ElasticSearch7.3学习(二十八)----聚合实战之电视案例

一、电视案例

1.1 数据准备

创建索引及映射

建立价格、颜色、品牌、售卖日期 字段

PUT /tvs
PUT /tvs/_mapping
{
  "properties": {
    "price": {
      "type": "long"
    },
    "color": {
      "type": "keyword"
    },
    "brand": {
      "type": "keyword"
    },
    "sold_date": {
      "type": "date"
    }
  }
}

插入数据

POST /tvs/_bulk
{"index":{}}
{"price":1000,"color":"红色","brand":"长虹","sold_date":"2019-10-28"}
{"index":{}}
{"price":2000,"color":"红色","brand":"长虹","sold_date":"2019-11-05"}
{"index":{}}
{"price":3000,"color":"绿色","brand":"小米","sold_date":"2019-05-18"}
{"index":{}}
{"price":1500,"color":"蓝色","brand":"TCL","sold_date":"2019-07-02"}
{"index":{}}
{"price":1200,"color":"绿色","brand":"TCL","sold_date":"2019-08-19"}
{"index":{}}
{"price":2000,"color":"红色","brand":"长虹","sold_date":"2019-11-05"}
{"index":{}}
{"price":8000,"color":"红色","brand":"三星","sold_date":"2020-01-01"}
{"index":{}}
{"price":2500,"color":"蓝色","brand":"小米","sold_date":"2020-02-12"}

1.2 统计哪种颜色的电视销量最高

不加query 默认查询全部

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "popular_colors": {
      "terms": {
        "field": "color"
      }
    }
  }
}

查询条件解析

  • size:只获取聚合结果,而不要执行聚合的原始数据
  • aggs:固定语法,要对一份数据执行分组聚合操作
  • popular_colors:就是对每个aggs,都要起一个名字,
  • terms:根据字段的值进行分组
  • field:根据指定的字段的值进行分组

返回

{
  "took" : 121,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 8,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "popular_colors" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "红色",
          "doc_count" : 4
        },
        {
          "key" : "绿色",
          "doc_count" : 2
        },
        {
          "key" : "蓝色",
          "doc_count" : 2
        }
      ]
    }
  }
}

返回结果解析

  • hits.hits:我们指定了size是0,所以hits.hits就是空的
  • aggregations:聚合结果
  • popular_color:我们指定的某个聚合的名称
  • buckets:根据我们指定的field划分出的buckets
  • key:每个bucket对应的那个值
  • doc_count:这个bucket分组内,有多少个数量,其实就是这种颜色的销量
  • bucket中的数据的默认的排序规则:按照doc_count降序排序

1.3 统计每种颜色电视平均价格

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "colors": {
      "terms": {
        "field": "color"
      },
      "aggs": {
        "avg_price": {
          "avg": {
            "field": "price"
          }
        }
      }
    }
  }
}

在一个aggs执行的bucket操作(terms),平级的json结构下,再加一个aggs,

这个第二个aggs内部,同样取个名字,执行一个metric操作,avg,对之前的每个bucket中的数据的指定的field,求一个平均值

返回:

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 8,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "colors" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "红色",
          "doc_count" : 4,
          "avg_price" : {
            "value" : 3250.0
          }
        },
        {
          "key" : "绿色",
          "doc_count" : 2,
          "avg_price" : {
            "value" : 2100.0
          }
        },
        {
          "key" : "蓝色",
          "doc_count" : 2,
          "avg_price" : {
            "value" : 2000.0
          }
        }
      ]
    }
  }
}

返回结果解析:

  • avg_price:我们自己取的metric aggs的名字
  • value:我们的metric计算的结果,每个bucket中的数据的price字段求平均值后的结果

相当于sql: select avg(price) from tvs group by color

1.4 每个颜色下,平均价格及每个颜色下,每个品牌的平均价格

多个子聚合

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "group_by_color": {
      "terms": {
        "field": "color"
      },
      "aggs": {
        "color_avg_price": {
          "avg": {
            "field": "price"
          }
        },
        "group_by_brand": {
          "terms": {
            "field": "brand"
          },
          "aggs": {
            "brand_avg_price": {
              "avg": {
                "field": "price"
              }
            }
          }
        }
      }
    }
  }
}

