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 tvswhere 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                }              }            ]          }        }      ]    }  }}