ElasticSearch6.x 之分组聚合

我会带着你远行 2023-10-18 22:21 151阅读 0赞

数据准备：新建索引my\_index,初始化数据

# 创建my_index索引
    Put 请求：http://192.168.1.74:9200/my-index/
    #  索引数据初始化
    PUT请求： http://192.168.1.74:9200/my-index/persion/1
    {
      "name":"张三",
      "age":27,
      "gender":"男",
      "salary":15000,
      "dep":"bigdata"
    }
    
    PUT请求： http://192.168.1.74:9200/my-index/persion/2
    {
      "name":"李四",
      "age":26,
      "gender":"女",
      "salary":15000,
      "dep":"bigdata"
    }
    
    PUT请求： http://192.168.1.74:9200/my-index/persion/3
    {
      "name":"王五",
      "age":26,
      "gender":"男",
      "salary":17000,
      "dep":"AI"
    }
    PUT请求： http://192.168.1.74:9200/my-index/persion/4
    {
      "name":"刘六",
      "age":27,
      "gender":"女",
      "salary":18000,
      "dep":"AI"
    }
    
    PUT请求： http://192.168.1.74:9200/my-index/persion/5
    {
      "name":"程裕强",
      "age":31,
      "gender":"男",
      "salary":20000,
      "dep":"bigdata"
    }
    PUT请求： http://192.168.1.74:9200/my-index/persion/6
    {
      "name":"hadron",
      "age":30,
      "gender":"男",
      "salary":20000,
      "dep":"AI"
    }

分组聚合之Terms 分组（Terms Aggregation）

【例子】根据薪资水平进行分组，统计每个薪资水平的人数

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob3V6aGl3ZW5nYW5n_size_16_color_FFFFFF_t_70][]

POST 请求：http://192.168.1.74:9200/my-index/_search/
    {
      "size": 0, 
      "aggs": {
        "group_count": {
          "terms": {"field": "salary"}
        }
      }
    }

【例子】统计上面每个分组的平均年龄

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob3V6aGl3ZW5nYW5n_size_16_color_FFFFFF_t_70 1][]

POST请求： http://192.168.1.74:9200/my-index/_search/
    {
      "size": 0, 
      "aggs": {
        "group_count": {
          "terms": {"field": "salary"},
          "aggs":{
            "avg_age":{
              "avg":{"field": "age"}
            }
          }
        }
      }
    }

分组聚合之Filter分组（Filter Aggregation）

Filter聚合用于过滤器聚合，把满足过滤器条件的文档分到一组。

【例子】计算男人的平均年龄

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob3V6aGl3ZW5nYW5n_size_16_color_FFFFFF_t_70 2][]

POST请求： http://192.168.1.74:9200/my-index/_search/
    {
      "size": 0, 
      "aggs": {
        "group_count": {
          "filter": {
            "term":{"gender": "男"}
          },
          "aggs":{
            "avg_age":{
              "avg":{"field": "age"}
            }
          }
        }
      }
    }

【例子】统计男女员工的平均年龄

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob3V6aGl3ZW5nYW5n_size_16_color_FFFFFF_t_70 3][]

POST请求： http://192.168.1.74:9200/my-index/_search/
    {
      "size": 0, 
      "aggs": {
        "group_count": {
          "filters":{
            "filters": [
              {"match":{"gender": "男"}},
              {"match":{"gender": "女"}}
            ]
          },
          "aggs":{
            "avg_age":{
                "avg":{"field": "age"}
            }
          }
        }
      }
    }

分组聚合之Range分组（RangeAggregation）

from..to区间范围是**\[from,to)**,也就是说包含from点，不包含to点

【例子】查询薪资在\[0,10000),\[10000,20000),\[2000,+无穷大)三个范围的员工数

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob3V6aGl3ZW5nYW5n_size_16_color_FFFFFF_t_70 4][]

POST请求： http://192.168.1.74:9200/my-index/_search/
    {
      "size": 0, 
      "aggs": {
        "group_count": {
          "range": {
            "field": "salary",
            "ranges": [
                {"to": 10000},
                {"from": 10000,"to":20000},  
                {"from": 20000}
            ]
          }
    
        }
      }
    }

【例子】查询发布日期在2016-12-01之前、2016-12-01至2017-01-01、2017-01-01之后三个时间区间的文档数

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob3V6aGl3ZW5nYW5n_size_16_color_FFFFFF_t_70 5][]

POST请求：http://192.168.1.74:9200/website/_search/
    {
       "size": 0, 
      "aggs": {
        "group_count": {
          "range": {
            "field": "postdate",
            "format":"yyyy-MM-dd",
            "ranges": [
                {"to": "2016-12-01"},
                {"from": "2016-12-01","to":"2017-01-01"},  
                {"from": "2017-01-01"}
            ]
          }
        }
      }
    }

分组聚合之Date分组（Date Aggregation）

专用于日期值的范围聚合。 这种聚合和正常范围聚合的主要区别在于，起始和结束值可以在日期数学表达式中表示，并且还可以指定返回起始和结束响应字段的日期格式。 请注意，此聚合包含from值并排除每个范围的值。

【例子】计算一年前之前发表的博文数和从一年前以来发表的博文总数

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3pob3V6aGl3ZW5nYW5n_size_16_color_FFFFFF_t_70 6][]

POST请求：http://192.168.1.74:9200/website/_search/
    {
      "size": 0, 
      "aggs": {
        "group_count": {
          "range": {
            "field": "postdate",
            "format":"yyyy-MM-dd",
            "ranges": [
                {"to": "now-12M/M"},
                {"from": "now-12M/M"}
            ]
          }
        }
      }
    }

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