返回

查看代码

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 8,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "group_by_color" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "红色",
          "doc_count" : 4,
          "color_avg_price" : {
            "value" : 3250.0
          },
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "长虹",
                "doc_count" : 3,
                "brand_avg_price" : {
                  "value" : 1666.6666666666667
                }
              },
              {
                "key" : "三星",
                "doc_count" : 1,
                "brand_avg_price" : {
                  "value" : 8000.0
                }
              }
            ]
          }
        },
        {
          "key" : "绿色",
          "doc_count" : 2,
          "color_avg_price" : {
            "value" : 2100.0
          },
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "TCL",
                "doc_count" : 1,
                "brand_avg_price" : {
                  "value" : 1200.0
                }
              },
              {
                "key" : "小米",
                "doc_count" : 1,
                "brand_avg_price" : {
                  "value" : 3000.0
                }
              }
            ]
          }
        },
        {
          "key" : "蓝色",
          "doc_count" : 2,
          "color_avg_price" : {
            "value" : 2000.0
          },
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "TCL",
                "doc_count" : 1,
                "brand_avg_price" : {
                  "value" : 1500.0
                }
              },
              {
                "key" : "小米",
                "doc_count" : 1,
                "brand_avg_price" : {
                  "value" : 2500.0
                }
              }
            ]
          }
        }
      ]
    }
  }
}

1.5 求出每个颜色的销售数量,平均价格、最小价格、最大价格、价格总和

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "colors": {
      "terms": {
        "field": "color"
      },
      "aggs": {
        "color_avg_price": {
          "avg": {
            "field": "price"
          }
        },
        "color_min_price": {
          "min": {
            "field": "price"
          }
        },
        "color_max_price": {
          "max": {
            "field": "price"
          }
        },
        "color_sum_price": {
          "sum": {
            "field": "price"
          }
        }
      }
    }
  }
}

返回:

查看代码

{
  "took" : 4,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 8,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "colors" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "红色",
          "doc_count" : 4,
          "color_avg_price" : {
            "value" : 3250.0
          },
          "color_min_price" : {
            "value" : 1000.0
          },
          "color_max_price" : {
            "value" : 8000.0
          },
          "color_sum_price" : {
            "value" : 13000.0
          }
        },
        {
          "key" : "绿色",
          "doc_count" : 2,
          "color_avg_price" : {
            "value" : 2100.0
          },
          "color_min_price" : {
            "value" : 1200.0
          },
          "color_max_price" : {
            "value" : 3000.0
          },
          "color_sum_price" : {
            "value" : 4200.0
          }
        },
        {
          "key" : "蓝色",
          "doc_count" : 2,
          "color_avg_price" : {
            "value" : 2000.0
          },
          "color_min_price" : {
            "value" : 1500.0
          },
          "color_max_price" : {
            "value" : 2500.0
          },
          "color_sum_price" : {
            "value" : 4000.0
          }
        }
      ]
    }
  }
}

返回结果解析

  • count:bucket,terms,自动就会有一个doc_count,就相当于是count
  • avg:avg aggs,求平均值
  • max:求一个bucket内,指定field值最大的那个数据
  • min:求一个bucket内,指定field值最小的那个数据
  • sum:求一个bucket内,指定field值的总和

1.6 划分范围 histogram(直方图),求出价格每2000为一个区间,每个区间的销售总额

GET /tvs/_search
{
  "size": 0,
  "aggs": {
    "price": {
      "histogram": {
        "field": "price",
        "interval": 2000
      },
      "aggs": {
        "income": {
          "sum": {
            "field": "price"
          }
        }
      }
    }
  }
}

histogram:类似于terms,也是进行bucket分组操作,接收一个field,按照这个field的值的各个范围区间,进行bucket分组操作

"histogram": {
    "field": "price",
    "interval": 2000
}

interval:2000,划分范围,左闭右开区间 ,[0~2000),2000~4000,4000~6000,6000~8000,8000~10000

bucket有了之后,一样的,去对每个bucket执行avg,count,sum,max,min,等各种metric操作,聚合分析

1.7 按照日期分组聚合,求出每个月销售个数

参数解析:

