Elasticsearch 7.X AggregationBuliders 相关聚合函数（一）计数指标

一、计数相关

count 计数聚合

不常使用，通常查询返回都有总个数avg 平均聚合

用于获取聚合文档中存在的任何数字字段的平均值请求正文：

{
   "aggs":{
      "avg_grade":{"avg":{"field":"fees"}}
   }
}

返回示例：

{
  ...
  "aggregations": {
    "avg_grade": {
      "value": 75.0
    }
  }
}

带权重的平均值计算，计算公式，加权平均值：∑(值*权重) / ∑(权重)1、需确定平均值字段和权重字段，字段都为数值类型
2、如果权重都相等，则和常规求平均结果一致
​ ​ ​ ​ ​ 如果权重都为0，则平均值为null
​ ​ ​ ​ ​ ​如果权重值不同，可以根据公式进行计算请求示例：

POST /exams/_search
{
  "size": 0,
  "aggs": {
    "weighted_grade": {
      "weighted_avg": {
        "value": {
          "field": "grade"
        },
        "weight": {
          "field": "weight"
        }
      }
    }
  }
}

返回示例：

{
  ...
  "aggregations": {
    "weighted_grade": {
      "value": 70.0
    }
  }
}

查找聚合文档中特定数字字段的最大值请求示例

POST /sales/_search?size=0
{
  "aggs": {
    "max_price": { "max": { "field": "price" } }
  }
}

返回示例：

{
  ...
  "aggregations": {
      "max_price": {
          "value": 200.0
      }
  }
}

查找聚合文档中特定数字字段的最小值

请求示例：

POST /sales/_search?size=0
{
  "aggs": {
    "min_price": { "min": { "field": "price" } }
  }
}

返回示例：

{
  ...

  "aggregations": {
    "min_price": {
      "value": 10.0
    }
  }
}

聚合计算聚合文档中特定数字字段的总和请求示例：

POST /sales/_search?size=0
{
  "query": {
    "constant_score": {
      "filter": {
        "match": { "type": "hat" }
      }
    }
  },
  "aggs": {
    "hat_prices": { "sum": { "field": "price" } }
  }
}

返回示例：

{
  ...
  "aggregations": {
    "hat_prices": {
      "value": 450.0
    }
  }
}

返回包括以上count,min,max,sum,avg聚合统计的数值请求示例：

POST /exams/_search?size=0
{
  "aggs": {
    "grades_stats": { "stats": { "field": "grade" } }
  }
}

返回示例：

{
  ...

  "aggregations": {
    "grades_stats": {
      "count": 2,
      "min": 50.0,
      "max": 100.0,
      "avg": 75.0,
      "sum": 150.0
    }
  }
}

对结果进行进一步分析请求示例：

GET /exams/_search
{
  "size": 0,
  "aggs": {
    "grades_stats": { "extended_stats": { "field": "grade" } }
  }
}

返回示例：

{
  ...

  "aggregations": {
    "grades_stats": {
      "count": 2,
      "min": 50.0,
      "max": 100.0,
      "avg": 75.0,
      "sum": 150.0,
      "sum_of_squares": 12500.0,
      "variance": 625.0,
      "variance_population": 625.0,
      "variance_sampling": 1250.0,
      "std_deviation": 25.0,
      "std_deviation_population": 25.0,
      "std_deviation_sampling": 35.35533905932738,
      "std_deviation_bounds": {
        "upper": 125.0,
        "lower": 25.0,
        "upper_population": 125.0,
        "lower_population": 25.0,
        "upper_sampling": 145.71067811865476,
        "lower_sampling": 4.289321881345245
      }
    }
  }
}