聚合

该框架收集搜索查询选择的所有数据,该框架由许多构建块组成，有助于构建复杂的数据摘要,聚合的基本结构如下所示

"aggregations" : {
   "<aggregation_name>" : {
      "<aggregation_type>" : {
         <aggregation_body>
      }
      [,"meta" : { [<meta_data_body>] } ]?
      [,"aggregations" : { [<sub_aggregation>]+ } ]?
   }
}

有不同类型的聚合，每种聚合都有自己的目的

度量聚合

这些聚合有助于根据聚合文档的字段值计算矩阵，有时一些值可以从脚本中生成。

数值矩阵可以像平均聚合一样是单值的，也可以像统计一样是多值的。

平均聚合

此聚合用于获取聚合文档中任何数字字段的平均值。例如，

POST  /schools/_search
{
   "aggs":{
      "avg_fees":{"avg":{"field":"fees"}}
   }
}

响应

{
   "took":44, "timed_out":false, "_shards":{"total":5, "successful":5, "failed":0},
   "hits":{
      "total":3, "max_score":1.0, "hits":[
         {
            "_index":"schools", "_type":"school", "_id":"2", "_score":1.0,
            "_source":{
               "name":"Saint Paul School", "description":"ICSE Affiliation",
               "street":"Dawarka", "city":"Delhi", "state":"Delhi", 
               "zip":"110075", "location":[28.5733056, 77.0122136], "fees":5000, 
               "tags":["Good Faculty", "Great Sports"], "rating":"4.5"
            }
         },
         {
            "_index":"schools", "_type":"school", "_id":"1", "_score":1.0,
            "_source":{
               "name":"Central School", "description":"CBSE Affiliation",
               "street":"Nagan", "city":"paprola", "state":"HP", "zip":"176115",
               "location":[31.8955385, 76.8380405], "fees":2200, 
               "tags":["Senior Secondary", "beautiful campus"], "rating":"3.3"
            }
         },
         {
            "_index":"schools", "_type":"school", "_id":"3", "_score":1.0,
            "_source":{
               "name":"Crescent School", "description":"State Board Affiliation",
               "street":"Tonk Road", "city":"Jaipur", "state":"RJ", 
               "zip":"176114", "location":[26.8535922, 75.7923988], "fees":2500, 
               "tags":["Well equipped labs"], "rating":"4.5"
            }
         }
      ]
   }, "aggregations":{"avg_fees":{"value":3233.3333333333335}}
}

如果该值不在一个或多个聚合文档中，默认情况下会被忽略。您可以在聚合中添加缺失字段，将缺失值视为默认值。

{
   "aggs":{
      "avg_fees":{
         "avg":{
            "field":"fees"
            "missing":0
         }
      }
   }
}

基数聚合

这种聚合给出了特定字段的不同值的计数。例如，

POST /schools*/_search
{
   "aggs":{
      "distinct_name_count":{"cardinality":{"field":"name"}}
   }
}

响应

………………………………………………
{
   "name":"Government School", "description":"State Board Afiliation",
   "street":"Hinjewadi", "city":"Pune", "state":"MH", "zip":"411057",
   "location":[18.599752, 73.6821995], "fees":500, "tags":["Great Sports"], 
   "rating":"4"
},

{
   "_index":"schools_gov", "_type": "school", "_id":"1", "_score":1.0,
   "_source":{
      "name":"Model School", "description":"CBSE Affiliation", "street":"silk city",
      "city":"Hyderabad", "state":"AP", "zip":"500030", 
      "location":[17.3903703, 78.4752129], "fees":700, 
      "tags":["Senior Secondary", "beautiful campus"], "rating":"3"
   }
}, "aggregations":{"disticnt_name_count":{"value":3}}
………………………………………………

注意：基数的值是3，因为name中有三个不同的值：Government, School和Model。

扩展统计信息聚合

这种聚合会生成聚合文档中特定数值字段的所有统计信息。例如，

POST  /schools/_search
{
   "aggs" : {
      "fees_stats" : { "extended_stats" : { "field" : "fees" } }
   }
}

响应

………………………………………………
{
   "aggregations":{
      "fees_stats":{
         "count":3, "min":2200.0, "max":5000.0, 
         "avg":3233.3333333333335, "sum":9700.0,
         "sum_of_squares":3.609E7, "variance":1575555.555555556, 
         "std_deviation":1255.2113589175156,
         "std_deviation_bounds":{
            "upper":5743.756051168364, "lower":722.9106154983024
         }
      }
   }
}
………………………………………………

最大聚合

此聚合会查找聚合文档中特定数值字段的最大值。例如，

POST /schools*/_search
{
   "aggs" : {
      "max_fees" : { "max" : { "field" : "fees" } }
   }
}

响应

………………………………………………
{
   aggregations":{"max_fees":{"value":5000.0}}
}
………………………………………………

最小聚合

此聚合会查找聚合文档中特定数值字段的最小值。例如，

POST /schools*/_search
{
   "aggs" : {
      "min_fees" : { "min" : { "field" : "fees" } }
   }
}

响应

………………………………………………
"aggregations":{"min_fees":{"value":500.0}}
………………………………………………

总和聚合

此聚合计算聚合文档中特定数字字段的和。例如，

POST /schools*/_search
{
   "aggs" : {
      "total_fees" : { "sum" : { "field" : "fees" } }
   }
}

响应

………………………………………………
"aggregations":{"total_fees":{"value":10900.0}}
………………………………………………

还有一些其他度量聚合在特殊情况下使用，如地理边界聚合和地理质心聚合，用于地理定位。

Bucket聚合

这些聚合包含许多用于不同类型聚合的bucket，这些聚合有一个标准，该标准决定文档是否属于该bucket。Bucket聚合描述如下：

子聚集

此Bucket聚合生成一组文档，这些文档映射到父Bucket聚合。类型参数用于定义父索引。例如，我们有一个品牌及其不同的模型，然后模型类型将具有以下_parent字段

{
   "model" : {
      "_parent" : {
         "type" : "brand"
      }
   }
}

还有许多其他特殊的bucket聚合，这些聚合在许多其他情况下很有用，它们是

  • Date Histogram Aggregation
  • Date Range Aggregation
  • Filter Aggregation
  • Filters Aggregation
  • Geo Distance Aggregation
  • GeoHash grid Aggregation
  • Global Aggregation
  • Histogram Aggregation
  • IPv4 Range Aggregation
  • Missing Aggregation
  • Nested Aggregation
  • Range Aggregation
  • Reverse nested Aggregation
  • Sampler Aggregation
  • Significant Terms Aggregation
  • Terms Aggregation

聚合元数据

您可以使用元标签在请求时添加一些关于聚合的数据，并可以得到响应。例如，

POST /school*/_search
{
   "aggs" : {
      "min_fees" : { "avg" : { "field" : "fees" } ,
         "meta" :{
            "dsc" :"Lowest Fees"
         }
      }
   }
}

响应

………………………………………………
{
   "aggregations":{"min_fees":{"meta":{"dsc":"Lowest Fees"}, "value":2180.0}}
}
………………………………………………

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