  • date_histogram,按照我们指定的某个date类型的日期field,以及日期interval,按照一定的日期间隔,去划分bucket
  • min_doc_count:即使某个日期interval,2017-01-01~2017-01-31中,一条数据都没有,那么这个区间也是要返回的,不然默认是会过滤掉这个区间的 extended_bounds,
  • min,max:划分bucket的时候,会限定在这个起始日期,和截止日期内
GET /tvs/_search
{
   "size" : 0,
   "aggs": {
      "date_sales": {
         "date_histogram": {
            "field": "sold_date",
            "interval": "month", 
            "format": "yyyy-MM-dd",
            "min_doc_count" : 0, 
            "extended_bounds" : { 
                "min" : "2019-01-01",
                "max" : "2020-12-31"
            }
         }
      }
   }
}

返回

查看代码

#! Deprecation: [interval] on [date_histogram] is deprecated, use [fixed_interval] or [calendar_interval] in the future.
{
  "took" : 11,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 8,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "date_sales" : {
      "buckets" : [
        {
          "key_as_string" : "2019-01-01",
          "key" : 1546300800000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2019-02-01",
          "key" : 1548979200000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2019-03-01",
          "key" : 1551398400000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2019-04-01",
          "key" : 1554076800000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2019-05-01",
          "key" : 1556668800000,
          "doc_count" : 1
        },
        {
          "key_as_string" : "2019-06-01",
          "key" : 1559347200000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2019-07-01",
          "key" : 1561939200000,
          "doc_count" : 1
        },
        {
          "key_as_string" : "2019-08-01",
          "key" : 1564617600000,
          "doc_count" : 1
        },
        {
          "key_as_string" : "2019-09-01",
          "key" : 1567296000000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2019-10-01",
          "key" : 1569888000000,
          "doc_count" : 1
        },
        {
          "key_as_string" : "2019-11-01",
          "key" : 1572566400000,
          "doc_count" : 2
        },
        {
          "key_as_string" : "2019-12-01",
          "key" : 1575158400000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2020-01-01",
          "key" : 1577836800000,
          "doc_count" : 1
        },
        {
          "key_as_string" : "2020-02-01",
          "key" : 1580515200000,
          "doc_count" : 1
        },
        {
          "key_as_string" : "2020-03-01",
          "key" : 1583020800000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2020-04-01",
          "key" : 1585699200000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2020-05-01",
          "key" : 1588291200000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2020-06-01",
          "key" : 1590969600000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2020-07-01",
          "key" : 1593561600000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2020-08-01",
          "key" : 1596240000000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2020-09-01",
          "key" : 1598918400000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2020-10-01",
          "key" : 1601510400000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2020-11-01",
          "key" : 1604188800000,
          "doc_count" : 0
        },
        {
          "key_as_string" : "2020-12-01",
          "key" : 1606780800000,
          "doc_count" : 0
        }
      ]
    }
  }
}

注意: 

#! Deprecation: [interval] on [date_histogram] is deprecated, use [fixed_interval] or [calendar_interval] in the future.

1.8 统计每季度每个品牌的销售额,及每季度的销售总额

GET /tvs/_search 
{
  "size": 0,
  "aggs": {
    "group_by_sold_date": {
      "date_histogram": {
        "field": "sold_date",
        "interval": "quarter",
        "format": "yyyy-MM-dd",
        "min_doc_count": 0,
        "extended_bounds": {
          "min": "2019-01-01",
          "max": "2020-12-31"
        }
      },
      "aggs": {
        "group_by_brand": {
          "terms": {
            "field": "brand"
          },
          "aggs": {
            "sum_price": {
              "sum": {
                "field": "price"
              }
            }
          }
        },
        "total_sum_price": {
          "sum": {
            "field": "price"
          }
        }
      }
    }
  }
}

