ElasticSearch7.3学习(二十八)

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                 }               }             ]           }         }       ]     }   } }

 

 

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