假设一个团队有很多成员，其中成员和团队都有关联的数据。
1.根据用户条件（例如，经验年限）筛选成员
1.然后将一些成员属性聚合到团队级别（例如，筛选成员的平均目标数）
1.然后对聚合进行筛选（例如，仅筛选成员的平均目标数〉= 17的团队）
1.然后对球队的任何属性进行排序（例如，获胜次数降序）。
在SQL术语中，这相当于WHERE、GROUP BY、HAVING、ORDER BY
如果有用的话，请看下面的样本数据。具体来说，这里有一个我想问的问题：

• 我希望所有球队的平均进球数至少有E年的经验的球员是〉G，按球队的胜利数降序排列。*

注意：因为用户可以过滤任何内容，所以在索引中保存预先聚合的数据不是一个选项。我需要搜索语法，使我能够动态地执行此操作。

模式

PUT sample
{
  "mappings": {
    "dynamic": "strict",
    "properties": {
      "teamId": { "type": "keyword", "index": true, "doc_values": true },
      "teamAge": { "type": "integer", "index": true, "doc_values": true },
      "wins": { "type": "integer", "index": true, "doc_values": true },
      "losses": { "type": "integer", "index": true, "doc_values": true },
      "memberId": { "type": "keyword", "index": true, "doc_values": true },
      "exp": { "type": "float", "index": true, "doc_values": true},
      "age": { "type": "integer", "index": true, "doc_values": true},
      "height": { "type": "integer", "index": true, "doc_values": true},
      "weight": { "type": "integer", "index": true, "doc_values": true},
      "goals": { "type": "integer", "index": true, "doc_values": true},
      "code": { "type": "keyword", "index": true, "doc_values": true}
    }
  }
}

样品数据

PUT sample/_doc/1  
{ "teamId" : "A", "teamAge":7 , "wins": 10, "losses": 5, "memberId" : "A1" , "exp": "1" , "age" : "21" , "code": "X", "goals": 12, "height": 72, "weight": 170 } 
PUT sample/_doc/2  
{ "teamId" : "A", "teamAge":7 , "wins": 10, "losses": 5, "memberId" : "A2" , "exp": "2" , "age" : "31" , "code": "Y", "goals": 15, "height": 75, "weight": 190 } 
PUT sample/_doc/3  
{ "teamId" : "A", "teamAge":7 , "wins": 10, "losses": 5, "memberId" : "A3" , "exp": "3" , "age" : "41" , "code": "Z", "goals": 20, "height": 80, "weight": 210 } 
PUT sample/_doc/4  
{ "teamId" : "B", "teamAge":6 , "wins": 12, "losses": 3, "memberId" : "B1" , "exp": "1" , "age" : "22" , "code": "Z", "goals": 20, "height": 80, "weight": 220 } 
PUT sample/_doc/5  
{ "teamId" : "B", "teamAge":6 , "wins": 12, "losses": 3, "memberId" : "B2" , "exp": "2" , "age" : "32" , "code": "X", "goals": 18, "height": 78, "weight": 200 } 
PUT sample/_doc/6  
{ "teamId" : "B", "teamAge":6 , "wins": 12, "losses": 3, "memberId" : "B3" , "exp": "3" , "age" : "42" , "code": "X", "goals": 16, "height": 76, "weight": 185 } 
PUT sample/_doc/7  
{ "teamId" : "B", "teamAge":6 , "wins": 12, "losses": 3, "memberId" : "B4" , "exp": "4" , "age" : "52" , "code": "Y", "goals": 12, "height": 72, "weight": 170 } 
PUT sample/_doc/8  
{ "teamId" : "C", "teamAge":5 , "wins": 11, "losses": 4, "memberId" : "C1" , "exp": "1" , "age" : "23" , "code": "Y", "goals": 16, "height": 76, "weight": 190 } 
PUT sample/_doc/9  
{ "teamId" : "C", "teamAge":5 , "wins": 11, "losses": 4, "memberId" : "C2" , "exp": "2" , "age" : "33" , "code": "X", "goals": 18, "height": 78, "weight": 195 } 
PUT sample/_doc/10 
{ "teamId" : "C", "teamAge":5 , "wins": 11, "losses": 4, "memberId" : "C3" , "exp": "2" , "age" : "43" , "code": "Z", "goals": 19, "height": 79, "weight": 225 }