返回

查看代码

#! Deprecation: [interval] on [date_histogram] is deprecated, use [fixed_interval] or [calendar_interval] in the future.
{
  "took" : 3,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 8,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "group_by_sold_date" : {
      "buckets" : [
        {
          "key_as_string" : "2019-01-01",
          "key" : 1546300800000,
          "doc_count" : 0,
          "total_sum_price" : {
            "value" : 0.0
          },
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [ ]
          }
        },
        {
          "key_as_string" : "2019-04-01",
          "key" : 1554076800000,
          "doc_count" : 1,
          "total_sum_price" : {
            "value" : 3000.0
          },
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "小米",
                "doc_count" : 1,
                "sum_price" : {
                  "value" : 3000.0
                }
              }
            ]
          }
        },
        {
          "key_as_string" : "2019-07-01",
          "key" : 1561939200000,
          "doc_count" : 2,
          "total_sum_price" : {
            "value" : 2700.0
          },
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "TCL",
                "doc_count" : 2,
                "sum_price" : {
                  "value" : 2700.0
                }
              }
            ]
          }
        },
        {
          "key_as_string" : "2019-10-01",
          "key" : 1569888000000,
          "doc_count" : 3,
          "total_sum_price" : {
            "value" : 5000.0
          },
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "长虹",
                "doc_count" : 3,
                "sum_price" : {
                  "value" : 5000.0
                }
              }
            ]
          }
        },
        {
          "key_as_string" : "2020-01-01",
          "key" : 1577836800000,
          "doc_count" : 2,
          "total_sum_price" : {
            "value" : 10500.0
          },
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "三星",
                "doc_count" : 1,
                "sum_price" : {
                  "value" : 8000.0
                }
              },
              {
                "key" : "小米",
                "doc_count" : 1,
                "sum_price" : {
                  "value" : 2500.0
                }
              }
            ]
          }
        },
        {
          "key_as_string" : "2020-04-01",
          "key" : 1585699200000,
          "doc_count" : 0,
          "total_sum_price" : {
            "value" : 0.0
          },
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [ ]
          }
        },
        {
          "key_as_string" : "2020-07-01",
          "key" : 1593561600000,
          "doc_count" : 0,
          "total_sum_price" : {
            "value" : 0.0
          },
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [ ]
          }
        },
        {
          "key_as_string" : "2020-10-01",
          "key" : 1601510400000,
          "doc_count" : 0,
          "total_sum_price" : {
            "value" : 0.0
          },
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [ ]
          }
        }
      ]
    }
  }
}

1.9 搜索与聚合结合,查询某个品牌按颜色销量

搜索与聚合可以结合起来。sql语句如下

select count(*)
from tvs
where brand like "%小米%"
group by color

注意:任何的聚合,都必须在搜索出来的结果数据中之行。

GET /tvs/_search 
{
  "size": 0,
  "query": {
    "term": {
      "brand": {
        "value": "小米"
      }
    }
  },
  "aggs": {
    "group_by_color": {
      "terms": {
        "field": "color"
      }
    }
  }
}

返回

{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "group_by_color" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "绿色",
          "doc_count" : 1
        },
        {
          "key" : "蓝色",
          "doc_count" : 1
        }
      ]
    }
  }
}

1.10 global bucket(全局桶):单个品牌与所有品牌销量对比

GET /tvs/_search 
{
  "size": 0, 
  "query": {
    "term": {
      "brand": {
        "value": "小米"
      }
    }
  },
  "aggs": {
    "single_brand_avg_price": {
      "avg": {
        "field": "price"
      }
    },
    "all": {
      "global": {},
      "aggs": {
        "all_brand_avg_price": {
          "avg": {
            "field": "price"
          }
        }
      }
    }
  }
}

返回

{
  "took" : 61,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "all" : {
      "doc_count" : 8,
      "all_brand_avg_price" : {
        "value" : 2650.0
      }
    },
    "single_brand_avg_price" : {
      "value" : 2750.0
    }
  }
}

返回结果解析:

  • 一个结果,是基于query搜索结果来聚合的;
  • 一个结果,是对所有数据执行聚合的

1.11 统计价格大于1200的电视平均价格

注意:单独使用filter 需加上constant_score

GET /tvs/_search 
{
  "size": 0,
  "query": {
    "constant_score": {
      "filter": {
        "range": {
          "price": {
            "gte": 1200
          }
        }
      }
    }
  },
  "aggs": {
    "avg_price": {
      "avg": {
        "field": "price"
      }
    }
  }
}

返回:

{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 7,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "avg_price" : {
      "value" : 2885.714285714286
    }
  }
}

