
  15.             "displayName": {
  16.               "query": "亚当斯"
  17.             }
  18.           }
  19.         },
  20.         {
  21.           "match": {
  22.             "country": {
  23.               "query": "亚当斯"
  24.             }
  25.           }
  26.         }
  27.       ]
  28.     }
  29.   }
  30. }

tie_breaker

上述查询中，只考虑匹配值最大的那个，不考虑其他字段的匹配度。但是假如也想将其他字段的检索结果纳入匹配考虑，此时就可以使用tie_breaker。

  1. GET /nba/_search
  2. {
  3.   "query": {
  4.     "dis_max": {
  5.       "tie_breaker": 0.7, 
  6.       "queries": [
  7.         {
  8.           "match": {
  9.             "teamCity": {
  10.               "query": "亚当斯"
  11.             }
  12.           }
  13.         },
  14.         {
  15.           "match": {
  16.             "displayName": {
  17.               "query": "亚当斯"
  18.             }
  19.           }
  20.         },
  21.         {
  22.           "match": {
  23.             "country": {
  24.               "query": "亚当斯"
  25.             }
  26.           }
  27.         }
  28.       ]
  29.     }
  30.   }
  31. }

boost

当然你也可以结合boost将某个字段的权重设置的比较高，然后 使用dis_max找出最高的分值的那个，同时考虑其他字段的影响tie_brekder

  1. GET /nba/_search
  2. {
  3.   "query": {
  4.     "dis_max": {
  5.       "tie_breaker": 0.7, 
  6.       "queries": [
  7.         {
  8.           "match": {
  9.             "teamCity": {
  10.               "query": "亚当斯"
  11.             }
  12.           }
  13.         },
  14.         {
  15.           "match": {
  16.             "displayName": {
  17.               "query": "亚当斯",
  18.               "boost": 3
  19.             }
  20.           }
  21.         },
  22.         {
  23.           "match": {
  24.             "country": {
  25.               "query": "亚当斯"
  26.             }
  27.           }
  28.         }
  29.       ]
  30.     }
  31.   }
  32. }