28、ElasticSearch 7.3 实战：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
}
}
]
}
}
]
}
}
}