1.12 bucket filter:统计品牌最近4年,3年的平均价格

注意:因为是最近的时间,所以读者实验的时候,需根据当前时间来自行设置查询范围

注意下面的区别

  • aggs.filter,针对的是聚合去做的
  • query里面的filter,是全局的,会对所有的数据都有影响
GET /tvs/_search 
{
  "size": 0,
  "query": {
    "term": {
      "brand": {
        "value": "小米"
      }
    }
  },
  "aggs": {
    "recent_fouryear": {
      "filter": {
        "range": {
          "sold_date": {
            "gte": "now-4y"
          }
        }
      },
      "aggs": {
        "recent_fouryear_avg_price": {
          "avg": {
            "field": "price"
          }
        }
      }
    },
    "recent_threeyear": {
      "filter": {
        "range": {
          "sold_date": {
            "gte": "now-3y"
          }
        }
      },
      "aggs": {
        "recent_threeyear_avg_price": {
          "avg": {
            "field": "price"
          }
        }
      }
    }
  }
}

返回

{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "recent_threeyear" : {
      "meta" : { },
      "doc_count" : 2,
      "recent_threeyear_avg_price" : {
        "value" : 2750.0
      }
    },
    "recent_fouryear" : {
      "meta" : { },
      "doc_count" : 2,
      "recent_fouryear_avg_price" : {
        "value" : 2750.0
      }
    }
  }
}

1.13 按每种颜色的平均销售额降序排序

GET /tvs/_search 
{
  "size": 0,
  "aggs": {
    "group_by_color": {
      "terms": {
        "field": "color",
        "order": {
          "avg_price": "desc"
        }
      },
      "aggs": {
        "avg_price": {
          "avg": {
            "field": "price"
          }
        }
      }
    }
  }
}

返回:

{
  "took" : 0,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 8,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "group_by_color" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "红色",
          "doc_count" : 4,
          "avg_price" : {
            "value" : 3250.0
          }
        },
        {
          "key" : "绿色",
          "doc_count" : 2,
          "avg_price" : {
            "value" : 2100.0
          }
        },
        {
          "key" : "蓝色",
          "doc_count" : 2,
          "avg_price" : {
            "value" : 2000.0
          }
        }
      ]
    }
  }
}

1.14 按每种颜色的每种品牌平均销售额降序排序

GET /tvs/_search    
{
  "size": 0,
  "aggs": {
    "group_by_color": {
      "terms": {
        "field": "color"
      },
      "aggs": {
        "group_by_brand": {
          "terms": {
            "field": "brand",
            "order": {
              "avg_price": "desc"
            }
          },
          "aggs": {
            "avg_price": {
              "avg": {
                "field": "price"
              }
            }
          }
        }
      }
    }
  }
}

返回

查看代码


{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 8,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "aggregations" : {
    "group_by_color" : {
      "doc_count_error_upper_bound" : 0,
      "sum_other_doc_count" : 0,
      "buckets" : [
        {
          "key" : "红色",
          "doc_count" : 4,
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "三星",
                "doc_count" : 1,
                "avg_price" : {
                  "value" : 8000.0
                }
              },
              {
                "key" : "长虹",
                "doc_count" : 3,
                "avg_price" : {
                  "value" : 1666.6666666666667
                }
              }
            ]
          }
        },
        {
          "key" : "绿色",
          "doc_count" : 2,
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "小米",
                "doc_count" : 1,
                "avg_price" : {
                  "value" : 3000.0
                }
              },
              {
                "key" : "TCL",
                "doc_count" : 1,
                "avg_price" : {
                  "value" : 1200.0
                }
              }
            ]
          }
        },
        {
          "key" : "蓝色",
          "doc_count" : 2,
          "group_by_brand" : {
            "doc_count_error_upper_bound" : 0,
            "sum_other_doc_count" : 0,
            "buckets" : [
              {
                "key" : "小米",
                "doc_count" : 1,
                "avg_price" : {
                  "value" : 2500.0
                }
              },
              {
                "key" : "TCL",
                "doc_count" : 1,
                "avg_price" : {
                  "value" : 1500.0
                }
              }
            ]
          }
        }
      ]
    }
  }
}

 

 

    原文作者:|旧市拾荒|
    原文地址: https://www.cnblogs.com/xiaoyh/p/16264715.html
    本文转自网络文章,转载此文章仅为分享知识,如有侵权,请联系博主进行删除。
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